Once they are deployed at scale, a fleet of automated vehicles (AVs) could serve as floating (or probe) sensors on a road network, acquiring and analyzing data collected from their own vicinities that can be used to make observations about traffic conditions. These observations, when sufficiently spatio-temporally dense, could be used to derive meaningful insights about how a transportation network is performing and changing. Transportation stakeholders could benefit from various types of traffic information, including, but are not limited to, travel speed, traffic density and traffic flow by vehicle classifications. The potential value of insights conceivably derivable from this data must be considered in the context of the significant cost and complexity associated with gathering, processing, modelling, transferring, storing, and securing this data, particularly at scale.

We further prove the concept of automated-vehicle-based traffic sensing by sensing traffic flow on surface streets in Pittsburgh through a fleet of automated vehicles from the Uber Advanced Technologies Group. In particular, we develop a general method for estimating traffic flow on roads, at the block level, and intersections using object detection/tracking data from automated vehicles. The proposed method is able to effectively extract various characteristics of traffic flow, including travel speed, traffic density, and traffic counts. Self-driving holds the potential to bring unprecedented changes to when, where and how people travel. This implies tremendous benefits and challenges to transportation systems, and our communities in general. Among many other challenges, how would transportation stakeholders design, plan and operate infrastructure in the era of automated vehicles? As transportation modality norms further diversify, what usage data can be leveraged in the maintenance of infrastructure?

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Publication

• Sean Qian, Shuguan Yang, Allison Plummer (2019), “High-resolution Traffic Sensing with Autonomous Vehicles.” [URL]