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| Multi-modal Travel Simulation and Travel Behavior Analysis: Case Study in Shanghai |
| HU Yue1,2,3, YANG Chao1,2, AXHAUSEN Kay W3 |
1. The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China;
2. College of transportation engineering, Tongji University, Shanghai 201804, China;
3. Institute for Transport Planning and Systems, ETH Zurich, Zurich 8093, Switzerland |
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Abstract This study aims to investigate the multi-modal travel behavior and obtain quantitative results for various indicators by building an eqasim/MATSim model, using Shanghai as the study area. Travel demand is mainly generated using mobile phone signaling data. For each mode, a travel cost model is formulated. Additionally, an MNL (Multinomial Logit) model is integrated into eqasim through the DMC (Discrete Mode Choice) module. The results demonstrate that using mobile phone signaling data to generate travel demand yields a high-quality representation of travel demand. Users prefer public transport over cars when travel distances are similar. Furthermore, for longer-distance travel, the combined bus and subway mode significantly reduces walking distance, travel time, and travel costs. The spatial accessibility of public transport strongly depends on the availability and coverage of the public transport infrastructure. In areas where public transport services are limited, cars can complement public transport by providing accessibility to areas with scarce public transport options. From a transportation system perspective, car trips during rush hours are similar to public transport and biking, while walking is consistently used throughout the day due to the shortest travel time. Home-based trips, particularly commuting trips, have the highest share. Understanding these travel patterns is essential for optimizing transportation planning and effectively addressing peak-hour travel demand. This study demonstrates the effectiveness of using mobile phone signaling data for studying multi-modal travel behavior. The results provide valuable insights for transportation planners and policymakers in developing efficient and sustainable transportation systems that meet the preferences and needs of travelers.
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Received: 14 March 2023
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2024, Vol. 18 
