Exploring Daily Rhythms of Interpersonal Contacts: Time-of-Day Dynamics of Human Interactions with Latent Class Cluster Analysis

Abstract

This study used activity-travel diary data to analyze the time-of-day dynamics of interpersonal contacts and examined their complex relationship with other activity-travel time allocation and personal accessibility dynamics. In total, 2,942 activity-travel diaries from 1,471 participants were used to identify five unique patterns of daily human interaction, with latent class analysis. Latent classes for the time-of-day dynamics of time allocation to activities and the time-of-day dynamics of experienced business employment density are also estimated independently and correlated with the five human interaction patterns. The analysis used a form of multinomial regression model (also called a three-step model in latent class analysis) to examine these relationships and to test the association of human interaction patterns and external explanatory variables simultaneously. Strong correlation was found between interpersonal contact patterns and activity participation patterns, along with day of the week, gender, and age. Other sociodemographic indicators and business employment density only partially explain these dynamics.

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References

Lynch

What Time Is This Place? MIT Press, Cambridge, Mass., 1972.

Chapin

F. S.

Human Activity Patterns in the City: Things People Do in Time and in Space. John Wiley and Sons, Hoboken, N.J., 1974.

Harvey

A. S.

Guidelines for Time Use Data Collection. Social Indicators Research, Vol. 30, No. 2–3, 1993, pp. 197–228. https://doi.org/10.1007/BF01078728.

Janelle

, and Goodchild

. Diurnal Patterns of Social Group Distributions in a Canadian City. Economic Geography, Vol. 59, No. 4, 1983, pp. 403–425. https://doi.org/10.2307/144166.

Goodchild

M. F.

, Klinkenberg

, and Janelle

D. G.

. A Factorial Model of Aggregate Spatio-Temporal Behavior: Application to the Diurnal Cycle. Geographical Analysis, Vol. 25, No. 4, 1993, pp. 277–294. https://doi.org/10.1111/j.1538-4632.1993.tb00299.x.

Weber

, and Kwan

M.-P.

. Bringing Time Back In: A Study on the Influence of Travel Time Variations and Facility Opening Hours on Individual Accessibility. Professional Geographer, Vol. 54, No. 2, 2002, pp. 226–240. https://doi.org/10.1111/0033-0124.00328.

Kim

H.-M.

, and Kwan

M.-P.

. Space-Time Accessibility Measures: A Geocomputational Algorithm with a Focus on the Feasible Opportunity Set and Possible Activity Duration. Journal of Geographical Systems, Vol. 5, No. 1, 2003, pp. 71–91. https://doi.org/10.1007/s101090300104.

Chen

, Ravulaparthy

, Deutsch

, Dalal

, Yoon

S. Y.

, Lei

, Goulias

K. G.

, Pendyala

R. M.

, Bhat

C. R.

, and Hu

H.-H.

. Development of Indicators of Opportunity-Based Accessibility. Transportation Research Record: Journal of the Transportation Research Board, No. 2255, 2011, pp. 58–68. https://doi.org/10.3141/2255-07.

Yoon

S. Y.

, Deutsch

, Chen

, and Goulias

K. G.

. Feasibility of Using Time-Space Prism to Represent Available Opportunities and Choice Sets for Destination Choice Models in the Context of Dynamic Urban Environments. Transportation, Vol. 39, No. 4, 2012, pp. 807–823. https://doi.org/10.1007/s11116-012-9407-8.

10.

Bhat

C. R.

, and Koppelman

F. S.

. Activity-Based Modeling of Travel Demand. In Handbook of Transportation Science ( Hall

R. W.

, ed.), Springer, 2003, pp. 35–61.

11.

Jones

P. M.

Developments in Dynamic and Activity-Based Approaches to Travel Analysis. Trove, Aldershot, England, 1990.

12.

Kitamura

An Evaluation of Activity-Based Travel Analysis. Transportation, Vol. 15, No. 1–2, 1988, pp. 9–34.

13.

Přibyl

, and Goulias

K. G.

. Simulation of Daily Activity Patterns Incorporating Interactions Within Households: Algorithm Overview and Performance. Transportation Research Record: Journal of the Transportation Research Board, No. 1926, 2005, pp. 135–141. https://dx-doi-org.web.bisu.edu.cn/10.3141/1926-16.

14.

