{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/36308"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/36308","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Estimating Pedestrian Crashes at Urban Signalized Intersections","abstract":"Crash prediction models are used to estimate the number of crashes using a set of explanatory variables. The highway safety community has used modeling techniques to predict vehicle-to-vehicle crashes for decades. Specifically, generalized linear models (GLMs) are commonly used because they can model non-linear count data such as motor vehicle crashes. Regression models such as the Poisson, Zero-inflated Poisson (ZIP), and the Negative Binomial are commonly used to model crashes. Until recently very little research has been conducted on crash prediction modeling for pedestrian-motor vehicle crashes. This thesis considers several candidate crash prediction models using a variety of explanatory variables and regression functions. The goal of this thesis is to develop a pedestrian crash prediction model to contribute to the field of pedestrian safety prediction research. Additionally, the thesis contributes to the work done by the Federal Highway Administration to estimate pedestrian exposure in urban areas. The results of the crash prediction analyses indicate the pedestrian-vehicle crash model is similar to models from previous work. An analysis of two pedestrian volume estimation methods indicates that using a scaling technique will produce volume estimates highly correlated to observed volumes. The ratio of crash and exposure estimates gives a crash rate estimation that is useful for traffic engineers and transportation policy makers to evaluate pedestrian safety at signalized intersections in an urban environment.","abstract_html":"Crash prediction models are used to estimate the number of crashes using a set of explanatory variables. The highway safety community has used modeling techniques to predict vehicle-to-vehicle crashes for decades. Specifically, generalized linear models (GLMs) are commonly used because they can model non-linear count data such as motor vehicle crashes. Regression models such as the Poisson, Zero-inflated Poisson (ZIP), and the Negative Binomial are commonly used to model crashes. Until recently very little research has been conducted on crash prediction modeling for pedestrian-motor vehicle crashes. This thesis considers several candidate crash prediction models using a variety of explanatory variables and regression functions. The goal of this thesis is to develop a pedestrian crash prediction model to contribute to the field of pedestrian safety prediction research. Additionally, the thesis contributes to the work done by the Federal Highway Administration to estimate pedestrian exposure in urban areas. The results of the crash prediction analyses indicate the pedestrian-vehicle crash model is similar to models from previous work. An analysis of two pedestrian volume estimation methods indicates that using a scaling technique will produce volume estimates highly correlated to observed volumes. The ratio of crash and exposure estimates gives a crash rate estimation that is useful for traffic engineers and transportation policy makers to evaluate pedestrian safety at signalized intersections in an urban environment.","abstract_has_math":false,"creators":["Kennedy, Jason Forrest"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Civil Engineering","degree_department":"Civil Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Rakha, Hesham A."],"committee_members":["Hancock, Kathleen L.","Murray-Tuite, Pamela M.","Kikuchi, Shinya","Inge, Patches Johnson"],"year":2008,"date_issued":"2008-12-10","date_published":"2008-12-10","updated_at":"2026-07-22T22:19:26Z","subjects":["Crash modeling","Exposure to risk","Signalized intersections","Crash risk estimation","Pedestrian crashes"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-12192008-163243"],"render_values":[{"text":"etd-12192008-163243","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/36308","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Rakha, Hesham A."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Hancock, Kathleen L.","Murray-Tuite, Pamela M.","Kikuchi, Shinya","Inge, Patches Johnson"]},{"key":"dc:contributor.department","label":"Department","values":["Civil Engineering"]},{"key":"dc:creator","label":"Author","values":["Kennedy, Jason Forrest"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-03-14T20:50:26Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-03-14T20:50:26Z","2009-01-07"]},{"key":"dc:date.issued","label":"Date","values":["2008-12-10"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Crash modeling","Exposure to risk","Signalized intersections","Crash risk estimation","Pedestrian crashes"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-12192008-163243"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/36308"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Crash prediction models are used to estimate the number of crashes using a set of explanatory variables. 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The results of the crash prediction analyses indicate the pedestrian-vehicle crash model is similar to models from previous work. An analysis of two pedestrian volume estimation methods indicates that using a scaling technique will produce volume estimates highly correlated to observed volumes. 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