Login | Register

Transit Network Complexity in the Context of Transit Itinerary Inference with Travel Survey Data and GTFS


Transit Network Complexity in the Context of Transit Itinerary Inference with Travel Survey Data and GTFS

Davey, Marshall (2019) Transit Network Complexity in the Context of Transit Itinerary Inference with Travel Survey Data and GTFS. Masters thesis, Concordia University.

[thumbnail of Davey_MSc_S2019.pdf]
Text (application/pdf)
Davey_MSc_S2019.pdf - Accepted Version
Available under License Spectrum Terms of Access.


Researchers and planners have taken great interest in the rich data-resource that smartphone and GPS travel surveys can now produce. The interpretation of this data has become a popular topic with methods such as transit itinerary inference (TII) from travel survey data and GTFS emerging as useful tools in the field of travel behavior analysis. This exploratory research develops metrics to quantify a characteristic of GTFS data that complicates the overlay processing of travel survey GPS points and bus route geometries in TII: the spatiotemporal overlap of bus routes in the GTFS record. Accurate route inference is difficult in regions where rider data coincides with overlapping routes and various TII approaches have been tested to address this challenge. In this research, detecting overlap, and quantifying the degree of overlap on road links is achieved in 5 study regions through the application of two proposed measures: The Overlapping Routes on Links (OROL) index, and the Probability of Passage (POP) score. The latter’s output is seen as one way to improve route matching rates in TII. These measures build off the traditional Line Overlapping Index (LOI) and improve upon it by providing previously unobtainable road-link level detail; the OROL index, in fact, represents a spatially precise decomposition of the LOI. To ensure accurate analysis between networks, an additional novel procedure is developed that converts GTFS data into a simplified stand-in road network representation, thus providing a base layer for disaggregate network measures, and replacing the need for additional road network sources entirely.

Divisions:Concordia University > Faculty of Arts and Science > Geography, Planning and Environment
Item Type:Thesis (Masters)
Authors:Davey, Marshall
Institution:Concordia University
Degree Name:M. Sc.
Program:Geography, Urban & Environmental Studies
Date:1 March 2019
Thesis Supervisor(s):Patterson, Zachary
Keywords:Transit Network, GTFS, Route overlap, GPS survey, travel survey, transit itinerary inference, network complexity, network indicators
ID Code:985393
Deposited By: Marshall Vincent Davey
Deposited On:27 Oct 2022 13:49
Last Modified:27 Oct 2022 13:49


