A review of research on public transport priority based on CiteSpace

1.   Introduction

With the acceleration of global urbanization and the continuous increase in the number of cars, the problems of traffic congestion and environmental pollution are becoming increasingly prominent. Insufficient development of road traffic infrastructure and outdated urban traffic control measures have exacerbated the contradiction between urban transportation demand and supply. Improving the urban transportation structure and optimizing transportation supply strategies, with a focus on balancing transportation supply-side reform and demand management, and enhancing the efficiency of transportation facility utilization, have become key priorities for urban transportation reform. As an important part of the urban transportation system, public transportation's role in alleviating traffic congestion, reducing exhaust emissions, and optimizing urban spatial layout is increasingly highlighted. Therefore, implementing a priority policy for public transportation is necessary. The concept of "public transportation priority" was first proposed by France in the 1960s (Zuo and Shao, 2012). Broadly speaking, "public transportation priority" refers to all policies and measures that can promote the development of public transportation. Narrowly speaking, it refers to the control measures under traffic control management, which give relative priority to public transportation vehicles on the road (Ji and Deng, 2004; Shaaban et al., 2021). Domestic and foreign urban development experience has shown that adjusting the residents' travel mode based on public transportation priority can maximize social benefits (Zhang et al., 2012). The priority development of public transportation can alleviate the future energy and environmental carrying capacity pressure of cities and has important practical significance for the sustainable development of energy and ecological environment (Cao et al., 2012).

The public transportation priority strategy presents a multifaceted approach to addressing a range of critical urban transportation issues. One of the most significant impacts of this strategy lies in its potential to foster sustainable transportation practices. By reducing the reliance on private cars and decreasing carbon emissions from transportation, public transportation priority can serve as a key driver for sustainable urban development. In addition to this, the strategy has demonstrated potential for improving the efficiency of urban transportation systems, particularly in terms of speed, reliability, and overall system performance. This improved efficiency can contribute to reducing road congestion and better accessibility, ultimately leading to a more sustainable and equitable urban transportation system. By enabling greater accessibility to public transportation systems, particularly among low-income and suburban populations, the public transportation priority strategy can help to promote greater equity in transportation. Given these potential benefits, the public transportation priority strategy is a critical area of research for enhancing the sustainability, efficiency, and equity of urban transportation systems. There are relatively few comprehensive review studies solely focused on public transit priority in both domestic and foreign literature databases. However, research on the development of public transportation has always been a hot topic in the transportation and urban planning fields. Scholars have been committed to improving the attractiveness of public transportation by improving the public transit system's network design, setting bus frequencies, minimizing operational costs, and implementing different real-time control strategies. Through literature review, Ibarra-Rojas et al. (2015) discussed the problems of network design, bus frequency setting, and minimizing operational costs of public transportation, as well as different real-time control strategies. Bhouri et al. (2015) reviewed the ground bus control system in multimodal urban transportation systems, including different types of control methods based on public transit priority at signalized intersections, dynamic control models, optimal control problems, and numerical experiments, and provided strategic recommendations. In 2015, Diakaki et al. (2015) reviewed the latest technology and practical results of public transit priority strategies, including infrastructure design based on public transit priority, signal control technology based on public transit priority, advanced public transit priority strategies, and implementation status in typical cities. Racehorse et al. (2015) reviewed the development history of BRT systems, typical city implementations, summarized key system components and infrastructure elements, and analyzed BRT's public acceptance. Hartmann et al. (2018) classified models developed to solve public transportation problems into four categories based on different objective functions and constraint conditions: minimizing total travel time, minimizing passenger waiting time, minimizing vehicle delay, and optimizing departure intervals, and provided a literature overview of each. Pojani and Stead (2015) analyzed nine measures to promote sustainable transportation system development in small and medium-sized cities, indicating that there is still room for small and medium-sized cities to expand and that they have more potential for sustainable development than large cities, making them more flexible in developing public transit systems. In the field of public transportation research, there are also many scholars in China who have summarized related content. Ji and Deng (2004) reviewed the research on intersection bus priority technology at home and abroad, analyzed the main problems and shortcomings of China's intersection bus priority technology from the perspective of delay calculation methods, phase and timing design, and provided development suggestions. Ma and Yang (2010a) reviewed the bus signal priority control technology and conducted a comprehensive analysis of passive priority, active priority, real-time priority, and signal priority control strategies combined with different facilities. The study believes that future research in this field should focus on considering the multi-objective optimization of social vehicles and the evaluation method of bus signal priority benefits. Ge et al. (2004) classified urban bus stops into three types: off-road bus stops, on-road bus stops, and intersection bus stops, and summarized the planning and setting methods of urban bus stops. Zhu et al. (2015) and Yang (2011) both conducted a comprehensive analysis of the research on urban bus subsidies and explored the impact of subsidy mechanisms. Ma et al. (2021) focused on the issues of route and network optimization in customized bus transportation, and conducted a comprehensive analysis of research on customized bus route optimization from the aspects of optimization objectives, optimization scenarios, and solution algorithms. The study believes that customized buses should meet different travel needs and provide differentiated services. The above-mentioned review papers on the development of public transportation have made a significant contribution to advancing research on public transportation priority strategies. Nevertheless, these review studies are not without potential drawbacks. For instance, the majority of papers concentrate on specific regions, such as developed countries or cities in China, which cannot represent the results of other areas in full. Additionally, some review studies overlook the trade-offs and conflicts between public transportation and other transportation modes when assessing the benefits of public transportation systems. Furthermore, existing review studies fail to provide actionable research recommendations that consider long-term strategic impact or sustainability. To address these limitations, this review study adopts a broader scope, encompassing multiple countries, and employs a unified knowledge visualization analysis method to objectively scrutinize and evaluate the progress of research in the public transportation priority field.

This study conducted a comprehensive literature review of the bus priority field from 2003 to 2021. The literature was selected based on clear objectives, accurate data, detailed content, reliable methods, and logical coherence. The study conducted a comprehensive analysis of research on bus priority, including research sources and institutions, knowledge foundation, research frontiers, and research hotspots, to identify key research questions and summarize the main research progress in the bus priority field. The study also analyzed the implementation of bus priority strategies, identified problems and limitations in bus priority research, and proposed future research directions and development trends in this field. This study can help researchers understand the development status of the bus priority field, grasp relevant theories and methods, identify research hotspots and frontier issues, and provide research ideas and directions based on the shortcomings and limitations of existing research on bus priority. Furthermore, as cities actively implement bus priority strategies, this study has practical guidance significance for urban public transportation system planning and development.

2.   Data and methods

2.1   Data sources

The data sources for this study included the Web of Science (WOS) database and the China National Knowledge Infrastructure (CNKI) database. In the WOS database, the search was conducted by using the "advanced search" method, and the search string was (TS = ("bus" OR "transit" OR "public tra^*") AND TS = ("priority")) OR TS = ("bus lane" OR "bus signal"). To ensure the accuracy and reliability of the data source, the literature search period for this review is from 2003 to 2021 because the "Science Citation Index Expanded" data on the website "https://www.web_of_science.com" only dates back to 2003. The type of literature selected was journal papers, conference papers, review papers, and online publications. A total of 1190 papers were extracted.

A specialized search was performed in the CNKI, and as the type of literature, academic journals and conference proceedings were selected. The source categories of journals were restricted to Science Citation Index (SCI) journals, Engineering Index (Ei) journals, the Peking University Core, Chinese Social Sciences Citation Index (CSSCI) journals, and Chinese Science Citation Database (CSCD) journals. The retrieval string was SU = ("bus" + "public transport") ^* ("priority" + "signal" + "dedicated lane"). The search period was 2003–2021, and 904 studies were extracted.

2.2   Analytical methods

This study used a combination of quantitative and qualitative methods and focused on the use of quantitative methods. Graphs and scientific knowledge maps of literature data were created based on a manual reading of the literature combined with bibliometric software for analysis. The chart shows the spatial and temporal distribution of the basic data. Scientific knowledge mapping is a means of visualizing the relevant literature on a research topic. Tools such as data mining, bibliometrics and mapping were utilized, the evolutionary path of a discipline or research field was demonstrated, research hotspots were uncovered, and the research frontiers were tracked.

CiteSpace takes the keywords, research institutions, research time and other information of the studied literature as the main object of analysis and visualizes the structure through pathfinding network algorithm theory and co-citation analysis (Chen et al., 2009).

3.   Discussion of the results

3.1   Characteristics of the temporal distribution

By analyzing the number of papers on public transport priority published each year, the development trend in recent years was described. The paper drew a histogram based on the number of papers on public transport priority published each year from 2003 to 2021 in the WOS and CNKI databases, as shown in Fig. 1.

Figure  1.  Number of published papers from 2003 to 2021.

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As shown in Fig. 1, there were fewer papers on the topic of public transport priority in the WOS database from 2003 to 2010, indicating that the field was at the frontier of exploration. The research interest continued to rise between 2011 and 2021, and the number of papers published showed a rapid growth trend. The number of published papers increased from less than 40 to nearly 150 in 2021, reaching a research peak in 2020 with an annual publication volume of 160 papers. Research in the field of public transport priority in the CNKI database showed a boom in research in 2011, 2012 and 2014, and then, the research popularity tended to be flat, with the research popularity showing a downward trend since 2017.

3.2   Analysis of research countries (regions)

The research countries (regions) were analyzed to explore the research intensity and cooperation relationships of the research countries (regions), focusing on public transport priority. Based on the results of the visual analysis, a total of 77 countries (regions) were represented in the literature in the field of public transport priority research. A larger annual nodal ring on the map represents a greater number of papers published in that country (region) on the topic of public transport priority. The color of the annual nodal ring corresponds to the time partition in which a paper was published, and the thickness of the annual nodal ring is proportional to the frequency in the corresponding time partition. As shown in Fig. 2, China, the United States, Australia, the United Kingdom, India and Canada were the most active in the field of public transport priority and were the main research forces in this field. The Netherlands, Chile and France had a close mutual partnership, and the UK had more partners. As shown in Table 1, in the WOS database, 354 papers were published in the United States from 2003 to 2021, accounting for 29.75%, and 343 papers were published in China, accounting for 28.82%. These results show that the U.S. and China were the main force in scientific research in the field of public transport priority. The citation half-life is an indicator that describes the aging of literature. The larger the half-life is, the more classic the literature is. Table 1 shows that the literature published in China in the area of public transport priority had the largest citation half-life, and the effective value of the literature was relatively high.

Figure  2.  Collaboration map of research countries (regions) in the WOS database.

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Table  1.  Table of study countries (regions) in the WOS database.

Ranking	Number of published papers	Country (region)	Year of first publication	Citation half-life
1	354	United States	2003	12.5
2	343	China	2003	14.5
3	81	Australia	2007	9.5
4	79	United Kingdom	2006	10.5
5	63	India	2008	9.5
6	56	Canada	2008	8.5

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3.3   Analysis of research institutions

The WOS data were used to generate a visualization map of research institutions, and the results are shown in Fig. 3 and Table 2. The number of papers from Tongji University, Southeast University, Monash University, Beijing Jiaotong University and the University of California, Berkeley was more than 20, accounting for 12.86% of all papers. The density of the cooperative network of research institutions was 0.0034, indicating a low level of cooperation among institutions.

Figure  3.  Cooperation map of research institutions in the WOS database.

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Table  2.  Research institutions in the WOS database.

Ranking	Number of published papers	Research institution	Year of first publication
1	39	Tongji University	2010
2	32	Southeast University	2014
3	30	Monash University	2007
4	26	Beijing Jiaotong University	2008
5	26	University of California, Berkeley	2004

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3.4   Knowledge base

Analysis of the knowledge base facilitates a further clarification of the nature of research frontiers. According to bibliometric theory, research frontiers are formed by citations, and the knowledge base is formed by cited literature (Persson, 1994). The frequency of citation reflects the academic influence and classicality of literature, and the citation status of the area of public transport priority could be derived by performing a citation frequency analysis of the cited literature. The ideas and methods in the highly cited literature are generally recognized by a large number of researchers, who further use the ideas and knowledge within the highly cited literature as the theoretical basis for their next research Qiu and Lyu (2013). The analysis of the knowledge base of the subject area includes an analysis of early seminal literature as well as an analysis of key literature with a high citation frequency and high centrality Zhao and Xu (2010).

3.4.1   Analysis of the knowledge base in the CNKI database

Papers cited more than 60 times in the CNKI database were screened for knowledge base analysis, and the details of the papers are shown in Table 3.

Table  3.  Highly cited papers in the area of public transport priority in the CNKI database.

Ranking	Name of the paper	Date of publication	Frequency of citation
1	Optimal signal-planning method of intersections based on bus priority	2004/09/30	211
2	Analysis of vehicle delay of intersections with pre-signals based on bus priority	2005/12/30	118
3	Efficiency analysis of transit signal priority strategies on isolated intersection	2008/06/23	105
4	Study on design methods of pre-signals based on bus priority of intersections	2004/06/15	97
5	Control strategies of urban transit signal priority	2004/05/15	96
6	Bus rapid transit and public transport priority strategy in China	2003/10/09	89
7	Situation and strategy planning of public transit priority development	2010/12/15	84
8	Impacts of traffic management measures on urban network microscopic fundamental diagram	2013/04/15	81
9	A coordinated intersection-group bus signal priority control approach	2009/02/15	81
10	Study on the design of signal phase based on bus priority intersections	2004/12/15	80
11	Bus signal priority method at arterial signal progression	2011/07/20	79
12	Transit passive priority control method based on isolated intersection of optimization of time-space	2007/05/15	78
13	Optimal location of exclusive bus lane and bus stops	2004/07/30	76
14	Transit oriented urban master plan: towards the spatial framework of transit city	2011/02/09	75
15	Transit signal priority strategies based on the consideration of bus frequency	2007/11/15	70
16	Study on the bus priority signal control theory of single intersection	2005/12/15	70
17	Bus-stop spacing optimization based on bus accessibility	2009/03/20	63
18	Bus-priority traffic signal multi-layer fuzzy control model	2006/09/30	63
19	Impact of bus stops on delay and capacity of shared approaches at signalized intersections	2003/01/30	63
20	Isolated transit signal priority control strategy based on logic rule	2008/09/15	60

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Analysis of the literature in Table 3 shows that the research base in the field of public transport priority in the CNKI database was mainly reflected in signal control strategies, bus stop settings, control optimization objectives and social vehicle traffic benefits. The identification of bus lanes was included as a key measure for building bus rapid transit (BRT) based on the analysis of BRT development prospects (Chen, 2003). In terms of bus stop locations, the mechanism of the impact of signal-controlled intersection bus stops on intersection traffic benefits such as vehicle delays and capacity were investigated (Wang and Yang, 2003), and the location of bus stops was analyzed to determine the location of bus stops in bus lanes (Yang and Yin, 2004). The intrinsic relationship between bus stop spacing and space-time accessibility was analyzed to optimize the setting of bus stop distances (Zhang et al., 2009). In terms of public transport priority control measures, the average vehicle delay, per capita delay, vehicle capacity, and traveler capacity were used as traffic benefit indicators to improve the public transport signal control strategy to minimize the loss of other social vehicle traffic benefits while prioritizing public transport (Ji et al., 2004; Li and Lu, 2006; Liu et al., 2004; Xu and Feng, 2010; Yin and Yang, 2005; Zhang et al., 2004, 2006; Zhang and Lu, 2005). In 2000, to improve the applicability of the public transport priority control strategy, a priority control strategy was developed for intersections without bus-only phases, where buses shared the inlet lane with other motor vehicles (Xu et al., 2008). The interaction between the bus departure frequency and priority signal was considered to optimize the bus signal timing based on the bus departure frequency. Research focused only on implementing priority strategies for late bus delays, and the negative impact of early bus arrivals on the transportation system was ignored. Therefore, different priority countermeasures were given for "late" and "early" buses with the control objective of minimizing the delay deviation of vehicles through a cluster of intersections (Ma et al., 2007, 2009; Ma and Yang, 2008). To increase the public transport share of urban travel, land use planning and bus-oriented planning were combined to build the overall planning framework of cluster-oriented cities under a public transport priority orientation (Yang and Zhang, 2011). The previous study of single-point bus signal priority was distinguished by considering the effect of timing parameter adjustment on the green waveband of the arterial and improving the arterial bus priority signal control strategy (Wang et al., 2011). The research scope was expanded, and the road network was taken as the research object to evaluate the impact of public transport control measures on the comprehensive benefits of the road network (Xu et al., 2013).

The research base in the CNKI database covers aspects such as bus lanes, bus stops, priority control measures for public transport, bus signal timing, and the integration of land use planning and bus-oriented planning. These research elements are mainly aimed at optimizing the operational efficiency and service quality of public transport systems and improving the attractiveness and competitiveness of public transport by analyzing the operational mechanisms and characteristics of transport systems.

3.4.2   Analysis of the knowledge base in the WOS database

Literature co-citation analysis was conducted through CiteSpace. As shown in Fig. 4, a larger node indicates a larger number of citations. The WOS database knowledge base analysis was performed based on the citation volume.

Figure  4.  Co-citation analysis map of papers in the WOS database.

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The analysis of the important nodes in Fig. 4 shows that the optimization objectives of the research base in the area of public transport priority in the WOS database were roughly classified into two stages. In the preliminary stage with the optimization objective of reducing vehicle delays, the adaptive bus signal priority optimization model was built (Li et al., 2011), and the priority was identified using real-time phase information segmentation time deviation to achieve real-time bus priority control (Lin et al., 2013; Zeng et al., 2014). On this basis, social vehicles were considered to coordinate the lane sharing between buses and social vehicles (Dai et al., 2016a), and signal intersection pre-signal adaptive control algorithms applicable to the traffic demand of bus and social vehicles were developed (Feng et al., 2015; He et al., 2016), achieving a priority strategy with minimal negative impact on other vehicles (Guler et al., 2016; Guler and Cassidy, 2012; Guler and Menendez, 2014a, 2014b). Under the concept outlined in the statements that "public transport priority was essentially a human priority", more attention was paid to the efficiency of human travel, and a priority strategy with a human target was proposed (Farid et al., 2015). With the goal of minimizing human delays at intersections, fixed signal priority was improved, and real-time signal control systems were developed (Christofa et al., 2013, 2016; Christofa and Skabardonis, 2011). To resolve the contradiction between multimode priority control, signal intersection priority was screened, and a dynamic decision control model was built to achieve integrated priority coordination control (He et al., 2014; Ma et al., 2014). With the development of Internet of Vehicles technology, bus signal collaboration and continuous signal coordination were achieved using vehicle information interconnection technology and bus signal priority logic based on a redistribution of the intersection signal green signal time (Hu et al., 2014, 2015). The impact of a series of public transport priority measures was assessed using a delay function (Truong et al., 2017a), and the delay function was used to evaluate the impact of a series of bus priority measures and road space priority measures, showing that the combined benefits of public transport priority were less than the sum of the benefits of the two measures (Truong et al., 2017b). Using a delay function, a series of bus priority measures were evaluated to determine their effects. Through spatiotemporal analysis, the combined effects of bus signal priority and road space priority measures were investigated. The results demonstrated that the overall benefits of bus priority were less than the sum of the benefits of the two individual measures (Truong et al., 2017b). However, combining the two measures may lead to a multiplier effect in reducing delays for one-way bus routes (Truong et al., 2017c). To minimize bus delays, a bus signal priority control model was subsequently proposed. This model is suitable for bus routes with dedicated bus lanes and is capable of identifying the optimal priority strategy for every possible intersection (Truong et al., 2019). The optimal adjustment of intersection signal timing was carried out with the goal of improving the reliability of bus services based on reducing delays (Chow et al., 2017).

