Urban Transit Coordination Using an Artificial Transportation System

An urban transit system usually consists of several modes, including busses, streetcars, a subway, and light rail. Unfortunately, coordination among different modes remains a challenging problem. Difficulties arise when modifying the transit network structure on a strategic level or when synchronizing timetables on a tactical level. Traditional transit network design and timetabling intend to solve a network-optimization problem based on static origin-destination (OD) information, with passenger assignment as a subproblem. In this paper, we propose an artificial urban transit system (AUTS) based on agent-based modeling and simulation. With AUTS, which is a special type of artificial transportation system (ATS), we are able to dynamically model the passenger's behavior and route choice and use the system to predict transit demand on a simplified transit network. The AUTS has the following important potential applications: forecasting transit flow; setting key parameters for urban transit networks - such as service frequencies and the capacity of subway trains - evaluating alternative modifications to subway rail and bus routes; and predicting the impact of special/ emergency events to the transit network. We create a demonstration system of the Beijing transit network and present its applications in experiments.

• Record URL:
  • https://doi.org/10.1109/TITS.2010.2060195
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Abstract reprinted with permission of IEEE.
• Authors:
  • Li, Lefei
  • Zhang, Han
  • Wang, Xiaofang
  • Lu, Wei
  • Mu, Zongping
• Publication Date: 2011

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 374-383
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 12
  • Issue Number: 2
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Artificial intelligence; Bus transit; Dynamic traffic assignment; Multi-agent systems; Network analysis (Planning); Public transit; Simulation; Subways; Timetables; Traffic forecasting
• Subject Areas: Data and Information Technology; Planning and Forecasting; Public Transportation; I71: Traffic Theory; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01352008
• Record Type: Publication
• Source Agency: UC Berkeley Transportation Library
• Files: TLIB
• Created Date: Sep 15 2011 7:31AM