arXiv subjects

Chunhui Yu

Publications and source records attributed to Chunhui Yu.

5 recordsLinked to original sources

arXiv2021

Optimization of bus scheduling and bus-berth matching at curbside stops under connected vehicle environment

It is commonly seen that buses are blocked by the ones in front serving passengers and have to queue outside a curbside bus stop although there are vacant berths at the stop. The resultant bus delays degrade the service level of urban public transportation. A potential solution is to reschedule the arrivals of the buses at the stop for full utilization of the berths with the aid of connected vehicle technologies. This study proposes a mixed-integer linear programming model to optimize the scheduling of bus arrivals and the bus-berth matching at a curbside stop under connected vehicle environment. The objective is the minimization of the bus delays weighted by the number of passengers on the buses. Bus arrival times at the stop and the assignment of berths are optimized together with bus departure times from the stop. Bus punctuality is also taken into consideration. The proposed model could be applied dynamically to cater to time-varying traffic conditions. Numerical studies validate the advantages of the proposed model over the first-come-first-service strategy and the relaxed model without bus punctuality in terms of weighted bus delays and bus punctuality. Sensitivity analyses show that: 1) the proposed model is robust to the fluctuation of bus service time; and 2) a smaller number of berths may be preferred on condition that the bus demand does not exceed the stop capacity.

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arXiv2021

Shared-phase-dedicated-lane based intersection control with mixed traffic of human-driven vehicles and connected and automated vehicles

Connected and automated vehicles (CAVs) and human-driven vehicles (HVs) are expected to coexist in the near future. CAV-dedicated lanes and phases have been explored to handle the uncertainty in the driving behavior of HVs in the mixed traffic environment. However, CAV-dedicated phases could significantly sacrifice HV benefits. This study proposes a shared-phase-dedicated-lane (SPDL)-based traffic control model at isolated intersections under the mixed traffic environment. Left-turn and through CAVs share CAV-dedicated lanes and cross the intersection during the shared phases with HVs. A three-level optimization model is developed. At the upper level, a standard NEMA (National Electrical Manufacturers Association) ring barrier structure is used for the signal optimization and barrier durations are optimized by dynamic programming to minimize the total vehicle delay. At the middle level, phase sequence and phase durations are optimized by enumeration for the given barrier from the upper level and the minimum vehicle delay is fed to the upper level. At the lower level, CAV platooning in the buffer zone and trajectory planning in the passing zone are conducted based on the signal timings of the barrier from the middle level and the travel time of CAVs is fed to the middle level. A rolling-horizon scheme is further designed for the dynamical implementation of the proposed model with time-varying traffic conditions. Numerical studies validate the advantages of the SPDL-based control over the blue-phase based control in previous studies in terms of average vehicle delay and intersection capacity. Further, the SPDL-based model is extended to serve as an alternative approach without the buffer zone.

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arXiv2021

Trajectory Planning for Connected and Automated Vehicles at Isolated Signalized Intersections under Mixed Traffic Environment

Trajectory planning for connected and automated vehicles (CAVs) has the potential to improve operational efficiency and vehicle fuel economy in traffic systems. Despite abundant studies in this research area, most of them only consider trajectory planning in the longitudinal dimension or assume the fully CAV environment. This study proposes an approach to the decentralized planning of CAV trajectories at an isolated signalized intersection under the mixed traffic environment, which consists of connected and human-driven vehicles (CHVs) and CAVs. A bi-level optimization model is formulated based on discrete time to optimize the trajectory of a single CAV in both the longitudinal and lateral dimensions given signal timings and the trajectory information of surrounding vehicles. The upper-level model optimizes lateral lane-changing strategies. The lower-level model optimizes longitudinal acceleration profiles based on the lane-changing strategies from the upper-level model. Minimization of vehicle delay, fuel consumption, and lane-changing costs are considered in the objective functions. A Lane-Changing Strategy Tree (LCST) and a Parallel Monte-Carlo Tree Search (PMCTS) algorithm are designed to solve the bi-level optimization model. CAV trajectories are planned one by one according to their distance to the stop bar. A rolling horizon scheme is applied for the dynamic implementation of the proposed model with time-varying traffic condition. Numerical studies validate the advantages of the proposed trajectory planning model compared with the benchmark cases without CAV trajectory planning.

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arXiv2020

Managing connected and automated vehicles with flexible routing at "lane-allocation-free'' intersections

Trajectory planning and coordination for connected and automated vehicles (CAVs) have been studied at isolated ``signal-free'' intersections and in ``signal-free'' corridors under the fully CAV environment in the literature. Most of the existing studies are based on the definition of approaching and exit lanes. The route a vehicle takes to pass through an intersection is determined from its movement. That is, only the origin and destination arms are included. This study proposes a mixed-integer linear programming (MILP) model to optimize vehicle trajectories at an isolated ``signal-free'' intersection without lane allocation, which is denoted as ``lane-allocation-free'' (LAF) control. Each lane can be used as both approaching and exit lanes for all vehicle movements including left-turn, through, and right-turn. A vehicle can take a flexible route by way of multiple arms to pass through the intersection. In this way, the spatial-temporal resources are expected to be fully utilized. The interactions between vehicle trajectories are modeled explicitly at the microscopic level. Vehicle routes and trajectories (i.e., car-following and lane-changing behaviors) at the intersection are optimized in one unified framework for system optimality in terms of total vehicle delay. Considering varying traffic conditions, the planning horizon is adaptively adjusted in the implementation procedure of the proposed model to make a balance between solution feasibility and computational burden. Numerical studies validate the advantages of the proposed LAF control in terms of both vehicle delay and throughput with different demand structures and temporal safety gaps.

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arXiv2019

Testing Scenario Library Generation for Connected and Automated Vehicles, Part I: Methodology

Testing and evaluation is a critical step in the development and deployment of connected and automated vehicles (CAVs), and yet there is no systematic framework to generate testing scenario library. This study aims to provide a general framework for the testing scenario library generation (TSLG) problem with different operational design domains (ODDs), CAV models, and performance metrics. Given an ODD, the testing scenario library is defined as a critical set of scenarios that can be used for CAV test. Each testing scenario is evaluated by a newly proposed measure, scenario criticality, which can be computed as a combination of maneuver challenge and exposure frequency. To search for critical scenarios, an auxiliary objective function is designed, and a multi-start optimization method along with seed-filling is applied. The proposed framework is theoretically proved to obtain accurate evaluation results with much fewer number of tests, if compared with the on-road test method. In part II of the study, three case studies are investigated to demonstrate the proposed methodologies. Reinforcement learning based technique is applied to enhance the searching method under high-dimensional scenarios.

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