Toward sustainable transportation: exploring transportation decision making in teleworking households in a mid-sized Canadian city.
|Subject:||Transportation industry (Forecasts and trends)|
Andrey, Jean C.
Burns, Kyle R.
Doherty, Sean T.
|Publication:||Name: Canadian Journal of Urban Research Publisher: Institute of Urban Studies Audience: Academic Format: Magazine/Journal Subject: Social sciences Copyright: COPYRIGHT 2004 Institute of Urban Studies ISSN: 1188-3774|
|Issue:||Date: Winter, 2004 Source Volume: 13 Source Issue: 2|
|Topic:||Event Code: 010 Forecasts, trends, outlooks; 290 Public affairs Computer Subject: Market trend/market analysis|
|Product:||Product Code: 4000000 Transportation; 4700000 Transportation Services NAICS Code: 488 Support Activities for Transportation SIC Code: 4100 LOCAL AND INTERURBAN PASSENGER TRANSIT|
|Geographic:||Geographic Scope: Canada Geographic Name: Canada; Canada Geographic Code: 1CANA Canada|
There is growing awareness that Canadian urban transportation systems are not sustainable. However, we have only a limited understanding of how travel decisions are affected by policy or other circumstantial changes that are purported to address sustainability concerns. This paper reports on an in-depth, small-sample experiment designed to explore issues that require nontraditional travel data. The locus is on household-level responses to a vehicle-reduction scenario in the context of teleworking households in a mid-sized Canadian city. The study design allows for comparisons of 'actual' versus 'gamed' activity and travel patterns. Two themes were explored: telework as an enabler of travel changes; and the persistence of and reasons for auto dependency within teleworking households. Results suggest that teleworking households have a high capacity to adapt to a vehicle-reduction scenario, while making only minor changes to activity patterns. Despite the ability to change, the six participating households displayed continued auto reliance. Barriers that prevent telework from reaching its potential as an auto-reduction strategy emerged during the game.
Keywords: Telework, transportation, activity diary, stated-adaptation game
Il y a une prise de conscience croissante que les systemes urbains canadiens de transport ne sont pas renouvelable. Cependant, nous avons une comprehension limitee de la facon dont la prise de decision de voyage est entache par la politique ou d'autres changements circonstanciels qui sont censes adresser des soucis de la viabilite ecologique. Dans cette etude, un jeu d'enoncer-adaptation a ate developpe et applique a un petit echantillon de menages canadiens qui teletravail a domicile afin d'explorer les processus du niveau du menage, la prise de decision de voyager dans une ville domine de voiture de serie intermediaire. Des menages qui ont autrefois eu deux ou trois vahicules ont eta invites reformuler leur semaine ayant seulement une automobile disponible. Le jeu fournit des points sur le teletravail comme un activateur de changements du comporternent de voyage. Les resultats suggerent que les menages qui teletravail ont une capacite elevee de s'adapter a un scenario de reduction de vehicule, tout en faisant seulement des changements mineurs aux modeles d'activite. En depit de la capacite de changer, tousles manages participants ont montre une continuita d'un besoin de voiture. Les resultats accentuent les obstacles empechant le teletravail d'atteindre sa pleine capacite comme stratagie de Gestion de la demande en transport (GDT).
Mots cles: Taletravail, transport, journal d'activite, jeu affirme-adaptation
Several reports highlight the high level of auto dependence in urban areas of Canada (Pucher 1998; The Centre for Sustainable Transportation 2002; Miller and Shalaby 2003), and much has been written about the environmental and social costs associated with such intensive auto use (e.g., Transport Canada 2001). There is thus growing interest in how both technology and policy may help to move Canadian cities in a more sustainable direction. Particular attention has been given to behavioural solutions under the umbrella of transportation demand management (TDM) (Litman 2002). TDM measures are intended to alter some aspects of transportation demand such as trip generation, travel mode, timing, route, or vehicle occupancy in order to improve the efficiency of the existing transport system and/or reduce the negative consequences of travel. In an operational sense, TDM initiatives are usually intended to reduce auto use in favour of other forms of communication or travel. Although TDM is being promoted in a number of forums (e.g. Transportation Climate Change Table 1999; Transport Canada 2002; Canadian Telework Association 2003), its formal integration into municipal plans has occurred only recently and to a limited extent (Lim 1997; Robinson 1997; Stewart and Pringle 1997).
