Rapid urbanisation and evolving trends in urban mobility planning have led to the restructuring of urban spaces into green zones (low-emissions 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 those critical points in a transit network. We can integrate shared autonomous vehicles (SAVs) with public transport (PuT) to address this issue. As a result, the objective of this research is to identify the strategies to integrate SAVs with PuT to reduce the passenger demand at critical points and evaluate the 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 SAVs 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 SAVs with transit bus lines to meet high passenger demand. When using this multimodal approach, the results show a significant reduction in travel time, particularly for the first and last miles of the journey. This integration improves the overall efficiency and connectivity of public transport.
Mitigation of Congestion of Public Transport Network with Inclusion of Shared Autonomous Vehicles
International Conference on Transportation and Development 2025 ; 2025 ; Glendale, Arizona
2025-06-05
Conference paper
Electronic Resource
English
Operation Model for Shared Autonomous Vehicles Which Affect Congestion
Springer Verlag | 2025
|DOAJ | 2021
|Shared Autonomous Vehicles Ontology
Springer Verlag | 2022
|Preferences for shared autonomous vehicles
Online Contents | 2016
|