Travel-time ratios for visits to the workplace: the relationship between commuting time and work duration

Transportation Research Part A 36 (2002) 573–592 www.elsevier.com/locate/tra

Travel-time ratios for visits to the workplace: the relationship between commuting time and work duration Tim Schwanen *, Martin Dijst Faculty of Geographical Sciences, Utrecht University, Urban Research Centre Utrecht (URU), NETHUR, P.O. Box 80.115, 3508 TC Utrecht, Netherlands Received 9 October 2000; received in revised form 19 March 2001; accepted 5 April 2001

Abstract For a better understanding of commuting behavior, the home-to-work journey has to be addressed in the context of daily time use. Although many studies have analyzed commuting times, the influence of the time spent working on the home-to-work travel time has only been investigated indirectly. This paper uses the travel-time ratio concept to investigate the association between work duration and commuting. We describe the theoretical framework of the travel-time ratio and analyze realized travel-time ratios for work activities with data from the 1998 Dutch National Travel Survey. It is shown that workers, on average, spend 10.5% of the time available for work and travel on commuting, which corresponds to 28 min (single trip) for an 8h workday. The travel-time ratio varies systematically with sociodemographic variables; urban form is of rather limited importance in the explanation of travel-time ratio values. Ó 2002 Elsevier Science Ltd. All rights reserved. Keywords: Activity-based analysis; Time use; Travel-time ratio; Commuting; Multilevel models; The Netherlands

1. Introduction Commuting has long been of central interest to transportation researchers and geographers. Attention has regularly focused on the presumed similarity and stability of commuting patterns. Cross-sectional empirical evidence from over the world suggests that average home-to-work trips mostly range from 25 to 35 min (Kenworthy and Laube, 1999). Others, however, have shown that

*

Corresponding author. Tel.: +31-30-253-22-24; fax: +31-30-254-04-06. E-mail addresses: [email protected] (T. Schwanen), [email protected] (M. Dijst).

0965-8564/02/$ - see front matter Ó 2002 Elsevier Science Ltd. All rights reserved. PII: S 0 9 6 5 - 8 5 6 4 ( 0 1 ) 0 0 0 2 3 - 4

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Table 1 The relationship between work duration and commuting time: findings from the literature Travel time required to access 1 h of working (min)a Golob and McNally (1997) Golob et al. (1995) Golob (2000) Lu and Pas (1999) Dijst and Vidakovic (2000) a

2.8 3.5 2.8 2.4 6.5

Data refer to single home-to-work trips only.

average commuting times vary either among metropolitan areas within countries (e.g. Gordon et al., 1989) or among different moments in time (e.g. Cervero and Wu, 1998). But generally speaking, differences in average commuting time – be it over time or in space – are not as large as differences in other commuting characteristics, such as distance traveled and mode choice (Levinson, 1998). Yet, large differences in commuting times can be observed at the level of the individual worker. Variations are caused by personal and household attributes, the spatial context of the commute, access to transportation and factors related to the activity and travel patterns of workers (e.g. Turner and Niemeier, 1997). Although many studies have analyzed commuting times, the influence of the time spent at the workplace on commuting time has only been investigated indirectly. As shown in Table 1, these studies differ considerably in the time needed to access 1 h of working time. Differences in the definition of work activities, sample design, and data handling are some of the reasons for this variation. As far as we know, the extent to which workers consider commuting time relative to work duration has not yet been analyzed thoroughly. In this paper we will try to fill this gap in several ways. First, we propose a theoretical framework to address the relationship between commuting time and duration of the workplace visit. Second, we empirically investigate to what extent workers balance commuting time and the duration of workplace visits. Our contention is that individuals engage in a balancing process in which commuting time and work duration are traded off against each other. Although the outcome of this balancing process may show a considerable degree of variation, we expect the differences to be smaller than the variation in mere commuting times. Further, we investigate how this balancing process varies systematically with person and household-related characteristics, urban form, and features of the travel/activity context of the commute. Building on previous research, we use the concept of the travel-time ratio for the present purpose. This ratio is defined as the total travel time (round trip) spent to visit an activity place divided by the sum of the total travel time to and activity duration at that destination (Dijst and Vidakovic, 2000). The following section discusses the theoretical framework of the travel-time ratio and its implications for commuting; section 3 describes the data and how we handled them. Section 4 gives a general description of travel-time ratios for visits to the workplace, while Section 5 presents a number of bivariate disaggregate analyses and Section 6 describes a multilevel regression analysis. In the Section 7, we formulate some conclusions, raise some issues for further research, and discuss some aspects relevant for policy making.

