TY - JOUR
T1 - What cities have is how people travel
T2 - Conceptualizing a data-mining-driven modal split framework
AU - Lee, Sujin
AU - Lee, Jinwoo
AU - Mastrigt, Suzanne Hiemstra van
AU - Kim, Euiyoung
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Data mining
KW - Data-driven decision making
KW - Modal split
KW - Mode choice
KW - Sustainable mobility
UR - http://www.scopus.com/inward/record.url?scp=85136092628&partnerID=8YFLogxK
U2 - 10.1016/j.cities.2022.103902
DO - 10.1016/j.cities.2022.103902
M3 - Article
AN - SCOPUS:85136092628
SN - 0264-2751
VL - 131
JO - Cities
JF - Cities
M1 - 103902
ER -