Development of a Secondary Crash Identification Algorithm and occurrence pattern determination in large scale multi-facility transportation network |
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| Institution: | 1. Department of Civil Engineering, University of Memphis, 3815 Central Avenue, Memphis, TN 38152, United States;2. Tennessee Department of Transportation, Nashville, TN 37243, United States;3. Intermodal Freight Transportation Institute, University of Memphis, Memphis, TN 38152, United States;1. School of Management, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan, PR China;2. Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China;3. College of Computer Science, Inner Mongolia University, Hohhot 010021, PR China;1. Department of Civil and Urban Engineering, New York University, Six Metrotech Center, 4th Floor, Brooklyn, NY 11201, USA;1. NEXTRANS Center, Purdue University, West Lafayette, IN 47906, USA;2. Goergen Institute for Data Science, University of Rochester, Rochester, NY 14627, USA;3. Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA;4. Department of Civil Engineering and Engineering Mechanics, The University of Arizona, AZ 85721, USA;5. Department of Electrical and Computer Engineering, Ohio State University, OH 43210, USA;6. Department of Civil, Structural and Environmental Engineering, University at Buffalo, the State University of New York, Buffalo, NY 14260, USA;1. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology (MIT), 77 Massachusetts Avenue, Cambridge, MA 0219, USA;2. Singapore-MIT Alliance for Research and Technology (SMART), 1 CREATE Way, #09-02 CREATE Tower, Singapore 138602, Singapore;3. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Room 1-181, 77 Massachusetts Avenue, Cambridge, MA 02139, USA;4. Department of Civil Engineering, Tsinghua University, Beijing, 100084, China |
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| Abstract: | Secondary crash (SC) occurrences are non-recurrent in nature and lead to significant increase in traffic delay and reduced safety. National, state, and local agencies are investing substantial amount of resources to identify and mitigate secondary crashes in order to reduce congestion, related fatalities, injuries, and property damages. Though a relatively small portion of all crashes are secondary, their identification along with the primary contributing factors is imperative. The objective of this study is to develop a procedure to identify SCs using a static and a dynamic approach in a large-scale multimodal transportation networks. The static approach is based on pre-specified spatiotemporal thresholds while the dynamic approach is based on shockwave principles. A Secondary Crash Identification Algorithm (SCIA) was developed to identify SCs on networks. SCIA was applied on freeways using both the static and the dynamic approach while only static approach was used for arterials due to lack of disaggregated traffic flow data and signal-timing information. SCIA was validated by comparison to observed data with acceptable results from the regression analysis. SCIA was applied in the State of Tennessee and results showed that the dynamic approach can identify SCs with better accuracy and consistency. The methodological framework and processes proposed in this paper can be used by agencies for SC identification on networks with minimal data requirements and acceptable computational time. |
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| Keywords: | Secondary crashes Dynamic approach Kinematic shockwave Crash pairing Impact area |
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