Modeling the Dynamics of Passenger Travel Demand by Using Structural Equations
Bibliographic Data
| ID | 4863025 |
|---|---|
| Authors | Thomas F Golob (University of California, Irvine), H Meurs (Bureau Goudappel Coffeng, Parkweg 4, 74115H Deventer, The Netherlands) |
| Year | 1988 |
| Volume | 20 |
| Issue | 9 |
| Pages | 1197-1218 |
| Publication date | 1988-09-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environment and Planning A Economy and Space (JOURNAL) |
| Journal identifiers | ISSN: 0308-518X • E-ISSN: 1472-3409 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1068/a201197 |
| OpenAlex | W2049177150 |
| Language | EN |
| Citations received | 6 |
| References cited | 23 |
Levels of demand over time are analyzed for five modes of passenger transportation. The data are for the modes: car driver, car passenger, train, bicycle, and public transit. These data are compiled from week-long travel diaries collected at six-month intervals from a nationwide panel in the Netherlands. Three types of empirical relationships are present in these panel data: (1) autocorrelative relationships, capturing temporal stability in demand for the same mode at different points in time; (2) contemporaneous relationships, capturing complementarity and competition among different modes at the same point in time; and (3) cross-lagged effects, potentially capturing systematic shifts in demand. Simultaneous equation systems are used to test the temporal stability of demand for each mode and the stationarity of the contemporaneous relationships among the modes. The dynamic structure both of trip rates and of travel times are modeled successfully according to several goodness-of-fit indices. The equation systems capture nonstationarity in the contemporaneous relationships, as well as important cross-lagged effects. These results quantify changes in the structure of demand over time in the Netherlands and are shown to be directly related to the event of a public transit fare increase
Econometrics · Economics · Goodness of fit · Panel data · Public transport · Statistics · Structural equation modeling · Transport engineering · Computer Science · Engineering · Human Mobility and Location-Based Analysis · Mathematics · Transportation Planning and Optimization · Urban Transport and Accessibility
Introduction to structural equation models
Structural Equation Models in the Social Sciences
Some Contributions to Maximum Likelihood Factor Analysis
Biases in response over time in a seven-day travel diary
Causal Inference from Panel Data
Maximum-Likelihood Estimation in Panel Studies with Missing Data
Significance tests and goodness of fit in the analysis of covariance structures
An Analysis of "Bias" in Survey Research
Techniques for estimating the source and direction of causal influence in panel data
More plausible rival hypotheses in the cross-lagged panel correlation technique
A critique of cross-lagged correlation
Education, Occupation, and Earnings
Structural Equation Models
| Unique citing works | 6 |
|---|---|
| Citations per year | 0.16 |
| Citation span | 1989 - 2014 (26) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 6 |