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2. Theory 2.1. The relationship between activity duration and travel time Because travel demand is derived from the need to pursue activities distributed in space and time, decisions about travel time and activity duration can be assumed to be interdependent. Indeed, a number of researchers have suggested or proven that travel time and activity duration are positively correlated (Hamed and Mannering, 1993; Kitamura et al., 1990, 1998; Levinson, 1999). The interdependence between activity duration and travel time reflects that individuals aim to meet two basic needs – sufficient time to engage in activities and larger choice sets of activity places (Dijst and Vidakovic, 2000). Numerous authors have assumed that the utility of an activity increases with activity duration (e.g. Becker, 1965; Kraan, 1996), and persons can thus be assumed to maximize participation in activities. On the other hand, the chance of finding a more attractive location increases, when individuals travel farther (Kitamura et al., 1997; Kraan, 1997). However, individuals are subject to time–space constraints, and spending additional time to reach a destination incurs the risk of not having sufficient time left for activity participation. Thus, individuals have to trade-off travel time and activity time (Dijst and Vidakovic, 2000; Thill and Horowitz, 1997). To address the association between activity duration and travel time, Dijst and Vidakovic (2000) proposed the travel-time ratio concept. They assume that an individual first trades off travel and activity duration in a cognitive process, which results in a planned travel-time ratio. This process is a heuristic search, the outcome of which may not be optimal, but falls within certain tolerance margins (cf. Simon, 1955). If individuals arrive at what they perceive of as a satisfactory travel-time ratio, they will carry out the visit(s) to the activity place. The planned outcome may, however, be distorted by unexpected events. As a consequence, realized travel-time ratios may differ from planned ones (for a more detailed discussion, see Dijst and Vidakovic, 2000). The assumption that individuals balance travel time and activity duration is reflected in the definition of the travel-time ratio. It is defined as the ratio between travel time and the sum of travel time and activity duration (Dijst and Vidakovic, 2000): Tt ; ð1Þ s¼ Tt þ Ta where s stands for the travel-time ratio, Tt indicates the travel time (round trip) and Ta denotes the activity duration. The duration of an activity and travel time are taken together in the denominator, since individuals make decisions about staying at or traveling to activity places within a given time budget. Additionally, an obvious advantage of the present definition is that values fall between zero and one, allowing for relatively easy interpretation. 2.2. Travel-time ratio for working Following the argument above, we assume that within certain time–space constraints, commuting time tends to increase with the work duration. More specifically, when making decisions about the separation between the home and work location, households determine the maximum acceptable commuting time for an individual worker in the household relative to the time the worker spends on working. Thus, when a worker is looking for a new home, the boundary of the

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search space from a given job location is dependent on the duration of the work duration. The result of this will be a certain degree of similarity in realized travel-time ratios for work activities. Yet, the variability in travel-time ratios for working may be expected to be larger than for nonwork activities for a number of reasons. First, job and residential location choice are impacted by a large variety of factors, many of which are related with a worker’s orientation towards his/her household and leisure (Salomon and Mokhtarian, 1997); decisions regarding commuting time are only partly motivated by work duration. Second, the degree of control workers have over their work situation and the strength of constraints at the workplace may differ among them. For example, some workers have rigid work hours which prohibits them to adjust work duration or timing of the commute. Third, workers may attach different meanings to commuting. Women especially seem to use commuting as a buffer between the different roles of being an employee and taking care of household members (Pazy et al., 1996; Blumen, 2000). Apart from interpersonal variability, a significant degree of intrapersonal variability in travel-time ratios for working can be expected. Day-to-day variation has been reported in various aspects of commuting behavior, such as trip-chaining propensity (Bhat, 1999), and route and mode choice (Mahmassani et al., 1997). As a consequence, realized travel-time ratios may vary from day to day for the same individual. The variability in travel-time ratios is also affected by the opportunities a worker has to adapt his or her situation. If the outcome of the balancing process has not been satisfying for some time, a worker can use several strategies to change the situation. Options are (cf. Salomon and Mokhtarian, 1997): 1. Economize on travel time by choosing an alternative transportation mode or adding non-work activities to the commute more often. 2. Accept long travel times, but use the time spent traveling for other purposes (e.g. work while seated in a train). 3. Change the duration and/or timing of workplace visits. 4. Change the workplace or the residential location. 5. Quit working. Commuters will not consider every strategy, as some may not be preferred and others will simply not be available. Different population segments will face different opportunities and apply different strategies to change unfavorable commuting conditions (Salomon and Mokhtarian, 1997; Raney et al., 2000). This may amplify the variation in travel-time ratios. Despite all the factors that may cause variation in the empirically observed travel-time ratios, we hypothesize that travel-time ratios for work activities are relatively similar to each other. Because of its stronger behavioral base, the variation in travel-time ratios should be smaller than the variation in commuting times. These hypotheses will be investigated in the empirical part of this paper. 3. Data 3.1. National travel survey For the analyses of travel-time ratios, we use the 1998 Dutch National Travel Survey (NTS). Implemented in 1978, this survey is a continuous inquiry into the travel behavior of households.

