Connected Vehicle–Based Adaptive Signal Control and Applications

Feng, Yiheng; Zamanipour, Mehdi; Head, Larry; Khoshmagham, Shayan

doi:10.3141/2558-02

Cited by 36 publications

(27 citation statements)

References 20 publications

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“…The vehicle arrival prediction model is an important part of traffic adaptive control algorithms or systems [34]. The research of vehicle arrival prediction can be categorized according to the following two categories.…”

Section: Literature Reviewmentioning

confidence: 99%

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A New Vehicle Arrival Prediction Model for Adaptive Signal Control in a Connected Vehicle Environment

Liu

Wang²,

Yao

2020

IEEE Access

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With the development of connected vehicles (CVs) technology, it has become a new research topic to capture the dynamic traffic system by using CVs data. The traditional vehicle arrival prediction models are limited to the fixed detectors, which can only recognize the passing information of vehicles, but cannot identify the state of vehicles. This paper proposed a new vehicle arrival prediction model of traffic signal control in a connected vehicle environment. First, the vehicle's identification number (ID), position, velocity, and acceleration were obtained in a CV environment. Second, a new vehicle arrival prediction model was developed by the joint probability distribution calibrated based on CV data. Then, the simulation experiment was designed for analyzing influence factors on model performance. The results show that the average relative error of each factor is less than 10%. Finally, the reliability of the proposed model was verified based on an adaptive signal control algorithm. Therefore, the proposed model can be used in the adaptive signal control system.

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Section: Literature Reviewmentioning

confidence: 99%

“…The proposed vehicle arrival prediction model was applied as a part of developing an adaptive signal control. The proposed model was used as the input of the adaptive signal control algorithm, named the COP algorithm [22], [24], [34]. A simulation experiment was developed by utilizing Vissim [42].…”

Section: Model Applicationmentioning

confidence: 99%

A New Vehicle Arrival Prediction Model for Adaptive Signal Control in a Connected Vehicle Environment

Liu

Wang²,

Yao

2020

IEEE Access

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“…The basic safety messages (BSMs) transmitted by the vehicles to the intersection contain latitude and longitude, speed, elevation, heading, braking information, and timestamp at 0.1 s intervals (23). Recent research has explored using CV to mitigate dilemma zone incursions in simulation, but there have been limited field studies done on the topic thus far (24)(25)(26)(27)(28). This study investigates the use of map-matching of BSMs to virtual waypoints in lanes upstream of a signalized intersection to determine vehicle position relative to the stop bar.…”

Section: Opportunities For Improved Dilemma Zone Detectionmentioning

confidence: 99%

Using Connected Vehicle Data to Reassess Dilemma Zone Performance of Heavy Vehicles

Platte

Mathew

et al. 2020

Transportation Research Record: Journal of the Transportation R

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The rate of fatalities at signalized intersections involving heavy vehicles is nearly five times higher than for passenger vehicles in the US. Previous studies in the US have found that heavy vehicles are twice as likely to violate a red light compared with passenger vehicles. Current technologies leverage setback detection to extend green time for a particular phase and are based upon typical deceleration rates for passenger cars. Furthermore, dilemma zone detectors are not effective when the max out time expires and forces the onset of yellow. This study proposes the use of connected vehicle (CV) technology to trigger force gap out (FGO) before a vehicle is expected to arrive within the dilemma zone limit at max out time. The method leverages position data from basic safety messages (BSMs) to map-match virtual waypoints located up to 1,050 ft in advance of the stop bar. For a 55 mph approach, field tests determined that using a 6 ft waypoint radius at 50 ft spacings would be sufficient to match 95% of BSM data within a 5% lag threshold of 0.59 s. The study estimates that FGOs reduce dilemma zone incursions by 34% for one approach and had no impact for the other. For both approaches, the total dilemma zone incursions decreased from 310 to 225. Although virtual waypoints were used for evaluating FGO, the study concludes by recommending that trajectory-based processing logic be incorporated into controllers for more robust support of dilemma zone and other emerging CV applications.

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“…For this study, six containers were run at the same time to operate six signalized intersections. The optimum signal timing data, which includes an optimal signal sequence and duration for each phase, are estimated by the phase allocation algorithm (9,10). The optimal solution is transmitted to the Econolite ASC/3 traffic controller inter-face.…”

Section: Mmitss Simulation Model Developmentmentioning

confidence: 99%

Multimodal Intelligent Traffic Signal System Simulation Model Development and Assessment

Ahn

Rakha

Kang

et al. 2016

Transportation Research Record: Journal of the Transportation R

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The study demonstrated the multimodal intelligent traffic signal system (MMITSS) simulation tool to assess the potential effects of a broader MMITSS deployment that would ultimately facilitate the site-independent analysis of MMITSS applications. The study also identified the most beneficial operational conditions for each MMITSS operational scenario by considering a combination of simulation variables and traffic demand levels. The MMITSS simulation platform consists of the Vissim microscopic traffic simulation software, the basic safety message distributor program, an Econolite ASC/3 traffic controller emulator that runs on a Windows platform, and a roadside equipment module that runs on a Linux platform. The study demonstrated that the intelligent traffic signal system reduced vehicle delay by up to 35% and increased average traffic stream speed by up to 27%. In addition, transit signal priority (TSP) reduced travel time for both transit and passenger vehicles on the corridor by up to 29% and 28%, respectively. However, TSP can increase systemwide delay because it reduces green times on the side streets. Freight signal priority (FSP) can be used effectively along major freight routes. Although FSP significantly reduced truck delay, networkwide delay was increased substantially, especially in the scenario with a large percentage of trucks in the traffic composition. The TSP and FSP bundle simulation study demonstrated that the bundle operation successfully executed a hierarchical level of priority and provided higher priority for trucks. MMITSS applications effectively improve vehicle travel time and delay for equipped and potentially nonequipped vehicles, depending on the scenarios considered. Typically, systemwide disbenefits are observed when TSP, FSP, or both are applied.

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Connected Vehicle–Based Adaptive Signal Control and Applications

Cited by 36 publications

References 20 publications

A New Vehicle Arrival Prediction Model for Adaptive Signal Control in a Connected Vehicle Environment

A New Vehicle Arrival Prediction Model for Adaptive Signal Control in a Connected Vehicle Environment

Using Connected Vehicle Data to Reassess Dilemma Zone Performance of Heavy Vehicles

Multimodal Intelligent Traffic Signal System Simulation Model Development and Assessment

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