Spatiotemporal intersection control in a connected and automated vehicle environment

Feng, Yiheng; Yu, Chunhui; Liu, Henry

doi:10.1016/j.trc.2018.02.001

Cited by 200 publications

(98 citation statements)

References 19 publications

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“…We have also applied the principle of opportunistically maximizing an expectation to drop the expectations in (52) from the problem. The optimization problem (53) can be stated in terms of the intersection movements, by appeal to (5) and (6): arg max p∈P Fig. 9: Layout of the calibrated single intersection in Abu Dhabi depicted in Fig.…”

Section: Stability Of Pwbpmentioning

confidence: 99%

“…This is well justified considering the vast opportunities and challenges that CAVs have to offer. The ability to control both trajectories and signals is one such opportunity that CAVs have to offer; we refer to [5,23,46] and references therein for recent examples. For more information we refer to [11], a recent review article that covers many aspects of intersection control.…”

Section: Introductionmentioning

confidence: 99%

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Position weighted backpressure intersection control for urban networks

Jabari

2019

Transportation Research Part B: Methodological

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Decentralized intersection control techniques have received recent attention in the literature as means to overcome scalability issues associated with networkwide intersection control. Chief among these techniques are backpressure (BP) control algorithms, which were originally developed of for large wireless networks. In addition to being light-weight computationally, they come with guarantees of performance at the network level, specifically in terms of network-wide stability. The dynamics in backpressure control are represented using networks of point queues and this also applies to all of the applications to traffic control. As such, BP in traffic fail to capture the spatial distribution of vehicles along the intersection links and, consequently, spill-back dynamics.This paper derives a position weighted backpressure (PWBP) control policy for network traffic applying continuum modeling principles of traffic dynamics and thus capture the spatial distribution of vehicles along network roads and spill-back dynamics. PWBP inherits the computational advantages of traditional BP. To prove stability of PWBP, (i) a Lyapunov functional that captures the spatial distribution of vehicles is developed; (ii) the capacity region of the network is formally defined in the context of macroscopic network traffic; and (iii) it is proved, when exogenous arrival rates are within the capacity region, that PWBP control is network stabilizing. We conduct comparisons against a real-world adaptive control implementation for an isolated intersection. Comparisons are also performed against other BP approaches in addition to optimized fixed timing control at the network level. These experiments demonstrate the superiority of PWBP over the other control policies in terms of capacity region, networkwide delay, congestion propagation speed, recoverability from heavy congestion (outside of the capacity region), and response to incidents.

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Section: Stability Of Pwbpmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Position weighted backpressure intersection control for urban networks

Jabari

2019

Transportation Research Part B: Methodological

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“…Zhao et al [24] proposed a connected-vehicle control strategy for a signalized intersection that employs model prediction control to guide connected vehicles. Finally, Feng et al [25] proposed a joint control method of vehicle trajectories and tra c signals for connected and automated vehicles at a signalized intersection.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Planning Method for Mixed Vehicles Considering Traffic Stability and Fuel Consumption at the Signalized Intersection

Fang

Yang

Wang

et al. 2020

Journal of Advanced Transportation

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Traffic lights force vehicles to stop frequently at signalized intersections, which leads to excessive fuel consumption, higher emissions, and travel delays. To address these issues, this study develops a trajectory planning method for mixed vehicles at signalized intersections. First, we use the intelligent driver car-following model to analyze the string stability of traffic flow upstream of the intersection. Second, we propose a mixed-vehicle trajectory planning method based on a trigonometric model that considers prefixed traffic signals. The proposed method employs the proportional-integral-derivative (PID) model controller to simulate the trajectory when connected vehicles (equipped with internet access) follow the optimal advisory speed. Essentially, only connected vehicle trajectories need to be controlled because normal vehicles simply follow the connected vehicles according to the Intelligent Driver Model (IDM). The IDM model aims to minimize traffic oscillation and ensure that all vehicles pass the signalized intersection without stopping. The results of a MATLAB simulation indicate that the proposed method can reduce fuel consumption and NOx, HC, CO2, and CO concentrations by 17%, 22.8%, 17.8%, 17%, and 16.9% respectively when the connected vehicle market penetration is 50 percent.

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“…Given the assumption that advanced communication systems are available between vehicles and the traffic controller, arterial traffic signals for multiple travel modes were optimized and simulated [50]. A joint control framework for isolated intersections was investigated, modeled as a two-stage optimization problem, and validated as being able to reduce both vehicle delay and emissions compared to fixed-time and adaptive signal control [51]. A coordinated signal control system for urban ring roads under a vehicle-infrastructure connected environment was proposed and tested using a VISSIM simulation model to improve the average delay, number of stops, and queue length compared with a conventional traffic control system [52].…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Left-Turn Signal Control Method for Improving Intersection Capacity in a Connected Vehicle Environment

et al. 2019

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Setting up an exclusive left-turn lane and corresponding signal phase for intersection traffic safety and efficiency will decrease the capacity of the intersection when there are less or no left-turn movements. This is especially true during rush hours because of the ineffective use of left-turn lane space and signal phase duration. With the advantages of vehicle-to-infrastructure (V2I) communication, a novel intersection signal control model is proposed which sets up variable lane direction arrow marking and turns the left-turn lane into a controllable shared lane for left-turn and through movements. The new intersection signal control model and its control strategy are presented and simulated using field data. After comparison with two other intersection control models and control strategies, the new model is validated to improve the intersection capacity in rush hours. Besides, variable lane lines and the corresponding control method are designed and combined with the left-turn waiting area to overcome the shortcomings of the proposed intersection signal control model and control strategy.

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Spatiotemporal intersection control in a connected and automated vehicle environment

Cited by 200 publications

References 19 publications

Position weighted backpressure intersection control for urban networks

Position weighted backpressure intersection control for urban networks

Trajectory Planning Method for Mixed Vehicles Considering Traffic Stability and Fuel Consumption at the Signalized Intersection

A Novel Left-Turn Signal Control Method for Improving Intersection Capacity in a Connected Vehicle Environment

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