What cities have is how people travel: Conceptualizing a data-mining-driven modal split framework

Lee, Sujin; Lee, Jinwoo; Hiemstra-van Mastrigt, S.; Kim, E.Y.

doi:10.1016/j.cities.2022.103902

What cities have is how people travel

Title

What cities have is how people travel: Conceptualizing a data-mining-driven modal split framework

Author

Lee, Sujin (Korea Advanced Institute of Science and Technology)
Lee, Jinwoo (Korea Advanced Institute of Science and Technology)
Hiemstra-van Mastrigt, S. (TU Delft Responsible Marketing and Consumer Behavior)
Kim, E.Y. (TU Delft Responsible Marketing and Consumer Behavior)

Date

2022

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.

Subject

Data mining
Data-driven decision making
Modal split
Mode choice
Sustainable mobility

To reference this document use:

http://resolver.tudelft.nl/uuid:ba3052b8-4e48-49aa-9754-c51ab961c002

DOI

https://doi.org/10.1016/j.cities.2022.103902

ISSN

0264-2751

Source

Cities: the international journal of urban policy and planning, 131

Part of collection

Institutional Repository