Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/97414
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dc.contributorDepartment of Civil and Environmental Engineeringen_US
dc.creatorLi, Hen_US
dc.creatorZhang, Zen_US
dc.creatorSze, NNen_US
dc.creatorHu, Hen_US
dc.creatorDing, Hen_US
dc.date.accessioned2023-03-06T01:18:18Z-
dc.date.available2023-03-06T01:18:18Z-
dc.identifier.issn0001-4575en_US
dc.identifier.urihttp://hdl.handle.net/10397/97414-
dc.language.isoenen_US
dc.publisherPergamon Pressen_US
dc.rights© 2021 Elsevier Ltd. All rights reserved.en_US
dc.rights© 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.en_US
dc.rightsThe following publication Li, H., Zhang, Z., Sze, N. N., Hu, H., & Ding, H. (2021). Safety effects of law enforcement cameras at non-signalized crosswalks: A case study in China. Accident Analysis & Prevention, 156, 106124 is available at https://dx.doi.org/10.1016/j.aap.2021.106124.en_US
dc.subjectDriver yielding behavioren_US
dc.subjectLaw enforcement cameraen_US
dc.subjectNon-signalized crosswalken_US
dc.subjectPedestrian safetyen_US
dc.subjectPedestrian-vehicle conflicten_US
dc.titleSafety effects of law enforcement cameras at non-signalized crosswalks : a case study in Chinaen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume156en_US
dc.identifier.doi10.1016/j.aap.2021.106124en_US
dcterms.abstractPedestrians are vulnerable when crossing the street, especially at non-signalized crosswalks. In China, in spite of the priority that laws entitle the pedestrians, the yielding rates at non-signalized crosswalks are relatively low. In light of this situation, law enforcement cameras have been used to increase the percentage of drivers yielding to pedestrians. This study investigates the effectiveness of law enforcement cameras on drivers yielding behavior and vehicle-pedestrian conflicts at non-signalized crosswalks. Using Unmanned Aerial Vehicle (UAV) and roadside video recording, information including pedestrian characteristics, vehicular characteristics and environmental factors are collected. The conflict indicators used include Post-Encroachment Time (PET), Time to Collision (TTC), and Deceleration to Safety Time (DST). In this study, a conflict classification framework based on PET, TTC and DST using Support Vector Machine algorithm is employed. A multinomial logit regression model is used to identify the factors contributing to the conflicts. Then, binary logit regression models are constructed to analyze the effects of law enforcement cameras on drivers yielding behavior. Conflict study reveals that the implementation of law enforcement cameras would increase the probability of slight conflict but decrease the probability of serious conflict. Yielding behavior analysis shows that the illegitimate yielding behavior percentages are over 10 %, indicating the necessity of improving the awareness of yielding rules, and the implementation of law enforcement cameras would increase the yielding and legitimate yielding probability. Moreover, factors including the adjacent vehicle yielding behavior, number of lanes between pedestrian and vehicle, pedestrian speed change, pedestrian waiting time, pedestrian accepted gap time, vehicle upstream speed and vehicle speed change are significantly associated with conflict severity and drivers yielding behavior. We recommend that supplementary facilities and measures should be used to improve the safety performance of law enforcement cameras.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationAccident analysis and prevention, June 2021, v. 156, 106124en_US
dcterms.isPartOfAccident analysis and preventionen_US
dcterms.issued2021-06-
dc.identifier.scopus2-s2.0-85104090310-
dc.identifier.pmid33873136-
dc.identifier.artn106124en_US
dc.description.validate202203 bcfcen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberCEE-0332-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextNational Key R&D Program of Chinaen_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS48344090-
dc.description.oaCategoryGreen (AAM)en_US
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