Revealing Daily Mobility Pattern Disparities of Monomodal and Multimodal Travelers through a Multi-Layer Cluster Analysis: Insights from a Combined Big Dataset

Jingyao Zhao (), Fan Zhang, Lei Gao, Chunhai Han and Xiongxiong Chen
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Jingyao Zhao: School of Transportation, Southeast University, Nanjing 211186, China
Fan Zhang: College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Lei Gao: College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Chunhai Han: College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Xiongxiong Chen: College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Sustainability, 2024, vol. 16, issue 9, 1-27

Abstract: More detailed and precise mobility patterns are needed for policies to reduce monomodal automotive dependency and promote multimodality in travel behaviors. Yet, empirical evidence from an integrated view of a complete door-to-door trip mode chain with daily mobility for pattern identification is still lacking. As an improvement and a solution on this issue, a multi-layer cluster model was designed and proposed for distinguishing 20 mobility pattern clusters, including six monomodal traveler groups, two non-transit multimodal traveler groups, and 12 transit multimodal based on big data mining. Statistical analysis with seven indicator measurements and a spatial distribution analysis with the Kernel density GIS maps of travelers’ residential location were carried out to reveal significant disparities across pattern clusters concerning spatial, social, and trip characteristics, based on which more precise and target policies for each group were discussed. This research may help provide more detailed information in establishing traveler mobility pattern profiles and solutions in filling the planning–implementation gap from the perspective of planners, policymakers, and travelers.

Keywords: mobility patterns; monomodal; multimodal; cluster analysis; big data mining (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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