While various studies document the impacts of individual TDMs in particular circumstances, research has also shown that commuters in different markets respond differently to the same mix of strategies (Winters 2000). Thus empirical generalizations may not necessarily lead to accurate predictions about behavioural responses to specific interventions. Rather, more attention must be given to understanding how and why responses come about, and also the potential for adaptation that exists in different circumstances. The latter is particularly important given the incremental and often unexpected ways in which changes to the transport system occur.
Teleworking (or telecommuting as it is often referred to in the United States) and other non-traditional work arrangements are considered TDM measures because of their potential to affect virtually every aspect of trip making--from trip frequency to travel mode--despite the fact that the primary, motivations for teleworking tend not to be transport-related, but rather pertain to job and/or personal factors (Doherty et al. 2000).
However, there is uncertainty about the total, household-level travel implications of telework as well as the effect of place-based variables such as city size. These uncertainties relate in part to the paucity of data at the household level, especially for non-work travel. Also of importance is the fact that there has been little serious work on the potential interaction between telework and other background variables, including auto-reduction initiatives or outcomes (Shiftan and Suhrbier 2002 is a notable exception) and other aspects of the transportation-land use system. Thus we are not in a position to predict household-level behavioural responses to the adoption or modification of telework. Nor are we able to anticipate the ease and/or readiness with which teleworking households might adjust to auto-reduction initiatives and/or pressures. This is a crucial area of research, however, as many of the empirical studies on the travel implications of telework have focused on early adopters with long commutes in heavily congested urban areas (Mokhtarian et al. 1995; Koenig et al. 1996; Saxena and Mokhtarian 1997), and the results may not be transferable to other groups or situations. Indeed, the Canadian literature on the travel implications of telework is very limited (Snow 1998; Tayyaran 2000; Bussiere and Lewis 2002; Mortimer 2002).
This paper uses a stated-adaptation gaming approach with a sample of six Canadian teleworking households in order to explore the processes of household-level, travel decision making. This pilot study is situated in a mid-sized city with a high level of auto dependence; and the gamed situation is one where households that formerly had two or three vehicles were asked to reformulate their week having only one automobile available.
This paper contributes to our understanding of the processes of travel decision making in teleworking households through the use of gaming. The goal of this research is to develop and implement a game that allows us to track and understand how teleworking households would respond to a vehicle-reduction scenario. The specific empirical objectives are to explore:
1. Telework as an enabler of travel change, and
2. The persistence, of and reasons for, auto dependency within teleworking households.
The reason for focusing on auto reduction is because a growing proportion of the population is aware of the environmental consequences of excessive auto use, and there are indications that some people would be willing to change their automobile use patterns if changes were feasible (Fisher 1999; Stradling et al. 2002). Various authors have explored why the car is so highly preferred and the corollary--obstacles to reduced car use (Fisher 1999; Jensen 1999; Wright and Egan 2000). According to Fisher (1999), the barriers to mode change may be organized into three groups: practical, psychological and social. The practical barriers include such things as perceived risk, convenience, comfort, reliability, speed, ability to carry loads, cost, and access to information. With respect to the psychological barriers, the power of the automobile may be seen as a symbol of power, status, freedom, adventure, omnipotence, virility, independence, and modernity; while public transit may be associated with negative images, such as loss of control and loss of privacy. Both practical and psychological barriers vary from individual to individual. The third type of barrier is sociological--concerns over a reduction in quality of life, which is considered unacceptable in a society that associates progress with individualism, speed and consumerism.
Since telework addresses some of the barriers identified above, it is viewed with optimism by planners and environmentalists alike as a strategy for vehicle use reduction. On a practical level, when the teleworker works from home the concerns over traveling convenience, risk, comfort, etc. are not germane. On a psychological level, teleworkers can sate their appetite for power and speed with the latest computer equipment. On a social level there is widespread belief that telework has the potential to improve one's quality of life by increasing work-life balance.
