00818737

Component

https://doi.org/10.3141/1752-08

http://scholar.google.com...zawa&publication_year=2001

It is often the case that a discrete choice model cannot be applied to forecasting because the choice set is unmanageably large and choice probabilities cannot be evaluated in a practical manner. For example, an activity-based analysis of travel behavior often involves an astronomical number of potential activity travel patterns, resulting in an enormous choice set when one attempts to formulate the behavior as a discrete choice. A colossal choice set makes it practically impossible to define the full choice set and to evaluate the choice probability of each pattern for forecasting. An algorithm is presented for the simulation of individuals' activity travel choice by sampling activity travel patterns from a colossal choice set, according to their choice probabilities as determined by a discrete choice model without enumeration of the full choice set. Numerical examples demonstrate the practicality and effectiveness of the algorithm in forecasting the effects on activity travel patterns of transportation policy measures.

This paper appears in Transportation Research Record No. 1752, Travel Patterns and Behavior; Effects of Communications Technology.

English

p. 53-61

2001

Transportation Research Record

0309072131

References (16) ; Tables (7)

Activity choices; Algorithms; Forecasting; Markov chains; Monte Carlo method; Sampling; Simulation; Transportation policy; Travel behavior; Travel patterns

Discrete choice

Highways; Planning and Forecasting; Policy; Public Transportation; I72: Traffic and Transport Planning

TRIS, TRB

Oct 1 2001 12:00AM

• ASSESSING URBAN CONTEXT-INDUCED CHANGE IN INDIVIDUAL ACTIVITY TRAVEL PATTERNS: CASE STUDY OF NEW RAILWAY STATION
• ASSOCIATION RULES IN IDENTIFICATION OF SPATIAL-TEMPORAL PATTERNS IN MULTIDAY ACTIVITY DIARY DATA
• COEVOLUTIONARY APPROACH TO EXTRACTING AND PREDICTING LINKED SETS OF COMPLEX DECISION RULES FROM ACTIVITY DIARY DATA
• DIRECT FORECASTING OF FREEWAY CORRIDOR TRAVEL TIMES USING SPECTRAL BASIS NEURAL NETWORKS
• DRIVERS' ROUTE CHOICE RULES AND NETWORK BEHAVIOR: DO DRIVERS BECOME RATIONAL AND HOMOGENEOUS THROUGH LEARNING?
• DYNAMIC TRAFFIC ANALYSIS WITH STATIC DATA: GUIDELINES WITH APPLICATION TO PARIS
• ESTIMATING TIME-DEPENDENT ORIGIN-DESTINATION TRIP TABLES WITH TRIPS ORIGINATING IN MULTIPLE TIME SLICES
• EXPERIMENTS WITH COMPUTERIZED SELF-ADMINISTRATIVE ACTIVITY SURVEY
• INDUCTIVE LEARNING APPROACH TO EVOLUTIONARY DECISION PROCESSES IN ACTIVITY-SCHEDULING BEHAVIOR: THEORY AND NUMERICAL EXPERIMENTS
• MODELING AUTOMOBILE DEMAND AND DRIVER POPULATION IN PALESTINIAN TERRITORIES: SIMULTANEOUS-EQUATION ESTIMATION METHOD
• MODELING EFFECTS OF ANTICIPATED TIME PRESSURE ON EXECUTION OF ACTIVITY PROGRAMS
• MODELING ROUTE CHOICE BEHAVIOR WITH STOCHASTIC LEARNING AUTOMATA
• MULTILEVEL ANALYSIS OF ACTIVITY AND TRAVEL PATTERNS: ACCOUNTING FOR PERSON- AND HOUSEHOLD-SPECIFIC OBSERVED AND UNOBSERVED EFFECTS SIMULTANEOUSLY
• PATTERN RECOGNITION IN COMPLEX ACTIVITY TRAVEL PATTERNS: COMPARISON OF EUCLIDEAN DISTANCE, SIGNAL-PROCESSING THEORETICAL, AND MULTIDIMENSIONAL SEQUENCE ALIGNMENT METHODS
• REAL-TIME VARIABLE MESSAGE SIGN-BASED ROUTE GUIDANCE CONSISTENT WITH DRIVER BEHAVIOR
• STOCHASTIC USER EQUILIBRIUM FORMULATION FOR GENERALIZED NESTED LOGIT MODEL
• TELECOMMUTING IMPLICATIONS FOR TRAVEL BEHAVIOR: CASE STUDIES FROM MINNESOTA
• TIMETABLE-BASED TRANSIT ASSIGNMENT USING BRANCH AND BOUND TECHNIQUES
• TRAVEL BEHAVIOR IMPLICATIONS OF INFORMATION AND COMMUNICATIONS TECHNOLOGY IN PUGET SOUND REGION
• WITHIN-HOUSEHOLD ALLOCATION OF TRAVEL: CASE OF UPPER AUSTRIA