Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Smooth traffic flow with a cooperative car navigation system
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
New Travel Demand Models with Back-Propagation Network
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
IEEE Transactions on Knowledge and Data Engineering
Adaptive Routing of Cruising Taxis by Mutual Exchange of Pathways
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Optimization of Vehicle Assignment for Car Sharing System
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
A statistical threat assessment
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Computer simulation method has been used to measure the effect of new Intelligent Transportation Systems (ITS). Prior works which make use of the method simulated simplified travel demand, thus it is necessary to represent the demand correctly. Most of existing researches for forecasting travel behaviors need survey data of travel activity in target city. The data is called Person Trip (PT) data. Therefore, they are not able to be applied to cities where the survey was not conducted. In this paper, we propose a method for modeling and estimating travel behaviors, using Bayesian network (BN). BN is constructed based on dependency zone and trip characteristics. The zones are characterized by the important facilities for travelers. Our method is able to apply to the cities since the zone characteristics are available without PT data. In addition, the dependency is represented as graph structure obtained by using K2 algorithm. Our experimental results show the effectiveness of our method for estimating the behaviors.