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Journal Article

Citation

Cheng L, Chen X, Lam WHK, Yang S, Lei D. Transp. Res. Rec. 2017; 2671: 10-19.

Copyright

(Copyright © 2017, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.3141/2671-02

PMID

unavailable

Abstract

In China, low-income commuters are usually concentrated in peripheral settlements outside downtown areas, where travel services are inadequately provided. These commuters are dependent on fewer travel options, considering their affordability. Based on the recognition that public transit is an important mode to enhance low-income commuters' travel mobility, a comprehensive attitude-based market segmentation analysis was performed to identify distinct market segments to best serve the needs of each segment and to develop plans to increase transit usage. First, a detailed household survey was conducted in Fushun, China, to obtain commuters' attitudes toward daily travel. Then, factor analysis was utilized to explore latent attitudinal factors. The structural equation modeling investigated the correlations between attitudes and public transit usage. The k-means clustering was then employed to partition the transit market into several subgroups. Finally, five segments of transit market with distinct attitudes were identified by three dividing variables, namely, the desire for comfort, the need for reliability, and environmental awareness. Low-income commuters in the same segment share homogeneous travel preferences while those in other segments possess different attitudes. The attitudinal characteristics, socioeconomic profile, and mode choice behavior in each segment were examined and discussed. Policies that best meet the needs of each submarket were proposed. These transit-related strategies included building a reliable operation environment, improving the level of service of existing facilities, implementing demand-response transit services, and providing public propaganda and education toward environmental protection.


Language: en

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