Despite the many studies on the travel implications of telework, little effort has been made to understand the ease/difficulty with which a teleworking household could make substantial changes to its travel patterns. The focus instead has been on documenting the travel outcomes of teleworkers, typically without any concurrent changes in other circumstances that would encourage or require adaptation. The current study thus offers a complement to the larger-sample, before-and-after or with-and-without studies on the travel impacts of telework by focusing on survey methodology, the capacity for change, and decision-making processes.
Primary data were collected using an electronic activity diary. Various authors emphasize the advantages of activity-based data/models for the analysis of TDM strategies generally, and telework more specifically, because they provide an opportunity to better understand responses to interventions, including secondary effects and induced trips (Doherty et al. 2002; Shiftan and Suhrbier 2002). Also, in the context of making travel modifications, Rose and Ampt (2001) argue that activity diaries provide a means of making travel activity tangible for the participants. The electronic activity diary used for the study was CHASE, Computerized Household Activity Scheduling Elicitor, as described in Doherty and Miller (2000). The activity data were collected over the course of one week (seven consecutive days) because multi-day data provide opportunities to explore how travel interacts with the underlying rhythms of life (Axhausen et al. 2002).
Other key characteristics that define the study approach are as follows:
* The study considered the household as the decision-making unit, due to the fact that travel results from activity and mode choices that are often interdependent, especially within a household. Complete data were recorded for all members of the six participating households.
* The game was structured as a stated-adaptation experiment, as defined in Lee-Gosselin (1995) and discussed in Tan et al. (2001). The advantage of this approach is that, while the constraints are mostly given, the behavioural outcomes are mostly elicited. Basically participants were asked to indicate what they would do differently due to the new situation.
* The game focused on goal implementation, i.e., on how to achieve the game's objective. As noted in Garling et al. (2002), this generally leads to better performance because it directs people's attention and actions. This was crucial to completing the game in a timely fashion.
* The interviewer provided procedural assistance during the game, including bus schedules, to encourage feasible solutions to the gamed situation, as in Stradling et al. (2000).
* The study explored both the processes by which travel decisions were made as well as travel outcomes. The processes were revealed using a think-aloud protocol, where the participants verbalized their thoughts while they brainstormed, negotiated and decided on changes. The entire interview was audio-taped. The outcomes were entered into the electronic diary, providing a before-and-after record of activity and travel patterns.
The study has in common with Brewer and Hensher (2000) its focus on telework and agency interdependency, except the focus here is on household members (rather than an employer and employee), and on a hypothetical change in auto availability (rather than work changes). The gaming outcome has similarities with travel awareness campaigns, such as 'travel blending' (Rose and Ampt 2001), where one of the objectives is to explore ways in which trip elimination, mode substitution or trip chaining could be achieved through small lifestyle changes. Unlike these campaigns, however, the current 'experiment' does not provide the participant with specific suggestions or program options. Rather, the participants develop strategies themselves, i.e., they explore the possibilities within their own life.
The approach outline above is designed to illuminate how travel decisions are made, including motivations and constraints. Nevertheless, the outcomes must be interpreted as possibilities rather than confident predictions. Despite the increasing use of stated adaptation techniques in travel research (e.g., Arentze et al. 2004; Loukopoulos et al. 2004), there are still a number of methodological challenges associated with its use--including the validity of the data obtained (Polak and Jones 1997): as well as its complexity and expense (D'Arcier et al. 1998), which tend to limit sample size.
The study was conducted in Waterloo Region, which is a mid-sized, auto-dependent community in southwestern Ontario. Canada. Waterloo Region includes three cities (Kitchener, Waterloo, Cambridge) and four rural townships, with a combined population of over 450,000. It is one of the fastest growing regions in the country: from 1997 to 2001, the Real Gross Domestic Product grew by 20 percent (CTT News 2003). Although more than 40 percent of the labour force works in manufacturing, an even larger proportion is employed in commercial/office or institutional settings, including many high-tech companies, several large financial firms, and two universities (Region of Waterloo 1998). Recent studies in the Region indicate a fairly high incidence of telework in these settings (Snow 1998; Mortimer 2002).
