Probe vehicle lane identification for queue length estimation at intersections

Rompis, Semuel Y. R.; Cetin, Mecit; Habtemichael, Filmon

doi:10.1080/15472450.2017.1300887

Cited by 30 publications

(9 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Instead of using video cameras or sensors, the mechanisms utilizing GPS-mounted probe vehicles have been proposed for the estimation of the vehicle queue length [26,27,28,29,30,31,32,33]. Probe vehicles are special purpose vehicles designed for monitoring road traffic situations and collecting trajectory data.…”

Section: Related Workmentioning

confidence: 99%

Internet of Vehicles and Cost-Effective Traffic Signal Control

Ahn

Choi

2019

Sensors

View full text Add to dashboard Cite

The Internet of Vehicles (IoV) is attracting many researchers with the emergence of autonomous or smart vehicles. Vehicles on the road are becoming smart objects equipped with lots of sensors and powerful computing and communication capabilities. In the IoV environment, the efficiency of road transportation can be enhanced with the help of cost-effective traffic signal control. Traffic signal controllers control traffic lights based on the number of vehicles waiting for the green light (in short, vehicle queue length). So far, the utilization of video cameras or sensors has been extensively studied as the intelligent means of the vehicle queue length estimation. However, it has the deficiencies like high computing overhead, high installation and maintenance cost, high susceptibility to the surrounding environment, etc. Therefore, in this paper, we propose the vehicular communication-based approach for intelligent traffic signal control in a cost-effective way with low computing overhead and high resilience to environmental obstacles. In the vehicular communication-based approach, traffic signals are efficiently controlled at no extra cost by using the pre-equipped vehicular communication capabilities of IoV. Vehicular communications allow vehicles to send messages to traffic signal controllers (i.e., vehicle-to-infrastructure (V2I) communications) so that they can estimate vehicle queue length based on the collected messages. In our previous work, we have proposed a mechanism that can accomplish the efficiency of vehicular communications without losing the accuracy of traffic signal control. This mechanism gives transmission preference to the vehicles farther away from the traffic signal controller, so that the other vehicles closer to the stop line give up transmissions. In this paper, we propose a new mechanism enhancing the previous mechanism by selecting the vehicles performing V2I communications based on the concept of road sectorization. In the mechanism, only the vehicles within specific areas, called sectors, perform V2I communications to reduce the message transmission overhead. For the performance comparison of our mechanisms, we carry out simulations by using the Veins vehicular network simulation framework and measure the message transmission overhead and the accuracy of the estimated vehicle queue length. Simulation results verify that our vehicular communication-based approach significantly reduces the message transmission overhead without losing the accuracy of the vehicle queue length estimation.

show abstract

Section: Related Workmentioning

confidence: 99%

Internet of Vehicles and Cost-Effective Traffic Signal Control

Ahn

Choi

2019

Sensors

View full text Add to dashboard Cite

show abstract

“…The method assumes a uniform arrival pattern for vehicles entering the intersection. There are other studies [28,29] in addressing the queue length estimation based on the data from moving sensors such as mobile sensors or probe vehicles.…”

Section: Literature Reviewmentioning

confidence: 99%

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

2019

View full text Add to dashboard Cite

This 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.

show abstract

“…The average arrival rate was inferred from the Bayesian network by applying the Expectation-Maximization algorithm. There is also a stream of literature that applies the shockwave theory to probe vehicle data (Ban et al, 2011;Cetin, 2012;Ramezani and Geroliminis, 2015;Li et al, 2017;Rompis et al, 2018), or combines probe vehicle data and loop detector data (Badillo et al, 2012;Cai et al, 2014;Wang et al, 2017;Shahrbabaki et al, 2018), to estimate or predict the queue lengths. Since these studies are not closely related to this paper methodologically, they will not be introduced in detail.…”

Section: Introductionmentioning

confidence: 99%

Various methods for queue length and traffic volume estimation using probe vehicle trajectories

Zhao

Zheng²,

Wong

et al. 2019

Transportation Research Part C: Emerging Technologies

View full text Add to dashboard Cite

The rapid development of connected vehicle technology and the emergence of ride-hailing services have enabled the collection of a tremendous amount of probe vehicle trajectory data. Due to the large scale, the trajectory data have become a potential substitute for the widely used fixed-location sensors in terms of the performance measures of transportation networks. Specifically, for traffic volume and queue length estimation, most of the trajectory data based methods in the existing literature either require high market penetration of the probe vehicles to identify the shockwave or require the prior information about the queue length distribution and the penetration rate, which may not be feasible in the real world. To overcome the limitations of the existing methods, this paper proposes a series of novel methods based on probability theory. By exploiting the stopping positions of the probe vehicles in the queues, the proposed methods try to establish and solve a single-variable equation for the penetration rate of the probe vehicles. Once the penetration rate is obtained, it can be used to project the total queue length and the total traffic volume. The validation results using both simulation data and real-world data show that the methods would be accurate enough for assistance in performance measures and traffic signal control at intersections, even when the penetration rate of the probe vehicles is very low.

show abstract

Probe vehicle lane identification for queue length estimation at intersections

Cited by 30 publications

References 18 publications

Internet of Vehicles and Cost-Effective Traffic Signal Control

Internet of Vehicles and Cost-Effective Traffic Signal Control

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

Various methods for queue length and traffic volume estimation using probe vehicle trajectories

Contact Info

Product

Resources

About