How routine is a routine? : An analysis of the day-to-day variability in prism vertex location

By Ryuichi Kitamura, Toshiyuki Yamamoto, Yusak O. Susilo, Kay W. Axhausen. 2006

48 citations recorded by the source corpus.

This study is concerned with how routine an individual’s routine really can be. This question is addressed by examining the day-to-day variability of the time co-ordinate of the vertex of a time–space prism; in other words, by examining how the timeframe which governs the individual’s daily schedule varies from day to day. When the timeframe varies, it is likely that the individual’s behavior also varies. When the timeframe is stable, on the other hand, a routine can be maintained. The analysis presented in this paper attempts to determine how much of the variation in travel is due to the variation in the timeframe. The origin vertices of workers’ morning prisms, which determine how early they can leave home in the morning, are examined in this study, along with the departure times of the first trips in the prisms, which are mostly supposedly routine commute trips. The results indicate that the vertices are located with a much smaller variance, but vary more systematically than do the departure times of the first trips in the prisms. This implies that a large degree of variability is introduced when a trip is made within the timeframe as determined by a prism vertex. It is also shown that the departure time varies from worker to worker according to unobserved heterogeneity—i.e., unexplained differences across individuals—much more than does the prism vertex. The study results indicate that large degrees of flexibility are associated with trip making, and suggest the presence of room for behavioral modification with respect to workers’ first trips in the morning.

Subfields: Automotive Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/3fcb2b0f411d65c0222e4e6b75b0c8e9de678275

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Xiaolei Ma
2. Yinhai Wang
3. Kay W. Axhausen

In Transportation

1. Yusak O. Susilo
2. Xiaolei Ma
3. Yao-Jan Wu
4. Yinhai Wang
5. Kay W. Axhausen
6. J. De Vos

Citations in the corpus

• Mining smart card data for transit riders’ travel patterns (2013, background): “Kitamura et al. (2006) and Morency et al. (2006, 2007) utilized multiple day smart card data to analyze transit riders’ travel variability and pointed out that developing a better understanding of travel variability can help reduce operational costs and manage demand.”
• Repetitions in individual daily activity–travel–location patterns: a study using the Herfindahl–Hirschman Index (2014, background): “Presumably, this is because, although the time frame (prism vertex) of the daily departure time of respondents has a relatively small variance, the real departure time of the morning trip tends to vary from day-to-day and is highly influenced by the respondents heterogeneity (Kitamura et al. 2006).”