Stability and safety evaluation of mixed traffic flow with connected automated vehicles on expressways

Yao, Zhihong; Hu, Rong; Jiang, Yangsheng; Xu, Taorang

doi:10.1016/j.jsr.2020.09.012

Cited by 97 publications

(39 citation statements)

References 34 publications

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“…In the simulation experiments, one simulation run period was 4,500 s, which included a warm-up period of 300 s, an efficient simulation period of 3,600 s, and a traffic dissipation period of 600 s. The simulation of every scenario in every penetration rate and volume is repeated 10 times with different random seeds ( 49 ). In addition, each vehicle is generated with a random number between zero and one.…”

Section: Case Study and Discussionmentioning

confidence: 99%

Kalman Filtering Method for Real-Time Queue Length Estimation in a Connected Vehicle Environment

Wang

Yao

Cheng

et al. 2021

Transportation Research Record: Journal of the Transportation R

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Queue length estimation is of great importance for measuring traffic signal performance and optimizing traffic signal timing plans. With the development of connected vehicle (CV) technology, using mobile CV data instead of fixed detector data to estimate queue length has become an important research topic. This study focuses on real-time queue length estimation for an isolated intersection with CV data. A Kalman filtering method is proposed to estimate the queue length in real time using traffic signal timing and real-time traffic flow parameters (i.e., saturated flow rate, traffic volume, and penetration rate), which are estimated using CV trajectories data. A simulation intersection was built and calibrated using field data to evaluate the performance of the proposed method and the benchmark method. Results show that when the CV penetration rate is at 30%, the average values of mean absolute errors, mean absolute percentage errors, and root mean square errors are just 1.6 vehicles, 20.9%, and 2.5 vehicles, respectively. The performance of the proposed model is also better than the benchmark method when the penetration rate of CVs is higher than 20%, which proves the validity of the proposed method. Furthermore, sensitivity analysis indicates that the proposed method requires a high penetration rate of at least 30%.

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Section: Case Study and Discussionmentioning

confidence: 99%

Kalman Filtering Method for Real-Time Queue Length Estimation in a Connected Vehicle Environment

Wang

Yao

Cheng

et al. 2021

Transportation Research Record: Journal of the Transportation R

Self Cite

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show abstract

“…e following necessary assumptions are made to facilitate modeling and analysis. that is, only the longitudinal movement is considered [43][44][45].…”

Section: Assumptionmentioning

confidence: 99%

A Two-Level Model for Traffic Signal Timing and Trajectories Planning of Multiple CAVs in a Random Environment

Jiang

Zhao

et al. 2021

Journal of Advanced Transportation

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Connected and automated vehicles (CAVs) trajectories not only provide more real-time information by vehicles to infrastructure but also can be controlled and optimized, to further save travel time and gasoline consumption. This paper proposes a two-level model for traffic signal timing and trajectories planning of multiple connected automated vehicles considering the random arrival of vehicles. The proposed method contains two levels, i.e., CAVs’ arrival time and traffic signals optimization, and multiple CAVs trajectories planning. The former optimizes CAVs’ arrival time and traffic signals in a random environment, to minimize the average vehicle’s delay. The latter designs multiple CAVs trajectories considering average gasoline consumption. The dynamic programming (DP) and the General Pseudospectral Optimal Control Software (GPOPS) are applied to solve the two-level optimization problem. Numerical simulation is conducted to compare the proposed method with a fixed-time traffic signal. Results show that the proposed method reduces both average vehicle’s delay and gasoline consumption under different traffic demand significantly. The average reduction of vehicle’s delay and gasoline consumption are 26.91% and 10.38%, respectively, for a two-phase signalized intersection. In addition, sensitivity analysis indicates that the minimum green time and free-flow speed have a noticeable effect on the average vehicle’s delay and gasoline consumption.

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“…They found that the application of CAVs can improve the stability and safety of traffic flow. Later, Yao et al (2020) [31] described the car-following behaviour of different types of vehicles by setting corresponding parameters in the IDM so as to obtain linear stability condition of heterogeneous traffic flow. The results showed that IDM with different parameters can express the car-following characteristics of different types of vehicles.…”

Section: Introductionmentioning

confidence: 99%

An Improved Intelligent Driver Model Considering the Information of Multiple Front and Rear Vehicles

et al. 2021

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This paper proposes an improved intelligent driver model (IDM) by considering the information of multiple front and rear vehicles to describe the car-following behaviour of CAVs (Connected and autonomous vehicles). The model involves the velocity and acceleration of multiple front and rear vehicles as well as the velocity difference and headway between the host vehicle and its surrounding vehicles. By introducing location-related parameters, the model quantitatively expresses the change in influence degree of a surrounding vehicle with its location to the host vehicle. To maximize traffic stability, we obtain the optimal value of the parameters in the model and the effect of specific time delays on the stability of traffic flow with numerical simulation. The results indicate that for a single vehicle control, the proposed model provides a much quicker and smoother acceleration and deceleration process to the desired speed than the IDM and multi-front IDM. And for platoon control, the proposed multi-front and rear IDM is superior to the other two models in decreasing the starting and braking time and increasing the stability of speed and acceleration. With effective car-following behaviour control, it is helpful to improve the operation efficiency of CAVs and enhance the stability of traffic flow. In addition to the car-following behaviour control, the model can be utilized for platoon control in the case of CAVs' homogeneous flow. This model can also serve as an effective tool to simulate car-following behaviour, which is beneficial for road traffic management and infrastructure layout in connected environments.

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Stability and safety evaluation of mixed traffic flow with connected automated vehicles on expressways

Cited by 97 publications

References 34 publications

Kalman Filtering Method for Real-Time Queue Length Estimation in a Connected Vehicle Environment

Kalman Filtering Method for Real-Time Queue Length Estimation in a Connected Vehicle Environment

A Two-Level Model for Traffic Signal Timing and Trajectories Planning of Multiple CAVs in a Random Environment

An Improved Intelligent Driver Model Considering the Information of Multiple Front and Rear Vehicles

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