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Spatio-temporal effects of bus arrival time information

Published: 01 November 2011 Publication History

Abstract

We analyze temporal and spatial variations in bus ridership that may result from using real-time bus arrival information. Using Random Effects Negative Binomial models of longitudinal average weekday ridership per bus route and controlling for operational, economic and social factors, we assess temporal variations in ridership by means of two types of time-varying coefficients: one that reflects "adjustment interval" effects after information becomes available on a route, during which users learn about information availability and potentially adapt their travel behavior, and the second, a "period" effect, that reflects changes in the underlying information and communications technology over time and ways in which people receive and use information. A k-means cluster analysis of bus stop service areas along routes that accrued the highest ridership allows us to associate the net effects of information to the sociodemographic, built environment, housing, economic, transportation and digital savviness characteristics of service areas. Four clusters of bus stops were identified: two where bus boardings gains were high after Bus Tracker, and two others where boarding gains were either modest or low. This strategy helped to determine the types of spatially-targeted Location-Based Service applications that may be developed to capitalize on basic bus arrival information.

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  • (2021)Combining K-means method and complex network analysis to evaluate city mobility2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)10.1109/ITSC.2016.7795782(1666-1671)Online publication date: 10-Mar-2021

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cover image ACM Conferences
CTS '11: Proceedings of the 4th ACM SIGSPATIAL International Workshop on Computational Transportation Science
November 2011
61 pages
ISBN:9781450310345
DOI:10.1145/2068984
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 November 2011

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Author Tags

  1. digital divide
  2. public transportation information system
  3. technology adoption

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View all
  • (2021)Combining K-means method and complex network analysis to evaluate city mobility2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)10.1109/ITSC.2016.7795782(1666-1671)Online publication date: 10-Mar-2021

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