Mitigation of Congestion of Public Transport Network with Inclusion of Shared Autonomous Vehicles

Abstract: Rapid urbanisation and evolving trends in urban mobility planning have led to the restructuring of urban spaces into green zones (low-emission and low-traffic zones), restricting private cars in specific areas. As a result, certain points in the transit system encounter substantial congestion due to high ridership rates (passenger demand), reducing the transit network's overall efficiency. As a result, there is a need to implement an optimal strategy to alleviate the congestion at critical points in a transit network. We can combine shared and connected automated mobility (SCAM) with public transport (PuT) to address this issue. As a result, the objective of this research is to formulate a methodology for multimodal transport that combines SCAM and PuT to improve network efficiency. The research evaluated public transport performance metrics, including passengers' total travel time (including walking, waiting, and vehicle time), after implementing the optimal multimodal transport strategy. The research focuses particularly on the feasibility of integrating transit buses with SCAM to meet passenger demand in certain areas. So, first, we identified the potential point (bus stop) of a network by evaluating the ridership rate using relation-based trip allocation in PTV Visum. Next, we compared the scenario by integrating the SCAM with transit bus lines to meet high passenger demand, while also comparing journey times. When
using this multimodal system, the results show a significant reduction in travel time, particularly for the first and last miles of the journey in that area. This integration improves the overall efficiency and connectivity of public transport.