Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision.In this article,these issues are handled...Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision.In this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things data.The framework integrates Kalman filtering and Q-learning.Unlike smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction error.Unlike traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction roads.For evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art approach.Hence,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.展开更多
Intelligent Transportation System （ITS） integrates information and communication technologies with location based technologies into roads, vehicles, traffic and transport management systems. Application of ITS can i...Intelligent Transportation System （ITS） integrates information and communication technologies with location based technologies into roads, vehicles, traffic and transport management systems. Application of ITS can improve situation in major cities where due to the increasing number of residents and level of motorization traffic congestion represents important issue. Another problem arises from this facts and that is that cities have contradictory needs as grooving need for goods within urban areas as well as need for less vehicles in the same area. The urban logistics activities by private companies within urban areas represent an integral part of city logistics that aims to improve the efficiency of urban freight transportation, reduce traffic congestion, mitigate environmental impacts, reduce costs and fuel consumption. This research presents application of ITS technologies and vehicle routing problem for night delivery scheme planning as one of the core techniques for modelling city logistics.展开更多
文摘Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision.In this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things data.The framework integrates Kalman filtering and Q-learning.Unlike smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction error.Unlike traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction roads.For evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art approach.Hence,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.
文摘Intelligent Transportation System （ITS） integrates information and communication technologies with location based technologies into roads, vehicles, traffic and transport management systems. Application of ITS can improve situation in major cities where due to the increasing number of residents and level of motorization traffic congestion represents important issue. Another problem arises from this facts and that is that cities have contradictory needs as grooving need for goods within urban areas as well as need for less vehicles in the same area. The urban logistics activities by private companies within urban areas represent an integral part of city logistics that aims to improve the efficiency of urban freight transportation, reduce traffic congestion, mitigate environmental impacts, reduce costs and fuel consumption. This research presents application of ITS technologies and vehicle routing problem for night delivery scheme planning as one of the core techniques for modelling city logistics.
