Shockwave-Based Queue Length Estimation Method for Presignals for Bus Priority

Queue length estimation is of great importance for signal performance measures and signal optimization. With the development of connected vehicle technology and mobile internet technology, using mobile sensor data instead of fixed detector data to estimate queue length has become a significant research topic. This study proposes a queue length estimation method using low-penetration mobile sensor data as the only input. The proposed method is based on the combination of Kalman Filtering and shockwave theory. The critical points are identified from raw spatiotemporal points and allocated to different cycles for subsequent estimation. To apply the Kalman Filter, a state-space model with two state variables and the system noise determined by queue-forming acceleration is established, which can characterize the stochastic property of queue forming. The Kalman Filter with joining points as measurement input recursively estimates real-time queue lengths; on the other hand, queue-discharging waves are estimated with a line fitted to leaving points. By calculating the crossing point of the queue-forming wave and the queue-discharging wave of a cycle, the maximum queue length is also estimated. A case study with DiDi mobile sensor data and ground truth maximum queue lengths at Huanggang-Fuzhong intersection, Shenzhen, China, shows that the mean absolute percentage error is only 11.2%. Moreover, the sensitivity analysis shows that the proposed estimation method achieves much better performance than the classical linear regression method, especially in extremely low penetration rates.

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A Real-Time Queue Length Estimation Method Based on Probe Vehicles in CV Environment

IEEE Access ◽

10.1109/access.2019.2898424 ◽

2019 ◽

Vol 7 ◽

pp. 20825-20839 ◽

Cited By ~ 1

Author(s):

Haiqing Liu ◽

Wenli Liang ◽

Laxmisha Rai ◽

Kunmin Teng ◽

Shengli Wang

Keyword(s):

Real Time ◽

Queue Length ◽

Estimation Method ◽

Probe Vehicles ◽

Length Estimation

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Left-Turn Spillback Probability Estimation in a Connected Vehicle Environment

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119837178 ◽

2019 ◽

Vol 2673 (4) ◽

pp. 753-761

Author(s):

Xiaowei Cao ◽

Jian Jiao ◽

Yunlong Zhang ◽

Xiubin Wang

Keyword(s):

Control Strategy ◽

Queue Length ◽

Estimation Method ◽

Signal Control ◽

Connected Vehicles ◽

Connected Vehicle ◽

Model Accuracy ◽

Left Turn ◽

Microscopic Traffic Simulation ◽

Length Estimation

At intersections in which the left-turn bay does not have sufficient length or the left-turn volume is relatively high, left-turn vehicles may spill back and block the adjacent through traffic. This paper aims to develop quantitative measures of the left-turn spillback, and by using the results on spillback probability, develop a suitable signal control strategy. We first develop an improved queue length estimation method for vehicles in the left-turn bay based on Comert and Cetin’s general queue length estimation method with connected vehicles, after which we propose a probabilistic model to measure the left-turn spillback probability at an intersection in a connected environment. The model accuracy is validated with results from microscopic traffic simulation. The effect of bay length is also studied. In the end, a signal control demonstration is presented to show the efficiency of the proposed method in signal control.

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A neural network algorithm for queue length estimation based on the concept of k-leader connected vehicles

Journal of Modern Transportation ◽

10.1007/s40534-019-00200-y ◽

2019 ◽

Vol 27 (4) ◽

pp. 341-354 ◽

Cited By ~ 3

Author(s):

Azadeh Emami ◽

Majid Sarvi ◽

Saeed Asadi Bagloee

Keyword(s):

Neural Network ◽

Queue Length ◽

Travel Demand ◽

Penetration Rate ◽

Estimation Method ◽

Traffic Signal ◽

Signal Control ◽

Traffic Signal Control ◽

Market Penetration ◽

Length Estimation

AbstractThis paper presents a novel method to estimate queue length at signalised intersections using connected vehicle (CV) data. The proposed queue length estimation method does not depend on any conventional information such as arrival flow rate and parameters pertaining to traffic signal controllers. The model is applicable for real-time applications when there are sufficient training data available to train the estimation model. To this end, we propose the idea of “k-leader CVs” to be able to predict the queue which is propagated after the communication range of dedicated short-range communication (the communication platform used in CV system). The idea of k-leader CVs could reduce the risk of communication failure which is a serious concern in CV ecosystems. Furthermore, a linear regression model is applied to weigh the importance of input variables to be used in a neural network model. Vissim traffic simulator is employed to train and evaluate the effectiveness and robustness of the model under different travel demand conditions, a varying number of CVs (i.e. CVs’ market penetration rate) as well as various traffic signal control scenarios. As it is expected, when the market penetration rate increases, the accuracy of the model enhances consequently. In a congested traffic condition (saturated flow), the proposed model is more accurate compared to the undersaturated condition with the same market penetration rates. Although the proposed method does not depend on information of the arrival pattern and traffic signal control parameters, the results of the queue length estimation are still comparable with the results of the methods that highly depend on such information. The proposed algorithm is also tested using large size data from a CV test bed (i.e. Australian Integrated Multimodal Ecosystem) currently underway in Melbourne, Australia. The simulation results show that the model can perform well irrespective of the intersection layouts, traffic signal plans and arrival patterns of vehicles. Based on the numerical results, 20% penetration rate of CVs is a critical threshold. For penetration rates below 20%, prediction algorithms fail to produce reliable outcomes.

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