Abstract: Sustainable travel behavior intervention is an essential strategy to promote the development of urban transportation. The interventions offer personalized strategies based on certain scenario and participants to promote its effectiveness over hard travel restrictions. However, personalized strategies may also bring about difficulties to identify the actual effect of the measures. Furthermore, based on current practice, to make full use of travel behavior interventions, it is necessary to construct a unified methodological evidence-based framework to assess the reliability and effectiveness of travel behavior intervention studies. In response to these issues, we applied evidence-based knowledge graph to the field of sustainable travel behavior interventions to help decision supporters design sustainable travel behavior interventions wisely and in turn avoid excessive use of hard travel restrictions. We introduced concept of evidence-based practice to conduct a systematic analysis concerning reliability and validity of current full volume empirical studies by dimensions of scenarios, types of interventions and targets. In addition, we took advantage of high extensivity and integrability of knowledge graph to organize evidence-based related elements. Result of the systematic analysis shows that in terms of reliability of evidence, school intervention is the best scenario, knowledge incentive is the best intervention type and promoting public transit and walking proportion are the best targets. Oppositely, the reliability of interventions in workplace, belonging to reward and threat along with aiming at changing travel patterns generally and lowering travel carbon emission need to be enhanced. From the study, various research prospects are raised to promote evidence quality in the field of travel behavior intervention implementation. As a pioneer study, our research contributes to the field of urban transportation in introducing concepts of evidence-based practice and enabling optimization and extension of our achievement via the usage of knowledge graph, enhancing reliability and objectivity in urban transportation decision-making.