In the early research on the WOS database, reducing vehicle delay was the main optimization objective. With the emergence of social vehicles, later research paid more attention to the negative impact of public transport priority on social vehicles. By introducing the concept that "public transport priority is essentially the priority of people, " the research focus gradually shifted from reducing vehicle delay to minimizing the delay of pedestrians at intersections. At the same time, research methods also gradually evolved from building adaptive public transport signal priority optimization models to using vehicle interconnection communication technology to achieve public transport signal cooperation and continuous signal coordination. Ultimately, through the use of delay functions evaluation and spatiotemporal analysis, it was found that combining public transport priority measures with road space priority measures can generate multiplier effects. These research results provide solid references and guidance for the theory and practice of public transport priority.

3.5   Research frontiers

Research frontiers are emerging theoretical trends and new themes in a field that represent the current state of thought in a research area. Research frontiers are important for research in the subject area, allowing researchers to accurately grasp the latest evolution of disciplinary research in a timely manner and to predict the direction of disciplinary development and hot issues that require further research in the future. In CiteSpace, the clustering of the knowledge base is determined by noun terminology extracted from the cited literature. Thus, the frontiers of research in the field were analyzed through literature co-citation clustering.

3.5.1   Frontier content

Table 4 shows the top 8 clusters with the largest co-citation cluster sizes in literature in the WOS database. The cluster label names are the research frontiers of public transport priority. The S value is the average profile value of clustering, and it is generally considered that the clustering is reasonable if S is greater than 0.5, and the clustering is convincing if S is greater than 0.7. The graph of the WOS database literature co-citation clustering analysis is shown in Fig. 5.

Table  4.  Co-citation clustering information of literature in the WOS database.

Cluster ranking	Cluster size	S value	Average year	Cluster label
#0	56	0.856	2012	Advanced public transit systems
#1	48	0.900	2016	Pre-signal
#2	43	0.911	2016	Bus bunching
#3	35	0.947	2017	Delay
#4	20	0.978	2013	Arterial coordination
#5	19	0.976	2015	Road vehicle speed
#6	14	0.954	2011	Bus lanes
#7	12	1.000	2012	Comparative assessment

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Figure  5.  Co-citation clustering of literature in the WOS database.

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(1) #0 advanced public transit systems. An advanced public transport system mainly includes the setting of bus lanes and the control strategy of bus signal priority. For bus lanes, the mixing of buses with social vehicles was studied based on the analysis of bus lanes, especially intermittent bus lanes (Chiabaut et al., 2014), bus lanes during double-peak hours were the focus of evaluation (Yao et al., 2015), the impact of bus signal priority was considered (Wu and Guler, 2018), the goal of total travel cost minimization was achieved (Zheng et al., 2017), the road space between social vehicles and buses was allocated in a coordinated manner, the optimal space sharing between service modes was determined, and the method of setting urban bus lanes was optimized (He et al., 2018). For bus signal priority, an improved control strategy was able to reduce the waiting time of bus passengers at downstream bus stops while ensuring no increase in travel delays for all passengers (Lin et al., 2013). Travel delay prediction was carried out based on advanced detection technology (Farid et al., 2018), and the minimum deviation between passenger delays and bus arrival times was taken as the dual objective to solve the signal priority conflict at intersections and optimize the control strategy at signalized intersection (Li et al., 2016; Xu et al., 2016). The optimal control method for the bus stop waiting time, signal timing and bus speed was given by coordinating the interaction between bus priority control and speed control with total cost minimization as the optimization objective (Wu et al., 2016). In order to improve the reliability of public transport services, a comprehensive evaluation index system for the reliability of urban public transport networks was proposed using dynamic microscopic simulation models (Sorratini et al., 2008). Regarding advanced technologies in public transport systems, research has considered the priority of public transport at signalized intersections, critically discussed the pros and cons of London's more advanced automatic vehicle location system for bus positioning, and analyzed how to use this system to build a more efficient public transport priority system (Hounsell et al., 2007, 2008). This clustering focuses on optimizing the setting of dedicated bus lanes by evaluating their mixing conditions with other vehicles and coordinating the allocation of road space. In terms of bus signal priority, it proposes methods to improve control strategies, predict travel delays in advance, and solve conflicts between signal priority for buses and other vehicles. It also provides strategies for optimizing signal control at intersections and improving other important performance metrics for public transport.

(2) #1 pre-signal. The control strategy of bus signal priority has always been a research hotspot in this area. To maximize the overall capacity of the roadway and eliminate traffic intermingling, an integrated design model was constructed (Zhao et al., 2017). The bus travel time of passengers was predicted (Yu et al., 2017), in order to evaluate the public transport signal priority methods and improve the overall reliability of the public transport system (Yang and Shi, 2017), a performance evaluation framework for public transport signal priority was constructed (Lai et al., 2020). This was achieved by using mobile phone location data to establish evaluation indicators for public transport priority and conducting parameter optimization (Huang et al., 2012). And the bus signal priority method was evaluated to improve the overall reliability of the bus system (Yang and Shi, 2017). The queue lengths of the pre-signal and main signal were predicted based on real-time vehicle information (Liang et al., 2018), and the signal parameters were optimized for adaptive control based on dynamic programming methods with the optimization objectives of minimizing vehicle delays and scheduling delays or stopping the least number of times while reducing delays of social vehicles (Bie et al., 2017; Ghanbarikarekani et al., 2018; Guo and Wang, 2021; Yang et al., 2019a). Based on real-time bus and traffic conditions, a bus signal control method was developed to determine priority. This method has been improved to enable real-time switching or modification of the priority scheme already implemented to adapt to changes in traffic conditions (Lee and Shalaby, 2013). An arterial bus signal priority assessment model was constructed for the study of urban arterial roads (Wu et al., 2020), drawing on the advanced development experience of urban arterial roads with high bus flows in typical Asian and European cities and combining with the actual situation in China to adjust the bus flow, dynamically controlled based on the traffic volume of the road section as well as traffic flow information (Chen et al., 2023; Kim et al., 2019). To unify the rapid transit system and public transport signal priority system, a system framework was proposed that simultaneously considered priority for bus-only lanes, public transport routes, and mixed-traffic lanes (Islam et al., 2018). Another study proposed a bandwidth-maximizing signal control model that could select the optimal transit signal scheme based on traffic volumes, transit ratios, and the geometric conditions of intersections (Cheng et al., 2018). A new scheduling strategy was proposed, which differs from the traditional method that compares the headway time of a bus with its scheduled headway time. Instead, it considers the headway time of the following bus to obtain better priority benefits (Hounsell and Shrestha, 2012). The main contribution of this cluster is the development of a comprehensive design model and a series of optimization and control models based on big data and dynamic programming methods, aiming to improve the overall reliability and capacity of the public transportation system. An evaluation model for the public transportation signal priority was constructed in consideration of practical situations. Additionally, more effective scheduling strategies were proposed to achieve better priority benefits, providing useful references for the field of public transportation signal priority.

(3) #2 bus bunching. The transit cluster problem has been a hot issue in recent years, and a large number of research results emerged in 2019 and 2020. In the context of bus lanes, the bus clustering problem was addressed by adjusting the speed of buses in bus lanes based on the effect of bus lanes on bus clustering (He et al., 2019c), which led to the construction of a cooperative multitransit control strategy (Zhou et al., 2019). With the shortest travel time as the optimization goal, bus and social vehicle lanes at sections and intersections have been replanned, reconfiguring bus and private vehicle lanes on road sections and intersections and integrating signal timing optimization in an unified framework, which in turn led to integrated control optimization (Zhao et al., 2019), the construction of an optimization model for bus guidance and priority control, and the provision of a variable bus lane design method (Shu et al., 2019). Research integrated bus headway spacing correction and bus priority to coordinate the passage of opposing buses in two-way lanes and proposed an integrated multiroute waiting control strategy to achieve bus headway spacing regulation on multiple routes in an integrated and synchronized manner (Seman et al., 2020a, b). The research of this cluster focused on the problem of bus clustering. Multiple coordinated control strategies were proposed, including adjusting the speed of buses on dedicated bus lanes, reconfiguring lanes for buses and private vehicles on road segments and intersections, and optimizing signal timing. In addition, a dynamic headway control method for high-frequency bus routes on dedicated lanes was proposed based on a model predicting public transportation travel times. By coordinating the passage of opposing buses on both directions of the roadway, the headway of buses on multiple routes was regulated.

