Abstract
The land-use characteristics of urban areas continually change, and thus the activity patterns of the significant trip generators evolve. Efficient public transit planning needs to perform frequent estimates of the spatio-temporal distribution and dynamics of different activities in urban areas and measure the likely consequences of changes. Automated data collection systems usually collect transit ridership data (e.g., automated passenger count (APC)). Many transit agencies also generate General Transit Feed Specification (GTFS) data and make them publicly available. This study explores the use of APC, GTFS, and land-use data to examine various land-use and transit ridership interactions at the stop, route, and zonal levels using visualization, data mining, and statistical analysis techniques. Results show that transit ridership at the bus stop level gives a better understanding of each bus stop's unique land use. Zonal-level transit ridership patterns reveal the different trip generations and attraction roles of the neighboring land usage. This study could provide additional insights on the interaction between the temporal changes in population from the perspective of transit use and the associated land uses.
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References
Alsger AA, Mesbah M, Ferreira L, Safi H (2015) Use of smart card fare data to estimate public transport origin-destination matrix. Transp Res Record 2535(1):88–96. https://doi.org/10.3141/2535-10
Andersen JLE, Landex A (2008) Catchment areas for public transport. WIT Trans Built Environ 101:175–184. https://doi.org/10.2495/UT080171
Badoe DA, Miller EJ (2000) Transportation-land-use interaction: empirical findings in North America, and their implications for modeling. Transp Res Part D 5(4):235–263. https://doi.org/10.1016/S1361-9209(99)00036-X
Barabino B, Di Francesco M, Mozzoni S (2013) Regularity analysis on bus networks and route directions by automatic vehicle location raw data. IET Intel Transport Syst 7(4):473–480. https://doi.org/10.1049/iet-its.2012.0182
Bast H, Brosi P and Storandt S (2015) Real-time movement visualization of public transit data. In: 22nd ACM SIGSPATIAL International Conference, pp 331–340. https://doi.org/10.1145/2666310.2666404
Batty M (2002) Thinking about cities as spatial events. Environ Plann B Plann Des 29:1–2. https://doi.org/10.1068/b2901ed
Becker RA, Cáceres R, Hanson K, Loh JM, Urbanek S, Varshavsky A, Volinsky C (2011) A tale of one city: using cellular network data for urban planning. IEEE Pervasive Comput 10(4):18–26. https://doi.org/10.1109/MPRV.2011.44
Bertaud A (2004) The spatial organization of cities: deliberate outcome or unforeseen consequence? IURD Working Paper Series. https://doi.org/10.1017/9781316271377.004.
Boarnet M, Crane R (2001) The influence of land use on travel behavior: specification and estimation strategies. Transp Res Part A 35(9):823–845. https://doi.org/10.1016/S0965-8564(00)00019-7
Calabrese F, Reades J, Ratti C (2010) Eigenplaces: segmenting space through digital signatures. IEEE Pervasive Comput 9(1):78–84. https://doi.org/10.1109/MPRV.2009.62
Calgary (2016) Open Calgary: The City of Calgary’s Open Data Portal
Calgary Transit (2020) Calgary Transit Ridership, Revenue and RouteAhead update. https://pub-calgary.escribemeetings.com/filestream.ashx?DocumentId=139367
Cervero R, Murakami J, Miller M (2010) Direct ridership model of bus rapid transit in Los Angeles County, California. Transp Res Record 2145:1–7. https://doi.org/10.3141/2145-01
Chakour V, Eluru N (2016) Examining the influence of stop level infrastructure and built environment on bus ridership in Montreal. J Transp Geogr 51:205–217. https://doi.org/10.1016/j.jtrangeo.2016.01.007
Chakraborty A, Mishra S (2013) Land use and transit ridership connections: implications for state-level planning agencies. Land Use Policy 30(1):458–469. https://doi.org/10.1016/j.landusepol.2012.04.017
Chang AYJ, Miranda-Moreno L, Clewlow R, and Sun L (2019) Trend or Fad? Deciphering the enablers of micromobility in the U.S. SAE International
Deal B, Schunk D (2004) Spatial dynamic modeling and urban land use transformation: a simulation approach to assessing the costs of urban sprawl. Ecol Econ 51(1–2):79–95. https://doi.org/10.1016/j.ecolecon.2004.04.008
Decraene J, Monterola C, Lee GKK, Hung TGG (2013) A quantitative procedure for the spatial characterization of urban land use. Int J Modern Phys C 24(01):1250092. https://doi.org/10.1142/S0129183112500921
Demissie MG, Correia G, Bento C (2015) Analysis of the pattern and intensity of urban activities through aggregate cellphone usage. Transportmetrica A 11:502–524. https://doi.org/10.1080/23249935.2015.1019591
Demissie MG (2014) Combining datasets from multiple sources for urban and transportation planning: emphasis on cellular network data. PhD thesis. Coimbra University
Demissie MG, Kattan L (2022) Estimation of truck origin-destination flows using GPS data. Transp Res Part E 145:102621. https://doi.org/10.1016/j.tre.2022.102621
Demissie MG, Kattan L, Phithakkitnukoon S, Correia GHA, 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
