Improving Rush Hour Traffic Flow by Computer-Vision-Based Parking Detection and Regulations

Mertz, Christoph; Qian, Sean; Chiang, Justin

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Improving Rush Hour Traffic Flow by Computer-Vision-Based Parking Detection and Regulations

2020-01-01
By Mertz, Christoph ; Qian, Sean ; Chiang, Justin

File Language:

English

Details

Creators:

Mertz, Christoph ; Qian, Sean ; Chiang, Justin
Corporate Creators:

Mobility21, Carnegie Mellon University
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
Subject/TRT Terms:

Computer Vision Multimodal Transportation On Street Parking Optimization Peak Hour Traffic Simulation Traffic Data Traffic Flow Travel Behavior
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Report
Corporate Publisher:

Mobility21, Carnegie Mellon University
Abstract:

To optimize the transportation network through a detailed analysis of all the traffic flows one needs to have two main ingredients. First it is necessary to have a holistic modeling framework that can manage the complexity of this multi-modal system. Second is comprehensive data for all the traffic statistics. In this project the authors want to demonstrate with an example how parking information can be incorporated into a multi-modal dynamic user equilibrium (MMDUE) model and develop a computer vision tool that can provide street parking data. This project allows the authors to collect time-varying parking data that could be used, in addition to existing traffic data, to understand travel behavior among various travel modes, such as driving (including solo-driving and carpooling), public transit, and park-and-ride. The travel behavior of all travelers in the network is then simulated to assess system performance and features of passenger/vehicular flow.
Format:

PDF
Funding:

69A3551747111
Collection(s):

University Transportation Centers
Main Document Checksum:

urn:sha-512:d6bdd7d1c77d1366940726c161565e3590a116a1efa32b257bf465ac86bc1c18cf8ce11daf99cf3e4478da3e64ceabf7cccbbf1fb639f0fcd15de2c031f89a6c
Download URL:

https://rosap.ntl.bts.gov/view/dot/67080/dot_67080_DS1.pdf
File Type:

[PDF - 2.32 MB ]

File Language:

English

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