Bhat

C. R.

, Goulias

K. G.

, Pendyala

R. M.

, Paleti

, Sidharthan

, Schmitt

, and Hu

H.-H.

. A Household-Level Activity Pattern Generation Model with an Application for Southern California. Transportation, Vol. 40, No. 5, 2013, pp. 1063–1086. https://doi.org/10.1007/s11116-013-9452-y.

15.

Bhat

C. R.

, and Pendyala

R. M.

. Modeling Intra-Household Interactions and Group Decision-Making. Transportation, Vol. 32, No. 5, 2005, pp. 443–448. https://doi.org/10.1007/s11116-005-6789-x.

16.

Dugundji

E. R.

, and Walker

J. L.

. Discrete Choice with Social and Spatial Network Interdependencies: An Empirical Example Using Mixed Generalized Extreme Value Models with Field and Panel Effects. Transportation Research Record: Journal of the Transportation Research Board, No. 1921, 2005, pp. 70–78. https://dx-doi-org.web.bisu.edu.cn/10.3141/1921-09.

17.

Carrasco

J. A.

, and Miller

E. J.

. Exploring the Propensity to Perform Social Activities: A Social Network Approach. Transportation, Vol. 33, No. 5, 2006, pp. 463–480. https://doi.org/10.1007/s11116-006-8074-z.

18.

Carrasco

J. A.

, Hogan

, Wellman

, and Miller

E. J.

. Collecting Social Network Data to Study Social Activity-Travel Behavior: An Egocentric Approach. Environment and Planning B: Planning and Design, Vol. 35, No. 6, 2008, pp. 961–980. https://doi.org/10.1068/b3317t.

19.

Habib

K. M. N.

, Carrasco

J. A.

, and Miller

E. J.

. Social Context of Activity Scheduling: Discrete-Continuous Model of Relationship Between “with Whom” and Episode Start Time and Duration. Transportation Research Record: Journal of the Transportation Research Board, No. 2076, 2008, pp. 81–87. https://doi.org/10.3141/2076-09.

20.

Arentze

, and Timmermans

. Social Networks, Social Interactions, and Activity-Travel Behavior: A Framework for Microsimulation. Environment and Planning B: Planning & Design, Vol. 35, No. 6, 2008, pp. 1012–1027. https://doi.org/10.1068/b3319t.

21.

Yoon

S. Y.

, Ravulaparthy

S. K.

, and Goulias

K. G.

. Dynamic Diurnal Social Taxonomy of Urban Environments Using Data from a Geocoded Time Use Activity-Travel Diary and Point-Based Business Establishment Inventory. Transportation Research Part A: Policy and Practice, Vol. 68, 2014, pp. 3–17. https://doi.org/10.1016/j.tra.2014.01.004.

22.

Sharmeen

, Arentze

, and Timmermans

. An Analysis of the Dynamics of Activity and Travel Needs in Response to Social Network Evolution and Life-Cycle Events: A Structural Equation Model. Transportation Research Part A: Policy and Practice, Vol. 59, 2014, pp. 159–171. https://doi.org/10.1016/j.tra.2013.11.006.

23.

Levinson

D. M.

Accessibility and the Journey to Work. Journal of Transport Geography, Vol. 6, No. 1, 1998, pp. 11–21. https://doi.org/10.1016/S0966-6923(97)00036-7.

24.

Handy

S. L.

Regional Versus Local Accessibility: Neo-Traditional Development and Its Implications for Non-Work Travel. Built Environment (1978–), Vol. 18, No. 4, 1992, pp. 253–267.

25.

Hägerstrand

What About People in Regional Science?

Papers in Regional Science, Vol. 24, No. 1, 1970, pp. 6–21. https://doi.org/10.1007/BF01936872.

26.

Kwan

M.-P.

Gender and Individual Access to Urban Opportunities: A Study Using Space-Time Measures. Professional Geographer, Vol. 51, No. 2, 1999, pp. 211–227. https://doi.org/10.1111/0033-0124.00158.

27.

Yoon

, and Goulias

. Impact of Time-Space Prism Accessibility on Time Use Behavior and Its Propagation Through Intra-Household Interaction. Transportation Letters, Vol. 2, No. 4, 2010, pp. 245–260. https://doi.org/10.3328/TL.2010.02.04.245-260.

28.

