A real-time adaptive signal control in a connected vehicle environment

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

doi:10.1016/j.trc.2015.01.007

Cited by 463 publications

(250 citation statements)

References 17 publications

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“…The signal planning in the I-SIG system uses a dual-ring version of the COP (Controlled Optimization of Phases) algorithm [36], [25]. The input of the COP algorithm is each approaching vehicle's estimated arrival time at the intersection, which is defined as the estimated remaining time for a vehicle to reach the stop bar of its current lane.…”

Section: ) Traffic Control Conceptsmentioning

confidence: 99%

“…If the COP algorithm only optimizes the signal plan for the equipped vehicles, its effectiveness is found to be largely reduced if the portion of the equipped vehicles is not sufficiently high, e.g., less than 95% [25]. Since it is estimated that the market penetration rate needs 25-30 years to reach at least 95% [16], the I-SIG system uses an algorithm called EVLS (Estimation of Location and Speed) to estimate the trajectory data of the unequipped vehicles.…”

Section: ) Traffic Control Conceptsmentioning

confidence: 99%

“…The use of COP and EVLS is chosen by the I-SIG designer, the team of USDOT-selected signal control experts, based on a 2015 paper published in Transportation Research Part C [25], a top-tier journal in transportation research. The COP algorithm is chosen because it is very suitable for the CV environment: its input is the arrival time for individual vehicles instead of aggregated traffic information, and thus can best leverage the per-vehicle trajectory data in the CV environment to effectively handle traffic dynamics.…”

Section: ) Traffic Control Conceptsmentioning

confidence: 99%

“…The unequipped vehicle estimation process, i.e., the EVLS algorithm [25], is detailed in the lower part of Fig. 5.…”

Section: B Spoofing Strategy For the Transition Period Onlymentioning

confidence: 99%

See 3 more Smart Citations

Exposing Congestion Attack on Emerging Connected Vehicle based Traffic Signal Control

Chen

Yu-cheng²,

Feng

et al. 2018

Proceedings 2018 Network and Distributed System Security Symposium

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Abstract-Connected vehicle (CV) technology will soon transform today's transportation systems by connecting vehicles and the transportation infrastructure through wireless communication. Having demonstrated the potential to greatly improve transportation mobility efficiency, such dramatically increased connectivity also opens a new door for cyber attacks. In this work, we perform the first detailed security analysis of the nextgeneration CV-based transportation systems. As a first step, we target the USDOT (U.S. Department of Transportation) sponsored CV-based traffic control system, which has been tested and shown high effectiveness in real road intersections. In the analysis, we target a realistic threat, namely CV data spoofing from one single attack vehicle, with the attack goal of creating traffic congestion.We first analyze the system design and identify data spoofing strategies that can potentially influence the traffic control. Based on the strategies, we perform vulnerability analysis by exhaustively trying all the data spoofing options for these strategies to understand the upper bound of the attack effectiveness. For the highly effective cases, we analyze the causes and find that the current signal control algorithm design and implementation choices are highly vulnerable to data spoofing attacks from even a single attack vehicle. These vulnerabilities can be exploited to completely reverse the benefit of the CV-based signal control system by causing the traffic mobility to be 23.4% worse than that without adopting such system. We then construct practical exploits and evaluate them under real-world intersection settings. The evaluation results are consistent with our vulnerability analysis, and we find that the attacks can even cause a blocking effect to jam an entire approach. In the jamming period, 22% of the vehicles need to spend over 7 minutes for an original halfminute trip, which is 14 times higher. We also discuss defense directions leveraging the insights from our analysis.

