key: cord-0881559-hqcmnxeo
authors: Wang, Kailai; Ozbilen, Basar
title: Synergistic and threshold effects of telework and residential location choice on travel time allocation
date: 2020-09-01
journal: Sustain Cities Soc
DOI: 10.1016/j.scs.2020.102468
sha: de5e707252b364fa2ab48b7610b6c395fbee5a77
doc_id: 881559
cord_uid: hqcmnxeo

Much of the literature shows a great interest in debating whether telework has a complementary or substitution effect on people’s travel demand. Relatively fewer studies analyze the modification effect of telework on a person’s daily activity-travel patterns. This study adopts a novel analytical approach to explore the influences of the duration of telework on sustainable travel. The empirical study builds upon a smartphone-based GPS travel survey conducted in the Puget Sound Region of Washington State. The merit of this research is twofold. We first investigate the threshold effects of the duration of telework and built environment characteristics on the shares of travel time spent riding public transit and engaging in active travel. The results can directly inform telework and land use policies. Then, we examine the synergistic effects of the duration of telework and the built environment on both travel outcomes. The findings suggest well-designed telework provisions could complement compact development policies aimed at shifting from automobile dependency to sustainable travel.

provides tax incentives to employers who encourage their employees to telecommute (Acitelli, 2019) . More recently, the COVID-19 (also known as coronavirus) disruption is demonstrating that telework is a valuable tool for maintaining physical distance that makes communities healthier and more resilient in times of disasters (e.g., Belzunegui-Eraso and Erro-Garcés, 2020).

A better understanding of the impacts of telework arrangements on an individual's activity-travel patterns is crucial for planning practices and policy responses.

J o u r n a l P r e -p r o o f have been well documented (Ewing and Cervero, 2010; Handy et al., 2002; Stevens, 2017) , little is known on how the time spent working online may change the relationships between the built environment and travel behaviors. In history, many telecommuting programs and land use aim to reduce auto dependency as well as the associated negative consequences. We expect that there exists a synergistic effect of the built environment and telework arrangements on promoting sustainable travel.

Encouraging the use of public transport, walking and biking is a tangible solution to a series of social, economic, and environmental problems caused by over-reliance on driving (e.g., Banister, 2008; Gudmundsson et al., 2016; Litman, 2019) . In this study, we treat the proportions of time spent riding public transit and engaging in active travel among the total amount of time spent traveling as sustainable travel outcomes. This study contributes to the literature by revealing the marginal contribution of the average time spent teleworking per day on an individual's travel time spent riding public transit and engaging in active travel. We further explore how such effects vary across different spatial contexts. A gradient boosting decision trees (GBDT) method is adopted to analyze the travel diary data from the 2017 Puget Sound Regional Travel Survey (PSRTS). We offer a detailed and insightful exploration of how the amount of time spent teleworking affect travel time spent on sustainable alternatives. The research findings offer nuanced guidance to the advocates of telework programs and sustainable mobility. Furthermore, the COVID-19 pandemic would change people's habits and out-of-home activity participation behavior to a certain level. Some employees may become more productive when working from home than working at the office, and thus tend to telework longer hours when the quarantine is lifted. Due to the above-mentioned reasons, we believe that an J o u r n a l P r e -p r o o f investigation of the effects of duration of teleworking on activity-travel patterns will be of interest to both the scientific community and policy makers in the post COVID-19 era.

According to the classical time geography theory, an individual's activity-travel patterns can be constrained to the spatial and temporal restrictions related to mandatory activities, as well as the travel budget constraints (Chapin, 1974; Hägerstrand, 1970) . The workplace locations and schedules are the major anchor points of an employee's activity-travel patterns. Teleworking helps workers remove such spatial and temporal constraints. As an alternative work arrangement, teleworking provides the freedom of managing daily activity patterns based on individual needs. Workers tend to have higher job-related positive affective well-being if they adopt teleworking as compared to working in the office (Anderson et al., 2014) . Telework is also found to have beneficial effects on improving work-life balance and reducing work-family conflict (Gajendran and Harrison, 2007) .

The initial goal of creating telecommuting programs is to reduce travel demand and ease peak-hour traffic congestion (Nilles, 1994) . Earlier research findings supported the view that advocating telecommuting programs can lead to substantial transport-related benefits (Hamer et al., 1991; Pendyala et al., 1991; Salomon, 1998) . Later, the possibly complementary effects of telecommuting had been gradually recognized (Mokhtarian et al., 1995; Mokhtarian, 1998 colleagues designed a series of empirical studies to identify the impacts of telecommuting on individuals' travel patterns from multiple aspects. Zhu (2012) investigated the influences of telecommuting on workers' one-way commute trips, and daily work and non-work trips. He found that telecommuting increases total travel distance (for both commuting and noncommuting trips), and therefore inferred telecommuting and personal travel are indeed complements, instead of substitutes. In line with this, Zhu (2013) extended his research by studying the effects of telecommuting status of one worker on his or her partner. The analytical results show that, at the household level, the presence of a regularly telecommuting employee increase both commute distance and duration significantly. However, this study did not find any significant relationships among household members regarding their telecommuting choice, and regular commute distance and duration. Zhu et al. (2018) examined the heterogeneous effects of telecommuting across different MSA (Metropolitan Statistical Areas) sizes. The results reveal that telecommuting policies have positive influences on both commute distance and duration, regardless of the size of MSAs. These studies plot a big picture illustrating the positive relationship between telecommuting and the overall vehicle travel demand. Although previous studies using data from the pilot programs have limitations related to the narrow scope and sample selection bias, they provide sound information based on the specific research scopes.

The connections between telecommuting and transportation can generate from four paths (Melo and Silva, 2017; Mokhtarian, 1990; 2002; Salomon, 1986) : 1) substitution if the usage of telecommuting results in a reduced need for travel ; 2) complementary if the use of telecommuting eventually increases travel demand; 3) modification if the use of telecommuting changes an employee's activity-travel patterns, such as transport mode choices and departure J o u r n a l P r e -p r o o f times, and 4) neutrality if there is no impact on actual travel demand. As discussed above, the debate on whether telecommuting exposes a complementary or substitution effect has been extensively revisited. Relatively fewer studies attempt to explore the modification effect of telework on an individual's daily activity-travel patterns. found that different types of telework arrangements have different impacts on increasing active travel and reducing traffic congestion. As compared to working at the workplace, working from home only is found to be associated with less overall travel time by an average of 13 minutes. Chakrabarti (2018) analyzed the 2009 NHTS data and found that telecommuting frequency can significantly promote active travel and physical activity. Individuals who telecommute more than four times per month are more likely to make at least one transit trip per month compared to nontelecommuters. For telecommuters, there exists a significant reduction in driving on telework days. However, the annual driving distance for telecommuters is usually longer than that for nontelecommuters (Chakrabarti, 2018) . Research findings in North America support that, if J o u r n a l P r e -p r o o f implemented appropriately, telecommuting programs can positively influence the process of shifting away from automobile dependency to sustainable alternatives.

Teleworkers are likely to generate new travel if the conventional needs could be satisfied at home. The net effect is a modification of existing travel patterns at both individual-level and system-wide (Salomon, 1985) . In light of all above, there is an imperative to carry out a detailed analysis of the amount of time spent teleworking on sustainable transport alternatives. This study contributes twofold to the literature by taking the advantages of the recently developed machine learning algorithms. First, it provides useful information regarding the degree to which the duration of telework will influence an individual's travel time spent on sustainable transport modes. Revealing the thresholds also reflect how an employee will allocate total travel time to other travel modes, particularly to automobiles, when his or her daily time spent teleworking changes. Second, this study presents the synergistic effects of the spatial context and the duration of telework on travel time spent riding public transit and engaging in active travel. The impact of telecommuting policy may differ when the amount of time spent teleworking falls into different ranges of the variable and different spatial contexts. The thresholds identified in this study offer crucial information for planners and decision-makers aimed at promoting the effectiveness of telework provisions.

This study develops the research dataset using the 2017 Puget Sound Regional Travel Survey (PSRTS). The survey collected household-and personal-level activity and travel pattern J o u r n a l P r e -p r o o f information from the Puget Sound Regional Council (PSRC)'s four-county region, namely, King, Pierce, Snohomish, and Kitsap counties (see Figure 1 ). The survey recorded participants' information about mode choices, time spent on travels, trip purposes, and some other trip features on multiple days. Puget Sound region is considered as an attractive area for young and tech-friendly people, as well as a relatively more transit-friendly built environment than other US metropolitan areas (Puget Sound Regional Council, 2019). The focus of this research is to analyze the effects of the duration of telework on sustainable travel, and how such effects vary across different spatial contexts. interest. The total number of valid participants is 3233. Individuals who do not have recorded times spent by different transport modes (i.e., auto, public transit, and active travel) were also removed from our analysis as they cannot be used for exploring relative travel time.

We measure the built environment at each participant's place of residence following the classical transportation planning literature (Ewing and Cervero, 2010) . We link the survey data with the Environmental Protection Agency (EPA)'s Smart Location Database (SLD) 1 using geocoded household locations. A Census Block Group (CBG) is selected as the spatial unit, which presents a reasonable scale to understand the neighborhood level spatial effects on near home activities. Table 2 presents the percentile distribution of continuous variables used in this study, namely, time spent online, built environment characteristics, and travel outcomes.

