Abstract
Traffic congestion is a condition where traffic demands exceed traffic capacity. It is a global problem in transportation that occurs around the world especially in metropolitan city. Dynamic traffic routing has been recognized as one of the methods that is capable of dispersing traffic congestions efficiently. This paper reviews the recent implementations of dynamic traffic routing in traffic congestion problems. Study on how the dynamic or online concept has been implemented in traffic routing focusing on definition of dynamic routing, traffic routing environment, traffic routing policy and routing strategy is reviewed in this paper. Some issues such as proactive routing and handling non-recurrent congestion are properly expounded while highlighting some limitations as well as suggestions for future research. As a conclusion, dynamic traffic routing is shown to be an important method in optimizing traffic congestion release. More studies need to be conducted in search of better solution.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Agatz, N., et al.: Optimization for dynamic ride-sharing: A review. European Journal of Operational Research 223(2), 295–303 (2012)
Cai, M., et al.: Realtime vehicle routes optimization by cloud computing in the principle of TCP/IP. In: 2013 10th International Conference on Service Systems and Service Management (ICSSSM), pp. 113–118 (2013)
Chen, B.Y., et al.: Finding reliable shortest paths in road networks under uncertainty. Networks and Spatial Economics 13(2), 123–148 (2013)
Chen, S., et al.: Traffic dynamics on complex networks: a survey. Mathematical Problems in Engineering 2012 (2011)
Chen, S.S., et al.: Research on Dynamic Route Guidance for an Emergency Vehicle Considering the Intersection Delay. Applied Mechanics and Materials, 848–852 (2014)
Cong, Z., et al.: Ant Colony Routing algorithm for freeway networks. Transportation Research Part C: Emerging Technologies 37, 1–19 (2013)
Dallmeyer, J., et al.: Don’t go with the ant flow: Ant-inspired traffic routing in urban environments. Journal of Intelligent Transportation Systems (2014) (just-accepted)
Dewen, S., et al.: Multiple Constrained Dynamic Path Optimization based on Improved Ant Colony Algorithm. International Journal of U- & E-Service, Science & Technology 7(6) (2014)
Dezani, H., et al.: Optimizing urban traffic flow using Genetic Algorithm with Petri net analysis as fitness function. Neurocomputing 124, 162–167 (2014)
Gubins, S., Verhoef, E.T.: Dynamic bottleneck congestion and residential land use in the monocentric city. Journal of Urban Economics 80, 51–61 (2014)
Güner, A.R., et al.: Dynamic routing under recurrent and non-recurrent congestion using real-time ITS information. Computers & Operations Research 39(2), 358–373 (2012)
Gurupackiam, S., et al.: A Snapshot of Lane-specific Traffic Operations under Recurrent and Non-recurrent Congestion. International Journal of Traffic and Transportation Engineering 3(4), 199–205 (2014)
Hoffman, K., et al.: Congestion pricing applications to manage high temporal demand for public services and their relevance to air space management. Transport Policy 28, 28–41 (2013)
Hu, P., Ma, Z.: Travel Efficiency in Urban Traffic Networks Based on Routing Strategies. Journal of Applied Science and Engineering Innovation 1(5) (2014)
Isa, N., et al.: A review on Recent Traffic Congestin Relief Approaches. In: 4th International Conference on Artificial Intelligence with Applications in Engineering and Techology. IEEE (2014)
Ishikawa, K., et al.: A decision support model for traffic congestion in protected areas: A case study of Shiretoko National Park. Tourism Management Perspectives 8, 18–27 (2013)
Jabbarpour, M.R., et al.: Ant-based vehicle congestion avoidance system using vehicular networks. Engineering Applications of Artificial Intelligence 36, 303–319 (2014)
Jahn, O., et al.: System-optimal routing of traffic flows with user constraints in networks with congestion. Operations Research 53(4), 600–616 (2005)
Jiang, B., Xu, X., Yang, C., Li, R., Terano, T.: Solving Road-Network Congestion Problems by a Multi-objective Optimization Algorithm with Brownian Agent Model. In: Corchado, J.M., et al. (eds.) PAAMS 2013. CCIS, vol. 365, pp. 36–48. Springer, Heidelberg (2013)
Jiang, B., et al.: Time-dependent pheromones and electric-field model: a new ACO algorithm for dynamic traffic routing. International Journal of Modelling, Identification and Control 12(1), 29–35 (2011)
Jiang, Z., Li, S.: Research on Optimized Control Model of Freeway Based on Dynamic Traffic Demand Estimation. Advances in Mechanical Engineering 2014 (2014)
