00818746

Component

https://doi.org/10.3141/1752-17

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

A new approach for extracting and predicting decision rules for linked choices is developed and explored for use in computational process models of activity-scheduling and activity-profiling decisions. This coevolutionary approach, represented by a new agent, coevolutionary heuristic algorithm for deriving rules from N-dimensional travel activity links (Chantal), has been implemented in the ALBATROSS model. The algorithm induces sets of decision rules by incorporating the observed choice for all other facets as conditions in the decision table formalism. In the predictive stage, probabilities of activities are iteratively updated until a predefined convergence level is reached. The performance of the coevolutionary approach is compared with independent and sequential modeling approaches. The coevolutionary model turns out never to perform worse and often performs better than the best alternative approach under various conditions.

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

English

p. 126-132

2001

Transportation Research Record

0309072131

References (16) ; Tables (4)

Activity choices; Algorithms; Decision making; Heuristic methods; Travel diaries

ALBATROSS (Computer model)

Activity scheduling; Coevolutionary model; Linked choices

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

TRIS, TRB, ATRI

Oct 2 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
• 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
• SAMPLING ALTERNATIVES FROM COLOSSAL CHOICE SET: APPLICATION OF MARKOV CHAIN MONTE CARLO ALGORITHM
• 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