Does commuting time tolerance impede sustainable urban mobility? Analysing the impacts on commuting behaviour as a result of workplace relocation to a mixed-use centre in Lisbon

Journal of Transport Geography 32 (2013) 38–48

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Journal of Transport Geography journal homepage: www.elsevier.com/locate/jtrangeo

Does commuting time tolerance impede sustainable urban mobility? Analysing the impacts on commuting behaviour as a result of workplace relocation to a mixed-use centre in Lisbon David S. Vale ⇑ CIAUD, Faculty of Architecture, Technical University of Lisbon, Polo da Ajuda, 1349-055 Lisbon, Portugal

a r t i c l e

i n f o

Keywords: Workplace relocation Commuting Sustainable urban form Acceptable travel time Commuting tolerance

a b s t r a c t Sustainable urban development policies promote the development of accessible mixed-use suburban centres. They are believed to reduce car dependency and stimulate sustainable mobility. We test this assumption through an analysis of workplace relocation to such a centre located in the inner suburbs of Lisbon, Portugal and examine its impact on commuting. We use primary data concerning previous and current commuting patterns, collected through a survey of employees working at the site. Binary and multinomial logistic models were developed to identify the explanatory variables of the observed impacts on commuting behaviour. Our findings showed a significant increase in commuting distance and the use of the car, and an insignificant change in commuting time. The relocation affected city centre residents most negatively. This demonstrates a strategy that aims to maintain commuting time within acceptable limits. Car use is greater when travel time increases and there is transportation mode inertia within the acceptable travel time. In this case, workplace relocation to a suburban mixeduse transit-oriented centre did not trigger the expected changes in mobility pattern, suggesting that the structure needs to be complemented by other travel demand measures to discourage workers from using their cars to commute. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction Driven by sustainable development objectives, compact city policies are promoted in metropolitan areas. Although the concept of compact city development lacks a clear definition, it is based on a combination of urban design and accessibility conditions. Of these, residential and employment density, mixed land use and multimodal transportation are probably the most important (Neuman, 2005). One specific policy measure is transit-oriented development (TOD), which can be described as mixed land use neighbourhoods located near to public transport stations, where housing, jobs and commercial areas co-exist within walking distance of each other (Cervero et al., 2004). One of the expected benefits of sustainable urban development policies such as living and working in a TOD is that residents and workers can use public and non-motorized transport. This reduces both private transport usage, and travel distance and time, which ultimately reduces commuting costs (Bertolini and Le Clercq, 2003; Newman and Kenworthy, 2006; Wheeler, 1998). In other words, sustainable urban development and mobility aim to reduce distances travelled

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in order to achieve a more sustainable distribution of transportation modes (Banister, 2008). The vast majority of the literature on travel behaviour has focused on the relationship with the built environment and has looked at the land use characteristics of residential areas. Moreover, most research is based on cross-sectional methods and compares individuals living in different neighbourhoods, which has lead to doubts about the direction of causality of the relationship. This is particularly relevant given what is designated as the self-selection hypothesis (Cao et al., 2009; Mokhtarian and Cao, 2008), although residential self-selection in itself may confirm the importance of the built environment (Næss, 2009). Much less is known about the influence of destinations on travel behaviour, especially when the destinations in question are mixed-use, transit-oriented suburbs of metropolitan areas. Likewise, little is known about geographical contexts, such as Mediterranean cities, where non-motorized and public transport still account for nearly half of commuter journeys. In this case study we analyse the travel behaviour of workers before and after their relocation to a new, mixed-use, transitoriented suburb in the Lisbon Metropolitan Area (LMA) in Portugal. We assume that attitudes to travel are not affected by the workplace relocation, and that self-selection is unlikely to influence the relationship between the built environment and

D.S. Vale / Journal of Transport Geography 32 (2013) 38–48

travel behaviour. Using a combined analysis of commuting mode, time and distance, we demonstrate the trade-offs that employees make when faced with workplace relocation. Our results suggest that employees try to keep commuting time within an acceptable value, and turn to private transport to compensate for the increased commuting distance created by the relocation. However, the opposite phenomenon does not occur (reduced distances do not lead to reduced use of private transport), which demonstrates a commuting time tolerance. Therefore, in order to achieve a significant change in travel modes, policies such as transit-oriented urban structures must be accompanied by additional ‘push’ measures (such as parking restrictions) that increase the overall cost of private transport. This paper is organized as follows. In the next section, we analyse existing research on the impact of workplace relocation on commuting, followed by a presentation of our methodology. Next, we present the results of our analysis and discuss their significance. Finally, we present some recommendations for sustainable urban development policies in the Portuguese context.

2. Literature review 2.1. Commuting behaviour and the built environment Although the relationship between land use and travel behaviour has been examined in detail, there is no consensus regarding the results. Socio-economic and other attitudinal characteristics have been found to be as important as land use in explaining commuting behaviour (Boarnet and Crane, 2001; Cao et al., 2009; Cervero, 2003; Crane, 2000; Ewing and Cervero, 2001, 2010; Meurs and Haaijer, 2001; Stead and Marshall, 2001). However, despite ambiguous results and differences between North American and European contexts, land use is still thought to have a clear relationship with travel behaviour. Seven main dimensions have been identified (known as the 7 Ds). These are, namely: (i) density, (ii) diversity/mixed use, (iii) design (including parking, and conditions for walking and cycling), (iv) destination accessibility (the location of new development at the regional scale, proximity to city centres and network connectivity), (v) distance to public transport (specifically, the accessibility of public transport), (vi) demand management, and (vii) demographics (Banister, 2011a; Ewing and Cervero, 2010; Litman, 2011). Although most research has focused on the influence of residential areas (the point of origin) on travel behaviour, workplaces (destinations) are equally important, as they have a clear impact on all seven dimensions. Three dimensions are particularly important: diversity (the land use mix and the jobs/ housing balance); design (the availability of parking); and destination accessibility (particularly disparities in the accessibility of transportation modes and the distance to public transport). Research has shown that suburban workplaces with ample free parking and poor public transport are associated with higher use of private transport (Aguilera, 2005; Aguilera et al., 2009; Guth, 2010; Ma and Kang, 2011; Næss and Sandberg, 1996; Priemus et al., 2001). However, when workplaces are located close to public transport stations, and business and residential areas are more mixed, private transport is less used (Hess, 2001; Verhetsel and Vanelslander, 2010). In this framework, the characteristics of the built workplace environment are expected to have a clear influence on commuting behaviour (especially modal share) as the set of transportation options differs. In the 1990s, the Dutch Government introduced an A–B–C location policy to overcome the car dependency of workers. The stated principle was ‘‘putting the right business at the right place’’ (Dijst, 1997; Martens and Griethuysen, 1999; Verroen et al., 1990). Locations were classified as type A, B or C according to their multimodal accessibility profile. Type A

