Microscopic simulation-based validation of a per-lane traffic state estimation scheme for highways with connected vehicles

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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Traffic state estimation using small imaging satellites and connected vehicles

Transportation Research Procedia ◽

10.1016/j.trpro.2018.11.007 ◽

2018 ◽

Vol 34 ◽

pp. 4-11 ◽

Cited By ~ 2

Author(s):

Toru Seo ◽

Takahiko Kusakabe

Keyword(s):

State Estimation ◽

Connected Vehicles ◽

Traffic State ◽

Traffic State Estimation

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Real-Time Traffic State Estimation With Connected Vehicles

IEEE Transactions on Intelligent Transportation Systems ◽

10.1109/tits.2017.2658664 ◽

2017 ◽

Vol 18 (7) ◽

pp. 1687-1699 ◽

Cited By ~ 28

Author(s):

Sakib Mahmud Khan ◽

Kakan C. Dey ◽

Mashrur Chowdhury

Keyword(s):

State Estimation ◽

Real Time ◽

Connected Vehicles ◽

Traffic State ◽

Real Time Traffic ◽

Traffic State Estimation

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Traffic State Estimation Using Connected Vehicles and Stationary Detectors

Journal of Advanced Transportation ◽

10.1155/2018/4106086 ◽

2018 ◽

Vol 2018 ◽

pp. 1-14 ◽

Cited By ~ 3

Author(s):

Ellen F. Grumert ◽

Andreas Tapani

Keyword(s):

State Estimation ◽

Traffic Management ◽

Effective Control ◽

Accurate Estimation ◽

Connected Vehicles ◽

Density Estimates ◽

Promising Alternative ◽

Traffic Conditions ◽

Traffic State ◽

Traffic State Estimation

Real-time traffic state estimation is of importance for efficient traffic management. This is especially the case for traffic management systems that require fast detection of changes in the traffic conditions in order to apply an effective control measure. In this paper, we propose a method for estimating the traffic state and speed and density, by using connected vehicles combined with stationary detectors. The aim is to allow fast and accurate estimation of changes in the traffic conditions. The proposed method does only require information about the speed and the position of connected vehicles and can make use of sparsely located stationary detectors to limit the dependence on the infrastructure equipment. An evaluation of the proposed method is carried out by microscopic traffic simulation. The traffic state estimated using the proposed method is compared to the true simulated traffic state. Further, the density estimates are compared to density estimates from one detector-based method, one combined method, and one connected-vehicle-based method. The results of the study show that the proposed method is a promising alternative for estimating the traffic state in traffic management applications.

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