Coupling Green Travel Mode Shift and Carpooling for Individualized Commute Guidance: A Citywide Trajectory-Based Assessment of Congestion Mitigation

Traffic congestion is a major factor constraining sustainable urban development and degrading residents’ travel experience, while green travel and shared mobility offer effective means of mitigating traffic congestion, and improving residents’ travel experience and living environment. However, it remains unclear how individualized guidance can be provided for green travel and shared mobility and how the benefits resulting from green travel guidance and shared mobility can be quantitatively evaluated at the city scale. Based on citywide license plate recognition data, all commute travelers were first identified, and their commute travel during the morning and evening peak periods was extracted. For all commute travel, algorithms were developed to quantify the green travel mode shift potential and carpooling potential of commute travelers. On this basis, the reductions in peak period travel demand and the congestion mitigation effects that could be achieved through green travel guidance and shared mobility under different scenarios were analyzed. The empirical results for Xiaoshan, China, showed that, under the illustrative upper-bound combined scenario constructed in this study, individualized guidance could reduce the peak number of vehicles traveling on the road by 28.73% in the morning peak and 25.96% in the evening peak, while the minimum network speed could be increased by 8.48% and 7.66%, respectively. Overall, an algorithmic framework for individualized travel guidance based on individual trajectory big data is proposed, and the congestion mitigation benefits achievable through green travel guidance and shared mobility are quantified.