Abstract
Public transit agencies have amassed substantial data through on-board and off-board sensors over the years. While data collection was the primary focus, there is now a shift towards deriving actionable insights from this wealth of information. As data-driven decision making becomes increasingly vital, there is a growing need for effective ways to visualize and convey complex insights to decision makers. This study addresses this need by introducing G2Viz, a visualizer for public transit operations. The development process of G2Viz spans requirement gathering, planning, and design, encompassing software architecture, data models, user interfaces, and system components. Rigorous implementation and testing ensure the tool’s functionality and effectiveness. G2Viz, designed to dynamically visualize public transit operations using General Transit Feed Specification (GTFS) data, is a web application accessible globally via any web browser. Its open-source nature, robustness, and versatility facilitate communication among transit agencies, users, researchers, and city authorities. G2Viz empowers transit planners to make well-informed decisions about public transportation. (Access G2Viz at https://g2viz.citycontext.info).
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References
Aemmer Z, Ranjbari A, MacKenzie D (2022) Measurement and classification of transit delays using GTFS-RT data. Public Transport 14:263–285. https://doi.org/10.1007/s12469-022-00291-7
Andrienko G, Andrienko N, Chen W, Maciejewski R, Zhao Y (2017) Visual analytics of mobility and transportation: State of the art and further research directions. IEEE Trans Intell Transp Syst 18(8):2232–2249. https://doi.org/10.1109/TITS.2017.2683539
Anwar A, Odoni A, Toh N (2016) BusViz: big data for bus fleets. Transportation Research Record: Journal of the Transportation Research Board 2544(1):102–109. https://doi.org/10.3141/2544-12
Berkow M, El-Geneidy AM, Bertini RL, Crout D (2009) Beyond generating transit performance measures: visualizations and statistical analysis using historical data. Transportation Research Record: Journal of the Transportation Research Board 2111:158–168. https://doi.org/10.3141/2111-18
Chen W, Guo F, Wang FY (2015) A survey of traffic data visualization. IEEE Trans Intell Transp Syst 16:2970–2984. https://doi.org/10.1109/TITS.2015.2436897
Demissie MG, Kattan L (2022a) Estimation of truck origin-destination flows using GPS data. Transp Research Part E: Logist Transp Rev 159:102621. https://doi.org/10.1016/j.tre.2022.102621
Demissie MG, Kattan L (2022b) Understanding the temporal and spatial interactions between transit ridership and urban land-use patterns: an exploratory study. Public Transport 14:385–417. https://doi.org/10.1007/s12469-022-00296-2
Demissie MG, Phithakkitnukoon S, Kattan L (2019) Trip distribution modeling using mobile phone data: emphasis on intra-zonal trips. IEEE Trans Intell Transp Syst 20(7):2605–2617. https://doi.org/10.1109/TITS.2018.2868468
Demissie MG, Kattan L, Phithakkitnukoon S, de Almeida H, Correia G, Veloso M, Bento C (2020) Modeling location choice of taxi drivers for passenger pick-up using GPS data. IEEE Intell Transp Syst Mag 13(1):70–90. https://doi.org/10.1109/MITS.2020.3014099
Deng X, Chen W, Zhou Q, Zheng Y, Li H, Liao S, Biljecki F (2023) Exploring spatiotemporal pattern and agglomeration of road CO2 emissions in Guangdong, China. Sci Total Environ 871:162134. https://doi.org/10.1016/j.scitotenv.2023.162134
Devunuri S (2024) gtfs-segments (2.1.1). GitHub. https://pypi.org/project/gtfs-segments
ESRI (2023) ArcGIS Pro. Environmental Systems Research Institute. https://pro.arcgis.com/en/pro-app/latest/get-started/get-started.htm
Fry B, Reas C (2023) Processing. GitHub. https://github.com/benfry/processing4/
Ge L, Sarhani M, Voß S, Xie L (2021) Review of transit data sources: Potentials, challenges and complementarity. Sustainability 13(20):11450. https://doi.org/10.3390/su132011450
Glick TB, Feng W, Bertini RL, Figliozzi MA (2015) Exploring applications of second-generation archived transit data for estimating performance measures and arterial travel speeds. Transportation Research Record: Journal of the Transportation Research Board 2538:44–52. https://doi.org/10.3141/2538-06
Godfrid J, Radnic P, Vaisman A, Zimányi E (2022) Analyzing public transport in the city of Buenos Aires with MobilityDB. Public Transport 14:287–321. https://doi.org/10.1007/s12469-022-00290-8
Guido G, Vitale A, Rogano D (2016) Assessing public transport reliability of services connecting the major airport of a low density region by using AVL and GIS technologies. In: International conference on environment and electrical engineering (EEEIC 2016), pp 1–5. https://doi.org/10.1109/EEEIC.2016.7555483
Guo F (2012) More than usability: The four elements of user experience, part IV. http://www.uxmatters.com/mt/archives/2012/04/more-than-usability-the-four-elements-of-user-experience-part-i.php. Accessed 30 Jan 2024
Herszenhut D, Pereira RHM, Andrade PR, Joao Bazzo I (2023) Introduction to gtfstools. https://cran.r-project.org/web/packages/gtfstools/vignettes/gtfstools.html
