, Assistant Professor, Civil, Environmental and Geo-Engineering
Connected autonomous vehicle (CAV) technologies have been used to develop and experimentally demonstrate constant distance-gap platooning, which can greatly increase road capacity by reducing spacing between vehicles. However, platooning is not compatible with legacy vehicles and requires CAV-only lanes. When connectivity is not available (e.g. a CAV following a legacy vehicle), adaptive cruise control (ACC) is used instead. Current ACC systems use constant-time headways, which provide little to no improvement in road capacities.
Since many traffic planning models have (incorrectly) assumed that ACC and other CAV technologies will create substantial increases in road capacity and mobility, there is a broad research need to correctly connect CAV technology development with traffic planning research. Traffic planning research needs to be better informed by CAV technologies, and the utility of CAV technology research could also be improved by better understanding traffic needs. This project aims to: 1) improve CAV technology, in terms of its impact on traffic characteristics and safety; 2) provide better traffic modeling of shared roads; 3) provide improved traffic planning to better utilize CAV technologies; and
4) foster a long-lasting collaboration between two complementary research groups.
This project is developing a non-linear spacing policy for ACC and analyzing its effects on city-wide traffic. By choosing a spacing that changes non-linearly with speed, the policy will be usable in shared roads yet improve the traffic capacity of existing infrastructure by reducing the average following headway. Once the flow-density relationship of the new spacing policy is established, this project will develop a multiclass traffic flow model to predict how ACC affects vehicle movement at different CAV market penetrations. By comparing road capacities from the new ACC policy and platooning, we will establish under what conditions non-linear ACC spacing is preferable to separate platooning lanes. The lane allocation decisions and traffic flow model will be integrated into dynamic traffic assignment to study future traffic impacts for large city networks under realistic user equilibrium route choice behavior.
This project interconnects the fields of CAV technology design and traffic planning, and it has the potential to lead to both safer roads and highly improved traffic flow.