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

Zhao, Yan; Zheng, Jianfeng; Wong, Wendy H.; Wang, Xingmin; Meng, Yuan; Liu, Henry

doi:10.1016/j.trc.2019.07.008

Cited by 78 publications

(30 citation statements)

References 34 publications

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“…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

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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.

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Section: Related Workmentioning

confidence: 99%

Internet of Vehicles and Cost-Effective Traffic Signal Control

Ahn

Choi

2019

Sensors

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“…While all these new sensing technologies address the coverage limitations of fixed sensors and allow for city-wide traffic state estimation, some of them present the other interesting advantage of providing infrastructure-less and cost-efficient solutions for traffic state estimation, making them particularly interesting for cities in developing countries that do not have road and sensing infrastructure of traffic monitoring. Probe Vehicles (PVs) [12]- [14] and Cell Phones [15] have been widely used for the implementation of infrastructure-less and cost-effective traffic state estimation solutions, mainly for travel time and signalized intersections' queue length estimation [14], [16]. But beside the penetration rate issue, these technologies cannot be used to directly estimate traffic flow.…”

Section: Introductionmentioning

confidence: 99%

Toward a Cost-Effective Motorway Traffic State Estimation From Sparse Speed and GPS Data

et al. 2021

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In this paper, we propose a new data-driven traffic state estimation model that estimates traffic flow based on average speed data only. The model is devised to implement a cost-effective framework that aggregates heterogeneous sources of vehicles' GPS and speed measurements to infer traffic flow using a novel triplet system called Conditionally Gaussian Observed Markov Fuzzy Switching Systems (CGOMFSM). Unlike its hard counterpart, CGOMFSM allows for a transient and gradual representation of traffic state transition and hence improves the estimation performance using a tractable scheme. The potential of the proposed model is illustrated through an application to the problem of traffic incident detection, particularly sporadic traffic congestion caused by unexpected road conditions. The performance of the proposed model is assessed using real traffic datasets from England highways. A simulation of traffic in the city of Salalah in Oman was conducted to evaluate the efficacy of the CGOMFSM-based traffic estimation and incident detection schemes with different penetration rates.

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“…However, there are spatial limitations in acquiring detailed and wide-ranging traffic information simultaneously in an entire network and there is a high cost for installing, operating and maintaining stationary detectors. Therefore, it is very difficult to collect traffic flow data on all the roadways across a country [ 2 , 3 , 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Effect of Enhanced ADAS Camera Capability on Traffic State Estimation

Kim

Chung

Kim

2021

Sensors

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Traffic flow data, such as flow, density and speed, are crucial for transportation planning and traffic system operation. Recently, a novel traffic state estimating method was proposed using the distance to a leading vehicle measured by an advanced driver assistance system (ADAS) camera. This study examined the effect of an ADAS camera with enhanced capabilities on traffic state estimation using image-based vehicle identification technology. Considering the realistic distance error of the ADAS camera from the field experiment, a microscopic simulation model, VISSIM, was employed with multiple underlying parameters such as the number of lanes, traffic demand, the penetration rate of ADAS vehicles and the spatiotemporal range of the estimation area. Although the enhanced functions of the ADAS camera did not affect the accuracy of the traffic state estimates significantly, the ADAS camera can be used for traffic state estimation. Furthermore, the vehicle identification distance of the ADAS camera and traffic conditions with more lanes did not always ensure better accuracy of the estimates. Instead, it is recommended that transportation planners and traffic engineering practitioners carefully select the relevant parameters and their range to ensure a certain level of accuracy for traffic state estimates that suit their purposes.

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

Cited by 78 publications

References 34 publications

Internet of Vehicles and Cost-Effective Traffic Signal Control

Internet of Vehicles and Cost-Effective Traffic Signal Control

Toward a Cost-Effective Motorway Traffic State Estimation From Sparse Speed and GPS Data

Effect of Enhanced ADAS Camera Capability on Traffic State Estimation

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