PREDICTING MULTI-FACETED ACTIVITY-TRAVEL ADJUSTMENT STRATEGIES IN RESPONSE TO POSSIBLE CONGESTION PRICING SCENARIOS USING AN INTERNET-BASED STATED ADAPTATION EXPERIMENT
planning - route design, ridership - mode choice, ridership - elasticity, ridership - commuting, ridership - commuting, ridership - demand, policy - congestion, economics - pricing, mode - mass transit, mode - bike
Work trips, Travel patterns, Travel models (Travel demand), Travel demand, Travel behavior, Transportation policy, Transit, Telecommuting, Stated preferences, Stated adaptation, Route selection, Route choice, Public transit, Policy analysis, Mode choice, Modal shift, Modal choice, Mathematical prediction, Mass transit, Local transit, Journey to work, Internet, Estimating, Elasticity (Economics), Departure time, Congestion pricing, Choice of transportation, Choice models, Bicycle usage, Bicycle travel, Activity choices
This study seeks to explain the effects of various congestion pricing policies by considering comprehensive activity-travel patterns. An agent for an activity-based model called Albatross model system is developed and implemented, which allows for the modeling of adaptation to transportation policies. The discrete choice models were estimated on data obtained in a stated adaptation experiment that was administered through the Internet and designed to examine how individuals adjust their activity-travel patterns in response to congestion pricing. An activity-based approach is used, meaning that all choice facets of activity patterns are taken into account as well as a complete set of activities. Estimates of price elasticities of travel demand are in line with other findings from the literature. Results of the stated adaptation experiment suggest that route change and departure time adjustment are the most important ways of adapting work trips. Changing to public transportation and working at home to reduce car trips have a smaller probability. Route change and switching to bike travel are the dominant responses in the case of non-work trips. The results also show that both the suggested adaptation experiment and the estimation are feasible. A similar approach could also be used to predict the effects of other specific policies.
Arentze, T, Hofman, F, Timmermans, H. (2004). PREDICTING MULTI-FACETED ACTIVITY-TRAVEL ADJUSTMENT STRATEGIES IN RESPONSE TO POSSIBLE CONGESTION PRICING SCENARIOS USING AN INTERNET-BASED STATED ADAPTATION EXPERIMENT. Transport Policy, Volume 11, Issue 1, p. 31-41.