Transportation Networks, Case-Based Reasoning and Traffic Collision Analysis: A Methodology for the 21st Century

Li, Kaidong; Waters, Nigel

doi:10.1007/3-540-28550-4_5

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…Using the CBR tool within the eGain KnowledgeAgent tool, Li and Waters [6] built a system for collision analysis and prevention. Cases are question-answer structures grouped into suitable clusters.…”

Section: Related Workmentioning

confidence: 99%

Case-Based Reasoning for Improving Traffic Flow in Urban Intersections

Kofod-Petersen

Andersen

Aamodt

2014

Case-Based Reasoning Research and Development

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Abstract. Congestion in urban areas is a main traffic challenge. Solutions to this problem include building more roads or have more people in each vehicle. Each of these presents their own challenges. However, one inexpensive approach is to improve the traffic flow, in particular by improving signal plans in intersections. We present a prototype case-based reasoning system, which can control traffic lights in an urban intersection. The system uses real historical vehicle counts from an intersection to make new signal plans. jCOLIBRI is used as the framework for the case-based reasoning system and an evolutionary algorithm is used for weighting the cases. Simulations carried out in Aimsun, a simulation tool used by the Norwegian Public Roads Administration, indicates that satisfactory signal plans can be made in a variety of scenarios.

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Section: Related Workmentioning

confidence: 99%

Case-Based Reasoning for Improving Traffic Flow in Urban Intersections

Kofod-Petersen

Andersen

Aamodt

2014

Case-Based Reasoning Research and Development

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“…Case-based reasoning (CBR) has been used in both cognitive science and artificial intelligence [32], it makes use of the most similar previous cases to solve new problems [32,30,27]. Since the early 1980s, CBR has been successfully applied in various fields such as legal reasoning [42,5,6,45], planning [28,14,25,15,34], E-commerce [41,18], medical diagnosis [4,19,9,40], incident management [49,24], and risk analysis [35,23].…”

Section: Introductionmentioning

confidence: 99%

A Case-Based Approach for Modelling the Risk of Driver Fatigue

Zhong¹,

Zhang²

2017

IFIP Advances in Information and Communication Technology

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Fatigue-related crashes are one of the major threats to road safety worldwide. Despite the substantial work in the domain of transportation science by both the industry and academia, there are few studies in applying casebased reasoning (CBR) approach to modelling the risk of driver fatigue. This research explores the potential for fatigued driving using a database of 16,459 traffic crashes reported from 21 cities in Guangdong province, China from 2006 to 2010. The CBR system under development differentiates between fatigueddriving and non-fatigued-driving cases based on various personal and environmental traffic characteristics. The advantage of using CBR in modelling fatigued driving has been demonstrated through empirical evaluation.

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“…A few other solutions proposed for traffic accident modeling and prediction are clustering techniques such as K-nearest neighbors [66], [48], C-means clustering [66], DBSCAN [67], case-base reasoning [68], [69] and ontologies [67].…”

Section: Other Techniques For Traffic Accident Modeling and Predictionmentioning

confidence: 99%

“…Jagannathan et al [68] use a system capitalizing on case-based reasoning to predict the outcome of current traffic flow conditions based on historical flow data cases that led to accidents and to differentiate between accident and no-accident conditions. Four groups of attributes are used in the decision-making process A case-based reasoning solution combined with GIS data for Metro LRT (Edmonton) traffic collision analysis is discussed in [69].…”

Section: Other Techniques For Traffic Accident Modeling and Predictionmentioning

confidence: 99%

A Framework for Traffic Collision Prediction Using Historical Accident Information and Real-Time Sensor Data: A Case Study for the City of Ottawa

Yanes¹,

Enrique²

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According to recent studies, beyond being a major worldwide problem with huge economic impact, traffic collisions are poised to become as well one of the most important leading causes of death. Proactive traffic enforcement and intervention should be based on a thorough analysis on the collision data available to identify leading causes of accidents, the most prone locations as well as to predict the conditions for collision occurrence. This thesis presents a novel framework for collision prediction that takes into consideration historical and real-time factors, such as weather, geospatial information and social event data that can be obtained with existing sensor technology. A prototype is proposed, implemented and evaluated for the city of Ottawa, Canada, to predict: (1) accident frequency (collision vs nocollisions) and (2) accident severity (in terms of fatal, injury and property damage only accidents). The best performance was achieved in both cases using gradient boosted trees.iii

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Transportation Networks, Case-Based Reasoning and Traffic Collision Analysis: A Methodology for the 21st Century

Cited by 8 publications

References 10 publications

Case-Based Reasoning for Improving Traffic Flow in Urban Intersections

Case-Based Reasoning for Improving Traffic Flow in Urban Intersections

A Case-Based Approach for Modelling the Risk of Driver Fatigue

A Framework for Traffic Collision Prediction Using Historical Accident Information and Real-Time Sensor Data: A Case Study for the City of Ottawa

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