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dc.contributor.authorAcker, Benjamin
dc.contributor.authorYuan, May
dc.date.accessioned2019-09-11T21:04:31Z
dc.date.available2019-09-11T21:04:31Z
dc.date.created2018-09-18
dc.identifier.issn1523-0406
dc.identifier.urihttps://hdl.handle.net/10735.1/6857
dc.descriptionFull text access from Treasures at UT Dallas is restricted to current UTD affiliates (use the provided Link to Article).
dc.description.abstractWe propose a novel approach to network-based event likelihood modeling that estimates the probabilities of event occurrences on a network and identifies the influences of site and situation characteristics. Our premise is that the occurrences of events that involve human activities are subject to site and situational characteristics, and an understanding of event occurrences serves the basis for preparation or mitigation. Using data from Dallas, Texas, USA, we take the proposed approach to estimate the likelihood of traffic accidents based on binary (event or nonevent) space–time atoms of 100-m road segments and 1-h intervals. We choose 12 variables representing time, site characteristics, and situational conditions based on literature reviews to develop logistic regression and random forest models. The traffic accident data on even days were used for model construction and data on odd days for model testing. Both models result in comparable accuracy at 84.11% (logistic regression) and 85.42% (random forest) with significant differences in the spatial patterns of how site and situation correlate to traffic accidents. The difference signals the dynamic influence of site and situation characteristics on the event likelihood over time. The proposed approach shall be applicable to other point events on a network. ©2018 Cartography and Geographic Information Society.
dc.description.sponsorshipThis work was performed under the financial assistance award # [60NANB17D180] from U.S. Department of Commerce, National Institute of Standards and Technology (NIST) Public Safety Innovation Accelerator Program (PSIAP).
dc.language.isoen
dc.publisherTaylor and Francis Inc.
dc.relation.urihttp://dx.doi.org/10.1080/15230406.2018.1515037
dc.rights©2018 Cartography and Geographic Information Society
dc.subjectSpace and time
dc.subjectModeling--Spatiotemporal
dc.subjectTraffic flow
dc.subjectTraffic accidents
dc.subjectUrban transportation
dc.subjectDecision trees
dc.subjectRegression analysis
dc.subjectSyntactics
dc.subjectTelecommunication—Traffic
dc.titleNetwork-Based Likelihood Modeling of Event Occurrences in Space and Time: A Case Study of Traffic Accidents in Dallas, Texas, USA
dc.type.genrearticle
dc.description.departmentSchool of Economic, Political and Policy Studies
dc.identifier.bibliographicCitationAcker, B., and M. Yuan. 2018. "Network-based likelihood modeling of event occurrences in space and time: A case study of traffic accidents in Dallas, Texas, USA." Cartography and Geographic Information Science 46(1): 21-38, doi: 10.1080/15230406.2018.1515037
dc.source.journalCartography and Geographic Information Science
dc.identifier.volume46
dc.identifier.issue1
dc.contributor.utdAuthorAcker, Benjamin
dc.contributor.utdAuthorYuan, May


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