Incorporating Crowdsourced Travel Time Data in Congestion Monitoring

Abstract

Accessing traffic engineering data sources has presented new challenges and opportunities when adopting cloud-based novel data mining techniques. This paper proposes the use of crowdsourced travel time data obtained from Google Distance Matrix Application Programming Interface (API) as possible mechanism to incorporate with traffic congestion monitoring. Study further reviewed its usefulness in transport planning and evaluation of congested points in the high-volume road network. Multiple traffic engineering applications have been analyzed to understand the data characteristics. Data validated by using state of art License Plate Recognition (LPR) computer system for short-distance trips in high volume By-pass Road which has high mobility in central Saitama, Japan. Results illustrate reasonable agreement between travel time data obtained from the Google and LPR method with the representative gap over time. Successful validation of methodology enabled transport planners to improve traffic management and transport decision-making in regional transport planning.