Ensemble-Based Methodology to Identify Optimal Personal Mobility Service Areas Using Public Data

• Published in: KSCE journal of civil engineering Vol. 26; no. 7; pp. 3150 - 3159
• Main Authors: Lee, Sangjae, Son, Seung-oh, Park, Juneyoung, Park, Jaehong
• Format: Journal Article
• Language: English
• Published: Seoul Korean Society of Civil Engineers 01.07.2022; Springer Nature B.V; 대한토목학회
• Subjects: Accessibility; Big Data; Cities; Civil Engineering; Data analysis; Distance; Economic analysis; Economic factors; Engineering; Future Urban Mobility with MaaS; Geographic information systems; Geographical information systems; Geotechnical Engineering & Applied Earth Sciences; Hexagonal cells; Industrial Pollution Prevention; Information systems; Mobility; Public transportation; Remote sensing; Service areas; Social factors; Socioeconomic aspects; Socioeconomic factors; Socioeconomics; Subway stations; Taxis; Transportation networks; Travel demand; Walking; 토목공학; Extreme gradient boosting; Personal mobility; Ensemble model; Big data; Random forest; Public transportation; Hexagonal grid
• ISSN: 1226-7988; 1976-3808
• DOI: 10.1007/s12205-022-1356-y

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.