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had very good public transport (PT) accessibility and poor car accessibility, type C locations had good car accessibility and poor PT accessibility, and type B areas were somewhere in between, offering good accessibility in several transport modes. A strict parking policy complemented accessibility profiles. Firms and organisations were classified according to the number of employees and visitors they attracted, and their space consumption per capita. The main objective of the policy was to encourage firms with high numbers of employees and visitors and low space consumption per capita to relocate to type A areas, firms with few employees and visitors and high space consumption per capita to move to type C locations, and firms with intermediate values to relocate to type B areas. The policy proved to be very hard to implement and the results were far from the desired travel patterns. Nevertheless, the policy has been successful to the extent that it has prevented a worse situation, i.e. if all workplaces were located in car dependent areas (Schwanen et al., 2004). Despite these findings, the influence of land use on travel behaviour has been challenged. The principal argument lies in the self-selection hypothesis, which posits that the desired travel mode is selected first and the choice of location reflects this decision (Boarnet and Sarmiento, 1998; Cao et al., 2009; Handy et al., 2005; Kitamura et al., 1997). Note that this hypothesis holds true for the workplace, as its location may reflect the desired commuting behaviour of both the employer and the employee. Employees may even self-select in order to work in a location that permits the desired travel behaviour (Van Wee, 2009). Although travel behaviour is usually assumed to be a derived demand (Fox, 1995), commuting time has been shown to be stable, suggesting that travel time has a greater value than travel distance (Maat et al., 2005; Metz, 2004, 2008). The ideal commuting distance seems to be ‘close, but not too close’ to the workplace, implying that travel has a positive utility or, at least, that an individual has a travel budget and is willing to use it for daily travel (Jain and Lyons, 2008; Mokhtarian and Chen, 2004; Næss, 2005). Therefore, in the sustainable mobility paradigm, travel must be conceived of as not only a derived demand but also as a valued activity. The traveller’s objective seems to be to achieve a reasonable travel time by reducing (rather than minimizing) travel distances and/or increasing travel speed (Banister, 2011a,b). 2.2. The impacts of workplace relocation on commuting There is little research on the impacts of employment suburbanization on commuting, although some studies were carried out at the beginning of the 1990s. Cervero and Landis’ (1992) study of the San Francisco Area revealed minimal impacts on average commuting distance, but a decrease in average commuting time, together with a significant increase in car use and a decrease in public transport use. The most negatively-affected employees were city-centre residents who experienced a significant increase in both commuting distance and time, while suburban residents benefitted from slightly reduced distances and significantly reduced commuting time, although there was also an increase in private car use. In a later study, Cervero and Wu (1998) reported an increase in vehicle miles travelled per employee, and an increase in both average commuting distance and time. There was also a slight increase in car use, although most commuters were already car users, suggesting inertia in the choice of transportation mode. However, as the suburban employment sites in the study were typically office parks located in places with poor public transport, these results were to be expected. Other studies have specifically focused on suburban areas with good public transport access. Bell (1991) looked at the relocation of a major retail firm from Melbourne city centre (Australia) to a suburban area with bus, tram and train access within walking

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D.S. Vale / Journal of Transport Geography 32 (2013) 38–48

distance, but also with good car access and ample free parking (enough for almost 100% of employees). This study reported both an increase in car usage and an increase in the number of employees who owned cars. Moreover, average commuting time decreased from 66 to 56 min, which may be linked to the increase in car usage. Similar research by Hanssen (1995) focused on the relocation of a major company from several locations in the central business district of Oslo (Norway) to a suburban area with good access to public transport, but also with free parking for around 45% of employees. As in the Melbourne case, there was a substantial increase in car use and a decrease in public transport use, while walking and cycling remained stable. The impacts were greater on residents of the city centre and the western sector (the new workplace location), but employees living in the southern and north-eastern sectors had higher increases in car usage. Another study in the city of Oslo (Aarhus, 2000) focused on five companies that relocated to suburban centres within walking distance of public transport, but also with ample parking. Again, this study showed an increase in private transport use for daily commuting. A contrasting example comes from Singapore (Malone-Lee et al., 2001; Sim et al., 2001), where workplace relocation to a planned regional suburban centre did not increase car use, but also did not reduce travel time nor travel distance. However, in Singapore the vast majority of employees (78%) commute by public transport, while only 5% commute by car, which may help to explain these different impacts on transport modes. Therefore, with the exception of Singapore, these case studies suggest substantial increases in car use for commuting when the workplace relocates to the suburbs. This can be attributed mainly to the availability of free parking at the new location (Hess, 2001), even when the new site has good access to public transport (Cervero, 2007). Additionally, studies show that commuting time decreases slightly or does not change significantly. At the same time commuting distance tends to have significantly different impacts, as it is directly linked to where the employee lives. On the one hand, employees who live close to new sites may have shorter and faster commutes, but do not necessarily stop using their car. On the other hand, employees living farther away from the new site have their commuting distance significantly increased, and in order to keep their commuting time within an acceptable value, start commuting by car. An explanation for this observed transportation mode inertia may arise from the motives for commuting by car, which are not simply practical, but also symbolic and affective (Gardner and Abraham, 2007; Steg, 2005). Habit has been shown to be another important factor in the choice of travel mode (Gardner and Abraham, 2008). Although a workplace relocation can constitute a new setting for travel behaviour decisions, commuting may be generalised across contexts, and relocation alone may be insufficient to change habits and therefore travel behaviour (Gardner, 2009).

3. Commuting time tolerance, distance and choice of transport mode The literature provides some support for the hypothesis that commuting time is the critical factor influencing travel behaviour. According to the ‘critical isochrone’ or ‘commuting tolerance’ hypothesis (Getis, 1969; Van Ommeren et al., 1997) it can affect the choice of commuting mode. This is supported by the concept of time–space prisms described by Hägerstrand (1970), which sees (accessible) space as the result of an individual’s spatio-temporal constraints. This hypothesis assumes that individuals have a travel time budget, which is heterogeneous at the individual level. It is derived from how time is valued, which is in turn related to wages.

