Use of Speed Measurements for Highway Traffic State Estimation: Case Studies on NGSIM Data and Highway A20, Netherlands

2016 ◽  
Vol 2559 (1) ◽  
pp. 90-100 ◽  
Author(s):  
Claudio Roncoli ◽  
Nikolaos Bekiaris-Liberis ◽  
Markos Papageorgiou
Keyword(s):  
State Estimation ◽  
Case Studies ◽  
Highway Traffic ◽  
Traffic State ◽  
Traffic State Estimation

scholarly journals Highway Traffic State Estimation With Mixed Connected and Conventional Vehicles

2016 ◽  
Vol 17 (12) ◽  
pp. 3484-3497 ◽  
Author(s):  
Nikolaos Bekiaris-Liberis ◽  
Claudio Roncoli ◽  
Markos Papageorgiou
Keyword(s):  
State Estimation ◽  
Highway Traffic ◽  
Traffic State ◽  
Traffic State Estimation

scholarly journals Highway traffic state estimation with mixed connected and conventional vehicles: Microscopic simulation-based testing

2017 ◽  
Vol 78 ◽  
pp. 13-33 ◽  
Author(s):  
Markos Fountoulakis ◽  
Nikolaos Bekiaris-Liberis ◽  
Claudio Roncoli ◽  
Ioannis Papamichail ◽  
Markos Papageorgiou
Keyword(s):  
State Estimation ◽  
Highway Traffic ◽  
Microscopic Simulation ◽  
Traffic State ◽  
Traffic State Estimation ◽  
Simulation Based

scholarly journals Highway Traffic State Estimation with Mixed Connected and Conventional Vehicles

2016 ◽  
Vol 49 (3) ◽  
pp. 309-314
Author(s):  
Nikolaos Bekiaris-Liberis ◽  
Claudio Roncoli ◽  
Markos Papageorgiou
Keyword(s):  
State Estimation ◽  
Highway Traffic ◽  
Traffic State ◽  
Traffic State Estimation

scholarly journals Effect of Enhanced ADAS Camera Capability on Traffic State Estimation

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1996
Author(s):  
Hoe Kyoung Kim ◽  
Younshik Chung ◽  
Minjeong Kim
Keyword(s):  
State Estimation ◽  
Traffic Engineering ◽  
Flow Density ◽  
Microscopic Simulation ◽  
Traffic Demand ◽  
Traffic System ◽  
Traffic Conditions ◽  
Traffic State ◽  
Vehicle Identification ◽  
Traffic State Estimation

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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