Research Article
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Congestion avoidance in city traffic
Rahul Kala,
Kevin Warwick,
Corresponding Author
Rahul Kala
School of Systems Engineering, University of Reading, Reading, U.K.
Robotics and Artificial Intelligence Laboratory, Indian Institute of Information Technology, Allahabad, India
Correspondence to: Rahul Kala, Robotics and Artificial Intelligence Laboratory, Indian Institute of Information Technology, Allahabad, Uttar Pradesh 211012, India. E-mail: [email protected]Search for more papers by this authorKevin Warwick
School of Systems Engineering, University of Reading, Reading, U.K.
Search for more papers by this authorRahul Kala,
Kevin Warwick,
Corresponding Author
Rahul Kala
School of Systems Engineering, University of Reading, Reading, U.K.
Robotics and Artificial Intelligence Laboratory, Indian Institute of Information Technology, Allahabad, India
Correspondence to: Rahul Kala, Robotics and Artificial Intelligence Laboratory, Indian Institute of Information Technology, Allahabad, Uttar Pradesh 211012, India. E-mail: [email protected]Search for more papers by this authorKevin Warwick
School of Systems Engineering, University of Reading, Reading, U.K.
Search for more papers by this authorSummary
The number of vehicles on the road (worldwide) is constantly increasing, causing traffic jams and congestion especially in city traffic. Anticipatory vehicle routing techniques have thus far been applied to fairly small networked traffic scenarios and uniform traffic. We note here a number of limitations of these techniques and present a routing strategy on the assumption of a city map that has a large number of nodes and connectivity and where the vehicles possess highly varying speed capabilities. A scenario of operation with such characteristics has not previously been sufficiently studied in the literature. Frequent short-term planning is preferred as compared with infrequent planning of the complete map. Experimental results show an efficiency boost when single-lane overtaking is allowed, traffic signals are accounted for and every vehicle prefers to avoid high traffic density on a road by taking an alternative route. Comparisons with optimistic routing, pessimistic routing and time message channel routing are given. Copyright © 2014 John Wiley & Sons, Ltd.
References
- 1 Gordon R. Chapter 3: functional ITS design issues. In Intelligent Freeway Transportation Systems. Springer: NY, 2009; 17–40.
- 2 Ma Y, Chowdhury M, Sadek A, Jeihani M. Real-time highway traffic condition assessment framework using vehicle-infrastructure integration (VII) with artificial intelligence (AI). IEEE Transactions on Intelligent Transportation Systems 2009; 10(4): 615–627.
- 3 Bishop R. Intelligent vehicle applications worldwide. IEEE Intelligent Systems and their Applications 2000; 15(1): 78–81.
- 4 Zito P, Amato G, Amoroso S, Berrittella M. The effect of Advanced Traveller Information Systems on public transport demand and its uncertainty. Transportmetrica 2011; 7(1): 31–43.
- 5 Reichardt D, Miglietta M, Moretti L, Morsink P, Schulz W. CarTALK 2000: safe and comfortable driving based upon inter-vehicle-communication, Proceedings of the 2002 IEEE Intelligent Vehicle Symposium, Vol. 2. 2002; 545–550.
- 6 Verhoef ET. Time, speeds, flows and densities in static models of road traffic congestion and congestion pricing. Regional Science and Urban Economics 1999; 29(3): 341–369.
- 7 Maniccam S. Adaptive decentralized congestion avoidance in two-dimensional traffic. Physica A: Statistical Mechanics and its Applications 2006; 363(2): 512–526.
- 8 Kala R, Warwick K. Planning autonomous vehicles in the absence of speed lanes using an elastic strip. IEEE Transactions on Intelligent Transportation Systems 2013a; 14(4): 1743–1752.
- 9 Kala R, Warwick K. Multi-level planning for semi-autonomous vehicles in traffic scenarios based on separation maximization. Journal of Intelligent and Robotic Systems 2013b; 72(3-4): 559–590.
- 10 Dia H. An object-oriented neural network approach to short-term traffic forecasting. European Journal of Operational Research 2001; 131(2): 253–261.
- 11 Kirby HR, Watson SM, Dougherty MS. Should we use neural networks or statistical models for short-term motorway traffic forecasting? International Journal of Forecasting 1997; 13(1): 43–50.
- 12 Zhang Y, Liu Y. Traffic forecasting using least squares support vector machines. Transportmetrica 2009; 5(3): 193–213.
- 13 Weyns D, Holvoet T, Helleboogh A. Anticipatory vehicle routing using delegate multi-agent systems, Proceedings of the IEEE Intelligent Transportation Systems Conference, 2007; 87–93.
- 14 Kaufman DE, Smith RL, Wunderlich KE. An iterative routing/assignment method for anticipatory real-time route guidance. Vehicle Navigation and Information Systems Conference 1991; 2: 693–700.
- 15 Claes R, Holvoet T, Weyns D. A decentralized approach for anticipatory vehicle routing using delegate multiagent systems. IEEE Transactions on Intelligent Transportation Systems 2011; 12(2): 64–373.
- 16
Kuwahara M,
Horiguchi R,
Hanabusa H. Traffic Simulation with AVENUE, Fundamentals of Traffic Simulation, 95-129. Springer: NY, 2010.
10.1007/978-1-4419-6142-6_3 Google Scholar
- 17 Pavlis Y, Papageorgiou M. Simple decentralized feedback strategies for route guidance in traffic networks. Transportation Science 1999; 33(3): 264–278.
- 18 Taniguchi E, Shimamoto H. Intelligent transportation system based dynamic vehicle routing and scheduling with variable travel times. Transportation Research Part C: Emerging Technologies 2004; 12(3-4): 235–250.
- 19 Sone G. Road traffic congestion display system. United States Patent, Patent Number 1994; 5: 313,200.
- 20 Summer R. L. In-Vehicle Traffic Congestion Information System. United States Patent, Patent Number 5,(1992)164,904.
- 21 Treiber M, Hennecke A, Helbing D. Congested traffic states in empirical observations and microscopic simulations. Physical Review E 2000; 62(2): 1805–1824.
- 22 Kala R. Motion planning for multiple autonomous vehicles, PhD Thesis, University of Reading, UK, 2013c.
- 23 Nilsson N J. (1980) Principles of Artificial Intelligence. Palo Alto, California: Tioga Publishing Company (1980).
- 24 Russel S, Norvig P. Solving Problems by Searching. Artificial Intelligence: A Modern Approach, 64-119. Upper Saddle River, NY: Pearson, 2010.
- 25 Openstreetmap. Online, available at: openstreetmap.org, accessed: August, (2012).
- 26 Davies P. The radio system-traffic channel, Proceedings of the Vehicle Navigation and Information Systems Conference, 1989; A44–A48.
- 27 Dresner K, and Stone P. Multiagent traffic management: a reservation-based intersection control mechanism, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, NY,(2004) 530-537.
- 28 Dresner K, Stone P. Multiagent Traffic Management: Opportunities for Multi-Agent Learning, Lecture Notes in Artificial Intelligence 3898. Berlin:Springer: Verlag, 2006; 129–138.
- 29 Dresner K, Stone P. Sharing the Road: Autonomous Vehicles Meet Human Drivers, Proceedings of the 20th International Joint Conference on Artificial Intelligence. Hyderabad: India, 2007; 1263–1268.
- 30 Vasirani M. and Ossowski S. A market-inspired approach to reservation-based urban road traffic management, Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems, 2009; 617–624.
- 31 Reveliotis SA, Roszkowska E. Conflict resolution in free-ranging multivehicle systems: a resource allocation paradigm. IEEE Transactions on Robotics 2011; 27(2): 283–296.
- 32 Kim S, Lewis ME, White CC III. Optimal vehicle routing with real-time traffic information. IEEE Transactions on Intelligent Transportation Systems 2005; 6(2): 178–188.
- 33 Wahle J, Bazzan ALC, Klügl F, Schreckenberg M. Decision dynamics in a traffic scenario. Physica A: Statistical Mechanics and its Applications 2000; 287(3-4): 669–681.
- 34 Furda A, Vlacic L. Enabling safe autonomous driving in real-world city traffic using multiple criteria decision making. IEEE Intelligent Transportation Systems Magazine 2011; 3(1): 4–17.
- 35 Ando Y, Fukazawa, Y. Masutani, O. Iwasaki H. Honiden S. Performance of pheromone model for predicting traffic congestion, Proceedings of the Fifth International Joint Conference on Autonomous agents and multiagent systems, New York, 2006; 73–80.
- 36 Narzt W, Pomberger G, Wilflingseder U, et al Seimel O, Kolb D, Wieghardt J, Hortner H, Haring R. Self-Organization in Traffic Networks by Digital Pheromones, Proceedings of the IEEE Intelligent Transportation Systems Conference. 2007; 490–495.
- 37 Song Q, Wang X. Efficient routing on large road networks using hierarchical communities. IEEE Transactions on Intelligent Transportation Systems 2011; 12(2): 132–140.
- 38 Li Q, Zeng Z, Yang B. Hierarchical model of road network for route planning in vehicle navigation systems. IEEE Intelligent Transportation Systems Magazine 2009; 1(2): 20–24.
- 39 Tatomir B, Rothkrantz L. Hierarchical routing in traffic using swarm-intelligence, Proceedings of the 2006 Intelligent Transportation Systems Conference, 2006; 230–235.
- 40 Fawcett J, Robinson P. Adaptive routing for road traffic. IEEE Comput Graph Appl 2000; 20(3): 46–53.
- 41 Kesting A, Treiber M, Schönhof M, Helbing D. Adaptive cruise control design for active congestion avoidance. Transportation Research Part C: Emerging Technologies 2008; 16(6): 668–683.
- 42 Naranjo JE, González C, García R, de Pedro T. Lane-change fuzzy control in autonomous vehicles for the overtaking maneuver. IEEE Transactions on Intelligent Transportation Systems 2008; 9(3): 438–450.
- 43 Jin-ying H, Hong-xia P, Xi-wang Y, Jing-da L. Fuzzy Controller Design of Autonomy Overtaking System, Proceedings of the 12th IEEE International Conference on Intelligent Engineering Systems. Miami: Florida, 2008; 281–285.
- 44 Hegeman G, Tapani A, Hoogendoorn S. Overtaking assistant assessment using traffic simulation. Transportation Research Part C 2009; 17(6): 617–630.
- 45 Wang F, Yang M, Yang R. Conflict-probability-estimation-based overtaking for intelligent vehicles. IEEE Transactions on Intelligent Transportation Systems 2009; 10(2): 366–370.