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Section: ) Traffic Control Conceptsmentioning

confidence: 99%

Section: ) Traffic Control Conceptsmentioning

confidence: 99%

Section: ) Traffic Control Conceptsmentioning

confidence: 99%

“…The unequipped vehicle estimation process, i.e., the EVLS algorithm [25], is detailed in the lower part of Fig. 5.…”

Section: B Spoofing Strategy For the Transition Period Onlymentioning

confidence: 99%

See 2 more Smart Citations

Exposing Congestion Attack on Emerging Connected Vehicle based Traffic Signal Control

Chen

Yu-cheng²,

Feng

et al. 2018

Proceedings 2018 Network and Distributed System Security Symposium

Self Cite

View full text Add to dashboard Cite

show abstract

“…A real-time adaptive signal phase allocation algorithm using CV data can be used for signal control by optimizing the phase sequence and duration with the two objective functions: minimization of total vehicle delay and minimization of queue length. Such control algorithm can reduce total delay by up to 16.33% in high and low penetration rate cases [17].…”

Section: Introductionmentioning

confidence: 99%

Bioinspired Psi Intelligent Control for Autonomous Dynamic Systems

Khoshnoud¹,

Silva²,

Esat³

2015

Control and Intelligent Systems

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Field implementation feasibility study of cumulative travel‐time responsive (CTR) traffic signal control algorithm

Choi

Park

Lee

et al. 2016

J of Advced Transportation

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The cumulative travel-time responsive (CTR) algorithm determines optimal green split for the next time interval by identifying the maximum cumulative travel time (CTT) estimated under the connected vehicle environment. This paper enhanced the CTR algorithm and evaluated its performance to verify a feasibility of field implementation in a near future. Standard Kalman filter (SKF) and adaptive Kalman filter (AKF) were applied to estimate CTT for each phase in the CTR algorithm. In addition, traffic demand, market penetration rate (MPR), and data availability were considered to evaluate the CTR algorithm's performance. An intersection in the Northern Virginia connected vehicle test bed is selected for a case study and evaluated within VISSIM and hardware in the loop simulations. As expected, the CTR algorithm's performance depends on MPR because the information collected from connected vehicle is a key enabling factor of the CTR algorithm. However, this paper found that the MPR requirement of the CTR algorithm could be addressed (i) when the data are collected from both connected vehicle and the infrastructure sensors and (ii) when the AKF is adopted. The minimum required MPRs to outperform the actuated traffic signal control were empirically found for each prediction technique (i.e., 30% for the SKF and 20% for the AKF) and data availability. Even without the infrastructure sensors, the CTR algorithm could be implemented at an intersection with high traffic demand and 50-60% MPR. The findings of this study are expected to contribute to the field implementation of the CTR algorithm to improve the traffic network performance. investigated as 18.15% and 15.31%, respectively. It is noted that the key to the success is not about the prediction accuracy but the correct identification of an approach with the highest CTT. The prediction accuracies to identify the highest CTT of the SKF and AKF were 89.6% and 92.1%, respectively. In addition, there are a few factors affecting the travel-time prediction accuracy. These include communication errors, vehicle types, MPR, etc. This paper only considered MPR in evaluating the performance of the Kalman filter because it is generally understood that communication errors are important for safety critical applications (i.e., not critical for travel-time estimation) and the vehicle mix on this corridor has less than 2% trucks. In addition, the Kalman filter used in this paper predicted quite well even without considering vehicle types.information to the signal head. Note that step 5 is not available to consider for indoor experiments. Thus, this study analyzed the CTR algorithm in the HILS configuration implementing step 1 through step 4. RESULTSThe effectiveness of the CTR algorithm over the actuated signal algorithm is summarized in Table III in terms of MPRs, volume scenarios, communication types, and Kalman filter algorithms compared. It is noted that the existing actuated signal control is considered to be up to date as the Northern Virginia traffic engineers well maintain the ti...

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A real-time adaptive signal control in a connected vehicle environment

Cited by 463 publications

References 17 publications

Exposing Congestion Attack on Emerging Connected Vehicle based Traffic Signal Control

Exposing Congestion Attack on Emerging Connected Vehicle based Traffic Signal Control

Bioinspired Psi Intelligent Control for Autonomous Dynamic Systems

Field implementation feasibility study of cumulative travel‐time responsive (CTR) traffic signal control algorithm

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