It has been shown to have a constant average value within a region (Hjorthol, 2001; Hupkes, 1982; Marchetti, 1994; Metz, 2004, 2008; Rouwendal and Nijkamp, 2004; Schafer, 2000; Zahavi, 1979). This suggests that commuting has a positive utility, not only because it offers a space–time transition between home and work (Jain and Lyons, 2008; Mokhtarian, 2005; Redmond and Mokhtarian, 2001), but also because through commuting, workplaces become significantly more accessible (Van Ommeren et al., 1997). As commuting time is a function of travel distance and transportation mode, distance is an important factor in the choice of travel mode, but only to the extent that it has an impact on commuting time. Nevertheless, distance is extremely important as it can be influenced by land use planning (Banister, 2011b), and active modes (walking and cycling) are especially sensitive to it (Pucher and Buehler, 2010). Therefore, we argue that although travel time may be the critical decision factor for the traveller, distance should also be taken into account. This approach includes active transport modes and emphasizes the influence of land use. We argue that for all transportation modes, the maximum commuting distance is a function of the maximum travel time, which in turn is a function of average travel speed in the different modes. Therefore, in line with Banister (2011b), we assume a clear trade-off between travel mode (in terms of speed), distance and time. Research supports both the existence of these trade-offs and the primordial importance of travel time. An analysis of time series data shows that overall travel time is stable at an international level. It is achieved through an increase in the proportion of faster transport modes, which offset increases in travel distances (Schafer, 2000). Moreover, information and communication technologies have increased ‘on-the-move’ productivity, enabling commuters to work while travelling. This transforms travel time into working time and increases its utility (Lyons et al., 2007, 2012; Lyons and Urry, 2005). Similarly, the rational locator theory (Levinson and Kumar, 1994; Levinson and Wu, 2005) supports the existence of trade-offs. This states that the observed stability in travel time has been achieved through the co-location of jobs and housing, which clearly assumes the existence of a commuting time tolerance that households seek to optimize, rather than minimize. Clear empirical evidence comes from a study in Seoul (South Korea), where observed travel time stability was obtained through a change to a faster mode. This finding supports the argument that commuter’s location decisions are more affected by time than distance (Ma and Kang, 2011). In summary, it is possible to identify three different, but interconnected concepts: travel time expenditure, travel time budget and acceptable travel time. Travel time expenditure simply refers to the amount of time spent travelling to and from the destination, and it is stable over time. It is known to vary according to the socio-economic situation and lifestyle of the traveller, the characteristics of the related activity, and those of the area where travel takes place (Mokhtarian and Chen, 2004). Travel time budget can be thought of as an ideal or optimum travel time. It can be interpreted as a law of travel behaviour (Marchetti, 1994; Mokhtarian and Chen, 2004; Zahavi, 1979), a left-over from the daily time allocation, or an input to decision making; none of these explanations are conclusive and even its existence is contested by some authors (Levinson and Wu, 2005; Schafer, 2000). It implies that a traveller will reduce their travel time if it is greater than the ideal amount, but will increase it if it is less (Hupkes, 1982; Mokhtarian and Salomon, 2001). Finally, acceptable travel time is implicit in the commuting tolerance and critical isochrone concepts (Getis, 1969; Van Ommeren et al., 1997). These two concepts reflect the maximum amount of travel time a person is willing to spend in order to perform a certain activity. However, unlike the travel budget concept, it also assumes indifference to reducing the amount of time if it is

D.S. Vale / Journal of Transport Geography 32 (2013) 38–48

Fig. 1. Relationship between travel utility and travel time.

already within the acceptable (maximum) value. If this hypothesis holds, an increase in travel speed might increase the distance travelled rather than save travel time (Metz, 2004, 2008). 3.1. Hypothesis Our hypothesis combines the idea of a minimum ideal travel time with commuting tolerance/critical isochrone concepts. We assume that travel utility increases with travel time up to a certain point, stabilizes for a certain interval and then decreases with further increases in travel time (Fig. 1). By assuming a fixed travel speed (i.e. transportation mode and level of congestion) we can translate this relationship from commuting time into commuting distance, where faster transport modes demonstrate a wider function and slower modes a narrower function. Although this hypothesis only relates to commuting, it may also be applicable to other travel purposes. Although the choice of transportation mode is also a result of socio-economic factors, such as conviviality (Banister, 2005), can be explained by symbolic and affective habits (Gardner, 2009; Steg, 2005), and depends on the friendliness of the route between home and workplace to different transportation modes, we propose a threefold hypothesis concerning the impacts of (suburban) employment relocation on commuting, as a result of the direct impact in terms of travel distance (see Fig. 2): (a) If the relocation substantially decreases the employee’s travel distance, travel time will decrease. In this case, as long as commuting time remains within the critical isochrone, the

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employee may change to an environmentally friendly and slower transportation mode, if available at both the origin and destination. Nevertheless, the employee may also maintain the same transportation mode when the new workplace location is within the critical isochrone. (b) If the relocation substantially increases the employee’s travel distance, travel time will increase. In this case, there is a propensity to continue or start commuting by car (a faster mode), in order to keep commuting time within the critical isochrone. However, if the new travel time remains within the critical isochrone, the employee may maintain the same transportation mode. (c) If the relocation has little impact on travel distance, travel time will only marginally change, assuming the same level of congestion. In this case, as long as commuting time remains within the critical isochrone, there will be no propensity to change commuting mode. In other words, small differences in travel distance and travel time are unlikely to trigger a change in commuting mode. However, if the new travel time exceeds the critical isochrone, there will be a propensity for the employee to change to a faster transport mode. Note that, in line with other findings (Krizek, 2003; Woo, 2005), our hypothesis implicitly assumes transportation mode inertia. A change of transportation mode only occurs if the new commuting time using the new transportation mode enables the employee to commute within the critical isochrone. In other words, we assume that, by itself, a new commuting distance is not sufficient to lead to a change in transportation mode. 4. Methodology A longitudinal or quasi-longitudinal methodology can be used to evaluate whether the built environment has a direct effect on travel patterns. This approach makes it possible to compare the travel patterns of the same individuals in different built environments (Krizek, 2003; Mokhtarian and Cao, 2008). Accordingly, we used a quasi-longitudinal approach (Cao et al., 2007) and focused on employees who had to adapt their daily commute to a new workplace. It is assumed that the decision to relocate the workplace was not taken by the employee. This means that self-selection processes are unlikely to influence the link between travel behaviour and the built environment. Nevertheless, it should be

Fig. 2. Workplace relocation impacts on commuting patterns.

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D.S. Vale / Journal of Transport Geography 32 (2013) 38–48

Fig. 3. Location of the Park of Nations in the Lisbon Metropolitan Area.

noted that some employees may have decided to change jobs rather that relocate to the new workplace because the change meant that the commuting mode or time became unacceptable. This may have limited our analysis to the impacts of workplace relocation on those employees who agreed to relocate. 4.1. The case study In order to test our hypothesis, we used the ‘Park of Nations’ (PN) as a case study. This is a mixed use centre located to the east of the city of Lisbon, Portugal, which was developed to host a major international exhibition in 1998. The site was created with the express objective of creating a new metropolitan centre (Carrière and Demazière, 2002; Castro and Lucas, 1999), and the necessary transportation infrastructure was put in place. This included a new metro line, train station, road bridge and several improvements to the local road network (see Fig. 3). As a result, the site is very accessible by both private and public transport and similar to the average for Lisbon; therefore higher than the average for the suburbs (Vale, 2009). The site has ample parking – five parking spaces per 100 square meters of commercial area – which means that around 75% of employees can park. In addition, there are several unofficial parking spaces around the site, which means that this figure may rise to 100%. 4.2. Data collection A self-completion questionnaire was designed for employees at the new site. It contained retrospective questions regarding the commuting patterns of the respondent. Following a pilot survey, the final questionnaire was distributed to 1016 employees working at randomly selected companies at the site. In total, 427 questionnaires were completed (42.9% response rate), which showed that 302 employees (78.9% of the completed questionnaires) had worked at another location in the previous five years.

