Improved traffic operations through real-time data collection and control.
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2016-05-01
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Abstract:Intersections are a major source of delay in urban networks, and reservation-based intersection control for
autonomous vehicles has great potential to improve intersection throughput. However, despite the high
flexibility in reservations, existing control policies are fairly limited. To increase reservation throughput,
we adapt two pressure-based policies for reservations in dynamic traffic assignment. The backpressure
policy is throughput optimal in communications networks, but communications networks are significantly
different from traffic networks. We propose that congestion propagation can be introduced by modeling
each cell in the cell transmission model as a link in a communications network. The finite-buffer limitation
on the maximum pressure per cell can be overcome by including queue spillback to previous cells and
links. However, a counterexample shows that local pressure-based policies such as backpressure cannot be
throughput optimal under user equilibrium route choice. Therefore, we also adapt the P0 policy to
reservations. Its adaptation is more straightforward, although dynamic traffic assignment also prevents
proving that P0 is throughput optimal. Nevertheless, results on the downtown Austin network show that
both backpressure and P0 performed significantly better than first-come-first-served, which has been used
in most previous work on reservations. Therefore, although backpressure and P0 cannot be proven to be
throughput optimal, they provide a better alternative to existing policies.
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