Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/31042
Title: | Identifying bicycle trip impediments by data fusion | Authors: | KNAPEN, Luk Holmgren, Johan |
Issue Date: | 2020 | Publisher: | Elsevier | Source: | Elsevier, p. 195 -202 | Abstract: | A set of GPS traces for bicyclists and a set of notifications by bicyclists of problematic situations (spots identified by GPS records) had been collected independently. The data collection periods did not coincide but overlapped and none was contained in the other one. The aim is to use both datasets to determine an optimal action plan for problem solving given a limited budget. First, problematic locations are clustered. Each cluster corresponds to an impediment. Impediments are then associated with trips using a distance function. The aim is to find out which impediments to solve under a given budget constraint in order to maximize the number of impediment free trips. Thereto the trip set is partitioned by matching each trip with the largest set of its affecting impediments. Solving all impediments in such set induces a cost and makes the associated part of trips impediment free. An optimizer is presented and evaluated. | Other: | - reviews.pdf contains the peer review - notesOnReviews contains my "response to reviewers" (which was not required in this case); it is added because i do not agree with particular technical details in the reviewers comments | Keywords: | bicyclist;GPS traces;clustering;data fusion | Document URI: | http://hdl.handle.net/1942/31042 | ISSN: | 1877-0509 | DOI: | 10.1016/j.procs.2020.03.025 | ISI #: | WOS:000582714500024 | Category: | C1 | Type: | Proceedings Paper | Validations: | ecoom 2021 |
Appears in Collections: | Research publications |
Files in This Item:
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1-s2.0-S1877050920304543-main.pdf | Published version | 1.32 MB | Adobe PDF | View/Open |
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