GRAVITY MODEL BASED ON PERCEIVED TRAVELTIMES
The purpose of this work is to show that while the minimum path travel time may be a good estimator of average travel time, it does not account for the perception differences between individual travellers. To demonstrate this, a strong statistical relationship (called the Minimum Path Deviation Function) between network-derived minimum path travel times and the corresponding disaggregate origin-destination survey data is developed. It is possible to predict the travel times perceived by various categories of the users of that system. The derivation of the MPDF from Los Angeles Regional Transportation Study (LARTS) data is described in some detail and a modification of the traditional gravity model is shown. Both the standard and modified model forms are tested using data from a subset of the LARTS area in northwest Orange County. Results of the testing showed that the MPDF gravity model performed slightly better than the standard gravity model.
-
Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/8674831
-
Corporate Authors:
American Society of Civil Engineers
345 East 47th Street
New York, NY United States 10017-2398 -
Authors:
- Bennett, J E
- Publication Date: 1980-1
Media Info
- Features: References;
- Pagination: p. 59-69
-
Serial:
- Journal of Transportation Engineering
- Volume: 106
- Issue Number: 1
- Publisher: American Society of Civil Engineers
- ISSN: 0733-947X
- Serial URL: https://ascelibrary.org/journal/jtepbs
Subject/Index Terms
- TRT Terms: Data collection; Forecasting; Gravity models; Highway transportation; Management; Mathematical models; Origin and destination; Routes; Surveys; Transportation; Travel demand; Travel time; Urban areas
- Old TRIS Terms: Route analysis
- Subject Areas: Administration and Management; Planning and Forecasting; Transportation (General);
Filing Info
- Accession Number: 00311154
- Record Type: Publication
- Source Agency: Engineering Index
- Files: TRIS
- Created Date: Jul 22 1980 12:00AM