Kponyo, J., et al.: Dynamic Travel Path Optimization System Using Ant Colony Optimization. In: Proceedings of the 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, pp. 142–147 (2014)
Kwoczek, S., et al.: Predicting Traffic Congestion in Presence of Planned Special Events (2014)
Liang, Z., Wakahara, Y.: Real-time urban traffic amount prediction models for dynamic route guidance systems. EURASIP Journal on Wireless Communications and Networking 2014(1), 1–13 (2014)
Lin, N., Liu, H.: Dynamic route guidance algorithm based on improved hopfield neural network and genetic algorithm. Int. J. Innov. Comput., Inf. Control. 10(2), 811–822 (2014)
Litman, T.: Factors to Consider When Estimating Congestion Costs and Evaluating Potential Congestion Reduction Strategies (2013)
Litman, T.: Smarter Congestion Relief in ASIAN Cities. Transport and Communications Bulletin for Asia and the Pacific 82(1) (2013)
Liu, Y., et al.: Decision Model for Justifying the Benefits of Detour Operation under Non-Recurrent Congestion. Journal of Transportation Engineering 139(1), 40–49 (2013)
Ma, T.-Y.: Distributed Regret Matching Algorithm for a Dynamic Route Guidance. In: Jezic, G., Kusek, M., Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) Agent and Multi-Agent Systems: Technologies and Applications. AISC, vol. 296, pp. 107–116. Springer, Heidelberg (2014)
Mun, L.S.: Blue Ocean Strategy in Traffic Management for Bandaraya Kuala Lumpur (2013)
Ochiai, J., Kanoh, H.: Hybrid Ant Colony Optimization for Real-WorldDelivery Problems Based On Real Time and Predicted Traffic in Wide Area ROad Network. Computer Science (2014)
Pan, Y., Xu, J.: Traffic network design problem with uncertain demand: A multi-stage bi-level programming approach. World Journal of Modelling and Simulation 9(1), 68–73 (2013)
Russell, S., Norvig, P.: Artificial intelligence: A modern approach. Prentice Hall Pa. (2009)
Sever, D., et al.: Dynamic shortest path problems: Hybrid routing policies considering network disruptions. Computers & Operations Research 40(12), 2852–2863 (2013)
Spears, W.M., Prager, S.D.: Evolutionary search for understanding movement dynamics on mixed networks. GeoInformatica 17(2), 353–385 (2013)
Spiliopoulou, A., et al.: Macroscopic traffic flow model validation at congested freeway off-ramp areas. Transportation Research Part C: Emerging Technologies 41, 18–29 (2014)
Suson, A.C.: Dynamic routing using ant-based control (2010)
Talbi, E.-G.: Metaheuristics: from design to implementation. John Wiley & Sons (2009)
Tian, D., et al.: Real-Time Vehicle Route Guidance Based on Connected Vehicles. In: Green Computing and Communications (GreenCom), 2013 IEEE and Internet of Things (iThings/CPSCom), IEEE International Conference on and IEEE Cyber, Physical and Social Computing, pp. 1512–1517 (2013)
Treiber, M., Kesting, A.: Traffic Flow Dynamics: Data, Models and Simulation. Springer, Heidelberg (2013) ISBN 978-3-642-32459-8
Tympakianaki, A., et al.: Real-time merging traffic control for throughput maximization at motorway work zones. Transportation Research Part C: Emerging Technologies 44, 242–252 (2014)
Wei, C., et al.: Formulating the within-day dynamic stochastic traffic assignment problem from a Bayesian perspective. Transportation Research Part B: Methodological 59, 45–57 (2014)
Weng, J., Meng, Q.: Estimating capacity and traffic delay in work zones: An overview. Transportation Research Part C: Emerging Technologies 35, 34–45 (2013)
Wilkie, D., et al.: Adaptive Route Planning for Metropolitan-Scale Traffic. In: SPARK 2013 (2013)
Yu, Z., et al.: Dynamic route guidance using improved genetic algorithms. Mathematical Problems in Engineering 2013 (2013)
Zhang, W., He, R.: Dynamic Route Choice Based on Prospect Theory. Procedia-Social and Behavioral Sciences 138, 159–167 (2014)
Zhao, D., et al.: Computational intelligence in urban traffic signal control: A survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 42(4), 485–494 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer Science+Business Media Singapore
About this paper
Cite this paper
Isa, N., Mohamed, A., Yusoff, M. (2015). Implementation of Dynamic Traffic Routing for Traffic Congestion: A Review. In: Berry, M., Mohamed, A., Yap, B. (eds) Soft Computing in Data Science. SCDS 2015. Communications in Computer and Information Science, vol 545. Springer, Singapore. https://doi.org/10.1007/978-981-287-936-3_17
Download citation
DOI: https://doi.org/10.1007/978-981-287-936-3_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-287-935-6
Online ISBN: 978-981-287-936-3
eBook Packages: Computer ScienceComputer Science (R0)