(4) #3 delay. The study proposed a public transportation delay prediction algorithm based on advanced detection, which could dynamically adjust the weights of performance indicators (Han et al., 2014). The use of bus lanes by social vehicles was dynamically controlled based on vehicle information interconnection technology with the optimization objective of vehicle delays or traveler delays (Zheng et al., 2020). A travel time prediction model for buses and social vehicles was constructed (Bayrak and Guler, 2020) to handle bus signal priority requests while considering dynamic queuing and queue overflow (Liu et al., 2021b), and a regional coordinated bus priority signal control method was given (Girijan et al., 2021; Li et al., 2021). The traffic benefits of nonpriority social vehicles were considered for priority decision-making based on delay reduction (Liu, 2021; Liu et al., 2021a), and an online adaptive system was developed for real-time signal decision-making (Zeng et al., 2021) to seek optimal economic benefits while minimizing delays (Hassan et al., 2021; Lin et al., 2020). Signal timing strategies were used to effectively improve traveler throughput at signalized intersections and reduce average travel delays (Mohammadi et al., 2021). Acceleration decay time was considered, and based on survival models, the arrival time of buses at downstream stops and intersections was predicted for different types of events (Sharmila et al., 2020). This clustering mainly addressed the issue of delay and proposed a delay prediction algorithm. The optimization objective considered vehicle delay or traveler delay, and the average travel delay was reduced while increasing traveler throughput through a prediction model of the travel time of public and social vehicles and a coordinated public transportation priority signal control method. The arrival time of public buses at downstream stations and intersections was predicted, and an online adaptive system was used for real-time signal decision-making to seek optimal economic benefits.

(5) #4 arterial coordination. A "self-organizing signal" control model was proposed for the coordinated optimization of trunk lines based on locally driven control (Cesme and Furth, 2014). A multimode urban transport system was taken as the research object. Based on the integrated bus signal priority system of trunk lines (Kim et al., 2018), the traditional signal control model was combined with a bus signal control strategy to balance the travel delays of social vehicle users and bus passengers by considering the impact of traffic demand and the geometric characteristics of trunk roads (Christofa et al., 2016; Cvitanic, 2017; Liu and Qiu, 2016). Joint control of real-time intersection signal timing was achieved by a mixed-integer planning approach with the objective of minimizing bus delays or travel times and minimizing the impact on other nonpriority social vehicles. A multimodal traffic signal priority control model was constructed by considering multirequest traffic signal priority in an interconnected vehicle environment (Lee et al., 2017; Song et al., 2018; Zamanipour et al., 2016). A study suggested that the time required for bus priority requests should be provided by the phase with the lowest traffic volume to minimize bus delays (Hua et al., 2017). Using motion wave theory and queuing theory, buses were considered as moving bottlenecks, and dynamic planning algorithms were developed to evaluate the benefits of implementing bus signal priority strategies (Wu and Guler, 2019). A coordinated traffic signal control model for continuous intersections was constructed with the objective of maximizing the effective bandwidth of vehicle green waves to reduce vehicle delays (Bai et al., 2018). An intelligent traffic signal integration control model was developed with the goal of breaking the limitations of traffic facilities and intersection width (Beak et al., 2018). This cluster's main contribution was proposing a self-organizing signal control mode based on the comprehensive public transport signal priority system on the mainline to balance travel delays for both vehicles and public transport passengers in urban transportation systems. This mode realized real-time intersection signal timing joint control and considered the priority of multiple requests for traffic signals in the connected vehicle environment. By using dynamic programming algorithms and continuous intersection traffic signal coordination control models, it overcame infrastructure limitations and intersection distance restrictions, improved traffic efficiency and passenger travel experience.

(6) #5 road vehicle speed. A public transport priority performance evaluation index system was established (Zhang et al., 2019). A travel time prediction model was constructed to improve the accuracy of traffic flow prediction (Cheng et al., 2019). Speed regulation by a control strategy was tested when two vehicles approached an intersection to ease the intersection vehicle traffic intermingling (Vaz, 2019). The maximum priority and minimum time deviation of the nontransit phase were used as the optimization objectives, and the migration state of the coordinated phase and the queuing state of nontransit vehicles were fully considered to propose a transit signal priority control method in the vehicle interconnection environment (Teng et al., 2019). It was concluded that decisions related to urban transit network expansion problems should make specific trade-offs based on objective circumstances rather than a unique optimal solution (Wei et al., 2019). There were studies focused on optimizing bus trajectories by considering both vehicle speed and traffic signal control. The problem was viewed as a multi-objective cost function problem. To address this issue, a novel optimization algorithm was proposed and implemented in Quebec City, Canada. The results of the experiment indicated that this method outperformed traditional control methods (Zimmermann et al., 2021). The main contribution of this cluster was the establishment of a performance evaluation index system for public transport priority and the proposal of a bus signal priority control method in a connected vehicle environment. This improved the efficiency of urban public transport services and traffic flow. The study also emphasized that the decision-making regarding the expansion of urban public transport networks should be based on objective circumstances rather than a single optimal solution. Additionally, a novel optimization algorithm was proposed and implemented to solve the problem of optimizing bus trajectories considering vehicle speed and traffic signal control.

(7) #6 bus lanes. Lane sharing between buses and social vehicles was implemented to resolve the issue of underutilized bus lanes and to increase the carrying capacity of the transportation network (Guler and Cassidy, 2012). This study conducted an empirical analysis on the impact of bus lanes on road safety. The research findings revealed that the implementation of bus lanes led to a significant reduction in road traffic accidents, with a significant decrease in the number of fatal and serious accidents (Goh et al., 2013, 2014). Related studies have demonstrated that bus lanes can reduce passengers' travel time to a certain extent (Bhattacharyya et al., 2019). The study focused on the bus lane network and explored its impact on heterogeneous traffic flow in urban areas (Arasan and Vedagiri, 2008). Bus lane networks were used as the study object, the combined benefits of bus lanes were quantified, and the formation of lane combinations was found to produce multiplier effects (Truong et al., 2015). A bilevel planning model of the urban bus lane layout considering accessibility and budget constraints was constructed to solve the problem of bus lane allocation in multimodal transport networks with the goal of minimizing the travel time and minimizing the differences in passenger comfort between all bus routes (Chen, 2015; Yu et al., 2015). The research conducted in this cluster demonstrated that the implementation of bus lanes improved road safety, reduced travel time for buses, and increased the carrying capacity of the transportation network. Multiple bus lanes produced a multiplier effect, where the benefits of travel time for both public transportation and overall transportation were directly proportional to the number of bus lanes. A bilevel planning model could be utilized to address the allocation of bus lanes in multimodal transportation networks.

(8) #7 comparative assessment. The development experience of advanced typical cities in other countries was evaluated and analyzed, from which ideas for the development of domestic public transport systems were derived. The impact of South Africa's public transport development strategy on users' travel choices was explored, and BRT development was found to increase the physical activity level of the South African population (Bartels et al., 2016). Indian scholars utilized the Delphi method to determine the priority of factors that need to be considered in the decision-making process for the development of public transportation systems in India (Lambat et al., 2019). It was discussed that local governments in Sweden value the role of public transport in society, and inspired by the advanced experience of Sweden, public transport should be given the same priority as other social functions (Stjernborg and Mattisson, 2016). The development experience of public transport in Lahore was analyzed, and the assessment method for the impact of public transport on the environment and society was used for reference, as well as the detection and identification of people's willingness to use public transport (Mansoor et al., 2016). This cluster summarized the public transportation development experiences of advanced typical cities in other countries, and analyzed case studies from South Africa, India, Sweden, and Lahore. It mainly emphasized the importance of drawing on the public transportation development experiences of other countries, including their impacts on user travel choices, priority factors to consider, equal priority of public transportation with other social functions, and evaluation methods for environmental and social impacts.

3.5.2   Frontier analysis

Through the summarization and analysis of cutting-edge content in the field of cluster analysis, we can discuss the research frontier in the area of bus priority from three different perspectives: theoretical trends, advanced technologies, and frontier themes.

(1) Theoretical trends. Based on the analysis above, the field of bus priority can be divided into three main aspects: multimodal transport integration, big data-driven decision-making, and intercity cooperation. The integration of public transportation with other transportation modes can lead to a more efficient, sustainable urban transportation system. Meanwhile, the rapid development of big data technology makes it possible to build intelligent transportation systems that utilize big data to drive strategic decision-making and real-time adjustments, resulting in more flexible and precise bus priority strategies. In addition, intercity cooperation is also an important theoretical trend in the field of bus priority, which focuses on achieving data exchange and accurate prediction, promoting technology and experience sharing, and ultimately improving the overall service level of urban transportation networks.

(2) Advanced technologies. With the rapid development of emerging technologies such as artificial intelligence and autonomous driving, it is essential to explore how to integrate and utilize these new technologies to introduce novel ideas and directions into the field of bus priority. In the process of developing urban public transportation networks, traffic network modeling technology is indispensable. Constructing urban traffic network models and analyzing the impact and benefits of various situations and strategies enable us to provide more scientific and reasonable priority strategies.

(3) Frontier themes. More and more scholars are shifting their focus towards the consideration of social equity when conducting research on bus priority strategies. The development of urban planning must not only ensure the efficiency of public transportation but also pay attention to the universality and fairness of urban residents, considering issues such as equal access to public transportation. With the increasing global concern for climate issues, China has proposed the target of reaching carbon neutrality. From city managers to urban residents, attention is gradually being paid to the environmental friendliness of travel. Green and low-carbon transportation has become a frontier hot topic in the transportation field, and addressing the important issue of reducing the impact of urban transportation on the environment through bus priority control measures is imperative.

