A framework for travel time variability analysis using urban traffic incident data

This study aims to develop a framework to estimate travel time variability caused by traffic incidents using integrated traffic, road geometry, incident, and weather data. The authors develop a series of robust regression models based on the data from a stretch in California's highway system during a two-year period. The models estimate highway clearance time and percent changes in speed for both downstream and upstream sections of the incident bottleneck. The results indicate that highway shoulder and lane width factor adversely impact downstream highway clearance time. Next, travel time variability is estimated based on the proposed speed change models. The results of the split-sample validation show the effectiveness of the proposed models in estimating the travel time variability. Application of the model is examined using a micro-simulation, which demonstrates that equipping travelers with the estimated travel time variability in case of an incident can improve the total travel time by almost 60%. The contribution of this research is to bring several datasets together, which can be advantageous to Traffic Incident Management.

• Record URL:
  • https://doi.org/10.1016/j.iatssr.2017.06.003
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0386111216300395
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/03861112
• Supplemental Notes:
  • © 2017 International Association of Traffic and Safety Sciences. Abstract reprinted with permission of Elsevier.
• Authors:
  • Javid, Roxana J
  • Javid, Ramina Jahanbakhsh
• Publication Date: 2018-4

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; Maps; References; Tables;
• Pagination: pp 30-38
• Serial:
  • IATSS Research
  • Volume: 42
  • Issue Number: 1
  • Publisher: Elsevier
  • ISSN: 0386-1112
  • Serial URL: http://www.sciencedirect.com/science/journal/03861112
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Bottlenecks; Data fusion; Highway traffic; Incident management; Linear regression analysis; Microsimulation; Multiple regression analysis; Time duration; Traffic congestion; Traffic data; Traffic incidents; Travel time; Urban highways; Weather conditions
• Geographic Terms: California
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Safety and Human Factors;

Filing Info

• Accession Number: 01670656
• Record Type: Publication
• Files: TRIS
• Created Date: May 29 2018 4:03PM