Traffic State Prediction: A Traveler Equity and Multi-modal Perspective (Core Project)

Project Abstract

Traffic congestion has become a major problem in many urban areas, and has related environmental, economic, and equity impacts. One potential method of reducing urban traffic congestion is developing tools that plan multi-modal trips to encourage more people to ride public transportation and to provide better driving alternatives for less affluent citizens. Traffic state prediction is the key component to planning multi-modal trips in a complex transportation network. This research attempts to address transportation system state prediction problems considering private vehicles, public transit, and bike share services within the context of a multimodal transportation system. For public transit service, the proposed effort focuses on developing real-time passenger demand prediction models using multiple data sources to enhance prediction accuracy. For bike share services, the proposed effort focuses on developing prediction models for the number and travel times of bikes. Finally, for private vehicles, this research develops a comprehensive traffic prediction tool by including different categories of prediction models. The proposed prediction algorithms and tools are evaluated by comparing their performance using the field data collected in multimodal transportation system to the performance of existing prediction methods using the same data.

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Outputs and Outcomes

Smart systems seamlessly integrate different modes of transportation. This research yield eight tools that can be used to convert current bikeshare systems into smart systems. Since traffic prediction is key for any system, this research also develops a comprehensive traffic prediction tool by including different categories of prediction models.

Universities and Sponsoring Organizations Involved

U.S. Department of Transportation Office of the Secretary-Research, Virginia Tech

Principal Investigator(s)

Hesham A. Rakha (VT) Email: hrakha@vt.edu

Funding Sources and Amounts (Split By Organization and Type of Funding) Format

UTC: $300,000

Completion Date

May 2019

Key Words

Multimodal Transportation System, Transit Passenger Demand Prediction, Bike Share System, Travel Time Prediction.