4.1. Workplace Relocation and Changes in Commuting Mode
Modal shifts related to workplace relocation can be related to modifications of (1) the Public Transport (PT) accessibility, (2) the road accessibility, (3) the parking provision, and (4) the share of employees with a short distance to work [16
]. Of course, a relocation from the city center to the suburbs has the potential to affect all of the aforementioned 4 elements.
The modal split variation is partially due to a modification of the distance to the Central Business District (CBD) and the urban density [24
]. Bell [6
] showed that after a decentralization from Melbourne downtown to a suburban area 8.5 km away from the CBD, car use increased from 34% to 76%. Hanssen [17
], using data from Oslo, indicated that the suburbanization of an insurance company increased car use from 25% to 41% despite that the new worksite was well served by public transport. More significant modal shift observations were provided by Wabe [43
], who indicated that a firm decentralization in London led to an increase in car use from 8% to 71%. However, as pointed out by Yang et al. [2
], the relocation of the employees is not necessarily leading to higher car use. Indeed, many studies show that relocation or decentralization of firms is indeed often associated with higher car use levels [6
]. However, counter examples can be found e.g., in the studies from Walker et al. [45
] and Sim et al. [21
], who mention a (possible) modal shift towards sustainable alternatives (mainly, from car to PT).
For the University of Luxembourg, changes in mode choices were very different from those forecasted by discrete choice models [31
] when forecasting the effects of relocating to Belval (Table 2
). A predicted increase of car use was not confirmed by the data, and, on the contrary, it decreased substantially in favor of a higher share of public transport trips. This may be due to the changes in transportation infrastructure and services, together with mobility management measures that were meanwhile adopted to guarantee a better accessibility to the new campus (new public transport lines, paid vs. free parking at the old campuses, public transport pass subsidies, introduction of a corporate car sharing system), together with long-term factors that are more difficult to be incorporated in travel prediction models (residential changes, company workforce turnover).
Often, people tend to stick to a commuting mode they are familiar with, as long as the commuting time remains below an acceptable threshold, hence showing mode selection habits [1
]. This travel mode inertia explains why, using data from Lisbon (Portugal), 73.3% of employees facing an office decentralization did not opt for a new mean of transport. In order to keep (or achieve) important share of public transport users after a relocation, the provision of good transit service at the new location is of paramount importance. However, Transit Oriented Development (TOD) with good public transport provision is not guaranteed to lead to lower share of car use among the commuters [17
]. On the other hand, free parking and good road accessibility can be important car incentives jeopardizing TOD goals. Indeed, compared to the CBD, suburban locations often enjoy a less congested road network. Moreover, due to cheap land availability, the provision of free parking spots is often a reality in suburban working sites and the less congested road conditions play a role as well. Cervero [47
] confirmed that, at least for the American context, suburban areas are also associated with free parking (because of cheap land availability) and poor public transport connections. Hanssen [17
] showed that after a company move from the center to the suburb the share of public transport users having to make one or more transfers increased from 8% to 28%. Hence, the employment suburbanization is sometimes leading to a less favorable public transport accessibility.
Interestingly, Walker et al. [45
] found that travel habits weakened immediately after a workplace relocation regardless if the employees shift to a new mode or not. Habits of workers who opted for a new mode did not disappear brutally but slowly decayed after the post-move period and during a period of 4 weeks. A disruptive event such as a workplace relocation is hence a good opportunity to foster modal shift but according to [45
] this “window of opportunity for change” can also be seen as a “window of vulnerability to relapse.” After a workplace decentralization, Bell [6
] observed that car started to be seen as a “faster, more reliable, less expensive, more comfortable, cleaner and more convenient” commuting mode. If a certain share of workers shifts from, for instance, public transport to car, this could partly explain why, in some job decentralization studies, the commuting distance increase but the commuting time remains roughly constant [1
Due to the enormous possible impact on travel behavior, numerous studies were undertaken to understand the aggregated effect on commuting time, distance, and mode. Workplace decentralization may not necessarily imply increasing commuting distances. Whereas some studies reported longer commuting times and distances (e.g., [11
], others found commuting distances to reduce as locations may get closer to the residential areas. Angel and Blei [48
] reported from a study involving 40 cities that average commuting distances were 1.6 times shorter than commuting to the CBD. This is in line with the co-location concept introduced by Gordon et al. [24
], which posits that companies may select suburban locations in order to locate themselves closer to their employees, who had slowly moved to the suburb. Kim [49
] provided an interesting study on the effect of co-location on commuting time stability and mentions that “little evidence contradicts the co-location hypothesis.” Despite a probable shortening of the commuting time, the overall environmental impact appears to be dramatic. Indeed, despite the intense debate on the co-location hypothesis regarding the commuting time or distance there is little doubt regarding the significant car use increase. Levinson and Kumar [44
] or Gordon et al. [24
] also underlined the fact that dispersed or polycentric metropolitan structures are associated with shorter commuting times. Regarding the commuting time, Wabe [43
], analyzing the London area data, observed that after a company suburbanization, the average commuting time of the employees was halved. The good road conditions and the massive shift towards car use are an explanation for this important commuting time decrease [32
]. Similarly, as observed in the Australian context, the decrease in the home-to-work time would be partially due to the non-congested road network state for reverse commuting (from the center to the suburbs) [33
]. Cervero and Landis [23
] proposed interesting workers submarket analysis and indicate that if the aggregated commuting time was decreasing due to switch to faster mode and stable commuting distance this situation was not verified for all types of residential areas. In analogy to [22
], Cervero and Landis [23
] showed that, for instance, reverse commuters (e.g., downtown resident whose new workplace is in the suburbs) were facing an important increase of their commuting time and distance.