3.6   Research hotspot analysis

A research hotspot is one or more topics of common interest to scholars in a certain field and has strong temporal characteristics. High-frequency keywords can reflect the research hotspots in a field, which have a key role in grasping the future development direction and change trend of the subject area (Ding and Zhong, 2015). This paper conducted a keyword co-occurrence analysis of the retrieved sample literature using CiteSpace. The node size reflects the frequency of occurrence in a given field (Shen et al., 2022; Zhang and Ma, 2007). Through keyword co-occurrence analysis, the correlation between various research topics in the subject field can be further grasped, which is helpful to further understand the system of the knowledge structure and current research hotspots of a certain subject (Zhong et al., 2008).

3.6.1   Analysis of research hotspots in literature in the CNKI database

Keyword analysis was performed on literature in the CNKI database to generate a co-occurrence knowledge network map, as shown in Fig. 6. In this network, there are 498 nodes and 605 connected lines, and the total network density is 0.0049. A node with a mediated centrality of more than 0.1 is generally considered a key node. The keywords signal timing, bus line network, bus metropolis, and conventional bus have high centrality, indicating that for domestic research results, these research topics are the key linkage points for the development of the field. The top 20 keywords with the highest frequency in the area of public transport priority in the CNKI database are shown in Table 4. Fig. 6 and Table 5 show that public transport priority, public transportation, traffic engineering, urban transportation, and signal priority appeared most frequently in the CNKI database and were the research hotspots in the field of public transport priority. However, considering that the search scope was the field of bus priority, it was inevitable that keywords covering a wide range of areas, such as public transport priority, public transportation, traffic engineering, and urban transportation, appeared more frequently. Thus, it was not reasonable to analyze the research hotspots based on the frequency of appearance. CiteSpace was used to detect keyword bursts in the CNKI database transit priority areas because research hotspots changed over time. In CiteSpace, emergent terms are those whose frequency of occurrence increases suddenly (i.e., at a faster rate of growth) in a relatively short period of time or whose rate of growth in frequency of use increases significantly (Yang and Wei, 2010). Emergent detection can demonstrate a sudden increase in the frequency of certain specific keywords in a specific time period, and "a set of emergent dynamic concepts and potential research questions" can represent a research hotspot in a certain field (Chen, 2006), as shown in Fig. 7.

Figure  6.  Keyword co-occurrence map of the CNKI database.

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Table  5.  High-frequency keywords of literature in the public transport priority field in the CNKI database.

Ranking	Keyword	Frequency	Centrality
1	Public transport priority	282	0.48
2	Public transportation	122	0.17
3	Transportation engineering	75	0.19
4	Urban transportation	71	0.12
5	Signal priority	48	0.17
6	Bus rapid transit	39	0.04
7	Regular bus	36	0.24
8	Traffic planning	33	0.19
9	Transit metropolis	28	0.35
10	Traffic simulation	27	0.08
11	Traffic congestion	26	0.23
12	Intersections	25	0.16
13	Signal timing	23	0.44
14	Delay	21	0.18
15	Bus line network	17	0.35
16	BRT	16	0.24
17	Low-carbon transportation	16	0.07
18	Green transport	14	0.00
19	Priority strategy	4	0.16
20	Bus lane	12	0.16

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Figure  7.  Keywords with the strongest citation bursts in the CNKI database.

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The burst keyword statistics in the area of public transport priority from 2003 to 2021 were obtained through burst detection analysis, and the development of the CNKI database in the area of public transport priority was roughly divided into three stages for analysis.

(1) The first phase. From 2003 to 2009, the main research hotspots focused on solving urban traffic congestion problems and improving the capacity of urban road traffic through public transport priority strategies. Public transport was identified as the preferred mode of transportation in China's cities, and the necessity of setting up bus-only lanes as well as bus signal priority was established (Zhang, 2003) to improve the route density and accessibility of public transport, ensure the absolute priority of right-of-way for surface public transport, and thus increase the share of public transport in total urban traffic (Zhang and Chen, 2003). On this basis, the influence of bus lanes on the capacity of signalized intersections was analyzed (Bai et al., 2004). A signal-controlled intersection bus priority phasing design was developed based on intersection traffic benefit indicators to analyze roadway capacity for mixed urban traffic flows (Ji et al., 2004). The effect of the location of bus stops near intersections on intersection capacity was quantitatively analyzed (Pei and Wu, 2004; Wang and Yang, 2003).

(2) The second phase. From 2010 to 2015, by applying priority control measures for public transportation to promote the economic and environmental energy sustainability of a city, research in the direction of low-carbon transportation, transit cities, green transportation, etc. proliferated.

Studies focused on urban transportation carbon emission assessment methods, quantified urban transportation carbon emissions based on residents' daily travel behavior, predicted carbon emission growth trends (Ma et al., 2011), and proposed a carbon emission conversion factor applicable to China (Su et al., 2012). It was found that the urban trip structure had a more significant impact on carbon emissions than total trips, and the differences in share rates due to changes in other low-carbon factors were analyzed (Shuai et al., 2012). By comparing the carbon emission intensity of various major transportation modes, it was concluded that public transportation development had an absolutely obvious advantage for carbon emission reduction (Chen and Zhang, 2010), and the route to low-carbon urban transportation structure was given by building an intelligent public transportation network starting from the urban spatial structure and urban transportation structure (Gong et al., 2013). Based on the low-carbon transportation system evaluation system, the harmonious development of people, vehicles, roads and the environment in low-carbon cities was promoted (Liu, 2013).

In terms of public transport cities, the public transport service quality evaluation index system and the quality index of the public transport city development index of the effectiveness of the construction of public transport cities were constructed (Chen and Yang, 2013; Yang and Chen, 2013). Additionally, the public transport city evaluation index system was optimized based on city construction (Wei and Dai, 2014). Based on a low-carbon travel perspective and drawing on the advanced experience of foreign transit-oriented development models, the construction priorities of China's public transport cities were clarified (Ding et al., 2013), with some studies suggesting that the construction priorities of public transport cities should be strengthening the overlay of slow-moving traffic and bus road networks to build a multilevel public transport system (Shi and Xu, 2014). In addition, some studies suggested that the construction priorities should focus on building a bus backbone network that meets the characteristics of travel and economic development (Zhang and Wang, 2015). Some scholars even argued that building a public transport city should focus not only on the indicators of public transport operation but also on the feelings of travelers (Zhou, 2015). It was generally agreed that the development of a transit city was not the same as the development of a transit-oriented city and that the proportion of public transport trips did not fully represent the level of development of a transit city. Therefore, a transit city is not only an enhancement of a transit-oriented city but also a synergistic development of multiple modes of transport (An et al., 2015; Sun et al., 2013).

Since the 2009 United Nations Climate Conference in Copenhagen a number of programs have been implemented in various areas to address climate change and achieve the goal of zero emissions. Among them, "green transportation" has been particularly effective. Combined with government policies and market mechanisms, the intrinsic causal relationships among three systems, i.e., the socioeconomic system, transportation supply and demand system, and transportation energy consumption and carbon emissions, have been clarified to analyze the influence mechanism of urban transportation energy consumption and carbon emissions (Zhou, 2012). For the development principle of green transportation, the research community has generally agreed that the proportion of public transport trips should be increased while ensuring the priority of walking. Based on the theory of travel as a service, the level of traffic refinement was improved, the connection between slow traffic and other modes of travel was optimized, and a "public transport + slow traffic"-led model was built, with the goal of building an integrated transport system based on public transport and slow traffic; additionally, comprehensive transport policies and control measures were proposed (Fan and Li, 2014; Wang et al., 2015a; Zhou and Zhou, 2010). Urban spatial travel segment differences were analyzed from the traveler perspective by measuring urban walkability, transit convenience accessibility, and car dependence (Wang et al., 2013). Inspired by the development of typical low-carbon cities in Japan and Korea from the perspective of urban construction, the traditional "big street district and wide road" planning model was broken, and green transportation theory was combined with ecological city construction to create a compact city layout by combining intelligent transportation and a hierarchical transportation system for the transportation planning of new ecological districts (Luan and Park, 2013; Wu et al., 2014).

(3) The third phase. From 2016 to 2021, the shift from "vehicle-centric" to "human-centric", the genetic algorithm, BRT, big data, the service level, small and medium-sized cities, and signal timing were emerging hotspots.

The genetic algorithm was mainly applied to solve multiobjective models in the area of public transport priority. In the area of bus lanes, the layout of the bus lane network, the location of bus lanes, the optimization of bus lanes, and the optimization of the travel paths of travelers were investigated by constructing a multiobjective planning model and applying genetic algorithms to solve it, taking the overall travel cost of travelers and the maximum benefit of the road network system as optimization objectives (Chen and Xu, 2016; Lu et al., 2016, 2017a, 2017b, 2017c). In bus signal control, a signal timing optimization model was constructed with the minimum total delay time and minimum per capita delay as optimization objectives, in addition to considering the negative influence of bus signal priority on the reliability of the road network. Furthermore, the real-time bus signal control strategy for intersections was given based on intersection clearance reliability and solved using genetic algorithms (Li et al., 2017; Qiao and Wang, 2017). The prediction of bus arrival times based on genetic algorithms combined with neural networks in bus line optimization provided a technical basis for improving the service quality of bus systems and developing intelligent transportation (Luo et al., 2016). In addition, genetic algorithms were used to optimize the vehicle scheduling and departure frequency of bus routes to improve the spatial accessibility of the overall bus route network and increase the attractiveness of bus trips (Dai et al., 2017; Jiang et al., 2016b; Wang et al., 2018).

