Potential impacts of connected-autonomous vehicles on congestion and safety : a look at Austin, Texas




Archer, Jackson Longstreet

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Data is a central component of Connected-Autonomous Vehicle (CAV) systems: the advantages and potential challenges of both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) CAV data underlie the question of wide scale CAV implementation. This report looks at the potential congestion and safety benefits of a vehicle system highly saturated with CAVs in Austin, Texas. Traffic factors such as capacity, intersection delay, and crash rate are examined with respect to their effect on an urban corridor in Austin. The case study relies almost entirely on collected field data to be used as a comparison against potential CAV advantages. In addition to a presentation of the quantitative benefits of CAVs, an infrastructure placement scheme that maximizes data transmission efficiency is also proposed. The results find that vehicle systems can see large improvements in capacity, intersection delay, and number of crashes, and at a relatively inexpensive cost.


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