Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/97437
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Civil and Environmental Engineering | en_US |
| dc.creator | Su, J | en_US |
| dc.creator | Sze, NN | en_US |
| dc.creator | Bai, L | en_US |
| dc.date.accessioned | 2023-03-06T01:18:28Z | - |
| dc.date.available | 2023-03-06T01:18:28Z | - |
| dc.identifier.issn | 0001-4575 | en_US |
| dc.identifier.uri | http://hdl.handle.net/10397/97437 | - |
| dc.language.iso | en | en_US |
| dc.publisher | Pergamon Press | en_US |
| dc.rights | © 2020 Elsevier Ltd. All rights reserved. | en_US |
| dc.rights | © 2020. 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.rights | The following publication Su, J., Sze, N. N., & Bai, L. (2021). A joint probability model for pedestrian crashes at macroscopic level: Roles of environment, traffic, and population characteristics. Accident Analysis & Prevention, 150, 105898 is available at https://dx.doi.org/10.1016/j.aap.2020.105898. | en_US |
| dc.subject | Accessibility | en_US |
| dc.subject | Crash prediction model | en_US |
| dc.subject | Joint probability model | en_US |
| dc.subject | Pedestrian safety | en_US |
| dc.subject | Walkability | en_US |
| dc.title | A joint probability model for pedestrian crashes at macroscopic level : roles of environment, traffic, and population characteristics | en_US |
| dc.type | Journal/Magazine Article | en_US |
| dc.identifier.volume | 150 | en_US |
| dc.identifier.doi | 10.1016/j.aap.2020.105898 | en_US |
| dcterms.abstract | Road safety is a major public health issue, with road crashes accounting for one-fourth of all documented injuries. In these crashes, pedestrians are more vulnerable to fatal and/or severe injuries than car occupants. Therefore, it is necessary to have a better understanding of the relationship between pedestrian crashes and possible influencing factors, including road environment, traffic conditions, and population characteristics. In conventional studies, separate prediction models were established for pedestrian crashes and other crash types, which could have ignored possible correlations among the different crash types. Additionally, these influencing factors can contribute to pedestrian crashes in two manners, i.e., contributing to crash occurrence and propensity of pedestrian involvement. Furthermore, extensive pedestrian count data were generally not available, affecting the estimation of pedestrian crash exposure. In this study, a joint probability model is adopted for the simultaneous modeling of crash occurrence and pedestrian involvement in crashes; effects of possible influencing factors, including land use, road networks, traffic flow, population demographics and socioeconomics, public transport facilities, and trip attraction attributes, are considered. Additionally, trip generation and pedestrian activity data, based on a comprehensive household travel survey, are used to determine pedestrian crash exposure. Markov chain Monte Carlo full Bayesian approach is then applied to estimate the parameters. Results indicate that crash occurrence is correlated to traffic flow, number of non-signalized intersections, and points of interest such as restaurants and hotels. By contrast, population age, ethnicity, education, household size, road density, and number of public transit stations could affect the propensity of pedestrian involvement in crashes. These findings indicate that better design and planning of built environments are necessary for safe and efficient access for pedestrians and for the long-term improvement of walkability in a high-density city such as Hong Kong. | en_US |
| dcterms.accessRights | open access | en_US |
| dcterms.bibliographicCitation | Accident analysis and prevention, Feb. 2021, v. 150, 105898 | en_US |
| dcterms.isPartOf | Accident analysis and prevention | en_US |
| dcterms.issued | 2021-02 | - |
| dc.identifier.scopus | 2-s2.0-85097338592 | - |
| dc.identifier.pmid | 33310648 | - |
| dc.identifier.artn | 105898 | en_US |
| dc.description.validate | 202203 bcfc | en_US |
| dc.description.oa | Accepted Manuscript | en_US |
| dc.identifier.FolderNumber | CEE-0452 | - |
| dc.description.fundingSource | RGC | en_US |
| dc.description.fundingSource | Others | en_US |
| dc.description.fundingText | Research Committee of the Hong Kong Polytechnic University | en_US |
| dc.description.pubStatus | Published | en_US |
| dc.identifier.OPUS | 41519970 | - |
| dc.description.oaCategory | Green (AAM) | en_US |
| Appears in Collections: | Journal/Magazine Article | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| SZE_Joint_Probability_Model.pdf | Pre-Published version | 893.92 kB | Adobe PDF | View/Open |
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