In order to analyse the impacts of workplace relocation on their commuting habits, we used the threefold workplace classification scheme for the Lisbon Metropolitan Area (LMA) developed by Seixas (2004). This divides the area into the city centre, the inner suburbs (the rest of the municipality of Lisbon, which includes the Park of the Nations) and the outer suburbs (see Fig. 3). These three categories represent different levels of public transport and car accessibility, as well as distance to the city centre. Accordingly, the city centre is the most easily accessible by public transport, while access is more limited in the inner suburbs (a ring five kilometres from the city centre), and workplaces in the more distant outer suburbs are dependent on private transport. We excluded respondents who had previously worked in ‘small cities’ (17 cases), who were considered as migrants. Consequently the final sample consisted of 285 employees. 4.3. Sample characteristics Although around one quarter of respondents had previously worked in the centre of Lisbon (27.3%), the largest group (46.1%) had previously worked in the inner suburbs (mainly north-east and north-west Lisbon). In the sample, 55.5% of respondents held managerial and skilled non-manual positions, 40.1% were semiskilled workers, and were 4.4% skilled manual and unskilled workers. As there is no data available about the total number of employees at PN, no analysis can be made of the representativeness of the sample. However, compared to averages for the LMA, the sample has fewer skilled manual and unskilled employees. The sample was well-distributed in terms of monthly household income. A sizeable number of respondents (30.3%) earned 750–1500 euros per month, but 12.5% were in lower income groups and 9.5% were in higher income groups. In terms of age, young adults dominated (46.2% were under 30), and there was only a small percentage of older adults (8.7% were aged over 50). Non-systematic observations of the site suggested that the sample was representative of

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D.S. Vale / Journal of Transport Geography 32 (2013) 38–48 Table 1 Commuting pattern before and after the workplace relocation. Commuting pattern

Before Number

Travel mode Walk Cycle Public transport Car/motorcycle Car and PT

After %

Number

%

15 0 112 136 22

5.3 0.0 39.3 47.7 7.7

3 0 78 167 37

1.1 0.0 27.4 58.6 13.0

Travel time Less than 15 min 16–30 min 31–60 min More than 60 min

68 85 96 36

23.9 29.8 33.7 12.6

44 113 91 37

15.4 39.6 31.9 13.0

Travel distance Less than 5 km 5–10 km 10–20 km 20–30 km More than 30 km

– – – – –

– – – – –

30 47 82 55 71

10.5 16.5 28.8 19.3 24.9

100.0

285

100.0

Total

285

the overall employee population, with perhaps a slight overrepresentation of employees under the age of 25. Women were also overrepresented (62.4% of the total number), and 45.2% of respondents were one of the two wage-earners in their household. 5. Overall impacts of workplace relocation on commuting The commuting patterns of employees at PN are, to some extent, representative of the mean values for the LMA, where the majority of employees commute 10–20 km and the commute takes 16–30 min each way (Vale, 2009). However, our results showed

that the number of car users is very high compared to the LMA average (58.6% vs 45.0%). This suggests that the workplace relocation increased the distance and percentage of car commuters and reduced the number of active and public transport commuters. At the same time, the overall impact on travel time was positive. Following the move, the majority of respondents commuted 16– 30 min, compared to more than 30 min prior to the relocation (see Table 1). This may be partly explained by the substitution of a faster mode of transport for a slower one. We test this explanation using the multinomial logistic model (see Section 6 below).

5.1. Impacts on commuting based on place of residence Respondents who lived in nearby areas (north and north-east Lisbon) were, as expected, those whose commuting distance decreased the most, as the relocation virtually brought their workplace to their doorstep (Table 2). However, for long-distance commuters (those living outside the LMA), the workplace relocation was also beneficial, probably due to the city’s ring roads that connect the suburbs. Commuting distance increased most for respondents living in the west and north-west, as they had to cross the entire city in their daily commute. City centre residents also reported a considerable increase in travel distance. Amongst this group, those that suffered most had previously lived and worked in the city centre (nine cases). In terms of mode of travel, the workplace relocation revealed substantial car usage inertia for daily commuting, regardless of the place of residence. Moreover, the number of active and public transport users considerably decreased. Similarly, multimodal commuting increased for all employees, except for nearby (north-east sector) residents. In terms of commuting time, those who benefited most from workplace relocation were residents of the nearby north and north-east

Table 2 Workplace relocation impacts on commuting based on place of residence. Place of residence City centre

Lisbon West Sector

Lisbon East Sector

Lisbon NorthWest

Lisbon NorthEast

Region West

Region North

Region South

Long distance commuting

Total

(N)

40.0 10.0 50.0

24.2 15.2 60.6

82.4 11.8 5.9

6.5 7.8 35.7

62.1 18.2 19.7

34.3 20.0 45.7

60.0 20.0 20.0

35.4 16.8 47.7

(101) (48) (136)

20.0 0.0

6.1 0.0

5.9 11.8

2.6 0.0

0.0 0.0

5.7 0.0

0.0 0.0

5.3 1.1

(15) (3)

50.0 60.0

33.3 21.2

76.5 47.1

15.6 9.7

45.5 28.8

40.0 34.3

20.0 0.0

39.3 27.4

(112) (78)

30.0 30.0

57.6 63.6

11.8 41.2

28.6 35.7

43.9 59.1

42.9 48.6

40.0 40.0

47.7 58.6

(136) (167)

0.0 10.0

3.0 15.2

5.9 0.0

3.2 4.5

10.6 12.1

11.4 17.1

40.0 60.0

7.7 13.0

(22) (37)

42.9 23.8

30.0 10.0

30.3 24.2

5.9 47.1

26.0 5.2

13.6 18.2

14.3 0.0

0.0 0.0

23.9 15.4

(68) (44)

33.3 57.1

40.0 60.0

57.6 48.5

29.4 41.2

31.2 23.4

22.7 53.0

8.6 25.7

20.0 40.0

29.8 39.6

(85) (113)

9.5 14.3

20.0 30.0

12.1 27.3

52.9 5.9

33.8 49.4

45.5 24.2

48.6 37.1

60.0 60.0

33.7 31.9

(96) (91)

14.3 4.8

10.0 0.0

0.0 0.0

11.8 5.9

9.1 22.1

18.2 4.5

28.6 37.1

20.0 0.0

10.9 13.0

(36) (37)

(21)

(10)

(33)

(17)

(77)

(66)

(35)

(5)

Impact on travel distance (%) Decrease 23.8 19.0 Same 9.5 28.6 Increase 66.7 52.4 Commuting mode (%) Walk Before 19.0 0.0 After 4.8 0.0 Public transport Before 38.1 28.6 After 42.9 9.5 Car/motorcycle Before 42.9 61.9 After 47.6 61.9 Car and public transport Before 0.0 9.5 After 4.8 28.6 Commuting time (%) Less than 15 min Before 52.4 After 28.6 16–30 min Before 33.3 After 38.1 31–60 min Before 14.3 After 23.8 More than 60 min Before 0.0 After 9.5 (N)

(21)