Patten

M. L.

, and Goulias

. Integrated Survey Design for a Household Activity-Travel Survey in Centre County, Pennsylvania. Presented at 83rd Annual Meeting of the Transportation Research Board, Washington, D.C., 2004.

29.

Goulias

K. G.

, and Kim

T.-G.

. An Analysis of Activity Type Classification and Issues Related to the with Whom and for Whom Questions of an Activity Diary. Elsevier, Oxford, UK, 2005. https://doi.org/10.1016/B978-008044581-6/50017-X.

30.

Goulias

K. G.

Travel Behavior Dynamics from a Lifespan Development Perspective. Presented at 12th International Conference on Travel Behavior Research, Jaipur, India, 2009.

31.

Goulias

K. G.

, and Yoon

S. Y.

. On the Relationship Among Travel Behavior, Time Use Investment and Expenditures in Social Networks. Presented at 16th HKSTS International Conference, Hong Kong, China, 2011.

32.

Lee

J. H.

, and Goulias

K. G.

. Examining Differences and Commonalities of Life Cycle Stages in Daily Contacts and Activity-Travel Time Allocation. Presented at 94th Annual Meeting of the Transportation Research Board, Washington, D.C., 2015.

33.

Goulias

K. G.

, and Henson

K. M.

. On Altruists and Egoists in Activity Participation and Travel: Who Are They and Do They Live Together? Transportation, Vol. 33, No. 5, 2006, pp. 447–462. https://doi.org/10.1007/s11116-006-8075-y.

34.

Allahviranloo

, and Recker

. Mining Activity Pattern Trajectories and Allocating Activities in the Network. Transportation, Vol. 42, No. 4, 2015, pp. 561–579. https://doi.org/10.1007/s11116-015-9602-5.

35.

Allahviranloo

, Regue

, and Recker

. Pattern Clustering and Activity Inference. Presented at 93rd Annual Meeting of the Transportation Research Board, Washington, D.C., 2014.

36.

Goulias

K. G.

Longitudinal Analysis of Activity and Travel Pattern Dynamics Using Generalized Mixed Markov Latent Class Models. Transportation Research Part B: Methodological, Vol. 33, No. 8, 1999, pp. 535–558. https://doi.org/10.1016/S0191-2615(99)00005-3.

37.

Joh

C.-H.

, Arentze

, Hofman

, and Timmermans

. Activity Pattern Similarity: A Multidimensional Sequence Alignment Method. Transportation Research Part B: Methodological, Vol. 36, No. 5, 2002, pp. 385–403. https://doi.org/10.1016/S0191-2615(01)00009-1.

38.

Liu

, Janssens

, Cui

, Wets

, and Cools

. Characterizing Activity Sequences Using Profile Hidden Markov Models. Expert Systems with Applications, Vol. 42, No. 13, 2015, pp. 5705–5722. https://doi.org/10.1016/j.eswa.2015.02.057.

39.

Wilson

Analysis of Travel Behavior Using Sequence Alignment Methods. Transportation Research Record: Journal of the Transportation Research Board, No. 1645, 1998, pp. 52–59. https://doi.org/10.3141/1645-07.

40.

Bakk

, Oberski

D. L.

, and Vermunt

J. K.

. Relating Latent Class Assignments to External Variables: Standard Errors for Correct Inference. Political Analysis, Vol. 22, No. 4, 2014, 520–540.

41.

Vermunt

J. K.

, and Magidson

. Technical Guide for Latent GOLD 5.0: Basic, Advanced, and Syntax. Statistical Innovations, Inc., Belmont, Mass., 2015.

42.

Morgan

G. B.

Mixed Mode Latent Class Analysis: An Examination of Fit Index Performance for Classification. Structural Equation Modeling, Vol. 22, No. 1, 2015, pp. 76–86. https://doi.org/10.1080/10705511.2014.935751.

43.

Nylund

K. L.

, Asparouhov

, and Muthén

B. O.

. Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study. Structural Equation Modeling, Vol. 14, No. 4, 2007, pp. 535–569. https://doi.org/10.1080/10705510701575396.

44.

Chen

, Ma

, Susilo

, Liu

, and Wang

. The Promises of Big Data and Small Data for Travel Behavior (aka Human Mobility) Analysis. Transportation Research Part C: Emerging Technologies, Vol. 68, 2016, pp. 285–299. https://doi.org/10.1016/j.trc.2016.04.005.

45.

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.