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A real-time online decision support system for intermodal passenger travel.
  • Published Date:
    2015-09-01
  • Language:
    English
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A real-time online decision support system for intermodal passenger travel.
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  • 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.
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