The built from of the tri-city area is described as dispersed, with most land being suburban in nature (Bunting and Filion 1999). Accordingly, nearly 88 percent of the one million daily trips are by private auto, 5.8 percent are walking trips, 3.4 percent are by public transit, and the remainder are by school bus, bicycle or 'other' (Region of Waterloo 1998).
The data were collected in February and March 2001. Participants were recruited by means of community posters, word-of-mouth, and articles in local newspapers. In order to qualify for the study, households had to Ca) be located within Waterloo Region; (b) have one member of the household telework as least eight hours per week: and (c) own or have access to at least two vehicles. Finding participants for the study was challenging, as has been found in other studies (Axhausen et al. 2002) because of both the restrictive criteria and also the time commitment required for participation (4 to 5 hours for each adult member of the household and a commitment to record data on a daily basis over a one-week period). Each participating household was given an honorarium--$50 for couples and $75 for couples with children.
The CHASE component of the field work began with a pre-interview with the adult members of a household in order to familiarize participants with the software and also to customize pull-down menus relating to household characteristics, routine activities and travel modes. This customization created deficiencies for data entry and gave a personal feel to the data entry. This interview took place as close as possible to the Sunday of the study week. At the end of the interview, a laptop computer with the CHASE program installed was left at the participating household for the duration of the study week.
On the Sunday of the study week, participants signed onto CHASE for the first time. The CHASE screen looks very much like many electronic organizers. There is a unique screen for each member of the household. During each log-on session, participants entered any activities for the following week that they had already planned. Any unplanned time slots were left blank. Then on each subsequent day of the reference week, each member of the household signed on to CHASE at least once each day to add and/or modify activities (child participants under the age of 14 worked with parents during data entry).
The gaming interview occurred at the close of the reference week, and involved the adult members of the household. Children were allowed to participate if they chose to, but children's schedules were used only insofar as they intersected with parental schedules. The first task of the interviewer was to review the household's schedule for the preceding week, which formed the 'revealed' basis for the stated adaptation interview. The second task was to have participants brainstorm a list of alternative ways of reducing auto use; this list was made available to participants during the game as cues for possible adaptations.
The next part of the interview was the game itself. First the hypothetical situation was introduced:
Participants were encouraged to use a think-aloud protocol while playing the game, so that the various options, reasons and decisions made during the game could be understood. As changes were made the interviewer probed for the feasibility of proposed solutions; for example, if the participant indicated that he/she would walk instead of drive, the interviewer asked how much earlier he/she would have to leave. In order to facilitate 'feasibility probes', several materials were prepared and brought to the interviews: all bus routes and schedules for the Region, a map of the Region with a calibrated measuring tape for distances; and regional cab fares. Once participants decided on how they would resolve conflicts, they entered the necessary changes to both activities and travel into the CHASE program. If, after 50 minutes, the household had not resolved all of the travel conflicts imposed by the scenario, participants were asked to look at the remaining days and think aloud about strategies they would use, without making changes to the CHASE program. The game was ended after one hour in order to avoid player fatigue.
The final part of the interview dealt with costs associated with car ownership and operation. More specifically, participants were asked about their yearly expenditures associated with insurance, fuel, maintenance and ownership/leasing. Then the participant indicated which of the vehicles in their household had been forfeited for the sake of the game, and the expenses were estimated for that vehicle and used to project the potential value of the savings (based on investment scenarios) at the expected date of retirement. After the investment value was calculated, participants were asked to comment on the value of their second car.
