Using Longitudinal Mobile Phone Data to Understand the Stability of Individual Travel Patterns

Abstract

Most travel demand models assume that individuals’ daily travel patterns are stable or follow a fixed routine. This hypothesis is being questioned by more and more researchers. In this study, longitudinal mobile phone data were used to study the stability of individual daily travel patterns from three aspects, including activity space, activity points, and daily trip-chain patterns. The activity space was represented by the number of nonhome activity points, the radius of nonhome activity points, and the distance from home. The visitation pattern of activity points was analyzed by entropy and predictability measures. The stability of trip-chain patterns was described by the number of distinct trip chains, the typical trip chain, and the typical trip-chain ratio. Analysis of 21 days of mobile phone data from three communities in Shanghai, China, revealed that individuals’ daily travel patterns showed considerable variation. Although individuals’ visitation patterns to activity points were very regular, the day-to-day variations of individual trip-chain patterns were quite significant. On average, an individual exhibited about eight types of daily trip chains during the 21-day period. The daily travel patterns of residents in the outskirts were more stable than those of residents in the city center. Individuals’ travel patterns on weekdays were more complex than those on weekends. As individuals’ activity spaces increased, the stability of their travel patterns decreased.

Get full access to this article

View all access options for this article.

References

Hanson

, and Huff

J. O.

. Assessing Day-to-Day Variability in Complex Travel Patterns. Transportation Research Record, No. 891, 1981, pp. 18–24.

Schönfelder

Urban Rhythms: Modelling the Rhythms of Individual Travel Behaviour. PhD dissertation. University of Dortmund, Germany, 2006.

Huff

J. O.

, and Hanson

. Repetition and Variability in Urban Travel. Geographical Analysis, Vol. 18, No. 2, 1986, pp. 97–114. https://doi.org/10.1111/j.1538-4632.1986.tb00085.x.

Jones

, and Clarke

. The Significance and Measurement of Variability in Travel Behaviour. Transportation, Vol. 15, No. 1–2, 1988, pp. 65–87. https://dx-doi-org.web.bisu.edu.cn/https://doi.org/10.1007/BF00167981.

Susilo

Y. O.

The Short-Term Variability and the Long-term Changes of Individual Spatial Behavior in Urban Areas. PhD dissertation. Kyoto University, Japan, 2005.

Marble

D. F.

, and Bowlby

S. R.

. Shopping Alternatives and Recurrent Travel Patterns. In Geographic Studies of Urban Transportation and Network Analysis ( Horton

F. E.

, ed.), Northwestern University Press, Evanston, Ill., 1968, pp. 42–75.

Shapcott

, and Steadman

. Rhythms of Urban Activity. In Human Activity and Time Geography ( Carlstein

, Parkes

, and Thrift

, eds.), John Wiley, New York, 1978, pp. 49–74.

Shen

, Chai

, and Guo

. Day-to-Day Variability in Activity-Travel Behavior Based on GPS Data: A Case Study in Suburbs of Beijing (in Chinese). Geographical Research, Vol. 32, No. 4, 2013, pp. 701–710.

Calabrese

, Colonna

, Lovisolo

, Parata

, and Ratti

. Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome. IEEE Transactions on Intelligent Transportation Systems, Vol. 12, No. 1, 2011, pp. 141–151. https://dx-doi-org.web.bisu.edu.cn/https://doi.org/10.1109/TITS.2010.2074196.

10.

White

, and Wells

. Extracting Origin Destination Information from Mobile Phone Data. In Proceedings of 11th International Conference on Road Transport Information and Control, 2002, pp. 30–34. https://doi.org/10.1049/cp:20020200.

11.

Iqbal

M. S.

, Choudhury

C. F.

, Wang

, and González

M. C.

. Development of Origin-Destination Matrices Using Mobile Phone Call Data. Transportation Research Part C: Emerging Technologies, Vol. 40, 2014, pp. 63–74. https://doi.org/10.1016/j.trc.2014.01.002.

12.

Liu

, Janssens

, Cui

, Wang

, Wets

, and Cools

. Building a Validation Measure for Activity-Based Transportation Models Based on Mobile Phone Data. Expert Systems with Applications, Vol. 41, No. 14, 2014, pp. 6174–6189. https://doi.org/10.1016/j.eswa.2014.03.054.

13.

Duan

, Liu

, and Wang

. MobilePulse: Dynamic Profiling of Land Use Pattern and OD Matrix Estimation from 10 Million Individual Cell Phone Records in Shanghai. In Proceedings of 19th International Conference on Geoinformatics, 2011, pp. 1–6. https://doi.org/10.1109/GeoInformatics.2011.5980928.

14.

Chen

, Bian

, and Ma

. From Traces to Trajectories: How Well Can We Guess Activity Locations from Mobile Phone Traces? Transportation Research Part C: Emerging Technologies, Vol. 46, 2014, pp. 326–337. https://doi.org/10.1016/j.trc.2014.07.001.

15.

González

M. C.

, Hidalgo

C. A.

, and Barabási

A. L.

. Understanding Individual Human Mobility Patterns. Nature, Vol. 453, No. 7196, 2008, pp. 779–782. https://doi.org/10.1038/nature06958.

16.

Song

, Qu

, Blumm

, and Barabási

A. L.

. Limits of Predictability in Human Mobility. Science, Vol. 327, No. 5968, 2010, pp. 1018–1021. https://doi.org/10.1126/science.1177170.

17.

, Bengtsson

, and Holme

. Predictability of Population Displacement After the 2010 Haiti Earthquake. In Proceedings of the National Academy of Sciences of the United States of America, Vol. 109, No. 29, 2012, pp. 11576–11581. https://doi.org/10.1073/pnas.1203882109.

18.

Cho

, Myers

S. A.

, and Leskovec

. Friendship and Mobility: User Movement in Location-Based Social Networks. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011, pp. 1082–1090. https://doi.org/10.1145/2020408.2020579.

19.

Schneider

C. M.

, Belik

, Couronné

, Smoreda

, and González

M. C.

. Unravelling Daily Human Mobility Motifs. Journal of the Royal Society, Interface, Vol. 10, No. 84, 2013. https://doi.org/10.1098/rsif.2013.0246.

20.

Jiang

Jr. , Ferreira

, and González

M. C.

. Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore. IEEE Transactions on Big Data, Vol. 3, No. 2, 2017, pp. 208–219. https://doi.org/10.1109/TBDATA.2016.2631141.

21.

, Cheng

, Li

, and Yang

. Rail Transit User Classification Using Mobile Phone Data: A Case Study of Three Communities in Shanghai, China. Presented at 95th Annual Meeting of the Transportation Research Board, Washington, D.C., 2016.

22.

, Cheng

, Duan

, Yang

, and Guo

. A Framework for Spatial Interaction Analysis Based on Large-Scale Mobile Phone Data. Computational Intelligence and Neuroscience, 2014. https://doi.org/10.1155/2014/363502.

23.

Cheng

Research on Travel Characteristics and Classification of Urban Residents Based on Mobile Phone Data (in Chinese). PhD dissertation. Tongji University, China, 2015.

24.

Cheng

, Li

, Jia

, Yang

, and Duan

. Analyzing Human Activity Patterns Using Cellular Phone Data: Case Study of Jinhe New Town in Shanghai, China. Presented at 92nd Annual Meeting of the Transportation Research Board, Washington, D.C., 2013.

25.

Fano

R. M.

Transmission of Information. MIT Press and Wiley, New York, 1961. https://doi.org/10.1063/1.3057290.