Disaggregated traffic conditions and road crashes in urban signalized intersections |
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| Institution: | 1. Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai, China;2. Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, Shenzhen, China;3. Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China;4. Guizhou Transportation Planning Survey & Design Co., Ltd, Guiyang, China;5. Department of Statistics and Data Science, Soutern University of Science and Technology, Shenzhen, China;1. Glenn Department of Civil Engineering, Clemson University, Clemson, SC 29634, USA;2. Applied Statistics, School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634, USA;3. Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC 29208 USA;1. Road Safety Research Centre, Faculty of Engineering, Universiti Putra Malaysia, 43400 Selangor, Malaysia;2. Civil Engineering Department, Faculty of Engineering, Universiti Pertahanan Nasional Malaysia, 57000 Kuala Lumpur, Malaysia;1. Department of Civil and Environmental Engineering, University of Louisville, Louisville, KY 40292, United States;2. Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran;3. Department of Civil and Architectural Engineering, University of Wyoming, Laramie, WY 82071, United States;1. Applied Economics & Management Research Group, Universidad de Sevilla, Spain;2. GIM-IREA, Universidad de Barcelona (Spain), Av. Diagonal, 690, 08034 Barcelona, Spain |
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| Abstract: | Introduction: Road safety studies in signalized intersections have been performed extensively using annually aggregated traffic variables and crash frequencies. However, this type of aggregation reduces the strength of the results if variables that oscillate over the course of the day are considered (speed, traffic flow, signal cycle length) because average indicators are not able to describe the traffic conditions preceding the crash occurrence. This study aims to explore the relationship between traffic conditions aggregated in 15-min intervals and road crashes in urban signalized intersections. Method: First, an investigation of the reported crash times in the database was conducted to obtain the association between crashes and their precursor conditions. Then, 4.1 M traffic condition intervals were consolidated and grouped using a hierarchical clustering technique. Finally, charts of the frequency of crashes per cluster were explored. Results: The main findings suggest that high vehicular demand conditions are related to an increase in property damage only (PDO) crashes, and an increase in the number of lanes is linked to more PDO and injury crashes. Injury crashes occurred in a wide range of traffic conditions, indicating that a portion of these crashes were due to speeding, while the other fraction was associated with the vulnerability of road users. Traffic conditions with: (a) low vehicular demand and a long cycle length and (b) high vehicular demand and a short cycle length were critical in terms of PDO and injury crashes. Practical Applications: The use of disaggregated data allowed for a stronger evaluation of the relationship between road crashes and variables that oscillate over the course of the day. This approach also permits the development of real-time risk management strategies to mitigate the frequency of critical traffic conditions and reduce the likelihood of crashes. |
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| Keywords: | Road safety Condition based Clustering Signal cycle length SCOOT |
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