Individual Speed Variance in Traffic Flow: Analysis of Bay Area Radar Measurements

The recent increase of mobile devices able to measure individual vehicles speed and position with high accuracy brings new opportunities to traffic engineers. The large amount of individual probe measurements allows the study of phenomena previously unobservable with conventional sensing technologies, and the design of novel traffic monitoring and control strategies. However, challenges inherent to the use of speed and location data arise. One of the main difficulties of measurements collected from individual vehicles lie in their ability to provide relevant information on the macroscopic properties of traffic flow. According to the classical triangular fundamental diagram, the relation between speed and flow can be inversed in the congestion phase but not in the uncongested phase. In the uncongested phase, the flow of vehicles cannot be retrieved from the speed of vehicles, assumed to be constant. This article proposes to investigate the nature of the relationship between flow and speed from joint measurements from radar data. Two different regression methods are proposed in this article to estimate traffic flow, based on individual speed measurements; regression of flow on speed and regression of flow on speed variance. The respective performance of these two methods during specific traffic periods is assessed, and recommendations on their relative strengths are provided. This empirical study is conducted using 112 NAVTEQ radars measuring speed and flow on highways in the San Francisco Bay area, California.