In the new era, BRT systems have become very important for urban transportation development. A BRT system was developed based on the principles of long-term sustainability and passenger experience, making full use of big data and "Internet+" resources to improve the operational efficiency of urban transportation systems (Wu et al., 2018). The integrated development of urban public transportation with "rail transit as the backbone and road transit as a supplement" was promoted, coordinating the coexistence of BRT and conventional public transportation and realizing the synergistic operation of both (Chen and Long, 2016; Dong et al., 2016). The timing strategies of BRT signals were given from the perspective of travelers and vehicles (Zhou et al., 2016). In addition, the land development and utilization strategy for the area around a rail transit station that serves bus priority was studied (Zhuo, 2016).

With the development and maturity of Internet+ and big data technologies, the use of big data to realize scientific decision-making and the management of urban transportation systems has become a research hotspot. In addition to mobile phone signaling data, studies have constructed multidimensional data models for urban bus travel based on intelligent bus system data to accurately and quickly analyze different dimensions and granularities of bus travel information (Chen et al., 2019). Based on big data, the selection characteristics of residents' bus travel were studied, operation management decisions were innovated, a smart transportation travel service platform was created, and public transport priority and green transport development were promoted (Fei, 2018; Lin, 2017; Yang et al., 2019b).

The level of service is an important attribute of urban public transport systems and an important measure of traveler perceptions. The level of service of bus lanes was graded from the passenger perspective to assess the overall benefits of installing bus lanes and to thus optimize the traffic organization scheme (Huo and Li, 2018; Jiang et al., 2016a). Passengers and bus operators were comprehensively considered to explore the optimization methods for bus stop setting, the model configuration scheme, bus departure intervals and coordinated scheduling to optimize the urban bus line network. Additionally, the service level and operational efficiency of public transportation were enhanced (Weng et al., 2019; Yang et al., 2017; Yao et al., 2020b). With the promotion of supply-side reforms, public transport attractiveness impact indicators were given for travelers with different options based on the interaction effect of the level of public transport service and transport demand management (Yao et al., 2020a). The relationship between the level of bus service and the impact of traffic energy consumption was quantified to achieve the goal of a dual carbon strategy, and bus priority control measures and route network optimization, such as departure intervals, were carried out to improve the level of bus service while reducing traffic energy consumption and carbon emissions (Wang et al., 2017; Xu et al., 2018).

From a supply-side perspective, public transport supply was the variable with the greatest impact on carbon emissions from commuting among land use factors, and the more adequate the supply of public transport was, the greater the probability that travelers would choose a low-carbon commute (Dai et al., 2016b). A study concluded that changing the transportation planning of small and medium-sized cities from a single mode to a multimodal transportation system (Li, 2019), strengthening the positioning of public transportation, implementing integrated urban and rural operation and management under the guidance of the government, and adopting public transport priority control measures to stimulate the development potential were an inevitable trend. Drawing on the development experience of typical small and medium-sized cities in foreign countries with less developed economies but successful green construction, it was proposed that small and medium-sized cities should not build subways but develop BRT systems to alleviate traffic congestion based on local conditions (Deng, 2015). In addition, some studies proposed an optimization idea based on regional conditions to optimize the travel structure and improve the proportion of public transport (Wang et al., 2015b). Based on a previous study, the direct access rate was maximized, the total direct access time was minimized, and the direct origin-destination (OD) ratio was innovatively further increased by setting a minimum transfer constraint to optimize the bus network in small and medium-sized cities (Cao et al., 2020).

The goal of intersection signal timing optimization is based on public transport priority. The early signal intersection timing model had the optimization goal of simply reducing the delay of public transport vehicles, and such a public transport priority signal timing strategy can reduce the delay of public transport vehicles but increase the delay of other vehicles at intersections (Feng et al., 2007). Research sought to reduce the negative impacts on social vehicles while implementing bus priority strategies, quantitatively assess the overall benefits of bus priority systems while considering intersection bus delays and social traffic delays, construct intersection signal control optimization models, and propose multiobjective optimal control methods to further secure the overall benefits of intersections (Ma and Yang, 2010b; Ma and Ye, 2013; Wang et al., 2010; Zhu and Long, 2014). In recent years, intersection signal timing optimization has been conducted more often with passenger delays as the optimization objective, and intersection reliability has been used as an important indicator for transit priority signal timing optimization while reducing passenger delays (Qiao and Wang, 2017; Lai and Ma, 2021). In addition, the number of intersection vehicle stops was considered in the development of timing strategies based on delay reduction (Liu et al., 2016; Zhang et al., 2017). Alternatively, multiobjective signal timing optimization was studied with intersection human delays and carbon emissions per capita (Liu and Wei, 2018; Wei and Li, 2016).

The evolution of the research hotspots at each stage in the CNKI database showed that in the early stage, domestic scholars simply devoted themselves to solving the urban road congestion problem, and in the middle stage, the traffic problem was included in the consideration of sustainable urban planning to achieve the synergistic development of the urban economy and environmental energy. With the growing maturity of the Internet and big data technology, data from various industries have gradually been shared, providing a more credible quantitative basis for urban traffic planning, enabling long-term planning visualization and prediction, and improving the reliability of urban traffic planning. In recent years, with increasing global attention to climate issues, China has proposed the strategic goal of "carbon peaking and carbon neutrality", and the transportation industry has assumed an important responsibility in the process of achieving this goal. How to reduce the energy consumption and carbon emissions of urban transportation systems has become a hotspot and a focus of future research.

Due to the fact that most of the research literature in the CNKI database is published by Chinese scholars, the analysis results from the CNKI database can to some extent represent the research trends of Chinese scholars. From the evolution of research hotspots in different stages in the CNKI database, it can be seen that in the early stages, domestic scholars were solely devoted to solving the problem of urban road congestion, while in the middle stages, they aimed to achieve the coordinated development of urban economy, environment, and energy by incorporating transportation issues into urban sustainable planning considerations. With the increasingly mature development of the Internet and big data technology, data in various industries are gradually interconnected and shared, providing more credible quantitative evidence for urban transportation planning and achieving long-term visualized predictions, thus enhancing the reliability of urban transportation planning. In recent years, with the growing global concern for climate issues, China has proposed the strategic goal of "carbon peak and carbon neutrality", and the transportation industry plays an important role in achieving this goal. As a result, how to reduce the energy consumption and carbon emissions of urban transportation systems has gradually become a research hotspot.

Upon conducting an analysis of the research topics in the CNKI database, it is evident that firstly, as China is one of the fastest urbanizing countries in the world, the level and quality of public transportation services are closely related to the quality of life of urban residents. Therefore, compared to other countries, research and practice on public transportation priority strategies are more important in China, and Chinese researchers place greater emphasis on the evaluation of these strategies. Secondly, due to the serious energy consumption and carbon emissions pressure faced by China's urbanization process, Chinese researchers are more concerned with exploring the relationship between public transportation priority strategies and sustainable urban development. However, public transportation priority strategy research in China mainly focuses on the technical aspects such as road priority, signal optimization, and dedicated bus lanes, while lacking comprehensive consideration of public transportation service quality and travel experience.

3.6.2   Analysis of research hotspots in literature in the WOS database

The literature from the WOS database was processed to generate a keyword co-occurrence knowledge graph in CiteSpace, as shown in Fig. 8. There are 415 network nodes, 626 connections and a network density of 0.0073 in the figure. The analysis found that the high-frequency keywords in the papers in the area of public transport priority in the WOS database were model, system, optimization, and priority. The top 20 keywords with the highest frequency in the WOS are shown in Table 6. Based on the principle of research hotspot analysis based on emergent keywords introduced in the previous section, the same emergent detection analysis of WOS database public transport priority areas were performed using CiteSpace. The results are shown in Fig. 9.

Figure  8.  Keyword co-occurrence map of the WOS database.

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Table  6.  High-frequency keywords of literature in the public transport priority field in the WOS database.

Ranking	Keyword	Frequency	Centrality
1	Model	135	0.14
2	System	103	0.08
3	Optimization	101	0.07
4	Priority	76	0.04
5	Transport	68	0.04
6	Time	61	0.02
7	Impact	61	0.01
8	Strategy	56	0.05
9	Algorithm	55	0.10
10	Bus priority	54	0.01
11	Transportation network	52	0.03
12	Design	49	0.12
13	Service quality	42	0.16
14	Assignment	40	0.17
15	Capacity	32	0.07
16	Choice	31	0.25
17	Transit signal priority	30	0.01
18	Allocation	30	0.06
19	Intersection	29	0.03
20	Operation	28	0.03

 | Show Table

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Figure  9.  Keywords with the strongest citation bursts in the WOS database.

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A map of the keywords with the strongest citation bursts in the WOS database in the field of public transport priority were generated, and the development of research hotspots in the area of public transport priority in the WOS database was divided into three stages through inductive analysis.

(1) The first phase. From 2003 to 2014, solving the problem of air pollution was a research hotspot in the area of public transport priority. Research has estimated that using only public transport signal priority strategy can reduce greenhouse gas emissions by 14% under congested conditions (Alam and Hatzopoulou, 2014). Policies on traffic congestion and pollution levels in major cities focused on the concept of sustainable transport systems, which would require a shift from private cars to public transport (Guttikunda and Kopakka, 2014). The changes in the travel decisions of travelers after the implementation of public transport priority management measures in cities were explored (Waterson et al., 2003). Some studies have proposed developing public transport system development strategies from the perspective of urban planning and exploring intervention and priority measures to implement low-carbon transportation through scenario analysis (Hickman et al., 2011). Taking a typical Indian city as an example, the carbon emission reduction brought by the public transport priority strategy was calculated based on data on the public transport system, residents travel, and carbon emissions, indicating that public transport services significantly reduce carbon emissions (Prabhu and Pai, 2012). The study also examined changes in travel decisions of commuters after the implementation of public transport priority management measures, constructed a travel behavior model to evaluate changes in travel patterns (Waterson et al., 2003), and used social cost-benefit theory to assess the impact of public transport priority strategy on improving the environment by reallocating road space (Currie et al., 2007). The operating costs and transport benefits of light rail, conventional buses, and subways were quantified to evaluate the positive role of public transport in decreasing urban congestion and mitigating air pollution under public transport priority control measures (Brand and Preston, 2003). Some studies took the perspective of developing countries and focused on analyzing policy approaches to reduce urban air pollution in developing countries, proposing a cost-benefit equivalence strategy for public transport priority development based on urban transport externalities (Timilsina and Dulal, 2011). A system dynamics model was constructed to calculate urban transportation carbon emissions, the positive effects of public transport priority measures on reducing vehicle energy consumption as well as carbon dioxide emissions were analyzed, and urban transportation management policies for building sustainable urban transportation systems were given (Cheng et al., 2015; Liu et al., 2010, 2015). Research from the perspectives of the European Union and the United Kingdom suggests that it is necessary to implement public transport priority policies related to low-carbon transportation. It also emphasizes the need to increase publicity efforts to improve public acceptance of public transport (Banister, 2007).

(2) The second phase. From 2015 to 2018, the main research hotspots shifted to transit stops as well as transportation networks and focused on passenger trip optimization.

Bus stops provide accessibility to public transportation services, but they can also affect transportation efficiency due to the additional dwell time. An integrated public transport reliability and speed evaluation framework was previously proposed, and the results showed that bus reliability was significantly correlated with bus stop density (Hu and Shalaby, 2017). The location of bus stops is the result of a trade-off between access coverage and mobility, and bus stop locations were determined as a result of the trade-off between access coverage and mobility. The impact of bus stop locations on stopping times was evaluated, considering the interaction between station buses and the number of passengers embarking and disembarking (Bian et al., 2015; Diab and EI-Geneidy, 2015). Thus, the effect of bus stops on the road traffic flow was analyzed (Zheng et al., 2016). In addition, the joint influence of the location and configuration of bus stops on the traffic performance of other vehicles at intersections was explored (Cvitanic, 2017), and a delay analysis of buses at bus stops was conducted (Huo et al., 2018). On the basis of quantitative assessment techniques, the bus stop setting method was studied, and the bus stop layout was developed at the road network level based on the impact of parking congestion on the road traffic flow with the objective of minimizing the total stop dwell time and the total number of bus stops (Chen et al., 2018). To improve the regularity of headway fluctuations, reduce bus clustering, and improve service reliability, a control strategy for bus stopping was given with bus capacity constraints (Wu et al., 2017). A dynamic bus stop model was combined with a static bus assignment model to realize optimal route decisions in the bus assignment process (Owais and Hassan, 2018). Based on the operation and parking characteristics of taxis and buses, the impact of parking behavior on the urban road traffic flow under different combinations of settings was explored, and better stopping distances were determined, which in turn provided a more solid basis for planning and controlling urban traffic (Zeng et al., 2018). In order to more accurately validate the effectiveness of implementing transport public priority strategies in the transportation network, a composite model based on system dynamics was constructed. This model can evaluate the pollutants and carbon emissions generated by the interaction between land use and road traffic network, and simulate the impact of government policies (Guzman and Orjuela, 2017). Subsequently, a study tested two priority schemes, namely bus lanes and bus signal priority, to quantify the environmental impact of bus priority measures at the network level. The results showed that fuel consumption and carbon emissions were effectively reduced (Karekla et al., 2018). The study identified the impact of conventional bus operating behavior characteristics, such as bus stop locations, routes, and schedules, on the accessibility of the bus network (Ho et al., 2011).

Big data were used to reveal traveler choice preferences and were combined with traditional choice modeling methods to assess the reliability of transit networks (Leahy et al., 2016). The reliability and vulnerability of public transport networks were analyzed for better real-time management of public transport networks, including the calculation of the probability of occurrence of adverse events and the impact of such disruptions on network functions in the context of multimodal transport networks. A risk criticality evaluation method was presented (Cats et al., 2016). Based on a previous study, the least reliable level in the urban public transport network was searched for, and the resulting social costs were calculated (Yap et al., 2018). Most studies up to that point focused on vulnerability until it was concluded that disruptions to the public transportation network would result in only a partial reduction in capacity, not a complete shutdown. The relationship between network performance and line capacity degradation was given by analyzing the impact of a series of capacity declines on the performance of public transport networks and refining the vulnerability analysis for complete failures (Cats and Jenelius, 2018). Scholars have been continuously studying the public transport network. Firstly, a bilevel planning model was developed with the optimization goal of minimizing the total travel time to find the optimal combination of bus lanes at the road network level. Further sensitivity analysis was conducted to examine the relative weights of factors in the objective function. Based on the previous research, a method for controlling the priority of public transport was proposed that considers both the mode choice, traffic assignment, and public transport allocation (Mesbah et al., 2008, 2010, 2011).

The stochastic integer planning model was developed to improve the level of service for passenger trips, reduce the randomness of bus travel times, and add idle time to the bus schedule with the objective of optimizing the passenger waiting time cost (Wu et al., 2015). Based on the building and land use status and bus accessibility of each region, the bus passenger flow was predicted, while passenger travel service level improvement and bus route network optimization were achieved (Yu et al., 2016). After analyzing the commuting habits of urban residents, it has been concluded that travel safety is the most important factor when choosing a mode of transportation, followed by reliability, travel cost, and comfort (Jain et al., 2014). However, other studies have found that the choice of transportation mode is primarily influenced by different travel needs (Cheranchery and Maitra, 2017). Using data from automatic vehicle location and passenger counting systems, the impact of public transport service strategies on passenger satisfaction has been studied (Diab and EI-Geneidy, 2012). To improve the public transport experience for users, opinions on the real-time information of transit were investigated (Rahman et al., 2013). Bus passenger assignments were customized to further ensure passenger benefits, balancing factors such as the travel time, the waiting time, delays, and economic costs (Cao and Wang, 2017). In the later stage, the intention was to seek a balance of benefits between bus operators and passengers based on a joint optimization model of the multimodal transport network to optimize the utilization of bus capacity while satisfying the individual demand requests defined by the spatiotemporal window (Tong et al., 2017). The influence of personal characteristics on passenger travel choice behavior was explored, and it was found that most passengers tend to avoid routes that require transfers (Fan et al., 2018). The travel time and waiting time of passengers were reduced by optimizing bus schedules (Li et al., 2018). A dynamic optimal route with fixed destinations was generated for multiple operating buses based on travel demand prediction and dynamic route planning for the "last-mile" travel problem (Kong et al., 2018). There were also studies that aimed to improve the quality of bus services and proposed a set of planning guidelines to guide transport professionals through the optimal design of bus networks (Grise et al., 2021).

(3) The third phase. From 2019 to 2021, research hotspots focused on transit operations, the travel time, and the application of genetic algorithms.

To improve the efficiency of public transport operation, starting by increasing the number of public transport passengers and based on a large amount of public transport operation data, a public transport passenger satisfaction evaluation method was proposed, and then, the impact of key factors on passenger satisfaction was obtained (Zhang et al., 2019). It was found that accessibility measures, social norms, and perceived costs play a positive role in passenger travel satisfaction. To assist in improving public transportation accessibility, a visual analysis system for public transportation accessibility was developed (Andrienko et al., 2020). Factors affecting the efficiency of public transportation operations were analyzed, revealing that external factors significantly impact public transportation efficiency. A model was constructed using key external factor indicators to evaluate the impact of external factors on the sustainable operating environment of public transportation, and appropriate methods were sought to eliminate environmental factors, thereby effectively improving public transportation efficiency (Georgiadis et al., 2020; Li et al., 2020). Data envelopment analysis was used to evaluate the efficiency of existing bus routes, and a multi-modal data model was proposed at the societal level to improve the efficiency of bus routes (Singh et al., 2019). Operational strategies to meet user needs were proposed to ensure future public transport ridership and to maintain the viability of transit operations (Ingvardson and Nielsen, 2019). With the advancement of research on improving the efficiency of bus operations in consideration of passengers combined with the interests of bus companies, with the goal of minimizing the passenger waiting time cost and maximizing the interests of bus companies, the utilization of public transportation resources and the public transportation passenger travel time were analyzed to construct a model of the bus intelligent scheduling problem and to find the optimal bus schedule (Lin and Tang, 2021). Priority strategies capable of independently regulating the priority of public transport and social vehicles have been proposed in order to effectively reduce queuing times for public transport, considering the complexity of Bimodal urban network (He et al., 2019a). A layered network model of the bus network and passenger flows was constructed to assess the suitability of the relationship between transportation supply and travel demand (Sui et al., 2019). Later, some studies proposed the Gini index to measure the imbalance of public transport demand and to more accurately predict the individual supply indicators of bus routes. It was found that a demand imbalance can lead to significantly higher line operating costs when the operating size is controlled (Sui et al., 2019). Most studies addressing public transport operations focused on increasing speed, but this focus on speed ignored reliability, another key dimension of the service level. By optimizing departure intervals based on the reliability of public transport trips, the total travel time, and operating costs, the overall level of service of public transport operations was improved (Munoz et al., 2020). In addition to factors such as accessibility, travel time, and travel costs, comfort is also one of the important factors affecting public transportation. A study conducted a meta-analysis of the factors affecting the comfort of public transportation passengers, and calculated the different customer comfort values for different comfort types (De Gruyter et al., 2019).

