{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T10:35:37Z","timestamp":1762079737683,"version":"build-2065373602"},"reference-count":71,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,2,17]],"date-time":"2020-02-17T00:00:00Z","timestamp":1581897600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CMMI-1635927 and CMMI-1634992"],"award-info":[{"award-number":["CMMI-1635927 and CMMI-1634992"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017186","name":"Ohio Supercomputer Center","doi-asserted-by":"publisher","award":["PMIU0138 and PMIU0162"],"award-info":[{"award-number":["PMIU0138 and PMIU0162"]}],"id":[{"id":"10.13039\/100017186","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014010","name":"American Society of Safety Professionals","doi-asserted-by":"publisher","award":["Advancing Safety Surveillance Using Individualized Sensor Technology (ASSIST)"],"award-info":[{"award-number":["Advancing Safety Surveillance Using Individualized Sensor Technology (ASSIST)"]}],"id":[{"id":"10.13039\/100014010","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Transportation Informatics Tier I University Transportation Center","award":["DTRT13-G-UTC48"],"award-info":[{"award-number":["DTRT13-G-UTC48"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the first part of the review, we observed that there exists a significant gap between the predictive and prescriptive models pertaining to crash risk prediction and minimization, respectively. In this part, we review and categorize the optimization\/ prescriptive analytic models that focus on minimizing crash risk. Although the majority of works in this segment of the literature are related to the hazardous materials (hazmat) trucking problems, we show that (with some exceptions) many can also be utilized in non-hazmat scenarios. In an effort to highlight the effect of crash risk prediction model on the accumulated risk obtained from the prescriptive model, we present a simulated example where we utilize four risk indicators (obtained from logistic regression, Poisson regression, XGBoost, and neural network) in the k-shortest path algorithm. From our example, we demonstrate two major designed takeaways: (a) the shortest path may not always result in the lowest crash risk, and (b) a similarity in overall predictive performance may not always translate to similar outcomes from the prescriptive models. Based on the review and example, we highlight several avenues for future research.<\/jats:p>","DOI":"10.3390\/s20041096","type":"journal-article","created":{"date-parts":[[2020,2,20]],"date-time":"2020-02-20T03:20:03Z","timestamp":1582168803000},"page":"1096","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A Review of Data Analytic Applications in Road Traffic Safety. Part 2: Prescriptive Modeling"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4438-1012","authenticated-orcid":false,"given":"Qiong","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Industrial and Systems Engineering, Auburn University, Auburn, AL 36849, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0170-6905","authenticated-orcid":false,"given":"Miao","family":"Cai","sequence":"additional","affiliation":[{"name":"College for Public Health and Social Justice, Saint Louis University, St. Louis, MO 63103, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7490-9928","authenticated-orcid":false,"given":"Nasrin","family":"Mohabbati-Kalejahi","sequence":"additional","affiliation":[{"name":"Jack H. Brown College of Business and Public Administration, California State University at San Bernardino, San Bernardino, CA 92407, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amir","family":"Mehdizadeh","sequence":"additional","affiliation":[{"name":"Department of Industrial and Systems Engineering, Auburn University, Auburn, AL 36849, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Ali","family":"Alamdar Yazdi","sequence":"additional","affiliation":[{"name":"Johns Hopkins Carey Business School, Baltimore, MD 21202, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3095-5986","authenticated-orcid":false,"given":"Alexander","family":"Vinel","sequence":"additional","affiliation":[{"name":"Department of Industrial and Systems Engineering, Auburn University, Auburn, AL 36849, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steven E.","family":"Rigdon","sequence":"additional","affiliation":[{"name":"College for Public Health and Social Justice, Saint Louis University, St. Louis, MO 63103, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2327-4429","authenticated-orcid":false,"given":"Karen C.","family":"Davis","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering, Miami University, Oxford, OH 45056, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2194-5110","authenticated-orcid":false,"given":"Fadel M.","family":"Megahed","sequence":"additional","affiliation":[{"name":"Farmer School of Business, Miami University, Oxford, OH 45056, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.trf.2015.01.014","article-title":"Testing extrinsic and intrinsic motivation as explanatory variables for the safety climate\u2013safety performance relationship among long-haul truck drivers","volume":"30","author":"Zohar","year":"2015","journal-title":"Transp. 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