The analysis was based on six households. In each case, one of the household members was a teleworker. In three of the households, the teleworker performed most work tasks at home, except for client meetings and an occasional visit to the corporate office. In the other three cases, each teleworker had a regular office but worked at home on a part-time basis (at least eight hours per week); in one case, there was a fixed telework routine while in the other two cases telework occurred throughout the week, but on no fixed schedule. Each of the households included a married couple and, in all cases, the spouse worked either full-time or part-time. Three of the couples had children who were in either elementary or high school, while the other three couples had no children. It is worth noting that, in all six households, one or both of the adults was a university graduate, and some participants had completed graduate degrees, so this is not a typical subset of the community population; but this profile is consistent with the tendency for teleworkers to have more formal education than the general labour force, as found in other studies. In terms of age, participants varied from mid-twenties to late forties. All owned their residence--five were single-detached home and one was a high-rise condo. Finally, each of the households had either two or three motor vehicles.
In terms of observed travel, 441 trips were recorded by the adult members of participating households during the reference week; each leg of a tour was recorded as a separate trip, as is typical in origin-destination studies. In terms of trip frequencies, the average was 5 trips per adult per day, but there was considerable variation from household to household and participant to participant.
By mode, auto travel dominated. Overall, 89 percent of trips were made in vehicles that were owned/leased by the participating households; another 4 percent were made in friends' or colleagues' vehicles. No trips were made by public transit. In two out of the six households, all trips were made by private auto. In two others, the only alternative mode was walking, and it accounted for a small minority of trips. In one household, one trip was made on roller blades but all others were by auto. Finally, household two was the only one to employ more than one alternative mode--walking and bicycling in this case--and to have alternative modes account for more than 20 percent of all trips.
Telework as an Enabler of Change
Three aspects of the game provide insights into telework as an enabler of changes in travel behaviour. The first was the ease with which participants played the game. The second was the role of flexible work arrangements in resolving conflicts created by the game, as revealed through the interview transcripts. The third was the response of participants to the proposition of long-term reduced vehicle ownership.
First, the results of the game reveal the ease with which most of the households adapted to a one-vehicle situation. In only 50 minutes, five out of the six households were able to reschedule at least four days. In combination, the six households rescheduled 30 out of the possible 42 days--even given the time restriction of having only one hour for a household to discuss, decide on and record the changes. Of particular note is the fact that 16 days needed no adjustment because there were no travel conflicts. Even for the 14 days with conflicts, the number of adjustment strategies was surprisingly low (19) notwithstanding the number of steps required to implement each strategy because of secondary or knock-on effects, as documented in Doherty et al. (2002) (1). In Table 1, the nature of each adjustment strategy is briefly explained, and each resolution is given a number for future reference. These observations together suggest that teleworking households may have a high capacity to adapt to a vehicle-reduction scenario, although it should be noted that the current sample already seemed to have incorporated a lair amount of flexibility into their lifestyles. Also, three of the six households did not include children. Both these attributes would seemingly contribute to their capacity to adapt to the scenario that was presented to them.
What are the main reasons for the ease of adjustment and, in particular, what is the role of telework and other flexible work arrangements? The interview transcripts reveal a number of factors that help to explain both the lack of conflicts on certain days and the choice of conflict resolution strategies on others. In some cases, two or more factors were used together to resolve a conflict. These factors are summarized and illustrated with quotations in Tables 2, 3 and 4 under the themes: work-related factors, travel options and factors related to non-work situations.
Three work-related factors emerged from the interviews (Table 2). The first was the substitution of telephone or email communications for face-to-face meetings (and thus travel). This factor was discussed by three of the six households and was used in resolution 4. In this instance, email replaced a meeting, which allowed the teleworker to rearrange his work schedule. The second was flexible work hours. This factor was discussed by all six households, and was used by three of the households to resolve conflicts (resolutions 1, 9, 10, 11 and 19). For example, in resolution 1, the teleworker changed the timing of a client meeting in order to free up the car for the non-teleworker during the day; and the non-teleworker, who was in sales, came home early so that the teleworker could have the car for a late-afternoon meeting. The third factor was spatial flexibility, i.e., the option to choose where work tasks were performed. This was discussed by four households and contributed to the resolution of four conflicts (3, 6, 12 and 19). For example in resolution 19, the teleworker worked in the office all day, after being chauffeured there by his spouse; whereas normally the teleworker would have split his workday--part at home and part at the office. The same type of adjustment strategy was used in resolution 12. Overall, work-related factors played a role in 9 of the 19 resolutions, which underscores the importance of flexible work arrangements in creating capacity for activity and travel adjustments.