The bivariate relationships between our variables of interest are presented in Table 3 . The duration of telework is statistically significant and positively associated with the share of travel time spent engaging in active travel. However, this factor is a negative predictor of the share of travel time spent riding public transit. All built environment features are significantly associated with two sustainable travel outcomes and follow the expected signs. The scales of the estimated coefficients suggest that the effects of spatial context around the residences tend to have a stronger influence on travel time spent on active travel compared to public transportation. For multivariate analysis, we first estimate linear regression models for riding public transit and engaging in active travel separately (as shown in Appendix A). Indeed, the pre-defined linearity assumption can lead to the estimates of multivariate analyses become severely biased. First, an explanatory variable can be an effective predictor of one outcome variable within a certain range; however, it may not influence the outcome variable significantly when it does not fall within such a range. Second, both the relationships shown in Table 3 and regression-based estimates neglect the existence of multiple confounding effects among explanatory variables (Ding et al., 2018a) . We follow with a novel machine learning approach.

This study adopts a gradient boosting decision trees (GBDT) model to explore the threshold effects of the duration of telework on the shares of travel time spent in sustainable travel (1)

The output in Step m (0<m≤M) is (Friedman, 2001) :

where ξ is the learning rate, and J is the number of regions partitioned by a decision tree. is the value of optimal gradient for the region that makes the current function ( ) obtain the smallest loss. I = 1 if ∈ and I = 0 otherwise. For a single decision tree T, a measurement to approximate the relative contribution of an explanatory variable in predicting the response has been well documented (Breiman et al., 1984):

where ̂2 is the improvement in squared error term when an explanatory variable is the splitting variable that refers to v(t) in the equation (3). For a series of decision trees, the relative contribution of a specific explanatory variable is obtained by averaging the information from all trees. The GBDT algorithm estimate the marginal effects of this explanatory variable following the same logic (Hastie et al., 2009; Friedman, 2001 ).

This study builds the GBDT model using the "gbm" package in R. The marginal effects (i.e., partial dependence) at different points are computed in "pdp" package. Two GBDT models are developed for the shares of travel time spent riding public transit and engaging in active travel. For each model, the predictors include all the variables listed in Table 4 . Three crucial parameters need to be predetermined. We fix the shrinkage or learning rate as 0.001 to make a balance between predictive variance and computing time ( Table 4 summarizes the relative contributions of independent variables in predicting travel time allocated to two forms of sustainable transport outcomes. Not surprisingly, the duration of telework is a significant predictor of both the shares of travel time spent riding public transit and engaging in active travel. The relative contributions of this factor are 6.2% and 7.8%, respectively. These relative contributions show that the effect of teleworking on travel time spent on sustainable travel is much larger than that of online shopping. Our descriptive statistics suggest that, an individual's average time spent teleworking is about six times as much as that of online shopping. Thus, duration of telework exacts a stronger modification effect on people's daily activities than that of online shopping. The findings regarding the impacts of teleworking on sustainable travel respond to the previous evidence that travel time savings due to teleworking can influence both transit rides and active travel (Chakrabarti, 2018) . sustainable travel modes will not change substantially.

As expected, population density has an overall positive relationship with the shares of travel time spent riding public transit and engaging in active travel (as shown in Figure 3 ). The effective range is between 5,000 and 30,000 people per square mile. Beyond this range, the relationships tend to be stable. We further find that the difference in the shares of total travel time spent in active travel could reach around 0.1 between residents living in low-density neighborhoods and those in high-density neighborhoods. Consistent with the bivariate statistics, as population density increases, engagement in active travel has a larger magnitude of the increment than that of riding public transit. Employment density has a small, negative, and almost linear relationship with travel time spent riding public transit (as shown in Figure 3 Figure 4 illustrates that, compared to the density variables, the margins of land use entropy index to travel outcomes are more erratic and less effective. The growth of the number of pedestrian-oriented intersections tend to increase the shares of travel time spent riding public transit and engaging in active travel (as shown in Figure 4) . Specifically, the difference in the shares of travel time spent engaging in active travel reaches approximately 0.05 between individuals who reside in neighborhoods with pedestrian-oriented street design and those living in communities with more auto-oriented street network. A similar relationship is observed for the share of travel time spent riding public transit. Somewhat surprisingly, as shown in Figure 5 , this study finds that the frequency of transit service during the evening peak period has a stronger effect on the share of travel time spent engaging in active travel than that of riding public transit. If an individual moves from a place with very low levels of peak-hour transit service frequency to a neighborhood with high levels of peak-hour transit service frequency, the share of his or her travel time spent engaging in active travel can increase by more than 0.1. This value is about 0.04 for travel time spent riding public transit. The larger influence of public transit supply on the relative travel time spent in active travel may be due to the fact that completing access/egress trips between transit stops and J o u r n a l P r e -p r o o f home/activity destinations involves non-motorized trips (i.e., bicycling, walking, etc.). Future studies are encouraged to explore the underlying reasons for the observed differences.

As explained above, the effects of built environment characteristics around the residences and the duration of telework are not simple, and most of them do not seem to correlate with the shares of travel time spent riding public transit and engaging in active travel linearly. Planning practices should be cautious about the thresholds related to local contexts. Another goal of this study is to explore the spatially varying effects of the duration of telework on travel time use patterns. Figures 6 and 7 present the changing trends of these effects, instead of predicting the exact numbers.

Generally, if an individual spends more time on teleworking, he or she will allocate less time to riding public transit, regardless of the choice of residential location (as shown in Figure   6 ). The result is somewhat interesting given the fact that the built environment around the residences collectively carries about 42% on the variation of the share of travel time spent riding J o u r n a l P r e -p r o o f public transit. The duration of telework contributes to about 6%, which is much less than the contribution of each built environment attribute. It can be inferred that the duration of telework has a more obvious effect on the share of travel time spent riding public transit than built environment factors. We speculate that this might be because the existence of multiple confounding effects from other factors representing incomes and vehicle ownership, and sociodemographics. In addition, the data analyzed in this study does not provide information regarding residential location preferences. These attitudinal factors may largely explain both the predictive power of the built environment and travel outcomes. In turn, implementing telework and compact development programs together would not be a straightforward path to encourage more public transit rides.

As shown in Figure 7 , the built environment around the residences has a more visible effect on changing the relationship between the duration of telework and the share of travel time spent engaging in active travel. People who spend more time on teleworking and live in neighborhoods with higher residential and employment densities, well-connected street networks, and sufficient public transit supplies are found to use active transport modes more than the others. Also, it is worth noting that promoting land use diversity does not modify the relationship between the duration of telework and the share of travel time spent engaging in active travel as much as the changes in other built environment characteristics. We argue that teleworking alone is not an adequate policy tool to encourage more transit trips.

People who study the impacts of teleworking on sustainable travel can utilize our findings, particularly those regarding active travel engagement. respectively. The difference shows that daily physical activity is more sensitive to the changes in the built environment characteristics around the residences as compared to riding public transit.

The relatively larger effect of built environment on active travel than public transit is consistent with the previous studies (Chakrabarti, 2018; Shin, 2019) . Admittedly, built environment characteristics affecting active travel are different than those affecting transit use (Ewing and Cervero, 2010) . Since the latter group includes variables that are household level rather than CBG level, such as proximity of places of residences to transit stops, and the number of stations per unit area. These variables are not available in our dataset, and they can explain the relatively weaker association between the built environment and public transit rides.

Furthermore, the verified synergistic effects between the built environment and the duration of telework on the shares of travel time spent engaging in active travel offer insightful implications to planners and decision-makers. Beyond reducing peak-hour traffic congestion and vehicle emissions, our results imply that telework programs complement compact development policies aimed at shifting from automobile dependency to sustainable travel. A possible explanation is that teleworkers have a relatively higher predisposition towards walking and bicycling, and they tend to engage in physical activities near home more often. Implementing appropriate telework arrangements should be adopted as a promising practice among transportation planners and professionals if they are pursuing a long-term cultural shift regarding vehicle ownership and usage.

Many people are supposed to travel less after the COVID-19 pandemic is over. They are very likely to avoid public transport and engage in active travel more frequently (recreationally or in case of short distances) (e.g., De Vos, 2020). It is crucial to know that exploring pathways to support active travel can potentially benefit physical health and enhances subjective wellbeing. In this study, the estimated threshold effects of built environment characteristics, at least, offer nuanced guidance to local authorities on specific short-term planning efforts.

This study finds that residential location choice can be a powerful predictor of an individual's travel time spent on sustainable travel. However, individuals may choose residential locations based on the predisposition towards specific modes or activities. People who place a high value on walking and bicycling are more likely to choose to live in compact development neighborhoods. This is referred to as residential self-selection in the literature (Cao et al., 2009 ).

We acknowledge the preexisting attitudes and preferences can largely influence an individual's decision to travel. Without controlling for the self-selection bias, we cannot predict the true effects of the built environment and the related synergistic effects accurately. Overall, the findings of this study provide concrete evidence to the advocates of sustainable mobility, indicating that telework programs can complement those land-use policies designed for reducing automobile dependency and encouraging active travel.

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The authors received no financial support for the research, authorship, and/or publication of this article. Table A1 presents the results of multiple linear regressions. Based on the size of standardized coefficients, it is feasible to rank the relative importance of all the independent variables.

However, the pre-defined linearity assumption can lead to the estimates of multiple linear regressions become severely biased. Multicollinearity may be a concern for the regression models. Moreover, the relationship between an independent variable and the dependent variable may be varying along the entire range of the independent variable. Estimating a multiple regression model is meaningful since it be regarded as complements to the tree-based ensemble approach by offering some statistical inferences. 

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