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D.S. Vale / Journal of Transport Geography 32 (2013) 38–48

Table 3 Impacts on commuting based on former workplace. Former workplace City centre

Inner suburb

Outer suburb

Total

(N)

Impact on travel distance (%) Decrease Same Increase

41.3 13.8 45.0

29.4 18.3 52.4

39.2 17.7 43.0

35.4 16.8 47.7

(101) (48) (136)

Impact on travel mode (%) Started using public transport Same Started using car

2.5 66.3 31.3

0.8 77.0 22.2

10.1 74.7 15.2

3.9 73.3 22.8

(11) (209) (65)

Impact on travel time (%) Decrease Same Increase

30.0 42.5 27.5

26.2 40.5 33.3

34.2 25.3 40.5

29.5 36.8 33.7

(84) (105) (96)

(N)

(80)

(126)

(79)

100.0

(285)

Table 4 Variables used in the logistic models. Variable

Type

Model 1

Model 2

Socio-economic Gender (Male) Age group Occupation ChildrenUnder12

Binary Categorical Categorical Binary

CoV CoV CoV CoV

CoV CoV CoV CoV

Former workplace

Categorical

IV

IV

Commuting pattern Travel distance Travel time

Categorical Categorical

IV IV

IV IV

Relocation commuting impacts Impact on distance Impact on time Impact on mode (car usage)

Categorical Categorical Binary

IV IV DV

IV DV IV

DV: Dependent Variable. IV: Independent Variable. CoV: Covariate.

sector. Nevertheless, it should be noted that the majority of respondents maintained a relatively short travel time (under 30 min) suggesting that commuting modes may change in order to keep travel time acceptable. We test this hypothesis using the multinomial logistic model shown in Section 6 below. 5.2. Impacts on commuting based on former workplace Compared to previous patterns, the new commuting distance increased for about half of employees and decreased for nearly one third, regardless of the former workplace (Table 3). However, in terms of travel time, workplace relocation had less impact and differences were based on the location of the former workplace. On the one hand, respondents whose former workplace was in the outer suburbs were most affected; more than 40% experienced an increase in commuting time. On the other hand, city centre residents were least affected; the vast majority (72.5%) experienced a decrease or no change in their commuting time. Finally, workplace relocation increased car use for 23% of employees; former city centre employees were most affected (31.3%). Nevertheless, the vast majority of respondents did not change transportation mode (73.3%), demonstrating significant transport mode inertia. 6. Results and discussion Our results suggest that the employee, when faced with a new workplace located within a certain travel distance, adapts to the new situation by adjusting the commuting mode to maintain the

commuting time within an acceptable value. In order to test this hypothesis, we developed two logistic regression models, using commuting mode impacts (Model 1) and commuting time impacts (Model 2) as independent and dependent variables (see Table 4). Following a collinearity diagnostic and further multiple correlation analyses, we excluded the following variables from our analysis: Education Level, Family Size, Dual Earner, Income, Number of Cars, Company Car and Time on Current Job. This was due to high correlations with other independent variables; variance inflation factors (VIF) were above five. Travel distance impact was only used as an independent variable, as we assume it is not possible to decrease it without changing the place of residence or job. Finally, we used socio-economic variables as control variables and the former workplace as an independent variable, as we assume that the workplace relocation can have an influence on the new travel pattern. 6.1. Commuting mode impact In order to analyse the impact of workplace relocation on commuting mode, we operationalized commuting mode impact as a dichotomous variable, comparing individuals who started to use their car (22.8%) with others. The number of respondents who started to commute by public transport was very low (3.9%), which made it difficult to develop a multinomial regression model that could include them. Therefore, we developed a binary logistic regression model (Model 1) to examine the effect of commuting distance and commuting time impact on the probability of a change to car (see Table 5). Model 1 is statistically significant (v2 = 42.13; df = 20; p = 0.003), although its explanatory power is low (R2 (Nagelkerke) = .21). Very low levels of multicollinearity were detected (VIF = 3.10 for impact on distance, 2.62 for impact on time, and 1.90 for travel time). However, the model showed that age, former workplace, new travel distance and travel distance impact were statistically significant variables (p < .05), which may explain the increase in the use of private transport for commuting. Younger workers were more affected by the workplace relocation and former inner suburb employees were more likely to start using private transport than former outer suburb employees (who already used private transport). Distance travelled is clearly a relevant factor in the probability of choosing private transport for daily commuting. On the one hand, workers who live further away from the new site showed a lower propensity to start using their car, which can be explained by their greater use of the car prior to the relocation. On the other hand, while an increase in travel distance significantly increased the probability of using the car (B = 1.24, p < .05), the opposite is

45

D.S. Vale / Journal of Transport Geography 32 (2013) 38–48 Table 5 Logistic regression of impacts on commuting and the characteristics of employees who start to use the car for daily commuting (Model 1). Variable (Constant) Socio-economic Gender (Male) Age group 31–45 years old vs less than 30 More than 45 years old vs less than 30 Occupation Skilled non-manual vs managerial Semiskilled vs managerial Skilled manual/unskilled vs managerial ChildrenUnder12 (dummy) Former workplace Inner suburb vs outer suburb City centre vs outer suburb Commuting pattern Travel distance 5 to 10 km vs less than 5 km 10 to 20 km vs less than 5 km 20 to 30 km vs less than 5 km More than 30 km vs less than 5 km Travel time 16 to 30 min vs less than 15 min 31 to 60 min vs less than 15 min More than 60 min vs less than 15 min Relocation commuting impacts Time increased vs same Time decreased vs same Distance increased vs same Distance decreased vs same R2 (Cox & Snell) R2 (Nagelkerke) Model v2 (df)

S.E.

Wald v2

1.50

.96

2.44

.22

.23

.36

.41

1.26

1.08 1.44**

.41 .50

6.89 8.40

.34 .24

.13 .42 .37 .44

.59 .64 .93 .38

.05 .43 .16 1.33

.87 1.52 1.45 1.55

.88* .91

.44 .47

4.08 3.66

2.41 2.47

.75 1.71** .65 1.41*

.58 .58 .62 .64

1.70 8.61 1.12 4.82

.47 .18 .52 .25

.35 .31 .19

.54 .62 .77

.43 .25 .06

1.42 .73 .83

.18 .35 1.24* .85

.44 .55 .59 .63

.16 .40 4.40 1.83

1.19 .71 3.44 2.34

B

**

Odds ratio

.14 .21 42.13 (20)

***

p < .001. p < .05. ** p < .01. *

not true. This suggests that an increase in travel distance may lead to a shift to a faster transport mode in order to reduce commuting time, whereas stability or a decrease in travel distance does not translate into a decrease in car commuting. Therefore, our results suggest that there is an acceptable commuting time, but not a minimum ideal time that would encourage commuters to change to a slower transportation mode. 6.2. Commuting time impact As previously mentioned, workplace relocation had various impacts on commuting time. For nearly one third (29.5%) of employees it decreased, it increased for another third (33.7%), and for the remainder (36.8%) the impact was negligible. In order to explain these differences, we developed a multinomial logistic model (Model 2), using ‘similar’ travel time as the reference category, and the two distinct impacts as comparison categories (see Table 6). We replicated the independent variables from the commuting mode impact model, but in this case the impact on transportation mode (started to use the car) was implemented as an independent variable. In order to control for socio-economic features, we constructed a model based solely on socio-economic variables, but none of the variables were significant. Therefore the model presented here includes all variables. This model is statistically significant and has significant explanatory power (R2 (Nagelkerke) = .74; v2 = 305.75; df = 38; p = 0.000). Very low levels of multicollinearity were detected (VIF = 2.30 for impact on distance, 1.82 for former workplace, and 1.79 for travel distance). As Model 2 shows, occupation, former workplace, new travel time and the impact of the workplace relocation on commuting

distance are the only statistically significant variables to explain the probability of an increase in commuting time. The new travel distance, the new travel time, and the impact of the workplace relocation on commuting distance are the only statistically significant variables that explain a decrease in commuting time. Therefore, only two variables are significant in both parts of the model: the impact on commuting distance and the new travel time. An analysis of the increase in travel time makes it clear that employees with a higher occupational status were less affected than skilled manual and unskilled workers. A move from an outer suburban workplace was also more likely to increase travel time than a move from the city centre, probably because it represents a substantial difference in distance. Some respondents reported a reduction in travel time. However, the values of the coefficients for shorter travel times (less than 15 min) are smaller than the coefficients for longer travel times (more than 60 min). Therefore, employees with longer travel times were more likely to have their travel time altered due to the workplace relocation. More importantly, as travel distance increased, travel time increased, whereas the opposite was not true. This finding is extremely relevant for our hypothesis. Our results demonstrated that a decrease in travel distance was not associated with a decrease in travel time. This shows therefore that while commuting times are insensitive to a reduction in travel distance, they are clearly sensitive to increases. A similar asymmetrical effect can be observed in the second part of the model, but in the opposite direction: shorter travel distances tend to lead to a decrease in travel time. Furthermore, an increase in distance had no significant effect on commuting time, raising the possibility that employee’s commuting patterns change to a faster mode of transport, but only if

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D.S. Vale / Journal of Transport Geography 32 (2013) 38–48

Table 6 Multinomial logistic regression of commuting impacts and employee characteristics on new commuting time (Model 2). Variable

Travel time increased (vs Same) B

(Constant)

S.E.

Travel time decreased (vs Same) 2

Wald v

.75

1.23

.37

.60

.41

2.10

Odds ratio

B

S.E. *

Wald v2

Odds ratio

3.70

1.83

4.09

.55

.59

.56

1.10

.55

Socio-economic Gender (Male) Age group Less than 30 years old vs more than 45 31–45 years old vs more than 45 Occupation Managerial vs skilled manual/unskilled Skilled non-manual vs skilled manual/unskilled Semiskilled vs skilled manual/unskilled ChildrenUnder12 (dummy)

.28 .20

.55 .50

.26 .16

1.32 .82

.16 .85

.73 .81

.05 1.09

1.18 2.34

2.56* 2.35* 2.31* .21

1.29 1.13 1.11 .43

3.91 4.29 4.29 .25

.08 .10 .10 1.24

1.05 .81 1.50 .31

1.35 1.19 1.16 .59

.61 .47 1.69 .27

.35 .44 .22 .74

Former workplace Outer suburb vs city centre Inner suburb vs city centre

1.43* .40

.57 .49

6.25 .67

4.18 1.49

.81 .04

.69 .63

1.35 .00

2.24 .96

1.47 .63 1.54* .21

.94 .89 .78 .87

2.43 .50 3.89 .06

.23 .53 .21 .81

Commuting pattern Travel distance Less than 5 km vs more than 30 km 5–10 km vs more than 30 km 10–20 km vs more than 30 km 20–30 km vs more than 30 km Travel time Less than 15 min vs more than 60 min 16–30 min vs more than 60 min 31–60 min vs more than 60 min

1.00 .11 .94 .46

1.03 .71 .55 .54

.94 .02 2.85 .72

2.71 1.12 2.55 1.58

4.63*** 2.20*** 1.59**

1.32 .65 .59

12.27 11.57 7.32

.01 .11 .20

5.29*** 3.49** 1.88

1.51 1.34 1.32

12.27 6.77 2.02

199.10 32.67 6.52

Relocation commuting impacts Distance increased vs same Distance decreased vs same Started commuting by car (Dummy)

3.03*** .02 .35

.60 .79 .45

25.13 .00 .58

20.61 .98 1.41

1.54 3.73*** .28

.90 .70 .59

2.91 28.37 .23

.21 41.63 .75

R2 (Cox & Snell) R2 (Nagelkerke) Model v2 (df)

.66 .74 305.75 (38)

*

p < .05. p < .01. *** p < .001. **

the increase in distance is larger than their commuting tolerance. Moreover, as before, the analysis of coefficients showed that respondents with shorter commuting times before the move experienced greater reductions in commuting time following the move than respondents with longer commuting times, which supports the previous finding. Also relevant in this model is the lack of significance of the impact on commuting mode (in this case, new car users), which suggests that despite the change to a faster transport mode, their travel time both increased and decreased.

7. Conclusions The development of a mixed-use centre in the LMA that is easily accessible by public transport has caused a considerable number of companies to relocate to the site. However, it has not created more sustainable commuting patterns. Our findings show that an increase in travel distance increases the probability of using private transport and increased commuting time, but the opposite is not true. This suggests that the decision to use the car to reach the new workplace has more to do with maintaining travel time within certain acceptable values, rather than reducing it to a minimum. Therefore, our findings suggest that a decrease in commuting distance, initiated by the creation of mixed use, accessible suburban centres is not enough to trigger a change to a more sustainable transport mode. This supports the hypothesis of a ‘frictionless commuting area’ resulting from a ‘critical isochrone’ proposed by Getis (1969). Additionally, high transportation inertia may be explained by symbolic and affective factors associated with private

transport (Steg, 2005), and by the strong effect of habit on the choice of commuting mode (Gardner, 2009). Indeed, the critical isochrone may in itself be a manifestation of commuting habits and workplace relocation is not enough to change the travel mode. Therefore, if the desired travel pattern is to be achieved, additional travel demand management measures are needed. These measures should increase the overall cost of private transport, such as reducing or eliminating free workplace parking. We must acknowledge some limitations of our findings. First, we did not look at short-term vs long-term adaptations. Although we examined relocations in the five years preceding the study, we did not carry out any time-based analyses. Second, we only analysed changes in commuting patterns that occurred as a consequence of a new commuting distance, which in turn was created by workplace relocation. Moreover, the significant effect of transportation mode inertia made it impossible to create a multinomial logistic model to analyse the impact on commuting mode. Consequently, we were only able to analyse the impacts of the new commuting distance on employees who had started to use the car compared to all other employees. Third, we did not collect data on the availability of public transport in the former workplace location. Therefore, relocations from places other than the city centre to the Park of the Nations may represent an increase or a decrease in public transport availability and accessibility. Nevertheless, our results suggest the existence of a commuting time tolerance and an associated critical isochrone, which guide how employees adapt their commute. However this adaptation appears to be unidirectional, as increases in travel distance have a clear impact on transport mode, but decreases in travel distance are