Du H, Mulley C (2012) Understanding spatial variations in the impact of accessibility on land value using geographically weighted regression. J Transp Land Use 5(2):46–59. https://doi.org/10.5198/jtlu.v5i2.225
Frias-Martinez V, Frias-Martinez E (2014) Spectral clustering for sensing urban land use using Twitter activity. Eng Appl Artif Intell 35:237–245. https://doi.org/10.1016/j.engappai.2014.06.019
Furth P, Mekuria M, SanClemente J (2007) Parcel-level modeling to analyze transit stop location changes. J Public Transp 10(2):73–91. https://doi.org/10.5038/2375-0901.10.2.5
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
Google (2016) “GTFS.” 2016. https://developers.google.com/transit/gtfs
Guido G, Vitale A, and 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: EEEIC 2016—International Conference on Environment and Electrical Engineering. https://doi.org/10.1109/EEEIC.2016.7555483
Gutiérrez J, Cardozo OD, García-Palomares JC (2011) Transit ridership forecasting at station level: an approach based on distance-decay weighted regression. J Transp Geogr 19:1081–1092. https://doi.org/10.1016/j.jtrangeo.2011.05.004
Hu N, Legara EF, Lee KK, Hung GG, Monterola C (2016) Impacts of land use and amenities on public transport use, urban planning and design. Land Use Policy 57:356–367. https://doi.org/10.1016/j.landusepol.2016.06.004
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 58(1):178–192. https://doi.org/10.1016/j.trc.2015.04.021
Kaeoruean K, Phithakkitnukoon S, Demissie MG, Kattan L, Ratti C (2020) Analysis of demand-supply gaps in public transit systems based on census and GTFS data: a case study of Calgary, Canada. Public Transport 12:483–516. https://doi.org/10.1007/s12469-020-00252-y
Kim K, Kyuhyup Oh, Lee YK, Kim SH, Jung JY (2014) An analysis on movement patterns between zones using smart card data in subway networks. Int J Geogr Inf Sci 28:1781–1801. https://doi.org/10.1080/13658816.2014.898768
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. https://doi.org/10.1109/tits.2021.3093061
Krizek KJ (2003) Operationalizing neighborhood accessibility for land use—travel behavior research and regional modeling. J Plan Educ Res 22(3):270–287. https://doi.org/10.1177/0739456X02250315
Kunama N, Worapan M, Phithakkitnukoon S, Demissie M (2017) GTFS-VIZ: tool for preprocessing and visualizing GTFS data. UbiComp/ISWC, pp 388–396. https://doi.org/10.1145/3123024.3124415
Lam W and Morrall J (1982) Bus passenger walking distances and waiting times: a summer–winter comparison. Transport Quart 36(3):407–421
Lee SG, Hickman M, Tong D (2013) Development of a temporal and spatial linkage between transit demand and landuse patterns. J Transport Land Use 6(2):33–46. https://doi.org/10.5198/jtlu.v6i2.268
Li D, Lin Y, Zhao X, Song H and Zou N (2011) Estimating a transit passenger trip origin-destination matrix using automatic fare collection system. Lect Notes Comput Sci 6637:502–513. https://doi.org/10.1007/978-3-642-20244-5_48
Liu Y, Wang F, Xiao Y, Gao S (2012) Urban land uses and traffic ‘source-sink areas’: evidence from GPS-enabled taxi data in Shanghai. Landsc Urban Plan 106(1):73–87. https://doi.org/10.1016/j.landurbplan.2012.02.012
Lumley T, Diehr P, Emerson S, Chen Lu (2002) The importance of the normality assumption in large public health data sets. Annu Rev Public Health 23:151–169. https://doi.org/10.1146/annurev.publhealth.23.100901.140546
Mazloumi E, Currie G, Rose G (2010) 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
Mungthanya W, Phithakkitnukoon S, Demissie MG, Kattan L, Veloso M, Bento C, Ratti C (2019) Constructing time-dependent origin-destination matrices with adaptive zoning scheme and measuring their similarities with taxi trajectory data. IEEE Access 7:77723–77737. https://doi.org/10.1109/ACCESS.2019.2922210
Ortúzar JD, Willumsen LG (2011) Modelling transport, 4th edn. Wiley. https://doi.org/10.1002/9781119993308
Padeiro M (2014) The influence of transport infrastructures on land-use conversion decisions within municipal plans. J Transport Land Use 7(1):79–93. https://doi.org/10.5198/jtlu.v7i1.373
Pei T, Sobolevsky S, Ratti C, Shaw SL, Li T, Zhou C (2014) A new insight into land use classification based on aggregated mobile phone data. Int J Geogr Inf Sci 28:1988–2007. https://doi.org/10.1080/13658816.2014.913794
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, 17 pages. https://doi.org/10.3390/ijgi10110773
Postsavee 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
Reades J, Calabrese F, Sevtsuk A, Ratti C (2007) Cellular census: explorations in urban data collection. IEEE Pervasive Comput 6(3):30–38. https://doi.org/10.1109/MPRV.2007.53
Rodrigue JP, Comtois C, Slack B (2016) The geography of transport systems, 4th edn. Routledge, London. https://doi.org/10.4324/9781315618159
Soliman A, Soltani K, Yin J, Padmanabhan A, Wang S (2017) Social sensing of urban land use based on analysis of Twitter users’ mobility patterns. PLoS ONE 12(7):e0181657, 16 pages. https://doi.org/10.1371/journal.pone.0181657
Soto V and Frias-Martinez E (2011) Robust land use characterization of urban landscapes using cell phone data. In: Proceedings of the 1st Workshop on Pervasive Urban Applications, in Conjunction with 9th Int. Conf. Pervasive Computing.
