Modeling Transit Patterns Via Mobile App Logs.

Details

• Creators:
  Tomasic, Anthony ; Steinfeld, Aaron ; Zimmerman, John ; Doryab, Afsaneh
• Corporate Contributors:
  Carnegie-Mellon University ; University of Pennsylvania ; United States. Department of Transportation. University Transportation Centers (UTC) Program
• Subject/TRT Terms:
  • Origin and destination
  • Urban transit
  • Traffic models
  • Traffic simulation
  • Mobile communication systems
  • Vehicle to infrastructure communications
  • Transportation planning Mobile App Logs
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  University Transportation Centers Program (U.S.)
• Abstract:
  Transit planners need detailed information of the trips people take using public transit in ; order to design more optimal routes, address new construction projects, and address the ; constantly changing needs of a city and metro region. Better transit plans lead to better ; service and lower costs. Unfortunately, good rider origin-destination information is almost ; universally unavailable. ; In this project we have developed a new method for inferring rider origin-destination (O-D) ; trip stops in support of transit planning. The meteoric adoption of smartphones along with ; the growth of transit apps that provide vehicle arrival information at a stop generates a new ; data resource. Every time a user requests arrival information, the mobile service logs the ; user’s location, the time, and the specific stop they requested information about. Over ; time, a user’s request history functions as “bread crumbs” revealing where and when they ; have travelled. ; The goal of this project is to develop machine-learning models that can infer O-D for a ; transit service based on the request logs of individual users of mobile transit apps. This ; project builds on already deployed and extensively used Tiramisu app. In addition to the ; request log, Tiramisu data includes O-D trips recorded by users that we can use as ground ; truth for training the machine learning models. We will use this data to build a transit ; model that can derive results based on model phone app usage. Thus, we can produce ; models of transit use at a fraction of the cost. This approach also allows continuous O-D ; modeling, unlike traditional survey and sampling techniques. Note that, as far as we know, ; the Tiramisu app is a unique source of exact, large-scale, O-D information collected for ; research purposes. Other researchers have collected O-D using smartphones in small ; studies, but not through an extensively deployed app with over four years of historical ; data.
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• Format:
  PDF
• Funding:
  DTRT12GUTG11
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/31317/dot_31317_DS1.pdf
• File Type:
  [PDF - 4.11 MB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:56ca676d41b82dd6858ff00a3f745dea356dd9f67e321117a04e99c3ddfa7931e4675e8bf9955f7bd6f6a0ef5634ddba6f310ccf8f76c0c97fc3739f010da066