Original scientific paper
https://doi.org/10.7307/ptt.v33i1.3585
Potential Travel Time Reduction with Autonomous Vehicles for Different Types of Travellers
Jamil Hamadneh
orcid.org/0000-0001-8565-1168
; Faculty of Transport Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Budapest, Hungary
Domokos Esztergár-Kiss
; Faculty of Transport Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Budapest, Hungary
Abstract
Autonomous Vehicles (AVs) have been designed to make changes in the travel behaviour of travellers. These changes can be interpreted using transport models and simulation tools. In this study, the daily activity plans were used to study the possibility of increasing the utility of travellers through minimizing the travel time by using AVs. Three groups of travellers were selected based on the benefits that they can obtain when AVs are on the market. The groups are (a) long-trip travellers (b) public transport riders, and (c) travellers with specified characteristics. Each group is divided into one or more scenarios based on the definition of each group and the collected data. A total of seven scenarios were derived from the collected data and simulated twice to include the existing transport modes and the presence of AVs. The simulations were conducted using Multi-Agents Transport Simulation (MATSim) that applies the concept of a co-evolutionary algorithm. MATSim simulates the current plans and the ones where AVs replace all or part of the existing conventional transport modes in the daily activity plans. The results have shown a reduction in the trip time: 13% to 42% for group (a), 33% for group (b), and 16% to 28% for group (c) compared with the original trip times. In conclusion, it can be claimed that AVs could reduce the travel time in all cases, which provides benefits for people to increase their utilities.
Keywords
agent-based modeling; autonomous vehicle; activity chains; optimization; MATSim
Hrčak ID:
253185
URI
Publication date:
5.2.2021.
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