The Experts below are selected from a list of 381123 Experts worldwide ranked by ideXlab platform
Jennifer Mcmanis - One of the best experts on this subject based on the ideXlab platform.
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next road rerouting a multiagent system for mitigating unexpected urban traffic Congestion
IEEE Transactions on Intelligent Transportation Systems, 2016Co-Authors: Shen Wang, Soufiene Djahel, Zonghua Zhang, Jennifer McmanisAbstract:During peak hours in urban areas, unpredictable traffic Congestion caused by en route events (e.g., vehicle crashes) increases drivers' travel time and, more seriously, decreases their travel time reliability. In this paper, an original and highly practical vehicle rerouting system, which is called Next Road Rerouting (NRR), is proposed to aid drivers in making the most appropriate next road choice to avoid unexpected Congestions. In particular, this heuristic rerouting decision is made upon a cost function that takes into account the driver's destination and local traffic conditions. In addition, the newly designed multiagent system architecture of NRR allows the positive rerouting impacts on local traffic to be disseminated to a larger area through the natural traffic flow propagation within connected local areas. The simulation results based on both synthetic and realistic urban scenarios demonstrate that, compared with the existing solutions, NRR can achieve a lower average travel time while guaranteeing a higher travel time reliability in the face of unexpected Congestion. The impacts of NRR on the travel time of both rerouted and nonrerouted vehicles are also assessed, and the corresponding results reveal its higher practicability.
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Next road rerouting: a multi-agent system for mitigating unexpected urban traffic Congestion
IEEE Transactions on Intelligent Transportation Systems, 2016Co-Authors: Shen Wang, Soufiene Djahel, Zonghua Zhang, Jennifer McmanisAbstract:During peak hours in urban areas, unpredictable traffic Congestion caused by en route events (e.g., vehicle crashes) increases drivers' travel time and, more seriously, decreases their travel time reliability. In this paper, an original and highly practical vehicle rerouting system, which is called Next Road Rerouting (NRR), is proposed to aid drivers in making the most appropriate next road choice to avoid unexpected Congestions. In particular, this heuristic rerouting decision is made upon a cost function that takes into account the driver's destination and local traffic conditions. In addition, the newly designed multiagent system architecture of NRR allows the positive rerouting impacts on local traffic to be disseminated to a larger area through the natural traffic flow propagation within connected local areas. The simulation results based on both synthetic and realistic urban scenarios demonstrate that, compared with the existing solutions, NRR can achieve a lower average travel time while guaranteeing a higher travel time reliability in the face of unexpected Congestion. The impacts of NRR on the travel time of both rerouted and nonrerouted vehicles are also assessed, and the corresponding results reveal its higher practicability
Shen Wang - One of the best experts on this subject based on the ideXlab platform.
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next road rerouting a multiagent system for mitigating unexpected urban traffic Congestion
IEEE Transactions on Intelligent Transportation Systems, 2016Co-Authors: Shen Wang, Soufiene Djahel, Zonghua Zhang, Jennifer McmanisAbstract:During peak hours in urban areas, unpredictable traffic Congestion caused by en route events (e.g., vehicle crashes) increases drivers' travel time and, more seriously, decreases their travel time reliability. In this paper, an original and highly practical vehicle rerouting system, which is called Next Road Rerouting (NRR), is proposed to aid drivers in making the most appropriate next road choice to avoid unexpected Congestions. In particular, this heuristic rerouting decision is made upon a cost function that takes into account the driver's destination and local traffic conditions. In addition, the newly designed multiagent system architecture of NRR allows the positive rerouting impacts on local traffic to be disseminated to a larger area through the natural traffic flow propagation within connected local areas. The simulation results based on both synthetic and realistic urban scenarios demonstrate that, compared with the existing solutions, NRR can achieve a lower average travel time while guaranteeing a higher travel time reliability in the face of unexpected Congestion. The impacts of NRR on the travel time of both rerouted and nonrerouted vehicles are also assessed, and the corresponding results reveal its higher practicability.
