AI Chat Paper
Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Search articles, authors, keywords, DOl and etc.
{{expandStatus?'Exit ':''}}Advanced Search
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.
HÖRL S, BECKER F, AXHAUSEN K W. Simulation of Price, Customer Behaviour and System Impact for a Cost-Covering Automated Taxi System in Zurich[J]. Transportation Research Part C: Emerging Technologies, 2021, 123: 102974.
GAO K, SUN L, YANG Y, et al. Cumulative Prospect Theory Coupled with Multi-Attribute Decision Making for Modeling Travel Behavior[J]. Transportation Research Part A: Policy and Practice, 2021, 148: 1-21.
OKE J B, AKKINEPALLY A P, CHEN S, et al. Evaluating the Systemic Effects of Automated Mobility-on-Demand Services Via Large-Scale Agent-Based Simulation Of Auto-Dependent Prototype Cities[J]. Transportation Research Part A: Policy and Practice, 2020, 140: 98-126.
AXHAUSEN K W, HORNI A, NAGEL K. The Multi-agent Transport Simulation MATSim[M]. London: Ubiquity Press, 2016: 9-21.
BALAKRISHNA R, MORGAN D, SLAVIN H, et al. Large-scale Traffic Simulation Tools for Planning and Operations Management[J]. IFAC Proceedings, 2009, 42(15), 117-122.
HÖRL S, BALAC M. Introducing The Eqasim Pipeline: From Raw Data to Agent-Based Transport Simulation[J]. Procedia Computer Science, 2021, 184: 712-719.
HÖRL S, BALAC M, AXHAUSEN K W. A First Look at Bridging Discrete Choice Modeling and Agent-based Microsimulation in MATSim[J]. Procedia Computer Science, 2018, 130: 900-907.
DENG Y, ZHAO P. The Impact of New Metro on Travel Behavior: Panel Analysis Using Mobile Phone Data[J]. Transportation Research Part A: Policy and Practice, 2022, 162: 46-57.
YANG C, ZHANG Y, ZHAN X, et al. Fusing Mobile Phone and Travel Survey Data to Model Urban Activity Dynamics[J]. Journal of Advanced Transportation, 2020, 2020(6): 5321385.1-5321385.17.
POLETTI F. Public Transit Mapping on Multi-modal Networks in MATSim[J]. Strasse Und Verkehr, 2017(8): 22-25.
DUAN Q, YE X, LI J, et al. Empirical Modeling Analysis of Potential Commute Demand for Carsharing in Shanghai, China[J]. Sustainability, 2020, 12(2): 620.
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.00/).
京公网安备11010802044758号