Robot motion planning with uncertainty in control and sensing
Artificial Intelligence
Dynamic Motion Planning for Mobile Robots Using Potential Field Method
Autonomous Robots
An investigation of real-time dynamic data driven transportation simulation
Proceedings of the 38th conference on Winter simulation
A dynamic and automatic traffic light control expert system for solving the road congestion problem
Expert Systems with Applications: An International Journal
Urban Trunk Road Traffic Signal Coordinated Control Based on Multi-Objective Immune Algorithm
CAR '09 Proceedings of the 2009 International Asia Conference on Informatics in Control, Automation and Robotics
On the performance of adaptive traffic signal control
Proceedings of the Second International Workshop on Computational Transportation Science
A coordinated urban traffic signal control approach based on multi-agent
INES'09 Proceedings of the IEEE 13th international conference on Intelligent Engineering Systems
Dynamic data driven application simulation of surface transportation systems
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part III
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part II
Neural Networks for Real-Time Traffic Signal Control
IEEE Transactions on Intelligent Transportation Systems
Capability-Enhanced Microscopic Simulation With Real-Time Traffic Signal Control
IEEE Transactions on Intelligent Transportation Systems
Cooperative, hybrid agent architecture for real-time traffic signal control
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Distributed dynamic data driven prediction based on reinforcement learning approach
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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With advance in distributed system technology, data has become ubiquitous and its dynamics has increased. Therefore, in this paper we proposed a new framework to integrate dynamic data driven application systems (DDDAS) with service-oriented architecture (SOA) and web services technology to tackle dynamic data issue in a real-time and resource-bounded environment. Nowadays, traffic management in Intelligent Transportation Systems (ITS) has been widely developed in major cities' urban areas around the world to provide more efficient way for solving traffic congestion problem. However, the problems in dynamic traffic management systems such as system flexibility, data standard common interface, transmission of required information, prediction performance, and real-time measurement data are all important issues but not totally supported. An efficient and effective service-oriented dynamic data-driven framework algorithm is designed in this paper to support prediction strategies for traffic management. The simulation results of vehicle navigation show that our algorithm outperforms the Dijkstra algorithm by improving 24.43% in average vehicle travelling time. On the other hand, the results of traffic signal control also show that our algorithm improves the performance in vehicles average number of stops by 43.7%, and 70.62% in average delay time that is compared with the fixed time control method.