D.S. Vale / Journal of Transport Geography 32 (2013) 38–48

associated with transport mode inertia. This in itself supports the hypothesis of commuting time tolerance, as it represents a strategy that aims to keep the commute within an acceptable (tolerated) travel time. These findings support the sustainable urban mobility paradigm, which states that transportation policies should aim to reduce travel speed and distance, rather than travel time (Banister, 2011b). Accordingly, we argue that land use planning should abandon car-dependent urban design solutions and focus on the promotion of diverse choices, whether in terms of providing a variety of transportation modes, dwelling types or densities. In other words, urban planning should promote a built environment that is diverse and has multimodal access. This should be applied to both homes and workplaces, and be based on a combination of push and pull measures, amongst which reduced availability of workplace parking is a key factor in reducing car usage. Acknowledgments We would like to thank the Gulbenkian Foundation for their financial support, and the two anonymous referees who provided useful insights on a previous version of this paper. Finally, we would like to thank Tim Schwanen for his valuable opinions and suggestions. References Aarhus, K., 2000. Office location decisions, modal split and the environment: the ineffectiveness of Norwegian land use policy. Journal of Transport Geography 8, 287–294. Aguilera, A., 2005. Growth in commuting distances in French polycentric metropolitan areas: Paris, Lyon and Marseille. Urban Studies 42, 1537–1547. Aguilera, A., Wenglenski, S., Proudlhac, L., 2009. Employment suburbanisation, reverse commuting and travel behaviour by residents of the central city in the Paris metropolitan area. Transportation Research Part A 43, 685–691. Banister, D., 2005. Unsustainable Transport: City Transport in the New Century. Routledge, London. Banister, D., 2008. The sustainable mobility paradigm. Transport Policy 15, 73–80. Banister, D., 2011a. Cities, mobility and climate change. Journal of Transport Geography 19, 1538–1546. Banister, D., 2011b. The trilogy of distance, speed and time. Journal of Transport Geography 19, 950–959. Bell, D.A., 1991. Office relocation - city or suburbs? Travel impacts arising from office relocation from city to suburbs. Transportation 18, 239–259. Bertolini, L., Le Clercq, F., 2003. Urban development without more mobility by car? Lessons from Amsterdam, a multimodal urban region. Environment and Planning A 35, 575–589. Boarnet, M.G., Crane, R., 2001. Travel by Design. The Influence of Urban Form on Travel. Oxford University Press, New York. Boarnet, M.G., Sarmiento, S., 1998. Can land-use policy really affect travel behaviour? A study of the link between non-work travel and land-use characteristics. Urban Studies 35, 1155–1169. Cao, X., Mokhtarian, P.L., Handy, S., 2007. Do changes in neighborhood characteristics lead to changes in travel behavior? A structural equations modelling approach. Transportation 34, 535–556. Cao, X., Mokhtarian, P.L., Handy, S., 2009. Examining the impacts of residential selfselection on travel behaviour: a focus on empirical findings. Transport Reviews 29, 359–395. Carrière, J.-P., Demazière, C., 2002. Urban planning and flagship development projects: lessons from Expo’98, Lisbon. Planning Practice and Research 17, 69– 79. Castro, A., Lucas, J., 1999. A Expo’98 – cronologia de um processo [Expo’98 – chronology of a process]. In: Matias Ferreira, V., Indovina, F. (Eds.), A Cidade da Expo’98 [The City of Expo’98]. Editorial Bizâncio, Lisbon, pp. 345–422. Cervero, R., 2003. The built environment and travel: evidence from the United States. European Journal of Transport and Infrastructure Research 3, 119–137. Cervero, R., 2007. Transit-oriented development’s ridership bonus: a product of selfselection and public policies. Environment and Planning A 39, 2068–2085. Cervero, R., Landis, J., 1992. Suburbanization of jobs and the journey to work: a submarket analysis of commuting in the San Francisco Bay Area. Journal of Advanced Transportation 26, 275–297. Cervero, R., Wu, K.-L., 1998. Sub-centring and commuting: evidence from the San Francisco Bay Area, 1980–90. Urban Studies 35, 1059–1076. Cervero, R., Murphy, S., Ferrell, C., Goguts, N., Tsai, Y.-H., 2004. Transit-Oriented Development in the United States: Experiences, Challenges and Prospects – TCRP Report 102. Transportation Research Board, Washington, DC. Crane, R., 2000. The influence of urban form on travel: an interpretative review. Journal of Planning Literature 15, 3–23.

