A Pedestrian Destination-Chain Choice Model from Bayesian Estimation of Pedestrian Activities using Sensors Data

Pedestrian modeling is emerging as a tool for designing new infrastructures and optimizing the use of current ones. Given sensor traces, we are interested in developing a dynamic model that can predict the destination chain of an individual in pedestrian facilities. As a first step, we developed a methodology to collect activity-episodes sequences from scarce data, directly modeling the imprecision in the measure. It generates several candidate lists of activity-episodes sequences associated with a corresponding likelihood.


Presented at:
2nd Symposium of the European Association for Research in Transportation (hEART 2013), Stockholm, Sweden, September 05, 2013
Year:
2013
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 Record created 2013-09-05, last modified 2018-03-17

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