We present a systematic method for generating activity-driven, multi-day alternative activity patterns that form choice sets for stated preference surveys. An activity pattern consists of information about an individual’s activity agenda, travel modes between activity episodes, and the location and duration of each episode. The proposed method adjusts an individual’s observed activity pattern using a hill-climbing algorithm, an iterative algorithm that finds local optima, to search for the best response to hypothetical system changes. The multi-day approach allows for flexibility to reschedule activities on different days and thus presents a more complete view of demand for activity participation, as these demands are rarely confined to a single day in reality. As a proof-of-concept, we apply the method to a multi-day activity-travel survey in Singapore and consider the hypothetical implementation of an on-demand autonomous vehicles service. The demonstration shows promising results, with the algorithm exhibiting overall desirable behavior with reasonable responses. In addition to representing the individual’s direct response, the use of observed patterns also reveals the propagation of impacts, that is, indirect effects, across the multi-day activity pattern.


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    Title :

    Alternative Activity Pattern Generation for Stated Preference Surveys


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2018-07-07




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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