Cumulative Travel-Time Responsive Real-Time Intersection Control Algorithm in the Connected Vehicle Environment

Lee, Joyoung; Park, Byungkyu Brian; Yun, Ilsoo

doi:10.1061/(asce)te.1943-5436.0000587

Cited by 133 publications

(78 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared to the benchmarking control method, the numerical experiment shows that the reduction in the total travel time and vehicle stops is notable under various simulation scenarios. Similar to the above method based on travel time, Lee et al [24] presented a cumulative travel-time responsive (CTR) real-time intersection control algorithm with connected vehicle data to minimize the cumulative travel time of vehicles. The cumulative travel time is defined as the elapsed time from when the vehicle entered the approaching link to the current position of the vehicle.…”

Section: Adaptive Signal Control Methodsmentioning

confidence: 99%

“…This is because the penetration rate of the equipped wireless vehicle will not reach up to 100% during recent years and the unequipped vehicle will have an influence on the intersection traffic signal control efficiency [17,26]. Several various objective functions were taken into consideration when designing a signal control system, such as minimizing the average delay of vehicle [27], minimizing the delay of total vehicle, minimizing the queue length [17] and improving the average speed [24]. The alternatives of objective function will determine the performance of the traffic signal control system.…”

Section: Quality Assessmentmentioning

confidence: 99%

“…Lee et al [24] applied the Kalman filtering (KF) technique to estimate traffic states for cumulative travel-time under imperfect connected vehicle penetration rates. Owing to the result of the feedback characteristic of the KF technique to recursively correct the errors within the boundary set by both process and measurements noise variances.…”

Section: Estimation Of Unequipped Vehicle Statusmentioning

confidence: 99%

“…Only a few recent works took into consideration the incomplete vehicle status information or unequipped vehicles. Traffic models [17] and statistical methods [24] are used to estimate the arrival status of unequipped vehicles. Although there has been certain positive achievement on models and solutions to research the adaptive traffic signal control in a connected vehicle environment, there are still many questions to be studied.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Adaptive Traffic Signal Control in a Connected Vehicle Environment: A Systematic Review

2017

View full text Add to dashboard Cite

Abstract:In the last few years, traffic congestion has become a growing concern due to increasing vehicle ownerships in urban areas. Intersections are one of the major bottlenecks that contribute to urban traffic congestion. Traditional traffic signal control systems cannot adjust the timing pattern depending on road traffic demand. This results in excessive delays for road users. Adaptive traffic signal control in a connected vehicle environment has shown a powerful ability to effectively alleviate urban traffic congestions to achieve desirable objectives (e.g., delay minimization). Connected vehicle technology, as an emerging technology, is a mobile data platform that enables the real-time data exchange among vehicles and between vehicles and infrastructure. Although several reviews about traffic signal control or connected vehicles have been written, a systemic review of adaptive traffic signal control in a connected vehicle environment has not been made. Twenty-six eligible studies searched from six databases constitute the review. A quality evaluation was established based on previous research instruments and applied to the current review. The purpose of this paper is to critically review the existing methods of adaptive traffic signal control in a connected vehicle environment and to compare the advantages or disadvantages of those methods. Further, a systematic framework on connected vehicle based adaptive traffic signal control is summarized to support the future research. Future research is needed to develop more efficient and generic adaptive traffic signal control methods in a connected vehicle environment.

show abstract

Section: Adaptive Signal Control Methodsmentioning

confidence: 99%

Section: Quality Assessmentmentioning

confidence: 99%

Section: Estimation Of Unequipped Vehicle Statusmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Adaptive Traffic Signal Control in a Connected Vehicle Environment: A Systematic Review

2017

View full text Add to dashboard Cite

show abstract

“…Their algorithm was platoon based and the objective was formulated to solve for an optimal signal plan based on current traffic condition, controller status, platoon data and priority requests. In [7] acumulative travel-time responsive real time intersection control algorithm with vehicular communication data was presented. The algorithm applied a kalman filter to estimate cumulative travel time under low PR.…”

Section: Introductionmentioning

confidence: 99%

Traffic Signal Control with Vehicle to Everything Communication

Salman

Özdemir

Çelebi³

2017

Computer Science &Amp; Information Technology (CS &Amp; IT)

View full text Add to dashboard Cite

show abstract

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

Choi

Park

Lee

et al. 2016

J of Advced Transportation

Self Cite

View full text Add to dashboard Cite

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...

show abstract

Cumulative Travel-Time Responsive Real-Time Intersection Control Algorithm in the Connected Vehicle Environment

Cited by 133 publications

References 18 publications

An Adaptive Traffic Signal Control in a Connected Vehicle Environment: A Systematic Review

An Adaptive Traffic Signal Control in a Connected Vehicle Environment: A Systematic Review

Traffic Signal Control with Vehicle to Everything Communication

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

Contact Info

Product

Resources

About