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Transportation

, Volume 8, Issue 1, pp 21–36 | Cite as

The relationship between transportation perceptions and behavior over time

  • Mary Lynn Tischer
  • Robert V. Phillips
Article

Abstract

The aim of this paper is to discuss cross-lagged panel analysis in terms of the causal inferences it generates about the relationship of beliefs about modes and mode choice behavior. Frequencies of use of the single-occupant automobile (SOA), bus and carpool, as well as beliefs about each of the modes, were collected from a sample of central business district commuters at two points in time. The belief variables for each mode were summed to form composite measures and were corrected for unreliability due to measurement error.

Perceptions of each mode and the frequency of its use were analyzed for influences operating over time. A time interval was assumed to exist during which the variables causally operated on each other. It was assumed that the time necessary for an individual to change modes based on his perception was equivalent to the interval required for a person to alter perceptions based on his experience. The causal structure relating the two variables was also assumed to be stable over time. An additional assumption was required to distinguish between third variable effects, or spuriousness, and dual causation: if a third variable were to be causing the relationship, it would be operating at a relatively constant rate over time.

A strong causal relationship was found to be operating between beliefs about SOA and bus and use of those modes over time. The relationship is mutually causative; beliefs determine behavior and behavior reinforces and changes perceptions. Analysis of the carpool data indicated that the causal structure had changed over time and could not be analyzed with this technique. In general, support is evidenced for an adaptation or learning process interpretation of the relationship between beliefs and mode choice behavior.

Keywords

Transportation Measurement Error Causal Relationship Additional Assumption Variable Effect 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Elsevier Scientific Publishing Company 1979

Authors and Affiliations

  • Mary Lynn Tischer
    • 1
  • Robert V. Phillips
    • 1
  1. 1.Federal Highway Administration, U.S. Department of TransportationUrban Planning DivisionWashingtonU.S.A.

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