Travel options that emerged from the interviews have been organized into two groups (Table 3). The first group deals with excess auto capacity, i.e., situations where one of the household vehicles would have been idle (typically parked in the driveway at home). All households commented on this matter, and each had extended periods of time where only one vehicle was needed. This was particularly true on weekends, but also during both normal work hours and some evenings. Overall, there were five instances--all pertaining to chauffeuring spouses and children to work/school where resolutions were linked to a combination of excess auto capacity and flexible schedules (resolutions 5, 9, 10, 11 and 16).
The second group of travel factors illustrate that alternative modes of travel were judged by some participants to be feasible strategies for resolving conflicts. All households, except household 1, discussed travel options other than using the household vehicle. These options included carpooling (or getting a ride from someone), transit, cycling and walking; they were used in resolutions 7, 13, 14, 15, 16, 17 and 18.
The final group of factors involves non-work activities (Table 4). Activity cancellation was discussed by five out of the six households, but was adopted only twice to resolve conflicts (resolutions 8 and 12). A second issue pertains to spatial flexibility, i.e., the option to do leisure/shopping activities at various locations. Interestingly, this option was discussed by only two households and was not used in any resolution. Finally in terms of temporal flexibility, three of the households discussed the possibility of doing activities at different times in order to avoid travel conflicts; however, this option was used only once, in resolution 2. These observations, taken together, highlight the rigidity with which participants adhered to their original schedules, which suggest that activity compromise is not likely to be an acceptable option in many cases. This is consistent with Garling et al.'s (2002 64) assertion that "... the preference is to maintain the status quo in terms of commitments, activities, and travel arrangements."
Finally, in the closing minutes of the interview, the potential investment value of each household's second vehicle was estimated. Comments reveal that, under the right circumstances, telework could facilitate reduced auto ownership. While households 1 and 2 were not open to discussing the possibility of becoming a one-vehicle household (household 2 had just recently become a two-vehicle household after many years of managing with one car and a bicycle), each of the other four households commented on how they could manage with one car. Indeed, household 5 expressed considerable enthusiasm, as illustrated by its response to the question: "Based on the amount that your car could be worth, do you think that it would affect your vehicle ownership?"
TELEWORKER: Holy shit. It's that much. (whispered)
NON-TELEWORKER: Holy shit. Oh my god. Yes.
TELEWORKER: Oh yes, definitely. Oh my god, yeah, can I get a copy of that program. 'Cause what I'm thinking is that $5436 ... we really love to travel. We went to Africa last year, and this year we're going to Peru, and that would be our trip right there, even less than that. We would have money to pocket, and put into an RRSP, and still be going to Peru. We could take that year off at 35 instead of 45, 'cause we'd like to go abroad for a year.
This comment, and those others by other participants, indicate that cost is a significant consideration in travel decisions, which is consistent with Stradling et al.'s (2002) findings that the cost of car use was the second most prevalent reason for wanting to use a car less; traffic congestion was the most prevalent reason. However, follow-up communication with the six participating households indicates that none of the participating households have made any changes in vehicle ownership in the time following the interview; indeed household 5 now has a baby and no longer considers auto reduction an option.
The second part of the analysis explored the persistence and reasons for auto dependence. Given the widespread appreciation for the feasibility of alternative travel modes, as revealed in the brainstorming session during the first part of the game (results reported in Doherty et al. 2002), one might reasonably expect that auto use would have declined in the gamed situation. A summary of travel patterns before and after the game is provided in Table 5. As shown here, there was a movement in the expected direction, but the degree of change was minor. For teleworkers in the study, the average daily time in a vehicle dropped by 4 minutes (7 percent); for non-teleworkers, the change was a one-minute reduction (1 percent). There were fewer single-occupancy trips (6.2 vs. 4.0 trips per household per day) due to carpooling (resolutions 2, 14 and 19) and chauffeuring (resolutions 5, 9, 10, 11, 12, 16 and 17), but only a marginal increase in public transit (resolution 17) and non-motorized modes (resolutions 4, 7, 13, 15, 17 and 18). This provides strong evidence of persistent auto dependency.
