00965453

Component

https://doi.org/10.3141/1836-10

http://scholar.google.com...Rhee&publication_year=2003

Trip-production rates presented in cross-classification tables are essential data for the planner's understanding of the travel characteristics of a region. Trip rates obtained from surveys, however, often show a pattern that is not consistent with what is expected by the analyst; for example, the greater the household size and auto ownership, the greater the number of trips generated. This pattern may not be found in the trip rates that are obtained directly by the survey. In such cases, analysts commonly adjust the irregularities manually. The way in which the values are adjusted affects the credibility of the trip table and, ultimately, the forecast travel demand. A method that adjusts the values of the trip table systematically is presented. The process uses the fuzzy linear programming method. The objective is to make the adjusted value as close to the observed value as possible. The constraints are to make the adjusted values adhere to the analyst's general expectations about the pattern of the values in the table, and to match the number of trips estimated from the adjusted trip table with the actual number of trips surveyed. An application example that uses real-world data is given.

This paper appears in Transportation Research Record No. 1836, Initiatives in Information Technology and Geospatial Science for Transportation.

English

p. 76-82

2003

Transportation Research Record

0309085721

Figures (4) ; References (16) ; Tables (3)

Equipment maintenance; Forecasting; Travel demand; Trip tables

Equipment adjustment; Fuzzy optimization; Trip rates

Highways; Maintenance and Preservation; Planning and Forecasting; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Nov 7 2003 12:00AM

• APPLICATION OF LIGHT DETECTION AND RANGING TECHNOLOGY TO HIGHWAY SAFETY
• BILEVEL FORMULATION FOR OPTIMAL TRAFFIC-INFORMATION DISSEMINATION
• BUILDING AND MAINTAINING A STATEWIDE TRANSPORTATION FRAMEWORK
• COMMON DATA FORMAT ARCHIVING OF LARGE-SCALE INTELLIGENT TRANSPORTATION SYSTEMS DATA FOR EFFICIENT STORAGE, RETRIEVAL, AND PORTABILITY
• DESIGN OF ARCHIVAL TRAFFIC DATABASES: QUANTITATIVE INVESTIGATION INTO APPLICATION OF ADVANCED DATA MODELING CONCEPTS
• DEVELOPED INCIDENT DETECTION ALGORITHM COMPARED WITH NEURAL NETWORK ALGORITHMS
• EXPLORING IMPUTATION TECHNIQUES FOR MISSING DATA IN TRANSPORTATION MANAGEMENT SYSTEMS
• FUZZY-NEURAL NETWORK TRAFFIC PREDICTION FRAMEWORK WITH WAVELET DECOMPOSITION
• INTEGRATION OF LIGHT DETECTION AND RANGING TECHNOLOGY WITH PHOTOGRAMMETRY IN HIGHWAY LOCATION AND DESIGN
• INTEGRATION OF SPATIAL POINT FEATURES WITH LINEAR REFERENCING METHODS
• LEARNING A CAUSAL MODEL FROM HOUSEHOLD SURVEY DATA BY USING A BAYESIAN BELIEF NETWORK
• MODULAR ARTIFICIAL NEURAL NETWORKS FOR SOLVING THE INVERSE TRANSPORTATION PLANNING PROBLEM
• OPTIMIZATION OF DYNAMIC NEURAL NETWORK PERFORMANCE FOR SHORT-TERM TRAFFIC PREDICTION
• REAL-TIME COORDINATED SIGNAL CONTROL THROUGH USE OF AGENTS WITH ONLINE REINFORCEMENT LEARNING
• ROUGH SETS AND PROBABILITY MASSES FOR DEMPSTER-SHAFER DATA FUSION AT A TRAFFIC MANAGEMENT CENTER
• SPATIOTEMPORAL OBJECT DATABASE APPROACH TO DYNAMIC SEGMENTATION
• TOWARD MORE EFFECTIVE TRANSPORTATION APPLICATIONS OF COMPUTATIONAL INTELLIGENCE PARADIGMS
• USE OF LOCAL LINEAR REGRESSION MODEL FOR SHORT-TERM TRAFFIC FORECASTING