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Next road rerouting: a multi-agent system for mitigating unexpected urban traffic Congestion
IEEE Transactions on Intelligent Transportation Systems, 2016Co-Authors: Shen Wang, Soufiene Djahel, Zonghua Zhang, Jennifer McmanisAbstract:During peak hours in urban areas, unpredictable traffic Congestion caused by en route events (e.g., vehicle crashes) increases drivers' travel time and, more seriously, decreases their travel time reliability. In this paper, an original and highly practical vehicle rerouting system, which is called Next Road Rerouting (NRR), is proposed to aid drivers in making the most appropriate next road choice to avoid unexpected Congestions. In particular, this heuristic rerouting decision is made upon a cost function that takes into account the driver's destination and local traffic conditions. In addition, the newly designed multiagent system architecture of NRR allows the positive rerouting impacts on local traffic to be disseminated to a larger area through the natural traffic flow propagation within connected local areas. The simulation results based on both synthetic and realistic urban scenarios demonstrate that, compared with the existing solutions, NRR can achieve a lower average travel time while guaranteeing a higher travel time reliability in the face of unexpected Congestion. The impacts of NRR on the travel time of both rerouted and nonrerouted vehicles are also assessed, and the corresponding results reveal its higher practicability
Zonghua Zhang - One of the best experts on this subject based on the ideXlab platform.
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next road rerouting a multiagent system for mitigating unexpected urban traffic Congestion
IEEE Transactions on Intelligent Transportation Systems, 2016Co-Authors: Shen Wang, Soufiene Djahel, Zonghua Zhang, Jennifer McmanisAbstract:During peak hours in urban areas, unpredictable traffic Congestion caused by en route events (e.g., vehicle crashes) increases drivers' travel time and, more seriously, decreases their travel time reliability. In this paper, an original and highly practical vehicle rerouting system, which is called Next Road Rerouting (NRR), is proposed to aid drivers in making the most appropriate next road choice to avoid unexpected Congestions. In particular, this heuristic rerouting decision is made upon a cost function that takes into account the driver's destination and local traffic conditions. In addition, the newly designed multiagent system architecture of NRR allows the positive rerouting impacts on local traffic to be disseminated to a larger area through the natural traffic flow propagation within connected local areas. The simulation results based on both synthetic and realistic urban scenarios demonstrate that, compared with the existing solutions, NRR can achieve a lower average travel time while guaranteeing a higher travel time reliability in the face of unexpected Congestion. The impacts of NRR on the travel time of both rerouted and nonrerouted vehicles are also assessed, and the corresponding results reveal its higher practicability.
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Next road rerouting: a multi-agent system for mitigating unexpected urban traffic Congestion
IEEE Transactions on Intelligent Transportation Systems, 2016Co-Authors: Shen Wang, Soufiene Djahel, Zonghua Zhang, Jennifer McmanisAbstract:During peak hours in urban areas, unpredictable traffic Congestion caused by en route events (e.g., vehicle crashes) increases drivers' travel time and, more seriously, decreases their travel time reliability. In this paper, an original and highly practical vehicle rerouting system, which is called Next Road Rerouting (NRR), is proposed to aid drivers in making the most appropriate next road choice to avoid unexpected Congestions. In particular, this heuristic rerouting decision is made upon a cost function that takes into account the driver's destination and local traffic conditions. In addition, the newly designed multiagent system architecture of NRR allows the positive rerouting impacts on local traffic to be disseminated to a larger area through the natural traffic flow propagation within connected local areas. The simulation results based on both synthetic and realistic urban scenarios demonstrate that, compared with the existing solutions, NRR can achieve a lower average travel time while guaranteeing a higher travel time reliability in the face of unexpected Congestion. The impacts of NRR on the travel time of both rerouted and nonrerouted vehicles are also assessed, and the corresponding results reveal its higher practicability
Soufiene Djahel - One of the best experts on this subject based on the ideXlab platform.
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next road rerouting a multiagent system for mitigating unexpected urban traffic Congestion
IEEE Transactions on Intelligent Transportation Systems, 2016Co-Authors: Shen Wang, Soufiene Djahel, Zonghua Zhang, Jennifer McmanisAbstract:During peak hours in urban areas, unpredictable traffic Congestion caused by en route events (e.g., vehicle crashes) increases drivers' travel time and, more seriously, decreases their travel time reliability. In this paper, an original and highly practical vehicle rerouting system, which is called Next Road Rerouting (NRR), is proposed to aid drivers in making the most appropriate next road choice to avoid unexpected Congestions. In particular, this heuristic rerouting decision is made upon a cost function that takes into account the driver's destination and local traffic conditions. In addition, the newly designed multiagent system architecture of NRR allows the positive rerouting impacts on local traffic to be disseminated to a larger area through the natural traffic flow propagation within connected local areas. The simulation results based on both synthetic and realistic urban scenarios demonstrate that, compared with the existing solutions, NRR can achieve a lower average travel time while guaranteeing a higher travel time reliability in the face of unexpected Congestion. The impacts of NRR on the travel time of both rerouted and nonrerouted vehicles are also assessed, and the corresponding results reveal its higher practicability.
