Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/24092
Title: GTFS Bus Stops Mapping to the OSM Network
Authors: VUURSTAEK, Jan 
CICH, Glenn 
KNAPEN, Luk 
YASAR, Ansar 
BELLEMANS, Tom 
JANSSENS, Davy 
Issue Date: 2017
Publisher: Elsevier B.V
Source: Procedia Computer Science,p. 50-58
Series/Report: Procedia Computer Science
Series/Report no.: 109
Abstract: Due to budget constraints public transportation (PT) can no longer be deployed in regions where it attracts insufficient customers. Novel techniques like demand-responsive collective transportation (DRT) are evaluated to cut costs. This requires detailed simulations that are able to predict travel demand and include trip execution. Simulating facilities acting as feeder services to time-table based PT services requires detailed and accurate information about the PT infrastructure on a network. However, there are no public data sources that combine network and PT infrastructure data with the preferred level of detail. A newly developed bus stop mapping technique is presented. It uses the OpenStreetMap (OSM) and General Transit Feed Specification (GTFS) open data sources, which are maintained independently. Merging the data into a single database requires alignment. Developing bus stop mapping algorithms is challenging due to (i) inaccurate location data, (ii) inconsistent data sources and (iii) the vastly interconnected PT network and services. Due to the inaccuracy in the GTFS stop locations and in the OSM network, pure geometric considerations might lead to multiple candidate solutions to map a stop to the network. The new technique handles all GTFS trips at once and operates under the assumption that PT operators minimize the total distance driven to complete all trips.
Notes: Vuurstaek, J (reprint author), Hasselt Univ, Transportat Res Inst IMOB, Agoralaan, B-3590 Diepenbeek, Belgium. jan.vuurstaek@uhasselt.be
Keywords: OpenStreetMap (OSM); General Transit Feed Specification (GTFS); micro-simulation; public Transport; mapping algorithm
Document URI: http://hdl.handle.net/1942/24092
DOI: 10.1016/j.procs.2017.05.294
ISI #: 000414533000006
Rights: (c) 2017 The Authors. Published by Elsevier B.V
Category: C1
Type: Proceedings Paper
Validations: ecoom 2018
Appears in Collections:Research publications

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