Shared Mobility Services (SMSs) are transforming urban transportation systems by offering flexible travel options. These services, which help reduce the number of cars on the roads, have the potential to enhance the transportation system’s performance, leading to improvements in travel times and emissions. This emphasizes the importance of assessing their impact on the system and users’ choices, particularly when integrated into complex multimodal systems that include public transport (PT). However, many studies overlook the synergies between SMSs and PT, leading to inaccurate traffic estimations and planning. This research presents an extensive review of multimodal transportation system models incorporating SMSs. It then introduces a multimodal traffic assignment model including a broad range of mobility options in urban transportation systems applicable in both continuous and integer settings, leading to a Mixed-Integer Bilinear Programming (MIBLP) formulation. The model comprises diverse travel options, including SMSs, and accounts for intermodality by allowing commuters to combine modes to optimize time and monetary expense. An in-depth examination of commuters’ mode and path choices on two test cases and an analysis of the price of anarchy reveals the disparities between user equilibrium and system optimum in such complex networks.
Kadem et al. (Thu,) studied this question.