Ji Y, Mishalani RG, McCord MR (2015) Transit passenger origin-destination flow estimation: Efficiently combining onboard survey and large automatic passenger count datasets. Transp Res Part C: Emerg Techn 58(B):178–192. https://doi.org/10.1016/j.trc.2015.04.021
Kim Y, Lee J, Kim J, Nakajima N (2021) The disparity in transit travel time between Koreans and Japanese in 1930s colonial Seoul. Findings. https://doi.org/10.32866/001c.25226
Kim Y, Kim J, Ha HJ, Nakajima N, Lee J (2022) Job Accessibility as a lens for understanding the urban structure of colonial cities: a digital humanities study of the colonial Seoul in the 1930s using GIS. ISPRS Int J Geo Inf 11(12):614. https://doi.org/10.3390/ijgi11120614
Kim J, Rapuri S, Chuluunbaatar E, Sumiyasuren E, Lkhagvasuren B, Budhathoki NR, Laituri M (2023) Developing and evaluating transit-based healthcare accessibility in a low- and middle-income country: A case study in Ulaanbatar, Mongolia. Habitat Int 131:102729. https://doi.org/10.1016/j.habitatint.2022.102729
Kinjarapu A, Demissie MG, Kattan L, Duckworth R (2021) Applications of passive GPS data to characterize the movement of freight trucks: a case study in the Calgary region of Canada. IEEE Trans Intell Transp Syst 23:9210–9225. https://doi.org/10.1109/tits.2021.3093061
Kujala R (2020) gtfspy-webviz. GitHub. https://github.com/CxAalto/gtfspy-webviz
Kunama N, Worapan M, Phithakkitnukoon S, and Demissie, M (2017). GTFS-VIZ: Tool for preprocessing and visualizing GTFS data. In: Adjunct proceedings of the ACM international joint conference on pervasive and ubiquitous computing and Proceedings of the ACM international symposium on wearable computers (UbiComp/ISWC 2017), pp 388–396. https://doi.org/10.1145/3123024.3124415
Kurkcu A, Miranda F, Ozbay K, Silva CT (2017). Data visualization tool for monitoring transit operation and performance. In: 5th IEEE international conference on models and technologies for intelligent transportation systems (MT-ITS 2017), pp 598–603. https://doi.org/10.1109/MTITS.2017.8005584
Li D, Lin Y, Zhao X, Song H, Zou N (2011) Estimating a transit passenger trip origin-destination matrix using automatic fare collection system. In: Database systems for adanced applications. Lecture notes in computer science, vol 6637, pp 502–513. https://doi.org/10.1007/978-3-642-20244-5_48
Ma X, Wang Y (2014) Development of a data-driven platform for transit performance measures using smart card and GPS data. J Transp Eng 140(12):04014063. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000714
Mazloumi E, Currie G, Rose G (2009) Using GPS data to gain insight into public transport travel time variability. J Transp Eng 136(7):623–631. https://doi.org/10.1061/(asce)te.1943-5436.0000126
McKinney W (2011) pandas: a foundational python library for data analysis and statistics. Python High Perform Sci Comput 14(9):1–9
Mesbah M, Currie G, Lennon C, Northcott T (2012) Spatial and temporal visualization of transit operations performance data at a network level. J Transp Geogr 25:15–26. https://doi.org/10.1016/j.jtrangeo.2012.07.005
Mueller M (2014) gtfs-visualizations. GitHub. https://github.com/cmichi/gtfs-visualizations
National RATP (2024) GTFS builder guidebook. https://www.nationalrtap.org/Technology-Tools/GTFS-Builder. Accessed 26 Apr 2024
Pereira RHM, Saraiva M, Herszenhut D, Braga CKV, Conway MW (2021) r5r: rapid realistic routing on multimodal transport networks with R5 in R. Transport Findings. https://doi.org/10.32866/001c.21262
Pereira RHM, Andrade PR, Vieira JPB (2023) Exploring the time geography of public transport networks with the gtfs2gps package. J Geogr Syst 25:453–466. https://doi.org/10.1007/s10109-022-00400-x
Phithakkitnukoon S, Hankaew S, Demissie MG, Smoreda Z, Ratti C (2022) Temporary migration flow inference and analysis from perspective of mobile phone network data. IEEE Access 10:23248–23258. https://doi.org/10.1109/ACCESS.2022.3154485
Phithakkitnukooon S, Patanukhom K, Demissie MG (2021) Predicting spatiotemporal demand of dockless e-scooter sharing services with a masked fully convolutional network. ISPRS Int J Geo Inf 10(11):773. https://doi.org/10.3390/ijgi10110773
Prommaharaj P, Phithakkitnukoon S, Demissie MG, Kattan L, Ratti C (2020) Visualizing public transit system operation with GTFS data: a case study of Calgary, Canada. Heliyon 6(4):e03729. https://doi.org/10.1016/j.heliyon.2020.e03729
Toso S, Oja R (2023) gtfs_functions. GitHub. https://github.com/Bondify/gtfs_functions
Funding
This work is funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grants (RGPIN/03037-2022). This work was supported in part by Chiang Mai University.
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Para, S., Wirotsasithon, T., Jundee, T. et al. G2Viz: an online tool for visualizing and analyzing a public transit system from GTFS data. Public Transp (2024). https://doi.org/10.1007/s12469-024-00362-x
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DOI: https://doi.org/10.1007/s12469-024-00362-x