Queue Spillover Management in a Connected Vehicle Environment

To alleviate the queue spillovers at intersections of urban roads during rush hours, a solution to the cross-spill problem based on vehicle networking technologies is proposed. This involves using connected vehicle technology, to realize the interactive information on vehicle and intersection signal control. The maximum control distance between intersections is determined by how vehicles are controlled and would travel in that connected environment. A method of calculating overflow tendency towards intersection queuing is also proposed, based on the maximum phase control distance. By this method, the intersection overflow is identified, and then the signal phases are re-optimized according to the requirements of different phases. Finally, overflow prevention control was also performed in this study. The VISSIM simulation results show that the method can better prevent the overflow of queues at intersections.

[1]  H. T. Mouftah,et al.  An Auction-Driven Multi-Objective Provisioning Framework in a Vehicular Cloud , 2015, 2015 IEEE Globecom Workshops (GC Wkshps).

[2]  H. T. Mouftah,et al.  Fairness-Aware Game Theoretic Approach for Service Management in Vehicular Clouds , 2017, 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall).

[3]  Yaser Jararweh,et al.  A collaborative mobile edge computing and user solution for service composition in 5G systems , 2018, Transactions on Emerging Telecommunications Technologies.

[4]  Ilsoo Yun,et al.  Quantifying benefits of a dynamic gap-out feature at an actuated traffic signalized intersection under cooperative vehicle infrastructure system , 2012 .

[5]  Ismaeel Al Ridhawi,et al.  QoS-Aware Service Composition in Mobile Cloud Networks , 2015, 2015 IEEE 7th International Conference on Cloud Computing Technology and Science (CloudCom).

[6]  H. T. Mouftah,et al.  Vehicle as a resource for continuous service availability in smart cities , 2017, 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[7]  Sebnem Karahancer,et al.  Optimization of Traffic Signal Timing at Oversaturated Intersections Using Elimination Pairing System , 2017 .

[8]  Ilsoo Yun,et al.  Cumulative Travel-Time Responsive Real-Time Intersection Control Algorithm in the Connected Vehicle Environment , 2013 .

[9]  H. T. Mouftah,et al.  Mitigating False Negative intruder decisions in WSN-based Smart Grid monitoring , 2017, 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC).

[10]  Wuping Xin,et al.  New Signal Control Optimization Policy for Oversaturated Arterial Systems , 2010 .

[11]  F. Webster.  TRAFFIC SIGNAL SETTINGS , 1958 .

[12]  Guohui Zhang,et al.  Optimizing Minimum and Maximum Green Time Settings for Traffic Actuated Control at Isolated Intersections , 2011, IEEE Transactions on Intelligent Transportation Systems.

[13]  Zhan Xiao-song.  Investigation and Study on Pedestrian Crossing Speed at Signalized Intersection , 2012 .

[14]  Wenbin Hu,et al.  Real-time traffic jams prediction inspired by Biham, Middleton and Levine (BML) model , 2017, Inf. Sci..

[15]  H. T. Mouftah,et al.  Trusted Third Party for service management in vehicular clouds , 2017, 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC).

[16]  Arnaud de La Fortelle.  Analysis of reservation algorithms for cooperative planning at intersections , 2010, ITSC.

[17]  Markos Papageorgiou,et al.  Delft University of Technology Resolving freeway jam waves by discrete first-order model-based predictive control of variable speed limits , 2017 .

[18]  Alexander Skabardonis,et al.  Estimating Queue Length under Connected Vehicle Technology , 2013 .

[19]  Nikolas Geroliminis,et al.  An empirical analysis on the arterial fundamental diagram , 2011 .

[20]  Byungkyu Park,et al.  Traffic Signal Control with Connected Vehicles , 2013 .

[21]  Byungkyu Brian Park,et al.  Development and Evaluation of a Cooperative Vehicle Intersection Control Algorithm Under the Connected Vehicles Environment , 2012, IEEE Transactions on Intelligent Transportation Systems.

[22]  Xiubin Bruce Wang,et al.  Queue Length Estimation Using Connected Vehicle Technology for Adaptive Signal Control , 2015, IEEE Transactions on Intelligent Transportation Systems.

[23]  Azzedine Boukerche,et al.  A novel self-adaptive content delivery protocol for vehicular networks , 2018, Ad Hoc Networks.

[24]  Dianhai Wang,et al.  A Method of Signal Timing Optimization for Spillover Dissipation in Urban Street Networks , 2013 .

[25]  Yang Wang,et al.  Vehicle-to-infrastructure communication-based adaptive traffic signal control , 2013 .

[26]  Henry X. Liu,et al.  Real-time queue length estimation for congested signalized intersections , 2009 .

[27]  S. Ilgin Guler,et al.  Using connected vehicle technology to improve the efficiency of intersections , 2014 .

[28]  Bin Ran,et al.  Queue Length Estimation of Intersection Traffic Flow Undera Connected Vehicles Environment , 2015 .

[29]  Xinkai Wu,et al.  Identification of oversaturated intersections using high-resolution traffic signal data , 2010 .

[30]  Darcy M. Bullock,et al.  Input–Output and Hybrid Techniques for Real-Time Prediction of Delay and Maximum Queue Length at Signalized Intersections , 2007 .

[31]  Nagui M. Rouphail,et al.  New Calculation Method for Existing and Extended HCM Delay Estimation Procedures , 2006 .

[32]  Christian Priemer,et al.  A decentralized adaptive traffic signal control using V2I communication data , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[33]  H. T. Mouftah,et al.  Multiagent/multiobjective interaction game system for service provisioning in vehicular cloud , 2016, IEEE Access.

[34]  Alexander Skabardonis,et al.  Identification and Analysis of Queue Spillovers in City Street Networks , 2011, IEEE Transactions on Intelligent Transportation Systems.