What cities have is how people travel: Conceptualizing a data-mining-driven modal split framework. (December 2022)

• Record Type: Journal Article
• Authors: Lee, Sujin; Lee, Jinwoo; Mastrigt, Suzanne Hiemstra-van; Kim, Euiyoung
• Is Part Of: Cities. Volume 131(2022)
• Publication Date: 2022-12
• Subjects: Modal split -- Mode choice -- Sustainable mobility -- Data mining -- Data-driven decision making; City planning -- Periodicals; Urban policy -- Periodicals; 711.4
• DOI: 10.1016/j.cities.2022.103902
• Languages: English
• ISSNs: 0264-2751

Abstract

Abstract: As city-level modal splits are outcomes of city functions, it is essential to understand whether and how city attributes affect modal splits to derive a modal shift toward low-emission travel modes and sustainable mobility in cities. This study elucidates this relationship between modal splits and city attributes in 46 cities worldwide, proposing a two-step data mining framework. First, using the K-Means method, we classify cities into private-vehicle-, public-transit-, and bicycle-dominant groups based on their modal splits. Second, we categorize city attributes into environmental, socio-demographic, and transportation planning factors and quantify their interlocked impacts on cities' modal splits via the decision tree method. We observe that the socio-demographic factor has the highest impact on determining the cities' modal splits. In addition, high population density and employment rate are positively associated with low-emission travel modes. High gasoline tax and low public transit and taxi fares often make people reconsider possessing private vehicles. On the other hand, extreme weather conditions (e.g., hot temperatures) can prevent bicycle usage. Our contribution expands the impact of introduced city planning and policies for modal shifts toward a real-world paradigm and we present implications of the proposed framework in developing practical modal shift strategies. Highlights: Propose a novel data-mining framework to explain the city-level modal split. Investigate environmental, socio-demographic, and planning variables for 46 cities. Socio-demographic variables are the most important in determining the modal split. Infrastructures and policies for a specific mode can affect demand for other modes. Observe a nonlinearity of the influence of elderly rate on modal split.