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dc.contributor.authorSevtsuk, Andres
dc.contributor.authorKalvo, Raul
dc.date.accessioned2021-01-05T22:47:00Z
dc.date.available2021-01-05T22:47:00Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/1721.1/128967
dc.description.abstractStreet attributes are thought to play an important role in influencing pedestrian route choices. Faced with alternatives, pedestrians have been observed to choose faster, safer, more comfortable, more interesting or more beautiful routes. Literature on pedestrian route choice has provided methods for assessing the likelihood of such options using discrete choice models. However, route choice estimation, which is data intensive and computationally challenging, remains infrequently deployed in planning mobility analysis practice. Even when coefficients from previous studies are available, operationalizing them in foot-traffic predictions has been rare due to uncertainty involved in the transferability of behavioral effects from one context to another, as well as computational challenges of predicting route choice with custom attributes. This paper explores a simpler method of route choice prediction, implemented in the Urban Network Analysis toolbox, which assigns probabilities to available route options based on distance alone. We compare the accuracy of distance-weighted approaches to the more detailed utility-weighted approach using a large dataset of observed GPS pedestrian traces that include numerous trips between same intersections pairs in downtown San Francisco as a benchmark. Even though a utility-weighted model matches observed pedestrian flows most accurately, a distance-weighted model is only marginally inferior, on average. However, shortest-distance and highest-utility route predictions are both significantly inferior to the utility-weighted and distance-weighted sample-enumeration methods. Our findings suggest that simplified assumptions can be used to predict pedestrian flow in practice with existing software, opening pedestrian flow predictions to a wider range of planning and transportation applications.en_US
dc.relation.isversionof10.1080/15568318.2020.1858377en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceAndres Sevtsuken_US
dc.titlePredicting pedestrian flow along city streets: a comparison of route choice estimation approaches in downtown San Franciscoen_US
dc.typeArticleen_US
dc.identifier.citationSevtsuk, Andres and Raul Kalvo. "Predicting pedestrian flow along city streets: a comparison of route choice estimation approaches in downtown San Francisco." Forthcoming in: International Journal of Sustainable Transportation.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Urban Studies and Planningen_US
dc.contributor.departmentMassachusetts Institute of Technology. School of Architecture and Planningen_US
dc.relation.journalInternational Journal of Sustainable Transportationen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.date.submission2021-01-05T15:18:12Z
mit.journal.volumeForthcomingen_US
mit.licenseOPEN_ACCESS_POLICY


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