Identifying bicycle trip impediments by data fusion | Zendy

Luk Knapen | Zendy; Johan Holmgren | Zendy

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Identifying bicycle trip impediments by data fusion

Author(s) -

Luk Knapen,

Johan Holmgren

Publication year - 2020

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2020.03.025

Subject(s) - trips architecture , computer science , set (abstract data type) , matching (statistics) , global positioning system , plan (archaeology) , function (biology) , constraint (computer aided design) , operations research , statistics , geography , mathematics , telecommunications , geometry , archaeology , evolutionary biology , parallel computing , biology , programming language

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.

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