Microscopic activity sequence generation: a multiple correspondence analysis to explain travel behavior based on socio-demographic person attributes

• Published in: Transportation (Dordrecht) Vol. 48; no. 3; pp. 1481 - 1502
• Main Authors: Ahmed, Usman, Moreno, Ana Tsui, Moeckel, Rolf
• Format: Journal Article
• Language: English
• Published: New York, NY Springer US 01.06.2021; Springer Nature B.V
• Subjects: Activity sequence patterns; Activity sequencing; Activity-based modeling; Attributes; Correspondence analysis; Demographics; Economic Geography; Economics; Economics and Finance; Engineering Economics; Innovation/Technology Management; Logistics; Marketing; Multiple correspondence analysis; Organization; Probabilistic models; Regional/Spatial Science; Sociodemographics; Travel; Trip surveys; Activity sequence patterns; Activity-based modeling; Multiple correspondence analysis; Activity sequencing
• ISSN: 1572-9435; 0049-4488; 1572-9435
• DOI: 10.1007/s11116-020-10103-1

Activity sequencing is a crucial component of disaggregate modeling approaches. This paper presents a methodology to analyse and predict activity sequence patterns for persons based on their socio-demographic attributes. The model is developed using household travel survey data from Germany. The presented method proposes an efficient approach to replace complex activity-scheduling modules in activity-based models. First, the paper describes a multiple correspondence analysis technique to identify the correlation between activity sequence patterns and socio-demographic attributes. Secondly, a probabilistic model is developed, which could predict likely activity sequence patterns for an agent based on the results of the multiple correspondence analysis. The model is predicting activity sequence patterns fairly accurately. For example, the activity sequence pattern home–work–home is well predicted ( R 2 = 0.99) for all the workers, and the activity sequence pattern home–education–home is rather well predicted ( R 2 = 0.90) for students. The model predicts the 112 most common activity sequence patterns reasonably well, which covers 72% of all activity sequence patterns observed.