Allen, G. W., & DiCesare, F. (1976). Transit Service Evaluation: Preliminary Identification of Varaibles Chracterizing Level of Service. Transportation Research Record, 606, 41–47.
Alter, C. H. (1976). Evaluation of public transit services: the level of service concept. Transportation Research Record, 606, 37–40.
Antrim, A., Barbeau, S. J., & Others. (2013). The Many Uses of GTFS Data - Opening the Door Transit and Multimodal Application. Location-Aware Information Systems Laboratory at the University of South Florida, 1–24. https://doi.org/
Birant, D., & Kut, A. (2007). ST-DBSCAN: An algorithm for clustering spatial–temporal data. Data & Knowledge Engineering, 60(1), 208–221. https://doi.org/https://doi.org/10.1016/j.datak.2006.01.013
Carrel, A., Lau, P. S. C., Mishalani, R. G., Sengupta, R., & Walker, J. L. (2015). Quantifying transit travel experiences from the users’ perspective with high-resolution smartphone and vehicle location data: Methodologies, validation, and example analyses. Transportation Research Part C: Emerging Technologies, 58, 224–239. https://doi.org/10.1016/J.TRC.2015.03.021
Catala, M., Downing, S., & Hayward, D. (2011). Expanding the Google transit feed specification to support opertions and planning.
Chen, C., Gong, H., Lawson, C., & Bialostozky, E. (2010). Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study. Transportation Research Part A: Policy and Practice, 44(10), 830–840. https://doi.org/10.1016/j.tra.2010.08.004
Derrible, S., & Kennedy, C. (2011). Applications of graph theory and network science to transit network design. Transport Reviews, 31(4), 495–519. https://doi.org/10.1080/01441647.2010.543709
Fielding, G. J., Glauthier, R. E., & Lave, C. A. (1978). Performance indicators for transit management. Transportation, 7(4), 365–379. https://doi.org/10.1007/BF00168037
Fortin, P., Morency, C., & Trépanier, M. (2016). Innovative GTFS Data Application for Transit Network Analysis Using a Graph-Oriented Method. Journal of Public Transportation, 19(4), 18–37. https://doi.org/10.5038/2375-0901.19.4.2
Gordon, J., Koutsopoulos, H., Wilson, N., & Attanucci, J. (2013). Automated Inference of Linked Transit Journeys in London Using Fare-Transaction and Vehicle Location Data. Transportation Research Record: Journal of the Transportation Research Board, 2343, 17–24. https://doi.org/10.3141/2343-03
Group, K. F. H. (2013). Transit capacity and quality of service manual.
Hadas, Y. (2013). Assessing public transport systems connectivity based on Google Transit data. Journal of Transport Geography, 33, 105–116. https://doi.org/10.1016/j.jtrangeo.2013.09.015
Liao, F., & van Wee, B. (2017). Accessibility measures for robustness of the transport system. Transportation, 44(5), 1213–1233. https://doi.org/10.1007/s11116-016-9701-y
Lu, Y., & Liu, Y. (2012). Pervasive location acquisition technologies: Opportunities and challenges for geospatial studies. Computers, Environment and Urban Systems, 36(2), 105–108. https://doi.org/10.1016/j.compenvurbsys.2012.02.002
Musso, A., & Vuchic, V. R. (1988). Characteristics of metro networks and methodology for their evaluation. Transportation Research Record, 1162, 22–33.
Nassir, N., Khani, A., Lee, S. G., Noh, H., & Hickman, M. (2011). Transit Stop-Level Origin–Destination Estimation through Use of Transit Schedule and Automated Data Collection System. Transportation Research Record, 2263(1), 140–150. https://doi.org/10.3141/2263-16
Nitsche, P., Widhalm, P., Breuss, S., & Maurer, P. (2012). A Strategy on How to Utilize Smartphones for Automatically Reconstructing Trips in Travel Surveys. Procedia - Social and Behavioral Sciences, 48, 1033–1046. https://doi.org/10.1016/j.sbspro.2012.06.1080
Nour, A. O. (2015). Automating and Optimizing a Transportation Mode Classification Model for use on Smartphone Data.
Shafique, A. M., & Hato, E. (2016). Travel Mode Detection with Varying Smartphone Data Collection Frequencies. Sensors . https://doi.org/10.3390/s16050716
Shen, L., & Stopher, P. R. (2014). Review of GPS Travel Survey and GPS Data-Processing Methods. Transport Reviews, 34(3), 316–334. https://doi.org/10.1080/01441647.2014.903530
Thiagarajan, A., Biagioni, J., Gerlich, T., & Eriksson, J. (2010). Cooperative transit tracking using smart-phones. Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems - SenSys ’10, 85. https://doi.org/10.1145/1869983.1869993
Wong, J. (2013). Leveraging the General Transit Feed Specification for Efficient Transit Analysis. Transportation Research Record: Journal of the Transportation Research Board, 2338, 11–19. https://doi.org/10.3141/2338-02
Wong, J. C. (2013). Use of the General Transit Feed Specification ( Gtfs ) in Transit Performance Measurement Use of the General Transit Feed Specification ( Gtfs ), (December).
Zahabi, S. A. H., Ajzachi, A., & Patterson, Z. (2017). Transit Trip Itinerary Inference with GTFS and Smartphone Data. Transportation Research Record: Journal of the Transportation Research Board, 2652, 59–69.
Zhao, F., Pereira, F. C., Ball, R., Kim, Y., Han, Y., Zegras, C., & Ben-Akiva, M. (2015). Exploratory Analysis of a Smartphone-Based Travel Survey in Singapore. Transportation Research Record: Journal of the Transportation Research Board, 2, 45–56. https://doi.org/10.3141/2494-06
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Research related to the current document (at the CORE website)
- Research related to the current document (at the CORE website)
Back to top Back to top