A number of reasons for the near-exclusive reliance on automobile travel emerged from the interviews. These can be organized into two groups--factors that encourage auto dependence and factors that discourage the use of other modes. With respect to factors that encourage auto dependence, the strongest theme that emerges is pressures--pressure to meet work expectations, family responsibilities and general time pressure. Others themes relate to the desire for spontaneity and independence, and the advantages of the automobile, especially when coordinating schedules. All of these are reflective of a society that has accepted the mobility imperative as being key to living a productive and happy life. Also, the practical benefit of being able to carry loads was mentioned in two of the interviews.
In terms of barriers to using other modes, seven factors were identified. The first three pertain to external variables that make travel by alternative modes difficult. The first is low quality of transit service (e.g., frequency, route configuration), which is consistent with other research that documents the perceived unsatisfactory nature of transit (e.g., Stradling et al. 2002). The other two external variables are the dispersed nature of the city, and inclement weather. The next two factors deal with personal well-being: the need to be somewhat physically fit to use a bicycle; and concerns over safety/security in cycling on busy roads and in using alternative travel modes at night. The latter issue was raised despite the fact that the study area experiences a low violent crime rate (6.4 per 1000 people) (Monteiro 2002, 2003). The final two factors pertain to people's mindsets and to social norms in general. As reflected in the interview transcripts, some participants have a strong aversion to using alternative modes, especially transit, and an attachment to the 'ideal' of one car per adult.
Overall, respondents provide a myriad of reasons for the high degree of auto dependence that was observed--both in life and during the game. These comments suggest that psycho-social barriers to mode change are as important as the practical barriers. As noted by Wright and Egan (2000, 289) when discussing the results of greening experiments, "... people may be amenable to changing their travel patterns to a modest extent when they are offered practicable alternatives, but they also raise deeper questions about the motives that govern car use, and how deeply those motives are embedded in people's minds." This suggests that interventions that address practical barriers only are not likely to be successful in achieving substantial modal shifts, as least not in the short term.
Results and Discussion
This paper reported on an in-depth, small-sample experiment designed to explore issues that require non-traditional travel data. The focus was on household-level responses to a vehicle-reduction scenario in the context of teleworking households in a mid-sized Canadian city. The study design allowed for comparisons of 'actual' versus 'gamed' activity and travel patterns. Two themes were explored: telework as an enabler of travel changes; and the persistence of and reasons for auto-dependency within teleworking households.
Despite the ability to change, all six participating households displayed continued auto dependence throughout the game. A summary of travel patterns before and after the game shows that automobile travel was reduced only minimally, and travel by alternative modes increased only marginally. A number of reasons for the near-exclusive reliance on automobile travel emerged from the interviews. Some are 'pull' factors that encourage auto use, and others are 'push' factors that discourage the use of other modes. Together they illustrate a strong aversion to transit use and an attachment to the 'ideal' of one car per adult; which underscores the importance of psycho-social factors in understanding travel decision making, implementing TDMs and planning for sustainable transportation. Indeed, these results raise an important question: Will telework reduce automobile travel in situations where other motivations for reducing car use (e.g. congestion, high cost of parking) are absent? These preliminary findings suggest that smaller urban areas with limited travel options and few impediments to auto use may have very different results than the oft-quoted telework studies from California and other large urban areas. Indeed, it may be that the transportation implications of telework are highly dependent on both place-based variables, such as city size and density, as well as individual variables, such as commute length. A more complete understanding of this important issue is necessary in order to predict the environmental consequences of this work trend.
