Graduation Year

2022

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Civil and Environmental Engineering

Major Professor

Xiaopeng (Shaw) Li, Ph.D.

Committee Member

Fred Mannering, Ph.D.

Committee Member

Henry Liu, Ph.D.

Committee Member

Yu Zhang, Ph.D.

Committee Member

Soo-Haeng Cho, Ph.D.

Committee Member

Weijun Xie, Ph.D.

Keywords

Optimal Control, Signal Optimization, Simulations, Trajectory Optimization

Abstract

Scheduling vehicles to pass a general conflict area is a common problem in traffic operations at various facilities, such as intersections, work-zones, and merging ramps. The advent of connected automated vehicle (CAV) technology provides unique opportunities for improving the traffic system performance and riding comfort at conflict areas. With the opportunities introduced by the emerging CAV technologies, this dissertation envisions building computationally efficient and applicable CAV-based control frameworks that can optimally serve traffic streams from different approaches while simultaneously or sequentially optimize CAV trajectories at conflict areas.

In our first study, we focus on a joint vehicle trajectories and signal timing optimization problem from the macroscopic perspective. One major challenge to this problem is the complexity in optimization of CAV trajectories, particularly with joint signal timing optimization. This study aims to propose an analytical model that can be solved to the exact optimum efficiently, is easy to scale up, and have a parsimonious model structure. The proposed model is posed to solve a joint vehicle multi-trajectory and signal optimization problem in the macroscopic level for two conflicting traffic approaches with an objective of simultaneously minimizing travel delay and fuel consumption. This study makes two simplifications to the original complex optimization problem. First, each vehicle trajectory is confined as a piecewise quadratic function with no more than five sections. This leads to the development of a simplified method to analytically (and thus very efficiently) populate near-optimum trajectories for all vehicles in both approaches. Comparison experiments also reveal that the objective values from the simplified trajectories populated with the proposed method are close to the true optima obtained from the numerical counterpart model. Second, we propose a simplified macroscopic measure that approximates fuel consumption as a function of the red light interval of the corresponding approach. Numerical experiments indicate that the proposed simplification method obtains a very high goodness-of-fit to a complex fuel consumption model. These two simplifications lead to elegant theoretical properties and consequentially an analytical exact solution to this otherwise extremely complex problem. The numerical experiments demonstrate the application of the proposed joint optimization model to different conflict points including a two-approach intersection and a two-lane work-zone. The experiments include verifying the near-optimality of the model solution with the exact solutions (which however require impractically long solution times), comparing the performance of the optimal solution with several benchmark cases and conducting sensitivity analysis on key parameters.

In our second study, we focus on the vehicle scheduling optimization problem. With the advent of CAV technology, vehicle scheduling at a conflict area can be as precise as to each individual vehicle. This study aims to address the research gaps in both fundamental methodologies and engineering applications on this general topic. The investigated problem seeks the optimal vehicle scheduling at a multi-conflict area considering heterogeneous vehicle headways and values of time to minimize the total travel time delay cost. A mixed integer programming (MIP) model is proposed to solve the exact optimal solution to this problem. Although small instances of the proposed model can be solved by existing commercial MIP solvers, their computational time increases almost exponentially as the numbers of vehicles and approaches increase. To ensure computational efficiency, we proposed a customized branch-and-bound algorithm that introduces a set of valid cuts to expedite the solution speed. A set of numerical experiments in various scenarios are tested to demonstrate the feasibility and effectiveness of the proposed model and algorithm. The comparison results show that coordination of vehicles with individual-vehicle-based control can significantly increase the capacity of the conflict area and reduce the vehicle travel time compared to existing well-known control strategies (e.g., stop signs and signals) while it is computationally tractable for real-world CAV applications.)

In our last study, we focus on a computationally efficient and applicable CAV application at conflict areas. In this study, we proposed an edge-computing-based cooperative highway operations (ECHO) framework for CAVs at stop-controlled and signalized intersections in the Transportation Systems Management and Operations (TSMO) context. The proposed framework focuses the infrastructure system only on critical high-level scheduling decisions while leaving complex low-level trajectory control and collision avoidance to individual CAVs in a decentralized manner. Thus, it much reduces operational complexity and associated risks and liabilities for traffic operators. Also, it distributes the computational burden among different entities in an edging computing structure and thus makes it much more suitable for real-time applications. Further, this study for the first time investigates different cooperation classes defined in the SAE J3216 standard for stop-controlled and signalized intersections. Simulation results show that all considered performance measures (e.g., throughput, energy consumption, travel delay, etc.) will be much improved and the backward shockwave propagation will be reduced as the cooperation class of C-ADS-equipped vehicles increases. This study produces fundamental algorithms to be implemented in the Federal Highway Administration (FHWA) CARMA open-source software.

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