Examination of the Impacts of Telecommuting on the Time Use of Nonmandatory Activities

How and to what extent telecommuting engagement affects time allocation among nonmandatory activities are examined to help understand the impacts of telecommuting on daily activity–travel patterns. Five categories of nonmandatory activities are considered: shopping, maintenance, discretionary, escort, and in-home shopping. The hypothesis is that telecommuting relaxes the temporal and spatial constraints related to work activities at the regular workplace, and telecommuters may allocate some of the time budget to other nonmandatory activities, which may or may not lead to additional travel. The structural equations model approach is applied to capture the impacts of telecommuting as well as the interactions among the nonmandatory activities. The activity durations by type along with the number of total daily trips are considered as endogenous (dependent) variables. By incorporating work hours at the regular workplace and daily telecommuting hours as exogenous variables, the models can reveal how people may reallocate their time among different nonmandatory activities given different levels of telecommuting engagement (either part-day or full-day). All types of telecommuting arrangements increased nonmandatory activity durations (compared with those of nontelecommuters). Full-day telecommuters have higher durations of discretionary activities, while part-day telecommuters have higher durations of maintenance and out-of-home shopping errands. Telecommuting also increased total daily trip rates for both telecommuters and their household members. This study used data obtained from the 2010–2011 Regional Household Travel Survey in the New York metropolitan region.

[1]  A. Koutsoyiannis,et al.  Theory of econometrics;: An introductory exposition of econometric methods , 1973 .

[2]  Upali Vandebona,et al.  A model for analysis of impacts of telecommuting on network travel time , 2007 .

[3]  van den P.E.W. Berg,et al.  A path analysis of social networks, ICT use and social activity-travel patterns in the Netherlands , 2010 .

[4]  P. Mokhtarian Telecommunications and Travel: The Case for Complementarity , 2002 .

[5]  Rick H. Hoyle,et al.  Handbook of structural equation modeling , 2012 .

[6]  Thomas F. Golob,et al.  A STRUCTURAL MODEL OF TEMPORAL CHANGE IN MULTI-MODAL TRAVEL DEMAND , 1987 .

[7]  S T Vu Analysis of impacts of telecommuting for reduction of environmental pollution , 2007 .

[8]  H. M. Zhang A mathematical theory of traffic hysteresis , 1999 .

[9]  Hamidreza Asgari,et al.  Toward a Comprehensive Telecommuting Analysis Framework , 2015 .

[10]  Dawn Iacobucci,et al.  Structural Equations Modeling: Fit Indices, Sample Size, and Advanced Topics , 2010 .

[11]  M. McNally,et al.  A MODEL OF ACTIVITY PARTICIPATION AND TRAVEL INTERACTIONS BETWEEN HOUSEHOLD HEADS , 1996 .

[12]  Glenn Lyons,et al.  An examination of determinants influencing the desire for and frequency of part-day and whole-day homeworking , 2009 .

[13]  E. I. Pas,et al.  Socio-demographics, activity participation and travel behavior , 1999 .

[14]  John Niles,et al.  Beyond telecommuting: A new paradigm for the effect of telecommunications on travel , 1994 .

[15]  R. Pendyala,et al.  A structural equations analysis of commuters' activity and travel patterns , 2001 .

[16]  John DiNardo,et al.  Econometric methods. 4th ed. , 1997 .

[17]  G. Judge,et al.  The Theory and Practice of Econometrics (2nd ed.). , 1986 .

[18]  T. Brijs,et al.  Assessing the Impacts of a Teleworking Policy on Crash Occurrence: The Case of Flanders, Belgium , 2013 .

[19]  Andreas Ritter,et al.  Structural Equations With Latent Variables , 2016 .

[20]  Thomas F. Golob,et al.  A Model of Activity Participation Between Household Heads , 1997 .

[21]  Patricia L. Mokhtarian,et al.  A Synthetic Approach to Estimating the Impacts of Telecommuting on Travel , 1997 .

[22]  Tron Foss,et al.  The Performance of ML, GLS, and WLS Estimation in Structural Equation Modeling Under Conditions of Misspecification and Nonnormality , 2000 .

[23]  Frank Douma,et al.  Telecommuting Implications for Travel Behavior: Case Studies from Minnesota , 2001 .

[24]  P. Bentler,et al.  Fit indices in covariance structure modeling : Sensitivity to underparameterized model misspecification , 1998 .

[25]  P. Mokhtarian,et al.  PLANNING FOR TELECOMMUTING: MEASUREMENT AND POLICY ISSUES , 1995 .

[26]  Pengyu Zhu,et al.  Telecommuting, travel behavior and residential location choice: Can telecommuting be an effective policy to reduce travel demand? , 2011 .

[27]  John W. Polak,et al.  Travel and Activity Participation as Influenced by Car Availability and Use , 1995 .

[28]  R. Kitamura,et al.  Impact of telecommuting on spatial and temporal patterns of household travel , 1991 .

[29]  Juan Zhicai,et al.  Activity-Travel Behavior Analysis Based on Structural Equation Model , 2009, 2009 International Conference on Electronic Commerce and Business Intelligence.

[30]  U. Vandebona,et al.  Evaluation of impacts of telecommuting in traffic assignment , 2008 .

[31]  Caspar G. Chorus,et al.  Information, communication, travel behavior and accessibility , 2013 .

[32]  I Salomon,et al.  EMERGING TRAVEL PATTERNS: DO TELECOMMUNICATIONS MAKE A DIFFERENCE? IN: IN PERPETUAL MOTION: TRAVEL BEHAVIOR RESEARCH OPPORTUNITIES AND APPLICATION CHALLENGES , 2002 .

[33]  Mei-Po Kwan,et al.  The impact of the Internet on human activity–travel patterns: analysis of gender differences using multi-group structural equation models , 2009 .

[34]  N. Ohmori,et al.  Exploring the Impacts of In-Home Virtual Activities on Daily Activity and Travel Behaviour : An Analysis Using Cairo Activity and Telecommunication Diary Data 2006 , 2010 .