A real-time online decision support system for intermodal passenger travel.

Details

• Creators:
  Hu, Mengqi ; Wu, Yao-Jan ; Chen, Yang ; Yang, Shu
• Corporate Contributors:
  Mississippi State University. National Center for Intermodal Transportation for Economic Competitiveness
• Subject/TRT Terms:
  • Decision support systems
  • Real time information
  • Intermodal transportation
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  Mississippi State University. National Center for Intermodal Transportation for Economic Competitiveness
• Abstract:
  The transportation system in the United States is disjointed and inefficient as a result of the ; different transportation modes in use and their respective industries which have developed ; independently. In addition, public transportation is not well used in passenger trips compared to ; other developed countries. For example, public transportation accounts for 20% of passenger trips ; in large U.S. cities compared to 50% in Europe. Also, development of the passenger intermodal ; transportation system has lagged behind development of the freight transportation system. ; To improve utilization of intermodal transit and efficiency in the U.S, we developed an ; intelligent decision support system for passenger travel decisions using real-time general transit ; feed specifications (GTFS) data. In our system, an automatic data collection strategy was created ; to collect GTFS and flight data across different platforms, and an “all-in-one” database was ; designed to store the data. The database was used to: 1) construct intermodal transit networks using ; a “node-link” scheme, and 2) estimate travel time and travel time reliability for links and transit ; routes. Using this real-time data, a data-driven travel decision model was developed to determine ; the best route based on passenger preferences. Several chance constraints were added in the ; decision model to guarantee the reliability of the travel route under uncertainties. Additionally, a ; user-friendly interface was developed in Python to allow travelers to plan their trips, and a ; geographic information system (GIS), Google Earth, was employed to allow users to visualize the ; optimized route options. ; The proposed system was validated using real-time GTFS data collected in Tucson, AZ, and ; Boston, MA. This validation demonstrated that the system can determine optimal travel routes for ; passengers. In addition, three sets of sensitivity analysis experiments were developed to investigate ; three model considerations: 1) the effect of chance constraints on path choice, 2) the effect of ; confidence levels on path choice, and 3) the difference between weekend and weekday travel ; planning. The results suggested that the optimal anticipated travel time increases with an ; increasing on-time arrival confidence level, and walking is preferred by passengers instead of ; transferring buses during peak hours. As an example, approximately 30% additional time serves ; as a reference for allocating travel buffer time to ensure a higher on-time arrival confidence level ; for transit trips to the Tucson International Airport.
  More +
• Format:
  PDF
• Funding:
  NCITEC Project No. 2013-13
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/30725/dot_30725_DS1.pdf
• File Type:
  [PDF - 7.31 MB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:2a33ef5a79b18da86103c5df8b1fc19727f5d0dd3864cff419a660ad38f391468c6e6f7d151288ec10672707071ffbb007a08ec533fc939da4bb319a6b647a70