Abstract
Public transportation networks are well established in main cities, but there are some inconveniences in using public transportation in some cities. Public transportation is less accessible and walking distance of getting to public transportation is too long in some cities. Compared to other cities, Seoul has a higher satisfaction rate with public transportation. There are many cases, however, where short-distance taxis are used because walking to destinations after using public transportation is inconvenient; instead, Personal mobility (PM) devices can be used for these short-distances trip. This study aims to find the optimal PM service area using GIS(Geographic Information System)-based public transportation big data analyses. Variables were generated by collecting socio-economic factors, public transportation data, and geographic data and Extreme gradient boosting and Random forest, which are representative ensemble methods, were used for evaluation. We divided Seoul into a hexagonal grid and developed the optimal PM location service model by creating hexagonal cell data units and analyzing the areas with the models. We found that residential complexes, parks, and near subway stations (all areas with high foot traffic) are best suited for optimal placement. We also determined deployment should be in lower sloped areas. We expect this work to help determine public transportation stop and shared mobility station locations as well as contribute to public transportation demand surveys and accessibility analyses.
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References
Barr S (2018) Personal mobility and climate change. Wiley Interdisciplinary Reviews: Climate Change 9(5):e542, DOI: https://doi.org/10.1002/wcc.542
Breiman L (2001) Random forests. Machine Learning 45(1):5–32, DOI: https://doi.org/10.1023/A:1010933404324
Campbell AA, Cherry CR, Ryerson MS, Yang X (2016) Factors influencing the choice of shared bicycles and shared electric bikes in Beijing. Transportation Research Part C: Emerging Technologies 67:399–414, DOI: https://doi.org/10.1016/j.trc.2016.03.004
Chang K-T (2004) Introduction to geographic information systems. McGraw-Hill Higher Education, Boston, MA, USA
Chen T, Guestrin C (2016) XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, August 13–17, San Francisco, CA, USA, DOI: https://doi.org/10.1145/2939672.2939785
Chen J, Yang S, Li H, Zhang B, Lv J (2013) Research on geographical environment unit division based on the method of natural breaks (Jenks). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 3:47–50, DOI: https://doi.org/10.5194/isprsarchives-XL-4-W3-47-2013
Cho S, Kim B, Kim N, Song J (2019) A study on the number of passengers using the subway stations in Seoul. The Korean Journal of Applied Statistics 32(1):111–128, DOI: https://doi.org/10.5351/KJAS.2019.32.1.111
Choi Y, Jeung I, Park J (2021) Comparative analysis of spatial impact of living social overhead capital on housing price by residential type. KSCE Journal of Civil Engineering 25(3):1056–1065, DOI: https://doi.org/10.1007/s12205-021-1250-z
Choi MH, Jung HY (2020) A study on the influencing factor of intention to use personal mobility sharing services. Journal of Korean Society of Transportation 38(1):1–13, DOI: https://doi.org/10.7470/jkst.2020.38.1.001
Currie G (2010) Quantifying spatial gaps in public transport supply based on social needs. Journal of Transport Geography 18(1):31–41, DOI: https://doi.org/10.1016/j.jtrangeo.2008.12.002
Dietterich TG (2000) An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning 40(2):139–157, DOI: https://doi.org/10.1023/A:1007607513941
Dowling R, Irwin J, Faulks I, Howitt R (2015) Use of personal mobility devices for first-and-last mile travel: The Macquarie-Ryde trial. In: Cameron I, Haworth N, McIntosh L (eds) Proceedings of the 2015 Australasian road safety conference (ARSC2015). Australasian College of Road Safety (ACRS), Pearce, Australia
Enticott G, Ward K, Ashton A, Brunton L, Broughan J (2020) Mapping the geography of disease: A comparison of epidemiologists’ and field-Level experts’ disease maps. Applied Geography 126