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Next road rerouting: a multi-agent system for mitigating unexpected urban traffic Congestion
IEEE Transactions on Intelligent Transportation Systems, 2016Co-Authors: Shen Wang, Soufiene Djahel, Zonghua Zhang, Jennifer McmanisAbstract:During peak hours in urban areas, unpredictable traffic Congestion caused by en route events (e.g., vehicle crashes) increases drivers' travel time and, more seriously, decreases their travel time reliability. In this paper, an original and highly practical vehicle rerouting system, which is called Next Road Rerouting (NRR), is proposed to aid drivers in making the most appropriate next road choice to avoid unexpected Congestions. In particular, this heuristic rerouting decision is made upon a cost function that takes into account the driver's destination and local traffic conditions. In addition, the newly designed multiagent system architecture of NRR allows the positive rerouting impacts on local traffic to be disseminated to a larger area through the natural traffic flow propagation within connected local areas. The simulation results based on both synthetic and realistic urban scenarios demonstrate that, compared with the existing solutions, NRR can achieve a lower average travel time while guaranteeing a higher travel time reliability in the face of unexpected Congestion. The impacts of NRR on the travel time of both rerouted and nonrerouted vehicles are also assessed, and the corresponding results reveal its higher practicability
Zaili Yang - One of the best experts on this subject based on the ideXlab platform.
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safety management of waterway Congestions under dynamic risk conditions a case study of the yangtze river
Applied Soft Computing, 2017Co-Authors: Di Zhang, Zaili YangAbstract:Abstract With the continuous increase of traffic volume in recent years, inland waterway transportation suffers more and more from Congestion problems, which form a major impediment to its development. Thus, it is of great significance for the stakeholders and decision makers to address these Congestion issues properly. Fuzzy Techniques for Order Preference by Similarity to an Ideal Solution (TOPSIS) is widely used for solving Multiple Criteria Decision Making (MCDM) problems with ambiguity. When taking into account fuzzy TOPSIS, decisions are made in a static scenario with fixed weights assigned to the criteria. However, risk conditions usually vary in real-life cases, which will inevitably affect the preference ranking of the alternatives. To make flexible decisions according to the dynamics of Congestion risks and to achieve a rational risk analysis for prioritising Congestion risk control options (RCOs), the cost-benefit ratio (CBR) is used in this paper to reflect the change of risk conditions. The hybrid of CBR and fuzzy TOPSIS is illustrated by investigating the Congestion risks of the Yangtze River. The ranking of RCOs varies depending on the scenarios with different Congestion risk conditions. The research findings indicate that some RCOs (e.g. “Channel dredging and maintenance”, and “Prohibition of navigation”) are more cost effective in the situation of a high level of Congestion risk, while the other RCOs (e.g. “Loading restriction”, and “Crew management and training”) are more beneficial in a relatively low Congestion risk condition. The proposed methods and the evaluation results provide useful insights for effective safety management of the inland waterway Congestions under dynamic risk conditions.
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an accident data based approach for Congestion risk assessment of inland waterways a yangtze river case
Proceedings of the Institution of Mechanical Engineers Part O: Journal of Risk and Reliability, 2014Co-Authors: Di Zhang, Zaili Yang, Jin WangAbstract:Inland waterway transportation is often claimed to be reliable, Congestion-free, economic and environmentally friendly. However, inland waterway transport accidents such as groundings cause Congestions that can easily reduce the navigational capability of the waterways with confined channel dimensions particularly during a dry season. An accident data-based approach is presented in this article to assess the Congestion risk of inland waterways using a case of the Yangtze River. Through a correlation analysis of historical failure data, the safety critical factors of Congestion are first identified and used to establish a Bayesian network for the analysis and prediction of the Congestion risk in the Yangtze River. A Congestion Risk Index is then developed by taking into account both probability and consequence of Congestion risks in order to evaluate the impacts of various safety critical factors (i.e. Visibility, Gross Tonnage, etc.) on the Congestion of the Yangtze River. The outcomes of this work can be used to effectively diagnose and predict the Congestion risks of inland waterways in general and the Yangtze River in specific. Language: en