47

Dijst, M., 1997. Spatial policy and passenger transportation. Netherlands Journal of Housing and the Built Environment 12, 91–111. Ewing, R., Cervero, R., 2001. Travel and the built environment: a synthesis. Transportation Research Record 1780, 87–114. Ewing, R., Cervero, R., 2010. Travel and the built environment: a meta-analysis. Journal of the American Planning Association 76, 265–294. Fox, M., 1995. Transport planning and the human activity approach. Journal of Transport Geography 3, 105–116. Gardner, B., 2009. Modelling motivation and habit in stable travel mode contexts. Transportation Research Part F: Traffic Psychology and Behaviour 12, 68–76. Gardner, B., Abraham, C., 2007. What drives car use? A grounded theory analysis of commuters’ reasons for driving. Transportation Research Part F: Traffic Psychology and Behaviour 10, 187–200. Gardner, B., Abraham, C., 2008. Psychological correlates of car use: a meta-analysis. Transportation Research Part F: Traffic Psychology and Behaviour 11, 300–311. Getis, A., 1969. Residential location and the journey from work. Proceedings of the Association of American Geographers, 55–59. Guth, D., 2010. Emergence of employment centres in German metropolitan areas: impacts on commuter traffic, 1987–2007. In: Schrenk, M., Popovich, V., Zeile, P. (Eds.), Proceedings of the Real Corp 2010 Conference, Vienna, pp. 235–247. Hägerstrand, T., 1970. What about people in regional science. Papers of the Regional Science Association 24, 7–21. Handy, S., Cao, X., Mokhtarian, P.L., 2005. Correlation or causality between the built environment and travel behavior? Evidence from Northern California. Transportation Research Part D 10, 427–444. Hanssen, J.U., 1995. Transportation impacts of office relocation: a case study from Oslo. Journal of Transport Geography 3, 247–256. Hess, D.B., 2001. Effect of free parking on commuter mode choice: evidence from travel diary data. Transportation Research Record 1753, 35–42. Hjorthol, R., 2001. Gendered aspects of time related to everyday journeys. Acta Sociologica 44, 37–49. Hupkes, G., 1982. The law of constant travel time and trip-rates. Futures 14, 38–46. Jain, J., Lyons, G., 2008. The gift of travel time. Journal of Transport Geography 16, 81–89. Kitamura, R., Mokhtarian, P.L., Laidet, L., 1997. A micro-analysis of land use and travel in five neighborhoods in the San Francisco Bay Area. Transportation 24, 125–158. Krizek, K.J., 2003. Residential relocation and changes in urban travel. Does neighborhood-scale urban form matter? Journal of the American Planning Association 69, 265–281. Levinson, D.M., Kumar, A., 1994. The rational locator: why travel times have remained stable. Journal of the American Planning Association 60, 319–332. Levinson, D.M., Wu, Y., 2005. The rational locator reexamined: are travel times still stable? Transportation 32, 187–202. Litman, T., 2011. Land Use Impacts on Transport – How Land Use Factors Affect Travel Behavior. Victoria Transport Policy Institute, Vitoria. Lyons, G., Urry, J., 2005. Travel time use in the information age. Transportation Research Part A 39, 257–276. Lyons, G., Jain, J., Holley, D., 2007. The use of travel time by rail passengers in Great Britain. Transportation Research Part A 41, 107–120. Lyons, G., Jain, J., Susilo, Y., Atkins, S., 2012. Comparing Rail Passengers’ Travel Time Use in Great Britain between 2004 and 2010. Mobilities, 1–20 (ahead-of-print). Ma, K.-R., Kang, E.-T., 2011. Time-space convergence and urban decentralisation. Journal of Transport Geography 19, 606–614. Maat, K., Van Wee, B., Stead, D., 2005. Land use and travel behaviour: expected effects from the perspective of utility theory and activity-based theories. Environment and Planning B 32, 33–46. Malone-Lee, L.C., Sim, L.L., Chin, L., 2001. Planning for a more balanced home-work relationship: The case study of Singapore. Cities 18, 51–55. Marchetti, C., 1994. Anthropological invariants in travel behavior. Technological Forecasting and Social Change 47, 75–88. Martens, M.J., Griethuysen, S.V., 1999. The ABC Location Policy in the Netherlands: The Right Business at the Right Place. INRO-TNO, Delft. Metz, D., 2004. Travel time – variable or constant? Journal of Transport Economics and Policy 38, 333–344. Metz, D., 2008. The myth of travel time savings. Transport Reviews 28, 321–336. Meurs, H., Haaijer, R., 2001. Spatial structure and mobility. Transportation Research Part D 6, 429–446. Mokhtarian, P.L., 2005. Travel as a desired end, not just a means. Transportation Research Part A 39, 93–96. Mokhtarian, P.L., Cao, X., 2008. Examining the impacts of residential self-selection on travel behavior: a focus on methodologies. Transportation Research Part B 42, 204–228. Mokhtarian, P.L., Chen, C., 2004. TTB or not TTB, that is the question: a review and analysis of the empirical literature on travel time (and money) budgets. Transportation Research Part A 38, 643–675. Mokhtarian, P.L., Salomon, I., 2001. How derived is the demand for travel? Some conceptual and measurement considerations. Transportation Research Part A 35, 695–719. Næss, P., 2005. Residential location affects travel behaviour – but how and why? The case of Copenhagen Metropolitan Area. Progress in Planning 63, 167– 257. Næss, P., 2009. Residential self-selection and appropriate control variables in land use: travel studies. Transport Reviews 29, 293–324. Næss, P., Sandberg, S.L., 1996. Workplace location, modal split and energy use for commuting trips. Urban Studies 33, 557–580.

48

D.S. Vale / Journal of Transport Geography 32 (2013) 38–48

Neuman, M., 2005. The compact city fallacy. Journal of Planning Education and Research 25, 11–26. Newman, P., Kenworthy, J.R., 2006. Urban design to reduce automobile dependence. Opolis: An International Journal of Suburban and Metropolitan Studies 2, 35– 52. Priemus, H., Nijkamp, P., Banister, D., 2001. Mobility and spatial dynamics: an uneasy relationship. Journal of Transport Geography 9, 167–171. Pucher, J., Buehler, R., 2010. Walking and cycling for healthy cities. Built Environment 36, 391–414. Redmond, L.S., Mokhtarian, P.L., 2001. The positive utility of the commute: modelling ideal commute time and relative desired commute amount. Transportation 28, 179–205. Rouwendal, J., Nijkamp, P., 2004. Living in two worlds: a review of home-to-work decisions. Growth and Change 35, 287–303. Schafer, A., 2000. Regularities in travel demand: an international perspective. Journal of Transportation and Statistics 3, 1–31. Schwanen, T., Dijst, M., Dieleman, F.M., 2004. Policies for urban form and their impact on travel: the Netherlands experience. Urban Studies 41, 579–603. Seixas, J., 2004. Desenvolvimento Económico e Competitividade Urbana de Lisboa [Economic development and urban competitiveness of Lisbon]. In: Seixas, J. (Ed.), Estudos Sócio-Económicos e Urbanísticos. Câmara Municipal de Lisboa, Lisboa. Sim, L.L., Malone-Lee, L.C., Chin, L., 2001. Integrating land use and transport planning to reduce work-related travel: a case study of Tampines Regional Centre in Singapore. Habitat International 25, 399–414. Stead, D., Marshall, S., 2001. The relationships between urban form and travel patterns. An international review and evaluation. European Journal of Transport and Infrastructure Research 1, 113–141.

Steg, L., 2005. Car use: lust and must. Instrumental, symbolic and affective motives for car use. Transportation Research Part A 39, 147–162. Vale, D., 2009. Sustainable Urban Form, Accessibility and Travel: The Relationship between Polycentric Urban Development and Commuting in Lisbon. Unpublished PhD Thesis, School of Architecture, Planning and Landscape. Newcastle University, Newcastle upon Tyne. Van Ommeren, J., Rietveld, P., Nijkamp, P., 1997. Commuting: in search of jobs and residences. Journal of Urban Economics 42, 402–421. Van Wee, B., 2009. Self-selection: a key to a better understanding of location choices, travel behaviour and transport externalities? Transport Reviews 29, 279–292. Verhetsel, A., Vanelslander, T., 2010. What location policy can bring to sustainable commuting: an empirical study in Brussels and Flanders, Belgium. Journal of Transport Geography 18, 691–701. Verroen, E.J., Jong, M.A., Korver, W., Jansen, B., 1990. Mobility Profiles of Businesses and Other Bodies. Rapport INRO-VVG 1990-03. Institute of Spatial Organisation TNO, Delft. Wheeler, S., 1998. Planning sustainable and livable cities. In: LeGates, R.T., Stout, F. (Eds.), The City Reader, second ed. Routledge, London, pp. 434–445. Woo, Y.-S., 2005. An analysis of commuting distance and mode changes of recent migrations to housing renewal areas. International Review of Public Administration 10, 95–108. Zahavi, Y., 1979. The ‘UMOT’ Project. Report Prepared for the US Department of Transport, Washington, DC and for the Ministry of Transport, Federal Republic of Germany, Bonn.