Grömping U (2009) Variable importance assessment in regression: Linear regression versus random forest. The American Statistician 63(4): 308–319, DOI: https://doi.org/10.1198/tast.2009.08199
Isaac Brodsky (2018) H3: Uber’s Hexagonal hierarchical spatial index. Uber Engineering, Retrived June 27, 2018, https://eng.uber.com/h3/
Jenks GF (1967) The data model concept in statistical mapping. International Yearbook of Cartography 7:186–190
Lee HM, Jeon GS, Jang JA (2020) Predicting of the severity of car traffic accidents on a highway using light gradient boosting model. The Journal of the Korea Institute of Electronic Communication Sciences 15(6):1123–1130, DOI: https://doi.org/10.13067/JKIECS.2020.15.6.1123
Liao C, Tesfa T, Duan Z, Leung LR (2020) Watershed delineation on a hexagonal mesh grid. Environmental Modelling & Software 128: 104702, DOI: https://doi.org/10.1016/j.envsoft.2020.104702
Litman T (2008) Evaluating accessibility for transportation planning. Victoria Transport Policy Institute, Victoria, BC, Canada
Lodha SK, Verma AK (2000) Spatio-temporal visualization of urban crimes on a GIS grid. Proceedings of the 8th ACM international symposium on advances in geographic information systems, November 6–11, Washington DC, USA, DOI: https://doi.org/10.1145/355274.355300
McMaster R (1997) In memoriam: George F. Jenks (1916–1996). Cartography and Geographic Information Systems 24(1):56–59, DOI: https://doi.org/10.1559/152304097782438764
Miller P, de Barros AG, Kattan L, Wirasinghe SC (2016) Public transportation and sustainability: A review. KSCE Journal of Civil Engineering 20(4):1076–1083, DOI: https://doi.org/10.1007/s12205-016-0705-0
Mollalo A, Vahedi B, Rivera KM (2020) GIS-based spatial modeling of COVID-19 incidence rate in the continental United States. Science of the Total Environment 728:138884, DOI: https://doi.org/10.1016/j.scitotenv.2020.138884
Murray AT, Davis R, Stimson RJ, Ferreira L (1998) Public transportation access. Transportation Research Part D: Transport and Environment 3(5):319–328, DOI: https://doi.org/10.1016/S1361-9209(98)00010-8
Opitz D, Maclin R (1999) Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research 11:169–198, DOI: https://doi.org/10.1613/jair.614
Park S, Kang J, Choi K (2014) Finding determinants of transit users’ walking and biking access trips to the station: A pilot case study. KSCE Journal of Civil Engineering 18(2):651–658, DOI: https://doi.org/10.1007/s12205-014-0073-6
Radzimski A, Dzięcielski M (2021) Exploring the relationship between bike-sharing and public transport in Poznań, Poland. Transportation Research Part A: Policy and Practice 145:189–202, DOI: https://doi.org/10.1016/j.tra.2021.01.003
Zefreh MM, Hussain B, Sipos T (2020) In-depth analysis and model development of passenger satisfaction with public transportation. KSCE Journal of Civil Engineering 24(10):3064–3073, DOI: https://doi.org/10.1007/s12205-020-1871-7
Zhang Y, Haghani A (2015) A gradient boosting method to improve travel time prediction. Transportation Research Part C: Emerging Technologies 58:308–324, DOI: https://doi.org/10.1016/j.trc.2015.02.019
Zhu R Zhang X, Kondor D, Santi P, Ratti C (2020) Understanding spatio-temporal heterogeneity of bike-sharing and scooter-sharing mobility. Computers, Environment and Urban Systems 81:101483, DOI: https://doi.org/10.1016/j.compenvurbsys.2020.101483
Acknowledgments
This research was supported by research project “Development of Sustainable MaaS (Mobility as a Service) 3.0+ Technology in Rural Areas” funded by the Korea Institute of Civil Engineering and Building Technology (KICT).
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Lee, S., Son, So., Park, J. et al. Ensemble-Based Methodology to Identify Optimal Personal Mobility Service Areas Using Public Data. KSCE J Civ Eng 26, 3150–3159 (2022). https://doi.org/10.1007/s12205-022-1356-y
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DOI: https://doi.org/10.1007/s12205-022-1356-y