Traffic control with connected and automated vehicles on urban roads

The connected and automated vehicle (CAV) is a promising technology that will reshape the transport. It has potentials in reducing fuel consumption as well as improving capacity and safety. The traffic control of CAVs on urban roads is investigated in the thesis, which consists of two aspects: intersection control and trajectory planning.

For the intersection control with CAVs, a bilevel programming model integrating intersection control with trajectory planning is proposed to improve the efficiency of the intersection when all vehicles are CAVs. The feedback structure adapts a wide range of conditions and improves capacity. The cooperation between the two levels and their linear properties ensure reasonable solving time. A platoon-based method is also proposed to improve the calculation speed.

For the trajectory planning with CAVs, a platoon-based eco-driving model is proposed using model predictive control. The model is used in mixed autonomy traffic and considers traffic efficiency, fuel consumption, and driving comfort. The cooperation between automated vehicles and human-driven vehicles reduces the negative impact of eco-driving on the following vehicles and reduces fuel consumption even further. The performance under different platoon sizes and penetrations of automated vehicles are also tested in the simulation.

At an intersection controlled by the adaptive traffic control system, the vehicle may not be able to get accurate information on the future signal timing. A multi-phase model predictive control model is proposed to reduce fuel consumption by considering the stochastic signal information. Two driving regimes are considered based on the state of the traffic signal at each time step: accelerating to pass the intersection when the light is green or keeping waiting for the possible green light in the next time step. This model is further extended to the case that the vehicle can receive the information on the future signal timing some seconds in advance. An additional eco-driving strategy is considered in this case.