Accurate public transportation travel time forecasting is the key to reliable travel planning for urban transportation systems. Public transport priority data was used in a study that concluded that a log-normal distribution was the best mathematical way to describe travel times (Kieu et al., 2015). Previous studies ignored the interaction between multiple buses, ridership and the traffic flow. Therefore, high accuracy and effective prediction of the travel time of bus routes were achieved by considering the transfer time and stop dwell time (Dai et al., 2019; He et al., 2019b). With the development of data technology, the combined data of buses and social vehicles was used to forecast the travel time and classify bus routes into different types to improve the prediction accuracy (Ma et al., 2019). Some researchers analyzed the statistical relationship between the proportion of travel time saved by travelers due to public transport priority and the travel time of cars, and then proposed a modified theoretical model for the secondary benefits of public transport priority (Currie and Sarvi, 2012). To improve the perception of the reliability of the bus system, the results of the effect of bus right-of-way changes on the travel time were obtained by predicting the bus travel time between adjacent stops by considering the changes in right-of-way in time and space (Chen et al., 2020). In addition to bus travel times, bus arrival and departure times are key pieces of information for creating high-quality public transportation. The effects of bus speed at different times of the day and bus network data on the real-time prediction results of bus arrival times were studied (Celan and Lep, 2020), and the distribution pattern of bus travel times during peak hours as well as off-peak hours was clarified (Chepuri et al., 2020).

Genetic algorithms were also mainly applied in the WOS database to solve multiobjective optimization models. To prevent bus congestion, a high-frequency route with bus lanes was set up as a research object, and a bus travel time prediction model under dynamic control was constructed and solved using a genetic algorithm, leading to the development of a dynamic headway time spacing control method (Bie et al., 2020). To realize real-time traffic flow control decisions in a big data environment, a traffic flow-based model was constructed to predict vehicle movements and to evaluate the control performance of candidate strategies, based on which a two-level hierarchical parallel genetic algorithm was designed to speed up the optimal solution (Zhang et al., 2021). To alleviate the urban traffic congestion problem, two network models, a simple network with basic macroscopic traffic characteristics and an advanced network considering vehicle turns and different driving routes between the origin and destination, were combined, and genetic algorithms were applied to solve the optimization problem (Li and Sun, 2019). A topological genetic algorithm-based safety evaluation system for urban rail transit networks was constructed by taking the urban rail transit road network as an example and combining the results of road network vulnerability analysis (Yu et al., 2020). A short-term traffic flow prediction algorithm combining a genetic algorithm and neural network was proposed to accurately predict the traffic flow in urban networks, and its good convergence and timeliness in a short-term traffic flow were verified (Zhang et al., 2020). The optimal traffic network design with the dual objective of road congestion charging efficiency optimization and the vehicle emission cost was obtained using a genetic algorithm with nondominated ranking (Zhou et al., 2021).

As shown by the statistical results of the development of research hotspots in the WOS database, attention to air pollution in the WOS database occurred earlier than in the CNKI database, and the positive role of public transport priority management measures in coping with urban traffic congestion and urban air pollution problems was found long ago. The energy consumption and carbon emissions of transportation systems were quantified, and thus, the costs and benefits of urban transportation were analyzed. Additionally, the importance of transit priority control measures in urban sustainability studies was established. To improve the efficiency of residents' trips and enhance the accessibility of public transport services, the focus was placed on considering the impact of bus stops on the traffic flow, and research was dedicated to proposing the bus stop layout method. At the same time, the research object gradually shifted from road sections to road networks and from improving vehicle speed to improving the level of service for passenger trips. How to effectively use urban infrastructure and land resources in the context of multimodal transportation networks, fully use big data to accurately predict public transport travel times, balance passenger travel costs and public transport operational benefits, improve the core competitiveness of public transport travel modes, and create an efficient, green and highly reliable public transport network have become the focus of research in recent years.

After summarizing and analyzing the research content of the WOS database, several trends can be observed. Firstly, with the advancement of emerging technologies, more and more research are utilizing real-time data collection and analysis through the application of big data, artificial intelligence, mobile signaling, and intelligent positioning. These emerging technologies are becoming important directions for public transport priority strategy research, leading to the development of intelligent traffic management systems based on public transport priority. Secondly, the attitudes and opinions of urban citizens towards public transport priority control measures have become a research focus. The public's subjective feelings can directly affect and reflect the implementation effects of public transport priority strategies, which can be used to optimize priority policies and improve government policy-making. Thirdly, previous research has mainly focused on the design and optimization of public transport priority strategies, with insufficient research on their implementation effects. Recently, research has started to focus on evaluation methods for implementation effects. Moreover, as public transport priority strategies not only impact the public transport system itself but also have an impact on the city's transportation network, recent research has paid more attention to the impact of public transport priority strategies on the city's transportation network and their sustainable effects. In addition, previous research has only focused on the benefits of the public transport system, while recent research is increasingly focused on the comprehensive evaluation of the impact and benefits of public transport priority strategies on the city's economy, society, environment, and other aspects.

4.   Conclusions

This study analyzed the history of research in the area of public transport priority in the WOS database and CNKI database between 2003 and 2021, and many theoretical research results in the area of public transport priority have been applied in practice. The research findings are divided into the following main points.

4.1   Discussions

This comprehensive review provides an analysis of research achievements in public transport priority across different regions worldwide and identifies regional variations in focus and development trends. The results highlight the importance of considering factors such as road environment, traffic conditions, public transportation demand, and policy orientation when implementing public transport priority strategies. Specifically, the review identifies the research focus and achievements in Europe, America, Asia, and Australia. In Europe and America, research has focused on improving the efficiency of public transportation vehicles in the road network and solving traffic congestion problems. Conversely, in Asia, the focus is on the integration and interconnection of public transportation with other modes of transportation, efficient operation and intelligent management of the public transportation system, and improving the quality of public transportation services. In Australia, the research focus is on environmental impact, public transportation safety, electric vehicles, and integration with walking and cycling.

Despite these regional variations, this review highlights the success achieved by several countries in implementing public transport priority strategies. For instance, Sweden and Denmark have implemented several strategies to improve public transport systems, such as Sweden's "Green Wave" system. The UK, France, and Germany have also achieved success with field validations of public transport priority strategies. China has been actively exploring this area and implementing public transport priority systems integrated with urban sustainable development. However, several challenges need to be addressed, including improving infrastructure, frequency, and comfort of public transport services.

In conclusion, the review emphasizes the potential of public transport priority strategies to improve the efficiency, reliability, and sustainability of public transportation systems worldwide. Future research should aim to address the identified challenges and ensure that these strategies meet the needs of communities and passengers, considering regional variations and specific contextual factors.

4.2   Conclusions

The study of public transport priority has encountered certain limitations and shortcomings. Firstly, there exists heterogeneity across different cities and regions in terms of public transport systems, traffic environments, and population characteristics, which current research often fails to acknowledge, leading to ineffective implementation of public transport priority strategies. Secondly, the absence of a comprehensive, unified, and accurate evaluation system creates difficulties in comparing and assessing public transport priority strategies across regions. Thirdly, the long-term impact of public transport priority strategies on the city's road network and transportation system often goes overlooked. Fourthly, the absence of interdisciplinary research hinders the breadth and accuracy of conclusions drawn from studies. Finally, the social, economic, and environmental impacts of public transport priority strategies are frequently disregarded, with a lack of research on the quantitative calculation of environmental impacts.

To address these limitations, research must consider the heterogeneity of cities and develop a comprehensive evaluation system that accounts for regional differences, adopts a long-term perspective, and conducts interdisciplinary research that examines the social, economic, and environmental impacts of public transport priority strategies. By doing so, public transport priority strategies that cater to the needs of various cities and regions can be developed and implemented, promoting the efficiency, reliability, and sustainability of public transportation systems.

4.3   Future works

The importance of public transportation priority strategy in the development of urban transportation cannot be overstated. As future research progresses, four main themes are expected to emerge: intelligence, sustainability, interdisciplinary fusion, and multimodal transportation integration.

(1) Intelligence: the application of intelligent technology will be a key area of focus for future research on public transportation priority strategies. Researchers will explore the use of artificial intelligence and the Internet of Things to optimize the dispatching and routes of public transportation vehicles. Big data technology can also be used to predict real-time passenger flow and to develop an adaptive public transportation priority strategy based on intelligent traffic signal control, thereby improving operational efficiency and service levels.

(2) Sustainability: sustainability will be an integral aspect of future research on public transportation priority. Research will comprehensively consider the impact of economic, social, and environmental factors, with particular attention to the energy consumption and carbon emissions of the urban transportation system. The promotion of green travel methods should be encouraged to further promote the sustainable development of cities.

(3) Interdisciplinary fusion: future research on public transportation priority will span multiple disciplines such as transportation engineering, urban planning, geographic information science, behavioral economics, and psychology, among others. Researchers will focus on interdisciplinary fusion to develop more adaptable public transportation priority strategies that fully consider the interaction between various roles in the public transportation system.

(4) Multimodal transportation integration: multimodal transportation integration will also be a key area of future research on public transportation priority. Researchers will comprehensively consider the public transportation system and other transportation modes, with a focus on the development of a more integrated multimodal transportation system. Optimizing the connection between the public transportation system and other transportation modes such as slow transportation, taxis, and ride-hailing services can improve the overall efficiency and reliability of the urban transportation system, create a more complete multimodal transportation system.

In conclusion, as research on public transportation priority strategies continues to evolve, there is a need to consider the application of intelligent technology, sustainable development, interdisciplinary fusion, and multimodal transportation integration to develop adaptable public transportation priority strategies that will promote the efficiency, reliability, and sustainability of urban transportation systems.