The effect of workplace relocation on individuals’ activity travel behavior

Abstract

On working days, homes and workplaces can be seen as anchor locations at the heart of daily mobility patterns, as well as being central to an employee’s activity pattern. In this study, we investigate how workplace relocation affects the entire daily activity-travel chain. While past research has shown that workplace decentralization is often associated with higher car use for the commuting trip, little is known about the effect on the whole activity travel pattern. Two waves of a two-week travel diary were completed by 43 employees of the University of Luxembourg: one before and one after the relocation of their office. Using descriptive statistics as well as standard deviational ellipses (SDE) theory combined with the results of a clustering analysis showed that workers’ activity spaces (represented by the standard deviational ellipses) were significantly modified due to the relocation of a single anchor activity location, i.e., their workplace.

Workplace location, polycentricism, and car commuting

“…In particular, a strong center-periphery gradient has been found for office workplaces. In a wider international context, similar patterns have been found in Melbourne (Bell, 1991); the San Francisco Bay area (Cervero & Landis, 1992); London and other large British cities (Dasgupta, 1994); the Dutch Randstadt area (Schwanen, Dieleman, & Diest, 2001); Atlanta and Boston (Yang, 2005); the Paris region (Aguiléra, Wenglenski, & and Proulhac, 2009;Aguiléra & Voisin, 2014), Chicago metropolitan area (Hu & Schneider, 2017); Luxemburg (Sprumont & Viti, 2018) and Lisbon (Vale, 2013;Vale et al, 2018).…”

“…Some studies have investigated effects of workplace relocation by comparing average modal shares and commuting distances among a company's employees before and after relocation (Monsen, 1983;Bell, 1991;Hanssen, 1995), or by analyzing aggregate-scale (e.g., metropolitan) changes over time in job locations and commuting behavior (e.g., Gordon, Kumar, & Richardson, 1989;Gordon & Lee, 2015). Quite few earlier studies have investigated changes in commuting behavior among individuals who have recently changed their workplace location (Cervero & Landis, 1992;Hanssen, 1995;Vale, 2013;Sprumont & Viti, 2018). To our knowledge, no previous studies of influences of workplace location on commuting have included qualitative interviewing.…”

Two-stage algorithms for visually exploring spatio-temporal clustering of avian influenza virus outbreaks in poultry farms

“…Although there is no universally feasible method to estimate the direction of transmission, the use of SDE to visualize the geographical distribution of a series of social, biological, or environmental events remains attractive 31 – 34 . In this study, SDE is applied to individual spatial clusters, defined by the Knox method, to reveal its local transmission by week.…”

A review of Machine Learning (ML) algorithms used for modeling travel mode choice

“…De Oña et al [58] used a CA method to assess passenger heterogeneity, where the CA method stratified the sample of passengers into clusters with similar features and therefore into clusters of homogeneous perceptions concerning the service. Other authors [76] used a CA to analyze the effect of workplace relocation on an individual's travel behavior and activity.…”