Ye Hong, Henry Martin, Yanan Xin, Dominik Bucher, Daniel J Reck, Kay W Axhausen, and Martin Raubal
Quantifying intra-person variability in travel choices is essential for the comprehension of activity–travel behavior. Due to a lack of empirical studies, there is limited understanding of how an individual’s travel pattern evolves over months and years. We use two high-resolution user-labelled datasets consisting of billions of GPS track points from ~3800 individuals to analyze individuals’ activity–travel behavior over the long term. The general movement patterns of the considered population are characterized using mobility indicators. Despite the differences in the mobility patterns, we find that individuals from both datasets maintain a conserved quantity in the number of essential travel mode and activity location combinations over time, resulting from a balance between exploring new choice combinations and exploiting existing options. A typical individual maintains ~15 mode–location combinations, of which ~7 are travelled with a private vehicle every 5 weeks. The dynamics of this stability reveal that the exploration speed of locations is faster than the one for travel modes, and they can both be well modelled using a power-law fit that slows down over time. Our findings enrich the understanding of the long-term intra-person variability in activity–travel behavior and open new possibilities for designing mobility simulation models.
Transportation Research Part C: Emerging Technologies, 146, 103979, 2023-01-01.