Abstract
Intersections become very congested when traffic volumes are high, creating inefficiency that results in user delay and frustration. There have been many approaches which focus on optimization signal of Traffic Light System and Vehicle Trajectory Analysis to improve traffic flow at intersection. However, to implement those approaches into reality become a challenges since real-time problem. In this study, inspired by recent advanced vehicle technologies, we propose an approach for traffic flow management at intersection. In particular, with the exploding at an enormous rate of Internet of Things (IoT), the connected object has been the most visible and familiar application. By this way, based on connected object, we design a model which communicating among objects to improve traffic flow at intersection with real time problem. Moreover, traffic congestion is also taken into consideration in case of high traffic volume. The simulation shows the potential results comparing with the existing traffic management system.















Similar content being viewed by others
Notes
Telefonica, “ Car Industry Report ”,Tech.rep.,2013
References
Amin HJ, Desai RN, Patel PS (2014) Modelling the crossing behavior of pedestrian at uncontrolled intersection in case of mixed traffic using adaptive neuro fuzzy inference system. J Traffic Logist Eng 2(4)
Bento LC, Parafita R, Nunes U (2012) Intelligent traffic management at intersections supported by v2v and v2i communications. In: The 15th International conference on intelligent transportation systems (ITSC 2012). IEEE, pp 1495–1502
Cajias R, Gonzȧlez-Pardo A, Camacho D (2011) A multi-agent simulation platform applied to the study of urban traffic lights. In: The 6th International conference on software and data technologies (ICSOFT 2011), pp 154–159
Cajias R, Gonzȧlez-Pardo A, Camacho D (2011) A multi-agent traffic simulation framework for evaluating the impact of traffic lights. In: The 3rd International conference on agents and artificial intelligence (ICAART 2011), pp 443–446
Chen XF, Shi Zk (2002) Real-coded genetic algorithm for signal timing optimization of a single intersection. In: The 1st International conference on machine learning and cybernetics (ICMLC 2002), pp 1245–1248
Coleri S, Cheung SY, Varaiya P (2004) Sensor networks for monitoring traffic. In: The 42nd Annual allerton conference on communication, control and computing (2004), pp 32–40
Conde Bento L, Parafita R, Santos S, Nunes U (2013) Intelligent traffic management at intersections: legacy mode for vehicles not equipped with v2v and v2i communications. In: The 16th International conference on intelligent transportation systems (ITSC 2013). IEEE, pp 726–731
Dua A, Kumar N, Bawa S (2014) A systematic review on routing protocols for vehicular ad hoc networks. Veh Commun 1(1):33–52
Fang Z, Li Q, Li Q, Han LD, Shaw SL (2013) A space–time efficiency model for optimizing intra-intersection vehicle–pedestrian evacuation movements. Transp Res Part C: Emerg Technol 31:112–130
Fangchun Y, Shangguang W, Jinglin L, Zhihan L, Qibo S (2014) An overview of internet of vehicles. Commun Chin 11(10):1– 15
Galvin PB, Gagne G, Silberschatz A (2013) Operating system concepts. Wiley
Gilbert GN (2008) Agent-based models. Sage, pp 153
Gokulan BP, Srinivasan D (2010) Distributed geometric fuzzy multiagent urban traffic signal control. IEEE Trans Intell Transp Syst 11(3):714–727
Iqbal Z (2006) Self-organizing wireless sensor networks for inter-vehicle communication. Ph.D. thesis, Citeseer
Jabbarpour MR, Jalooli A, Shaghaghi E, Marefat A, Noor R, Jung JJ (2015) Analyzing the impacts of velocity and density on intelligent position-based routing protocols. J Comput Sci 11:177–184
Jung JJ (2011) Semantic preprocessing for mining sensor streams from heterogeneous environments. Expert Syst Appl 38(5):6107–6111
Jung JJ (2012) Semantic optimization of query transformation in a large-scale peer-to-peer network. Neurocomputing 88:36– 41
Lai Y, Zheng Y, Cao J (2007) Protocols for traffic safety using wireless sensor network. In: Algorithms and architectures for parallel processing. Springer, pp 37–48
Lu N, Cheng N, Zhang N, Shen X, Mark JW (2014) Connected vehicles: solutions and challenges. IEEE Internet Things J 1(4):289–299
Ni Y, Deng T, Li K (2014) Pedestrian accommodation in intersection signal system: rule-based dynamic pedestrian control strategy. In: The 93rd Annual meeting on transportation research board (TRB 2014), pp 14–1440
Qiao J, Yang N, Gao J (2011) Two-stage fuzzy logic controller for signalized intersection. IEEE Trans Syst Man Cybern Part A: Syst Humans 41(1):178–184
Qureshi KN, Abdullah AH (2013) A survey on intelligent transportation systems. Middle-East J Sci Res 15(5):629–642
Rajkumar R, Gagliardi M, Sha L (1995) The real-time publisher/subscriber inter-process communication model for distributed real-time systems: design and implementation. In: The 1st Internaltional conference on real-time technology and applications (RTAS 1995), pp 66–75
Ren F, Lin C (2011) Modeling and improving tcp performance over cellular link with variable bandwidth. IEEE Trans Mob Comput 10(8):1057–1070
Sadeghi-Niaraki A, Varshosaz M, Kim K, Jung JJ (2011) Real world representation of a road network for route planning in GIS. Expert Syst Appl 38(10)
Schneider R, Arnold L, Ragland D (2009) Pilot model for estimating pedestrian intersection crossing volumes. Transp Res Record: J Transp Res Board 2140:13–26
Srinivasan D, Choy MC, Cheu RL (2006) Neural networks for real-time traffic signal control. IEEE Trans Intell Transp Syst 7(3):261–272
Tang Y, Zhang C, Gu R, Li P, Yang B (2015) Vehicle detection and recognition for intelligent traffic surveillance system. In: Multimedia tools and applications, pp 1– 16
Wu W, Zhang J, Luo A, Cao J (2015) Distributed mutual exclusion algorithms for intersection traffic control. IEEE Trans Parallel Distrib Syst 26(1):65–74
Yousef KM, Al-Karaki MN, Shatnawi AM (2010) Intelligent traffic light flow control system using wireless sensors networks. J Inf Sci Eng 26(3):753–768
Zhao D, Dai Y, Zhang Z (2012) Computational intelligence in urban traffic signal control: a survey. IEEE Trans Syst Man Cybern Part C: Appl Rev 42(4):485–494
Zhao L, Peng X, Li L, Li Z (2011) A fast signal timing algorithm for individual oversaturated intersections. IEEE Trans Intell Transp Syst 12(1):280–283
Zhou B, Cao J, Zeng X, Wu H (2010) Adaptive traffic light control in wireless sensor network-based intelligent transportation system. In: The 72nd Semiannual international academic conference on vehicular technology conference (VTC 2010). IEEE, pp 1–5
Acknowledgments
This research was supported by Chung-Ang University Research Grants in 2016.
Author information
Authors and Affiliations
Corresponding author
Additional information
This paper is significantly revised from an earlier version presented at The 7th International Conference on Ambient Intelligence (ISAmI’2016) in June 2016.
Rights and permissions
About this article
Cite this article
Bui, KH.N., Camacho, D. & Jung, J.E. Real-Time Traffic Flow Management Based on Inter-Object Communication: a Case Study at Intersection. Mobile Netw Appl 22, 613–624 (2017). https://doi.org/10.1007/s11036-016-0800-y
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11036-016-0800-y