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Every year, approximately 70,000 households are asked to participate in the survey. It yields data on the travel behavior of some 130,000 individuals. In addition to information on household and personal attributes, respondents are asked to complete a trip diary for an entire day, a section of which requires filling in the starting and ending times of each trip. Thus, the travel times in the NTS dataset are self-reported door-to-door travel times (Central Bureau for Statistics, 1998). Because of the expected day-to-day variability, travel-time ratios should, ideally, be investigated with a data set comprising at least several working days. The NTS data, however, only represent a respondent’s travel pattern on one day. This makes it impossible to demonstrate workers’ balancing of commuting time and work duration on a day-to-day basis. Another drawback to the NTS dataset is that some important information is not recorded. As a consequence, the effects of occupation type cannot be directly investigated. The major advantages of the data, however, are the large sample size, and the fact that it covers all spatial environments to be found in Netherlands. Additionally, a broad range of households and individuals are represented. 3.2. Data handling and variable descriptions In the case of direct home-to-work commutes (no non-work activities conducted en route), the travel-time ratio is calculated by doubling the home-to-work travel time, divided by the sum of time spent at the workplace and twice the home-to-work travel time (Dijst and Vidakovic, 2000). For calculating the travel-time ratio in the case of trip-chains on the way to work, the time required for traveling directly between the home and work location needs to be known. This can be determined by computing the network distance between the two locations with a GIS and relating this to the speed of the longest trip in the trip-chain, so as to take into account the effects of congestion. The data, however, are too aggregated spatially for accurate estimates of the travel distance between and within zones. As a consequence, home-to-work distances in trip-chains cannot be established, and the results presented here refer to direct home-to-work commutes only. For the present study, data on 19,957 direct home-to-work journeys from the 1998 NTS are used to determine travel-time ratios for stops at the workplace. Only visits to workplaces made by the main breadwinner and his or her partner (if present) are used in the analysis, because their work patterns are thought to play the most important role in the total organization of the household’s activities (Damm, 1979). Since the NTS collects no information about on-site activity participation, a work activity is assumed to start at the moment the respondent’s work trip ends, and to last until he or she returns home. The NTS data do not allow us to determine whether the work activity takes place at a fixed address (a workplace whose location in space is the same for longer periods of time). However, it may be assumed that most commuting trips end at a regular workplace. We have only included the most probable observations (90%) in our analysis; the shortest and longest visits to workplaces (5% each) are left out of consideration. These observations most likely contain many occasional, short stops at the workplace as well as a number of exceptional visits with a long duration. In addition, a large part of the 5% longest observations seem to originate from wrongly administered trip diaries. Tests to assess whether this decision causes selection bias in our results have indicated that results do not change much, and that the relationships among variables are not altered.

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A typology of households has been devised. Households with working heads of households are classified along three main dimensions: presence of children (<12 yr) in the household, the employment status of the single or both wage earners (the head of the household and his or her partner), and household size (Table 2). Employment status is subdivided into two categories: working full-time ( P 30 h per week), or part-time (between 12 and 30 h per week). Individuals within household are also classified along the dimensions of income, education and gender. To analyze the effects of urban form characteristics on travel-time ratios, the present study uses a classification of Dutch municipalities based on Van der Laan (1998) (Fig. 1). Van der Laan demarcated Daily Urban Systems (DUSs) or Standard Metropolitan Statistical Areas (SMSAs) in Netherlands on the basis of commuter sheds. Within DUSs, core cities, suburban municipalities and (former) growth centers are discerned. Core cities are the major cities that used to attract lots of commuters residing in the surrounding suburbs. Although DUSs have increasingly become policentric, core cities still accommodate a large proportion of jobs within the local labor market. Growth centers are considered as the centerpiece of the national spatial planning policy of the 1970s and are the Dutch equivalent to the New Towns, which were developed in various European countries in the 1960s and 1970s. They were intended as suitable locations for firms and households suburbanizing from the larger cities, but evolved into dormitory towns (Van der Laan, 1998), whose inhabitants exhibit travel patterns that deviate from other municipality types (Schwanen et al., 2001). As a consequence, they are considered as a separate residential environment category here. All municipalities in the non-metropolitan areas outside DUSs are called open space municipalities. Table 2 Household classification Household type

Description

One full-time worker One part-time worker Two-earner couple

Single person, working P 30 h per week Single person, working <30 h per week Two-person household with head and partner working P 30 h per week head working P 30 h and partner working <30 h Two-person household with a working head and a non-working partner both head and partner working <30 h per week Two-or-more-person household with at least one child (<12 yr) and a head working P 30 h; no partner present Two-or-more-person household with at least one child (<12 yr) and a head working <30 h; no partner present Three-or-more-person household with head and partner and at least one child (<12 yr) with head and partner working P 30 h per week head working P 30 h and partner working <30 h Three-or-more-person household with heads and partner and at least one child (<12 yr) with a working head and a non-working partner both head and partner working <30 h per week Households with non-family members, households with only children of P 12 yr and senior households

Traditional couple

One-parent family with full-time working head One-parent family with part-time working head Two-earner family

Traditional family

Other household

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Fig. 1. Residential environments within Netherlands.

