Understanding Factors Affecting Arterial Reliability Performance Metrics

Avinash Unnikrishnan, Portland State University



With worsening congestion, travel time reliability is increasingly becoming as critical as average travel times in affecting travel choices. This research project will first study the data requirements for existing travel time reliability metrics being used or being considered for use by practitioners and researchers. The research team will then use bluetooth travel time data from arterial corridors in Portland to get an understanding of the temporal variation in travel time reliability metrics, travel time distributions, distribution of the travel time reliability metrics, variation of travel time reliability metrics with weather and other special events. 

We are particularly interested in understanding if the reliability metrics are similar or different across various arterial corridors.  The research team will then adopt a regression approach to quantify the impact of geometry, signal timing, volume counts, weather etc. on travel time reliability. This research project is unique due to the following aspects. We are studying travel time reliability metrics on arterial corridors with signalized intersections, bicycle, pedestrians, and transit corridors, and not on freeways.  We are attempting to quantify and rank the impact of geometric, traffic flow characteristics on travel time reliability in a formal statistical framework. We will be basing our work on archived bluetooth real world travel time data and not use a simulation based framework. 


The research project will first attempt to answer the following questions through literature review and interaction with practitioners:  
•	What reliability related metrics are currently being used or being considered for use in traffic planning and operations, by practitioners and researchers for quantifying travel time reliability along arterial corridors?
•	What are the data requirements for quantifying the metrics? 
•	How easy or difficult is it to get an accurate estimate for each metric with existing data collection practices? 

Project Details

Project Type:
Project Status:
In Progress
End Date:
June 30,2019
UTC Grant Cycle:
NITC 16 Round 1
UTC Funding: