A rental system of electrical cars in Amsterdam

References

• Abdelfatah A, Mahmassani H. 2001. A simulation-based signal optimization algorithm within a dynamic traffic assignment framework, in Intelligent transportation systems, Proc. IEEE, Oakland CA, USA, IEEE, 428–433.
   Google Scholar
• Braess D, Nagurney A, Wakolbinger T. 2005. On a paradox of traffic planning, J. Transp. Sci., 39, 446–450.
   Web of Science ®Google Scholar
• Cario GD, Dorigo M. 1998. AntNet: distributed stigmergetic control for communication networks, IEEE, J. Artif. Intell. Res., 9, 317–365.
   Web of Science ®Google Scholar
• Cornet B, Rothkrantz LJM. 2003. Recognition of car license plates using a Neocognitron neural network, Neural Netw. World, 2, 115–132.
   Google Scholar
• Dantzig GB. 1957. Discrete-variable extremum problems, Oper. Res., 5, 266–277.
   Web of Science ®Google Scholar
• Dijkstra EW. 1959. A note on two problems in connexion with graphs, Numer. Math., 1, 269–271.
   Web of Science ®Google Scholar
• Dorigo M, Socha K. 2007. An introduction to ant colony optimization, in Handbook of approximation algorithms and metaheuristics, (ed. , Gonzalez T F, ed), 26·1–26·14, Boca Raton, FL, Chapman & Hall/CRC.
   Google Scholar
• Dorigo M, Stützle T. 2002. The ant colony optimization metaheuristic: algorithms, applications, and advances, in Handbook of Metaheuristics, Vol. 57, 251–285, Norwell, MA, Kluwer Academic Publishers.
   Google Scholar
• Dorigo M, Stützle T. 2004. Ant colony optimization, Cambridge, MA, The MIT Press.
   Google Scholar
• Fisk C. 1979. More paradoxes in the equilibrium assignment problem, Transp. Res. Part B: Methodol., 13, (4), 305–309.
   Web of Science ®Google Scholar
• Friesz TL, Luque J, Tobin RL, Wie BW. 1989. Dynamic network traffic assignment considered as a continuous time optimal control problem, Oper. Res., 37, (6), 893–901.
   Web of Science ®Google Scholar
• Gamma E, Helm R, Johnson R, Vlissides J. 1995. Design patterns, elements of reuseable object-oriented software, Reading, MA, Addison-Wesley.
   Google Scholar
• Giridhar A, Kumar PR. 2006. Scheduling automated traffic on a network of road, IEEE Trans. Veh. Technol., 55, 1467–1474.
   Web of Science ®Google Scholar
• Hall MD, Van Vliet D, Willumsen LG. 1980. SATURN: a simulation assignment model for the evaluation of traffic management schemes, Traffic Eng. Control, 21, (4), 168–176.
   Google Scholar
• Ho JK. 1980. A successive linear optimization approach to the dynamic traffic assignment problem, Transp. Sci., 14, (4), 295–305.
   Web of Science ®Google Scholar
• Hobeika A, Kim CK. 1994. Traffic flow-prediction systems based on upstream traffic, Eur. J. Oper. Res., 131, 253–261.
   Google Scholar
• Ishak S, Al-Deek H. 2002. Performance evaluation of short-term time-series traffic prediction model, J. Transp. Eng., 128, (6), 490–498.
   Web of Science ®Google Scholar
• Kalal Z, Matas J, Mikolajczyk K. 2010. P-N learning: bootstrapping binary classifiers by structural constraints, IEEE Comput. Soc. Conf. on ‘Computer vision and pattern recognition’, San Francisco CA, USA, IEEE, June, 49–56.
   Google Scholar
• Kassabalidis I, El-Sharkawi MA, Marks RJ, Arabshahi P, Gray AA. 2002. Adaptive-SDR: adaptive swarm-based distributed routing, IEEE Proc. Int. Joint Conf. on ‘Neural networks’, Honolulu HI, USA, May, IEEE, 351–354.
   Google Scholar
• Kerner BS. 2004. The physics of traffic, Berlin, New York, Springer.
   Google Scholar
• Kisgy K, Rilett LR. 2002. Travel time prediction by advanced neural network, Period. Polytech. Ser. Civ. Eng., 46, 2002.
   Google Scholar
• Li Y, McDonald M. 2002. Link travel time estimation using single GPS-equipped probe vehicle, Proc. IEEE 5th Int. Conf. on ‘Intelligent transportation systems’, Singapore, September, IEEE, 932–937.
   Google Scholar
• Lint van JWC, Hoogendoorn SP, van Zuylen H. 2005. Accurate freeway travel time prediction with state-space neural networks under missing data, Transp. Res. Part C: Emerg. Technol., 13, (5–6), 347–369.
   Web of Science ®Google Scholar
• Lum KM, Fan HSL, Lam SH, Olszewski P. 1998. Speed-flow modeling of arterial roads in Singapore, J. Transp. Eng., 124, (3), 213–222.
   Web of Science ®Google Scholar
• Merchant DK, Nemhauser GL. 1978. A model and an algorithm for the dynamic traffic assignment problems, Transp. Sci., 12, (3), 183–199.
   Google Scholar
• Nagel K, Schrekenberg M. 1992. A cellular automaton model for freeway traffic, J. Phys. France, 2, 2221–2229.
   Google Scholar
• Pasula H, Russell S, Ostland M, Ritov Y. 1999. Tracking many objects with many sensors, Proc. Int. Joint Conf. on ‘Artificial intelligence’, Stockholm, Sweden, July–August, NISAC, 1160–1171.
   Google Scholar
• Radu AA, Rothkrantz LJM, Novak M. 2012. Digital traveler assistant. Informatics in control, automation and robotics, Vol. 174, 101–114, Berlin, Springer Verlag.
   Google Scholar
• Rothkrantz LJM, Ehlert PAM. 2001. Microscopic traffic simulation with reactive driving agents, IEEE Intell. Transp. Syst. Conf. Proc., Oakland CA, USA, August, IEEE, 860–865.
   Google Scholar
• Schadschneider A. 2000. Statistical physics of traffic flow, Physica A, 285, (1–2), 101–120.
   Google Scholar
• Schoonderwoerd R, Holland OE, Bruten JL, Rothkrantz LJM. 1996. Ant based load balancing in telecommunication networks, Adapt. Behav., 5, 169–207.
   Web of Science ®Google Scholar
• Tatomir B, Boehlé JL, Rothkrantz LJM. 2005. Dynamic routing in traffic networks and MANNETs using Ant Based Algorithms, Proc. 7th Int. Conf. on ‘Artificial evolution’, Lille, France, October, EA’05, 1–12.
   Google Scholar
• Tatomir B, Dibowsky H, Rothkrantz LJM. 2004a. Hierarchical routing in traffic networks, Proc. 16th Belgian-Dutch Conf. on ‘Artificial intelligence’, Groningen, The Netherlands, October, ALICE-BNAIC, 75–82.
   Google Scholar
• Tatomir B, Kroon R, Rothkrantz LJM. 2004b. Dynamic routing in traffic networks using AntNet, Ant Colony Optimization and Swarm Intelligence ANTS 2004, Lecture Notes in Computer Science 3172, 424–425.
   Google Scholar
• Tatomir B, Rothkrantz LJM. 2006. Hierarchical routing in traffic using swarm-intelligence, Proc. 9th Int. IEEE Conf. on ‘Intelligent transportation systems’, Toronto, Canada, September, IEEE, 230–235.
   Google Scholar
• Taub A H, (ed.). 1961–1963. John von Neumann: collected works, 1903–1957, 6 Vols. Oxford, Pergamon Press.
   Google Scholar
• Taylor NB. 1980. Contram dynamic traffic assignment model, Netw. Spatial Econ., 3, 297–322.
   Google Scholar
• Wagner P, Nagel K, Wolf DE. 1996. Realistic multi lane traffic rules for cellular automata, Physica A, 234A, 687–698.
   Google Scholar
• Wardrop J. 1952. Some theoretical aspects of road traffic research, Proc. Inst. Civ. Eng., Part II, 1, (36), 352–362.
   Google Scholar
• Yu J, Chang GL, Ho H, Liu Y. 2008. Variation based online travel time prediction using clustered neural networks, Proc. 11th Int. IEEE Conf. on ‘Ïntelligent transportation systems', Beijng, China, October, 2008, IEEE, 85–90.
   Google Scholar
• Ziliaskopoulos AK. 2000. A linear programming model for the single destination system optimum dynamic traffic assignment problem, Transp. Sci., 34, (1), 37–49.
   Web of Science ®Google Scholar