4. General results The mean and median travel-time ratios for visits to the workplace are 0.105 and 0.085, respectively. The difference between the two values reflects the sensitivity of the mean to the fact that a certain number of people face high travel-time values. For most people, however, total travel time is less than 10% of work and travel time together. The mean of 0.105 corresponds to a commuting time (single trip) of 3.5 min per hour spent working or, alternatively, 28 min for an 8-h workday, which is very much comparable to the result by Golob et al. (1995) (Table 1). Use of the overall mean conceals the variation in realized travel-time ratios, which can be visualized in a scatterplot with work duration and travel-time ratio on the axes (Fig. 2). The

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Fig. 2. Variation in travel-time ratios by work duration.

diagram shows that the variation in ratios is lower when the workplace visits last longer. In the range between 7 and 9 h spent working, where most observations are concentrated, the variation is rather small, but still considerable. Yet, a comparison of the variation in travel-time ratios with that in commuting times shows that the former is smaller (Fig. 3). The histogram for the former is more peaked and its coefficient of variation (the S.D. divided by the mean) is smaller for the travel-time ratio than for commuting. This indicates perhaps that individuals do not treat travel

Fig. 3. Histograms of travel-time ratio and commuting time.

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time as an independent phenomenon, but as an integral part of an activity episode that is related to activity duration. The extent to which realized travel-time ratios and commuting times vary with work duration has also been scrutinized. Fig. 4 shows mean travel-time ratios and corresponding average hometo-work commuting times per 30 min work duration intervals. The diagram can be divided into three parts: 1. Up to work durations of 4 h, commuting time is constant and the travel-time ratio is declining, which may indicate that, up to a certain threshold, commuters are indifferent to commuting time. 2. From 4 to 8 h, the travel-time ratio tends to be stable and commuting time rises monotonically with work duration. 3. For workplace visits of 8 h or more, both travel-time ratio and commuting time tend to decrease with work duration. These results indicate that commuting time is positively related to work duration within certain time–space constraints. These constraints, however, become more binding as the activity episode (work and commuting together) consumes too much time. In that case individuals tend to economize on commuting time rather than work duration. Physical capability constraints (H€ agerstrand, 1970) may come into play in such a situation: commuting extensively after a long workday is too tiring. In addition, individuals may economize on commuting time to save time to pursue non-work activities. A regression model has been estimated to determine how the travel-time ratio is related to the duration of the stop at the workplace. Preliminary analysis showed that an inverse polynomial function is preferable

Fig. 4. Mean travel-time ratio and commuting time by work duration.

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s¼

1 ; aW  bW 2 þ cW

ð2Þ

3

where s indicates the travel-time ratio, W the work duration (in h) and a, b and c are parameters to be estimated. Estimation yields values of 0.036 (20.0) for a, 0.635 (37.3) for b and 3.997 (59.7) for c (t-statistics in parentheses). Graphic display of the function shows a curve similar to the one depicted for average travel-time ratios in Fig. 4. But the R2 of the estimated Eq. (2) is merely 0.083, mainly because of the variation in travel-time ratios. In summary, this section has revealed that average commuting time tends to rise with work duration until time–space constraints become more binding, and that the variation in travel-time ratios is smaller than in commuting times, although it is still considerable. In subsequent sections, we explicitly address this variation and investigate to what extent the travel-time ratio varies systematically with sociodemographic and urban form variables.

5. Disaggregate analysis of travel-time ratios 5.1. Personal and household characteristics This subsection examines how travel-time ratios vary with personal and household attributes. Table 3 shows that the variation in travel-time ratios across household types is rather small. Only single part-time workers and one-parent families with full-time workers show values that are clearly deviating from the overall mean. t-tests on the means for each household type support the exceptional position of single part-time workers (Table 4). Differences between the single-parent families and other household types, however, are less often significant, suggesting that the values in Table 3 can, to a large degree, be attributed to the small number of observations in these categories. Furthermore, the t-tests show that two-earner couples have significantly lower mean travel-time ratios than two-earner families and traditional couples have. Although travel-time ratios for various household types tend to be similar, Table 4 indicates that subtle differences across categories exist. Table 3 Travel-time ratio by household type Mean

S.D.

Median

One full-time worker One part-time worker Two-earner couple Traditional couple One-parent family with full-time worker One-parent family with part-time worker Two-earner family Traditional family Other household

0.103 0.129 0.103 0.110 0.080 0.101 0.108 0.106 0.103

0.081 0.092 0.076 0.083 0.057 0.099 0.078 0.076 0.076

0.082 0.108 0.083 0.088 0.064 0.067 0.089 0.087 0.081

Total

0.105

0.078

0.085

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Table 4 Results of t-tests on pairwise differences of mean travel-time ratios by household type 1 2 3 4 5

One full-time worker One part-time worker Two-earner couple Traditional couple One-parent family with full-time worker One-parent family with part-time worker Two-earner family Traditional family Other household

6 7 8 9 *

1

2

3

4

)4.42 0.32 )2.55 1.87

4.68 3.21 4.77

)3.32 1.93

3.38

1.84

)0.09

0.69

)1.22

3.67 4.07 4.69

)3.30 )1.70 0.13

0.84 1.77 3.27

)2.35 )2.19 )1.92

0.16 )2.02 )1.05 0.40

5

6

7

8

)0.45 )0.37 )0.07

1.21 3.18

1.71

P 6 0:05. P 6 0:01.

**

Table 5 Mean travel-time ratio by employment status, gender and household type One One Twofull-time part-time earner worker worker couple Employment status Part-time Full-time t-statistic for pairwise difference Gender Male Female t-statistic for pairwise difference *

0.129

0.107 0.102 1.59

0.121 0.107 2.75

0.104 0.110 )2.17

0.126 0.105 2.63

0.102 0.103 )0.43

0.108 0.104 2.76

0.130 0.129 0.06

0.108 0.097 5.24

0.109 0.113 )0.74

0.110 0.105 1.65

0.105 0.118 )1.62

0.106 0.096 4.00

0.107 0.101 5.94

0.103

0.107 0.099 2.19

Traditional Two-earn- Traditional Other Total couple er family family household

P 6 0:05. P 6 0:01.

**

Within households, role-related variables, such as gender and employment status, affect the travel-time ratio. Individuals working between 12 and 30 h a week have somewhat higher traveltime ratios than full-time workers (Table 5). 1 This pattern varies among household types: the tstatistics in Table 5 indicate that full-time workers have significantly lower travel-time ratios than traditional couples and traditional families, while for two-earner couples and other households no significant differences exist. Only in two-earner families have part-time workers lower travel-time ratios than full-time workers. Because the latter household category faces the greatest problems in completing the household’s activity program, part-time workers (presumably female) may try to 1

Because of the small group size, no average and median values are presented for the single-parent families in Tables 5–8.

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save time by working near the place of residence, which could result in lower travel-time ratios. The lower travel-time ratio for women in two-earner families is consistent with this supposition, although the difference is not statistically significant. Generally speaking, women have lower travel-time ratios than men. This is in accordance with the large literature on gender differences in commuting time, which states that women work relatively near their homes because they have to combine work with household maintenance activities, such as shopping and childcare (Turner and Niemeier, 1997). The gender difference also varies among household types. Only in two-earner couples, single full-time worker households and other household have women significantly lower travel-time ratios. Along with time pressure within the household, availability of monetary resources seems to impact the consideration of commuting time and work duration. Table 6 shows that, beyond a certain threshold, travel-time ratios are positively related to personal income. Furthermore, travel-time ratios tend to rise with educational attainment of workers, which primarily reflects larger commuting times for higher-educated workers. Since income and education are indicators of the types of jobs to be reached, these results indicate that occupational class may also affect the travel-time ratio for workplace visits. 5.2. Travel-time ratios in various spatial contexts Analysis of the data shows that the type of residential location affects the travel-time ratio (Table 7). For workers residing in central cities or suburbs, the average ratio is higher than for those outside DUSs, while workers living in growth centers face the highest travel-time ratios. Variation in average travel-time ratios between residential environments seems at least as considerable as that between households or individuals. Similarly, travel-time ratios vary with the location of the workplace. Commuting toward central cities of DUSs and growth centers tends to result in rather high travel-time ratios. In general, the results presented here are consistent with the findings for commuting times by Levinson and Kumar (1994). Suburb-to-suburb commutes have lower travel-time ratios than the traditional suburb-to-core city home-to-work trips. When only travel-time ratios for commuting within DUSs were analyzed, largely similar results are obtained. 5.3. Travel-time ratios and mode choice People traveling to work by public transportation generally encounter the highest travel-time ratios (Table 8). This is primarily due to their extremely long commuting time. Some variation between public transportation modes can be seen. Travel-time ratios for commuting by train are higher than for home-to-work journeys by bus, tram or metro. These results align with analysis for income and educational level to support prior knowledge that higher-status groups in Netherlands often commute by train. Captive and non-captive public transportation travelers tend to have the same travel-time ratios. To differentiate captive travelers from non-captives, we used Hanson and Hanson’s car availability index which is defined as the number of cars present in the household divided by the number of valid driver’s licenses (Hanson and Hanson (1981)). If a person does not have a driver’s license, the index is set to zero. For both train and bus/tram/metro average and median values by car availability are virtually similar. Thus the high travel-time ratio for mass transit do not seem

Annual personal income Low (Euroa < 12,200) Medium (Euro 12,200–18,999) Medium-High (Euro 19,000–26,199) High (Euro P 26,200) Highest educational attainment No (primary school) Low (secondary school) Medium (intermediate vocational training) High (higher vocational training/ university) a *

Euro 1.00 is approx. US$ 0.90. N < 30.

One partTwo-earner One fulltime worker time worker couple

Traditional couple

Two-earner family

Traditional family

Other household

Total

0.096 0.099 0.105

0.127 0.133

0.092 0.098 0.113

0.104 0.097 0.109

0.097 0.101 0.116

0.090 0.095 0.105

0.095 0.090 0.109

0.096 0.097 0.110

0.124



0.129

0.126

0.130

0.123

0.118

0.124

0.095 0.092 0.093



0.140 0.115

0.090 0.087 0.096

0.093 0.100 0.107

0.097 0.093 0.102

0.081 0.092 0.103

0.088 0.094 0.107

0.090 0.093 0.100

0.120

0.136

0.124

0.128

0.127

0.125

0.112

0.123



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Table 6 Mean travel-time ratio by personal income, educational attainment and household type

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Table 7 Mean travel-time ratio by spatial context From/to

Core city

Suburb

Growth center

Open space

Total

Core city Suburb Growth center Open space

0.099 0.133 0.146 0.152

0.129 0.078 0.161 0.127

0.153 0.132 0.079 0.153

0.145 0.136 0.142 0.080

0.109 0.108 0.121 0.099

Total

0.122

0.100

0.119

0.089

0.105

to stem from the fact that transit users have no travel options available other than public transportation. Travel-time ratios for car drivers and car passengers are roughly the same. Table 8 does not provide evidence of adverse effects of car pooling on the balancing of travel time and stay time. Travel-time ratios for stops at the workplace by cycling and walking are significantly lower than for visits by car or public transportation. This is mostly the result of lower average commute times, reflecting the limited physical distance covered with these modes. However, visits to the workplace, too, tend to be shorter if slow transportation modes – especially walking – are used to reach the work address. This is, among other things, due to the fact that people walking or cycling to work are more inclined to return home during the work day, e.g. during the lunch break.

6. Multilevel regression analysis The results presented in the previous section seem to indicate that spatial and transportationrelated variables offer a better explanation for the variation in travel-time ratios for workplace visits than individual or household variables. This section aims to validate this assertion, and addresses the association between the travel-time ratio and a number of independent variables in a multivariate framework. Section 6.1 presents the multilevel model specification, and Section 6.2 describes the estimation results.

Table 8 Mean travel-time ratio by transportation mode and household type

Car driver Car passenger Bus/tram/metro Train Bicycle Walking Other *

N < 30.

One fullOne partTwo-earner Traditional Two-earner Traditional Other time worker time worker couple couple family family household

Total

0.106 0.108 0.141 0.215 0.069 0.048

0.111 0.107 0.159 0.222 0.073 0.050 0.096



0.133   

0.100  

0.107 0.098 0.149 0.213 0.069 0.045 0.090

0.115 0.113 0.166 0.234 0.080 0.065 

0.116 0.112 0.171 0.222 0.076 0.049 0.090

0.112 0.116 0.172 0.223 0.073 0.043 0.116

0.110 0.107 0.156 0.242 0.074 0.052 0.095

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6.1. Model specification Ordinary least-squares regression models are based on the assumption that residual variance is constant and does not depend on the explanatory variables (homoscedasticity assumption). Residual variance is, therefore, summarized by a single random parameter. However, in hierarchical datasets the homoscedasticity assumption is violated, because elementary units are clustered in higher-level units (e.g. individuals within residential environments). The use of ordinary regression methods may in such instances result in biased parameters of estimators and reduced variation. Multilevel regression models recognize the clustering of data and contain more than one random term (Snijders and Bosker, 1999). This makes them an appropriate analysis tool for data with complex clustering patterns like the Dutch NTS. Multilevel models can be specified with different degrees of complexity. Here, we restrict ourselves to a random-intercept model, since more complicated multilevel models either yielded no statistically significant results or were not accommodated by the present data. In a randomintercept model only the intercept-term is allowed to vary, which implies that the association between the response and the predictor variables is assumed to be similar in each higher-level unit. Conceptually, four levels of analysis can be specified when analyzing decisions regarding the balancing of commuting time and the duration of workplace visits: 1. 2. 3. 4.

Activity episode i (level I). Individual worker j (level II). Household k (level III). Residential municipality l (level IV).

The four-level random-intercept model with one predictor variable at level i can be written as (cf. Goldstein, 1995): yijkl ¼ b0 þ b1 X1ijkl þ ðf0l þ v0kl þ u0jkl þ e0ijkl Þ;

ð3Þ

where yijkl is the response variable, in this case the travel-time ratio with an overall mean b0 . The parentheses in Eq. (3) indicate the random part of the model. The random term e0ijkl refers to the activity episode, u0jkl to the individual worker, v0jkl to the household, and f0l to the residential environment. These random terms are assumed to have a mean of 0, to be mutually independent, and can be summarized by their variances r2e0 ; r2u0 ; r2v0 , and r2f 0 , respectively (Goldstein, 1995). In addition, b1 is the slope coefficient for the predictor variable X1ijkl . This formulation shows that activity episodes are assumed to be nested within individuals in households which are grouped in residential environments. In the following subsection, the estimation results for an intercept-only model are described first. This is a random-intercept model without predictor variables (Snijders and Bosker, 1999). When all variance terms in this model are significantly larger than zero, the four-level specification is correct, and activity episodes are indeed clustered within workers within households within municipalities. Additionally, we calculated intra-class correlation coefficients from the estimates of the random terms in the intercept-only model. The intra-class correlation gives an indication of the proportion of variance that is accounted for by a given level of analysis (Snijders and Bosker,

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1999). At a given level, this measure is computed by dividing the variance estimate for that level by the sum of all variance estimates. Predictor variables were added stepwise to the intercept-only model until the final specification of the random-intercept model was reached. First, we included ‘transportation mode’ (categories as in Table 8) and ‘departure time from home’ which consists of three values: ‘before 7 A.M.’, ‘between 7 and 9 A.M.’, and ‘after 9 A.M.’. Next, we added the discrete variables ‘employment status’, ‘gender’ and ‘educational level’ (categories as in Tables 5 and 6), ‘car availability index’, ‘income’ (mean of eight income classes) and ‘age’ (mean of 11 age classes). ‘Household type’ was also included, but some of the previous categories were collapsed to avoid multicollinearity problems. The classes used in the analysis are ‘single workers’, ‘two-earner couples’, ‘traditional couples’, ‘single-parent families’, ‘two-earner families’, ‘traditional families’, and ‘other household types’. At the highest level, the residential environments ‘core city’, ‘suburb’, ‘growth center’ and ‘open space municipality’ were used, together with a continuous density variable, 2 defined as the number of addresses (both residential and non-residential) per square kilometer. The logit of the travel-time ratio was taken as the dependent variable. 3 6.2. Empirical results The specification of four levels of analysis is empirically supported by the results for the intercept-only model, because the estimates for all variance parameters are significantly larger than zero (Table 9). The intraclass coefficients indicate that most of the variance in travel-time ratios is explained by the levels of the individual worker and the activity episode: 31.1% and 21.6%, respectively. Although day-to-day variation cannot be addressed with the present data, the variance estimate for the activity episode indicates that the intrapersonal variation in realized travel-time ratios is probably considerable. The household level accounts for a further 9.5% of the variance. With only 2.3% the spatial context is relatively unimportant. The low variance estimate seems to be at odds with the descriptive results in Table 7. This indicates, however, that most of the variation shown in Table 7 is due to aggregation of variance between lower-level units, and that the role of urban form in the variation in travel-time ratios is fairly limited. Conversely, the significant parameter shows that urban form does matter, and that estimation of a four-level model is justified. Adding the independent variables to the model results in a significant model improvement, as indicated by the decrease in the deviance, which is an estimate of misfit of the model (Snijders and Bosker, 1999). For multilevel models no straightforward measure of the explained variance exists. As a possible surrogate for R2 , Snijders and Bosker (1999) propose to use the following measure: one minus the proportional reduction in the sum of the estimated variances after the inclusion of

2

This density variable is not included in the 1998 NTS; it was obtained from the statline database from the Netherlands Central Bureau for Statistics and linked to the data. 3 Since the dependent variable is a proportion, a generalized linear model that incorporates the transformation of the dependent variable should have been estimated (McCullagh and Nelder, 1989). However, the data set do not permit the estimation of such a model. Therefore, we have chosen to apply an empirical transformation of the dependent variable and used the logit of the travel-time ratio as the dependent variable.

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Table 9 Estimation results for the multilevel model Fixed part

Intercept-only model b coefficient

Intercept Level I (activity episode) Train Bus/tram/metro Cycling Walking Before 7 A.M. After 9 A.M. Level II (individual worker) Personal year income Medium education High education Full-time worker Female Age Level III (household) Single worker Two-earner couple Traditional couple Single parent family Other household Level IV (spatial context) Density of the residential environment Residing in core city Residing in suburb Residing in growth center Random part e0ijkl (variance at level 1) u0jkl (variance at level 2) v0kl (variance at level 3) f0l (variance at level 4) Deviance Model improvement

)2.372



Random-intercept model t-statistic )273.5

b coefficient )2.222

)96.3

0.881 0.501 )0.422 )0.954 0.200 0.091

35.2 16.0 )34.1 )28.2 13.3 7.0

3.7E)03 0.091 0.227 )0.252 )0.042 4.4E)03

8.8 6.6 14.8 )13.9 )3.0 6.4

)0.049 )0.032 )0.030 )0.321 )0.054

Variance 0.223 0.328 0.084 0.014 42,969.1

t-statistic 23.6 20.7 6.9 6.2

t-statistic



)2.6 )2.2 )1.4 )4.1 )3.2

1.9E)05

2.4

0.037 0.051 0.134

1.6 3.3 4.6

Variance 0.226 0.219 0.048 0.002

t-statistic 23.9 16.1 5.1 2.2

38,464.6 4,504.5

N ¼ 18,201 activity episodes. * P 6 0:05. ** P 6 0:01.

the predictor variables. In the present case, 23% (1)0.496/0.648) of the total variance is explained by the independent variables. Concerning the effects of separate variables, the results largely corroborate the conclusions drawn on basis of the disaggregate analyses in Section 5. Transportation mode and all individualworker level variables have the expected signs. The parameters for departure time from home show that, everything else being equal, commuting during the A.M. peak results in the lowest

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travel-time ratio. This reflects that workers leaving home before 7 A.M. need to travel relatively long, while workers leaving home after 9 A.M. tend to have shorter work durations. In addition, the model indicates that age is positively related to the travel-time ratio. Consistent with earlier results, car availability had no significant effect, and was therefore omitted from the model. The results for the household typology are largely consistent with the descriptive analyses. Traditional and two-earner families do not differ significantly from each other (as a consequence, both categories are defined as reference category here). Yet, their travel-time ratios are somewhat higher than those for couples, although the difference with traditional couples is not significant. Single workers and other households differ significantly from the family household types, but not from the traditional and two-earner couples. Single-parent families clearly have the lowest values for the travel-time ratio. Overall, however, differences between household types are rather limited. Table 9 further shows that density of the residential environment is positively related to the travel-time ratio. Controlling for density, travel-time ratios of workers in households residing in suburbs tend to be higher than of those in core cities or open space municipalities (reference category). Residents of growth centers clearly face the highest travel-time ratios. The results for the four types of residential environments are somewhat different than those presented in Table 7, which is mostly due to the inclusion of the density variable in the model. In sum, the multilevel analysis has largely confirmed our earlier assertions about the effects of the independent variables. The results also indicate that the residential context matters, although its importance is small.

7. Discussion For a better understanding of commuting behavior, the home-to-work journey has to be addressed in the context of daily time use. This paper hypothesized that commuting time is related to work duration, and that individuals tend to balance the time spent at the work place and the time needed to access the work location. Data from the 1998 Dutch NTS shows that workers, on average, spend 10.5% of the time available for working and traveling on commuting. This corresponds to a single home-to-work trip of 28 min for an 8-h workplace visit, a result comparable to those obtained in other studies. As we have shown, variation around this average value is considerable. The variation in commuting times is, however, even larger, a fact that has been ignored in many previous studies on commuting time. Despite the variation in travel-time ratios, we believe that a maximum tolerable travel-time ratio for workplace visits exists. More precise quantification remains an important future task; however, we think that, for individuals visiting the workplace for more than 4 h, the maximum travel-time ratio may be at a level of 0.20–0.25. In other words, individuals in the longer term might not be prepared to spend more than one-fourth of time available for commuting and working on traveling to and from the workplace. The balancing of commuting time and work duration is impacted by the traditional determinants of commuting time, such as time and monetary constraints on workers, time of day, commuting mode or spatial context. The multilevel analysis has indicated that the variation in travel-time ratios due to the residential environment of workers is rather small. Although density and type of municipality affect the travel-time ratio, most of the variation among municipalities is the result of aggregation of differences among individuals and households.

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Unfortunately, the data did not permit investigation of some deeper questions regarding the association between commuting time and work duration, which remain avenues for further research. Some interesting issues are the relationships between the value of the travel-time ratio and the number of days commuted per week, and the impact of worktrip-chaining on the realized travel-time ratio. In addition, the variation among occupational groups may be investigated, as well as the day-to-day variation in travel-time ratios. Lastly, the reasons why people accept high travel-time ratios, for instance when traveling by public transportation, merit future research. Do these workers accept traveling long periods of time because they can use commuting time as a buffer between different roles in their private life (Blumen, 2000; Pazy et al., 1996), or does it provide them opportunities to conduct other activities while traveling? The concept of the travel-time ratio may in the future be used in land use/transportation policy. Currently, commuting times are often used to delineate labor market areas for which specific policies are formulated. As we have argued and shown, however, commuting is not an independent phenomenon, but the propensity to commute is related to work duration. Use of the travel-time ratio concept would incorporate both the travel and the activity duration dimension in policy making. Policies related to job access could benefit from application of the concept. If the travel-time ratio were used, policy could not only aim to enhance access to suitable jobs, but it could also attempt to tune job access to work duration. Based on work duration estimates, maximum acceptable distances between residences and workplaces for subgroups of the population at specific locations in urban areas can be estimated. These can be used to ensure that workers can access a sufficiently large number of jobs with a given work duration that meet their preferences and constraints from their home location. Before the concept can be applied, however, more research must be undertaken to address the remaining questions. Acknowledgements This research was sponsored by the Dutch National Science Foundation, grant 425-13-003 to the Urban Research centre Utrecht, Utrecht University. The authors would like to thank the three anonymous reviewers for their valuable comments and suggestions for improvement of the manuscript. References Becker, G.S., 1965. A theory of the allocation of time. The Economic Journal 75, 493–517. Bhat, C., 1999. An analysis of evening commute stop-making behavior using repeated choice observations from a multi-day survey. Transportation Research B 33, 495–510. Blumen, O., 2000. Dissonance in women’s commuting? The experience of exurban employed mothers in Israel. Urban Studies 37, 731–748. Central Bureau for Statistics, 1998. National Travel Survey 1998: Documentation for Tape Users. CBS, Voorburg/ Heerlen (in Dutch). Cervero, R., Wu, K.-L., 1998. Sub-centering and commuting: evidence from the San Francisco Bay Area. Urban Studies 35, 1059–1076. Damm, D., 1979. Towards a model of activity scheduling behavior. Ph.D. Thesis, Massachusetts Institute of Technology, USA. Dijst, M., Vidakovic, V., 2000. Travel time ratio: the key factor in spatial reach. Transportation 27, 179–199.

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