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Abstract

Transit agencies collect a vast amount of data on vehicle positions, passenger loading and, increasingly, origin–destination flows. Collecting and synthesizing these data to support operations and planning are significant challenges and can be constrained by information silos within transit agencies. In this paper, an open-source bus performance and journey visualization dashboard, Ridership and Operations Visualization Engine, is presented, which integrates multiple disparate data sources into a flexible and iterative analysis tool. It differs from existing commercial products by including origin–destination flows along with standard performance metrics, and is designed to be adaptable and relevant to any transit agency. Two case studies are presented to demonstrate the functionality of the dashboard: planning transit priority infrastructure and evaluating network design changes. The dashboard was developed in partnership with Chicago Transit Authority and Massachusetts Bay Transportation Authority, and practical details from the installation and maintenance procedures are included for prospective users.

Keywords

public transportation operations performance measures planning ridership automated passenger counters (APC)automatic vehicle location (AVL)big data GTFS ridership analysis

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References

Fielding

G. J.

Babitsky

T. T.

Brenner

M. E.

Performance Evaluation for Bus Transit. Transportation Research Part A: General, Vol. 19, No. 1, 1985, pp. 73–82.

Prioni

Hensher

D. A.

Measuring Service Quality in Scheduled Bus Services. Journal of Public Transportation, Vol. 3, No. 2, 2000, p. 4.

Kittelson & Associates, United States Federal Transit Administration, Transit Cooperative Research Program, and Transit Development Corporation. A Guidebook for Developing a Transit Performance-Measurement System, Vol. 88. Transportation Research Board, Washington, D.C., 2003.

Furth

P. G.

Hemily

Muller

T. H.

Strathman

J. G.

Using Archived AVL-APC Data to Improve Transit Performance and Management. Transit Cooperative Research Program, No. Project H-28. Transportation Research Board, Washington, D.C., 2006.

Hammerle

Haynes

McNeil

Use of Automatic Vehicle Location and Passenger Count Data to Evaluate Bus Operations: Experience of the Chicago Transit Authority, Illinois. Transportation Research Record: Journal of the Transportation Research Board, 2005. 1903: 27–34.

Wang

Development of a Data-Driven Platform for Transit Performance Measures Using Smart Card and GPS Data. Journal of Transportation Engineering, Vol. 140, No. 12, 2014, p. 04014063.

Chen

Liu

Xia

Chien

S. I.

A Dynamic Bus-Arrival Time Prediction Model Based on APC Data. Computer-Aided Civil and Infrastructure Engineering, Vol. 19, No. 5, 2004, pp. 364–376.

Shalaby

Farhan

Prediction Model of Bus Arrival and Departure Times Using AVL and APC Data. Journal of Public Transportation, Vol. 7, No. 1, 2004, p. 3.

Egge

Whitmore

Silbermann

Qian

Z. S.

Understanding Transit System Performance Using AVL-APC Data: An Analytics Platform With Case Studies for the Pittsburgh Region. Journal of Public Transportation, Vol. 21, No. 2, 2018, p. 2.

10.

Bast

Brosi

Storandt

Real-Time Movement Visualization of Public Transit Data. Proc., 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Dallas, TX, 2014, pp. 331–340.

11.

Kimpel

T. J.

Data Visualization as a Tool for Improved Decision Making Within Transit Agencies. Tech. Rep. TNW2006-14. Transportation Northwest, University of Washington, Seattle, 2007.

12.

Miller

H. J.

Shaw

S.-L.

Geographic Information Systems for Transportation: Principles and Applications. Oxford University Press on Demand, Oxford, 2001.

13.

Kurkcu

Miranda

Ozbay

Silva

C. T.

Data Visualization Tool for Monitoring Transit Operation and Performance. Proc., 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), Naples, Italy, IEEE, New York, 2017, pp. 598–603.

14.

Stewart

Diab

Bertini

El-Geneidy

Perspectives on Transit: Potential Benefits of Visualizing Transit Data. Transportation Research Record: Journal of the Transportation Research Board, 2016. 2544: 90–101.

15.

McHugh

Pioneering Open Data Standards: The GTFS Story. In Beyond Transparency: Open Data and the Future of Civic Innovation ( Goldstein

Dyson

eds.), America Press, San Francisco, CA, 2013, pp. 125–135.

16.

Interline. Transitland. 2021. https://www.transit.land/.

17.

Transportation Research Board. TCRP G-18: Improving Access and Management of Transit ITS Data. TRB, 2021. https://apps.trb.org/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=4687. Accessed November 9, 2021.

18.

Gordon

J. B.

Koutsopoulos

H. N.

Wilson

N. H.

Attanucci

J. P.

Automated Inference of Linked Transit Journeys in London Using Fare-Transaction and Vehicle Location Data. Transportation Research Record: Journal of the Transportation Research Board, 2013. 2343: 17–24.

19.

Figliozzi

M. A.

Stoll

N. B.

A Study of Bus High-Resolution GPS Speed Data Accuracy. Transportation Research Record: Journal of the Transportation Research Board, 2018. 2672: 187–198.

20.

Sánchez-Martínez

G. E.

Inference of Public Transportation Trip Destinations by Using Fare Transaction and Vehicle Location Data: Dynamic Programming Approach. Transportation Research Record: Journal of the Transportation Research Board, 2017. 2652: 1–7.

21.

Zhao

Rahbee

Wilson

N. H. M.

Estimating a Rail Passenger Trip Origin-Destination Matrix Using Automatic Data Collection Systems. Computer-Aided Civil and Infrastructure Engineering, Vol. 22, No. 5, 2007, pp. 376–387. https://doi.org/10.1111/j.1467-8667.2007.00494.x.

22.

Valhalla. Valhalla-Open Source Routing Engine for OpenStreetMap. GitHub, 2020. https://www.github.com/valhalla. Accessed October 8, 2020.

23.

OpenStreetMap Contributors. Planet Dump. OSM, 2020. https://planet.osm.org. https://www.openstreetmap.org. Accessed November 22, 2020.

24.

J.-Q.

Match Bus Stops to a Digital Road Network by the Shortest Path Model. Transportation Research Part C: Emerging Technologies, Vol. 22, 2012, pp. 119–131. https://doi.org/10.1016/j.trc.2012.01.002.

25.

Grinberg

Flask Web Development: Developing Web Applications With Python. O’Reilly Media, Inc., Sebastopol, CA, 2018.

26.

El-Geneidy

A. M.

Horning

Krizek

K. J.

Analyzing Transit Service Reliability Using Detailed Data From Automatic Vehicular Locator Systems. Journal of Advanced Transportation, Vol. 45, No. 1, 2011, pp. 66–79.

27.

Massachusetts Bay Transportation Authority. See How Some Bus Routes Changed on December 22, 2019. MBTA, 2021. https://www.mbta.com/projects/better-bus-project/update/see-how-some-bus-routes-changed-december-22-2019. Accessed April 4, 2021.

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Ridership and Operations Visualization Engine: An Integrated Transit Performance and Passenger Journey Visualization Engine

Abstract

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