4.2. Activity Pattern Modification and Changes in the Daily Mobility
When analyzing the commuting behavior, often studies focus on the commuting trip. This may provide a short-sighted vision of the impacts of workplace relocation for two reasons. First, commuting represents only 1 out of 3–4 trips performed on average by an individual, and chained activities, performed during the day, may equally affect commuting mode choice (e.g., business trips, picking up or dropping off children, etc.). Second, travel behavior may differ significantly across weekdays, making it difficult to really capture the temporal variability of mode choices.
Workplace relocations are affecting the commuting trip characteristics (road and PT accessibility, parking provision, commuting distance) and when the home-work-home trip is routinized, the entire daily activity pattern is also affected. Moving may be associated with a change in the lifecycle stage and household scheduling. Aguilera et al. [22
] showed that job suburbanization was associated with a decrease in the number of daily journeys performed by working central city residents. Bell [6
] showed that relocating a workplace to an isolated new site can have important impact on the daily activities performed. In total, the workplace relocation led to a 10% decrease in the number of activities performed during a day (from 2.2 to 2 activities). The modification of the activity pattern is the example of a short-term adaptation due to a workplace decentralization. Bell [6
] shows that the number of shopping activities performed per day decreased from 23.8% to 15.2%.
Sprumont and Viti [41
] showed that, after the relocation of the Walferdange campus, most of the activity locations close to the former working site that were previously visited were not re-visited after the relocation. This is showcased in Figure 4
, where the location of all activities visited by three individuals, each representative of the three distinct clusters of staff members derived from the data analysis (see [41
] for more details), is presented, together with their resulting Standard Deviational Ellipse (SDE), which geometrically and synthetically represents the spatial coverage of the activity locations. In this theory, home and work locations are seen as anchor points, and the ellipses delimit the space where individuals can seek for the locations of the other (chained) activities. The figure reveals that not only a large number of activity locations changed once the employee workplace has been relocated, but also the number of performed activities in a working day changed. By performing this analysis, Sprumont and Viti [41
] concluded that the national objective, which was meant to decrease pressure (in terms of trips mainly) from Luxembourg city, was achieved also because only very few respondents still had activities close to their former working place.
Apart from the influence of workplace relocation to chained activities, mobility habits may be affected because of new induced trips, or because the daily routine of an employee may significantly differ from one day of the week to another. In this sense the literature on workplace relocation we found misses to provide sufficient evidence and therefore it may be an opportunity for future research. Its relevance is demonstrated by the results of the 2020 Travel Survey of the University of Luxembourg, where it was asked for the first time to report a non-typical day, if this was occurring non-sporadically. Interestingly, 42.4% of the respondents stated to have a non-typical day, and 66% of these respondents indicated to perform this alternative commuting pattern at least once a week. Table 3
shows how typical and atypical days may differ in terms of commuting behavior. Notably, non-typical days are characterized by a lower number of modes used over the day, in accordance with a higher car usage and a higher number of activities chained to the commuting trip.
Another interesting finding, when collecting information about atypical days, is that stated satisfaction may be very different for those days. As welfare and satisfaction have been recently deemed fundamental metrics for company’s performance and employees’ productivity and attachment to the employer [50
], and it has been more extensively researched in the last years also in the context of workplace relocation (e.g., [7
]), it will be relevant in future studies to draw more attention to collecting different mobility habits beyond the single commuting trip, and to identify those critical aspects that affect commuting satisfaction.
Focusing in particular on the role of atypical days, this may partly explain the changes in commuting satisfaction after workplace relocation, as reported in Figure 5
. Since in 2020 the survey included the option ‘Neither satisfied nor dissatisfied’ a fair comparison of the results could not be performed and hence the 2020 results have been separated from the other three survey results. Figure 5
a shows how commuting satisfaction had changed overall during the first part of the relocation phase, and in particular how the share of satisfied and very satisfied commuters had reduced in 2016 (60%) with respect to the previous years (69% and 63%, respectively, in 2012 and 2014), when campus Belval was populated by a minor share of employees. In 2020 (Figure 5
b), the satisfied and very satisfied commuters have notably reduced (48%, probably also due to the high number of ‘neutral’ respondents), but this number is even lower when looking at the atypical days (37%).