Stewart C, Diab E, Bertini R, El-Geneidy A (2016) Perspectives on transit: potential benefits of visualizing transit data. Transp Res Record 2544(1):90–101. https://doi.org/10.3141/2544-11
Sun L, Lee DH, Erath A, Huang X (2012) Using smart card data to extract passenger’s spatio-temporal density and train’s trajectory of MRT system. Proc ACM SIGKDD Int Conf Knowl Discover Data Min, pp 142–148. https://doi.org/10.1145/2346496.2346519
Tan PN, Steinbach M, Kumar V (2005) Cluster analysis: basic concepts and algorithms, chapter 8 in introduction to data mining. Addison-Wesley
Toole JL, Ulm M, González MC, Bauer D (2012) Inferring land use from mobile phone activity. Proc ACM SIGKDD Int Workshop Urban Comput. https://doi.org/10.1145/2346496.2346498
TRB (1996) Transit and urban form. http://onlinepubs.trb.org/onlinepubs/tcrp/tcrp_rpt_16-1.pdf
Tsai CH, Mulley C, and Clifton G (2012) The spatial interactions between public transport demand and land use characteristics in the Sydney greater metropolitan area. 35th Australasian Transport Research Forum, Perth, Australia
Upchurch C, Kuby M, Zoldak M, Barranda A (2004) Using GIS to generate mutually exclusive service areas linking travel on and off a network. J Transport Geograph 12(1):23–33. https://doi.org/10.1016/j.jtrangeo.2003.10.001
Waddell P (2002) Urbansim: modeling urban development for land use, transportation, and environmental planning. J Am Plann Assoc 68:297–314. https://doi.org/10.1080/01944360208976274
Wakamiya S, Lee R, Sumiya K (2011) Urban area characterization based on semantics of crowd activities in Twitter. Lecture Notes Comput Sci 6631:108–123. https://doi.org/10.1007/978-3-642-20630-6_7
Wang W, Attanucci JP, Wilson NHM (2011) Bus passenger origin-destination estimation and related analyses using automated data collection systems. J Public Transp 14 (4):131–150. https://doi.org/10.5038/2375-0901.14.4.7
Wong J (2013) Use of the general transit feed specification (GTFS) in transit performance measurement. MSc thesis, Georgia Institute of Technology
Yu C, He ZC (2017) Analysing the spatial-temporal characteristics of bus travel demand using the heat map. J Transp Geogr 58:247–255. https://doi.org/10.1016/j.jtrangeo.2016.11.009
Zhan X, Ukkusuri SV, Zhu F (2014) Inferring urban land use using large-scale social media check-in data. Netw Spat Econ 14:647–667. https://doi.org/10.1007/s11067-014-9264-4
Acknowledgements
We thank Calgary Transit and the City of Calgary for letting us use their data. This work is funded by the NSERC Discovery grant RT735236 (RGPIN/03942-2020); the Alberta Innovate Strategic Grant on Integrated Urban Mobility 10024748 (G2018000894); the NSERC CREATE on Integrated Infrastructure for Sustainable Cities (IISC) RT1002081 (CREATE/511060-2018); the Urban Alliance Chair in Transportation Systems Optimization RT756247; and the Schulich School of Engineering Startup Grant.
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Demissie, M.G., Kattan, L. Understanding the temporal and spatial interactions between transit ridership and urban land-use patterns: an exploratory study. Public Transp 14, 385–417 (2022). https://doi.org/10.1007/s12469-022-00296-2
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DOI: https://doi.org/10.1007/s12469-022-00296-2