01516085

Component

With the goal of developing procedures for predicting activity/travel patterns of individuals given their socio-demographic characteristics, the authors cluster individuals based on their activity patterns using a two-stage clustering technique to infer activity time windows. The two-stage technique is a combination of affinity propagation and K-means clustering methods. Activity patterns are created by segmenting daily activities into ten-minute intervals, carrying information about activity types, duration, schedule and travel distance. The authors test different combinations of two error measures: sequential alignment and agenda dissimilarity to compute the distance between each pair of patterns. In order to analyze the effectiveness of clustering on inferring activity patterns, the authors further test the prediction accuracy for two population, clustered and un-clustered. The results indicate that updating activity time windows based on the arrival time distribution of the clustered data, has higher accuracy than using those distributions with un-clustered data.

This paper was sponsored by TRB committee ADB40 Transportation Demand Forecasting.

01503729

14-1274

English

16p

2014

Transportation Research Board 93rd Annual Meeting

Digital/other

Figures; References; Tables

Activity choices; Cluster analysis; Data collection; Time duration; Time windows; Travel patterns; Trip length

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

Transportation Research Board Annual Meeting 2014 Paper #14-1274

TRIS, TRB, ATRI

Jan 27 2014 2:29PM