Finally, it is worth commenting on the game itself, as pilot studies of this type are essentially about developing methodology for exploring complex issues. The stated-adaptation game that was developed for this study was successful in tracking the interdependent nature of household-level travel decision-making and exploring possible changes in travel outcomes. The authors assert that a key element of the game was the requirement that respondents resolve household-level conflicts in making travel or activity changes, as this provides more realistic results than asking respondents to merely scope out alternatives (e.g. Mackett 2002). Since "... it is now commonly agreed that sustainable transportation presents a challenge to the traditional transportation planning methodology, which is heavily dependent on the standard four-stage model ..." (Loo 2002 221), this type of methodological development is important for anticipating the effects of policy interventions in different contexts. Future work should focus on how adaptation strategies vary across different subsets of teleworkers, stratified by personal and place-based characteristics.
Although the sample size is too small to generalize about travel changes, three aspects of the game provide insights into telework as an enabler of change in travel behaviour. More specifically, the results of the game reveal the ease with which most of the households adapted to a one-vehicle situation; the importance of both flexible work and excess auto capacity (both of which are linked to telework) in resolving travel conflicts, and the feasibility of reduced auto ownership in the long term. Together these suggest that teleworking households might have a high capacity to adapt to a vehicle-reduction scenario, while making only minor changes to activity patterns.
The authors would like to acknowledge the financial support of the Social Sciences and Humanities Research Council of Canada and the University of Waterloo. The authors are also grateful to the three anonymous reviewers for their critical comments.
(1) Households 1,2, 3, 5, 6 and 7 in Doherty et al. (2002) correspond to households 1, 2, 3, 4, 5 and 6 in this study.
Arentze, T., Hofman, F., and H. Timmermans. 2004. Predicting multi-faceted activity-travel adjustment strategies in response to possible congestion pricing scenarios using an Internet-based stated adaptation experiment. Transport Policy 11:31-41.
Axhausen, K.W., A. Zimmerman, S. Schonfelder, G. Findsfuser, and T. Haupt. 2002. Observing the rhythms of daily life: a six-week travel diary. Transportation 29: 95-124.
Brewer, A.M., and D.A.Hensher. 2000. Distributed work and travel behaviour: the dynamics of interactive agency choices between employers and employees. Transportation 27:117-148.
Bunting, T.E., and P. Filion. 1999. Dispersed city form in Canada: A Kitchener CMA case example. The Canadian Geographer 43: 268-286.
Bussiere, Y., and R Lewis. 2002. Impact of telework and flextime on reducing future urban travel demand: the case of Montreal and Quebec (Canada), 1996-2016. Eighth International Conference on Urban Transport and the Environment in the 21st Century, Seville, Spain.
Canadian Telework Association. 2003. www.ivc.ca
The Centre for Sustainable Transportation. 2002. Sustainable Transportation Monitor 7: 1-12 http://www.cstctd.org/index.html
Choo, S., P.L. Mokhtarian, and I. Salomon. 2003. Does telecommuting reduce vehicle-miles travelled? An aggregate time series analysis for the U.S. CDROM Proceedings of the 82nd Annual Meeting of the Transportation Research Board, Washington, D.C.
CTT News. 2003. Waterloo Region armed with socio-economic performance paper. Feb. 4. http://www.techtriangle.com/
D'Arcier, B.F., O. Andan, and C. Raux. 1998. Stated adaptation surveys and choice process: some methodological issues. Transportation 25: 169-185.
Doherty, S.T., J.C. Andrey, and L.C. Johnson. 2000. The economic and social impacts of telework. In Telework: The New Workplace of the 21st Century Conference Proceedings, 73-97. Washington, D.C.: U.S. Department of Labor.
Doherty, S.T., M. Lee-Gosselin, K.Burns, and J. Andrey. 2002. Household activity rescheduling in response to automobile reduction scenarios. Transportation Research Record 1807: 174-182.
Doherty, S.T., and E. Miller. 2000. A computerized household activity scheduling survey. Transportation 27: 75-97
Fisher, J. 1999. Psychological factors related to transportation mode change in the United States. In CD-ROM Proceedings of the 78th Annual Meeting of the Transportation Research Board. Washington: D.C.
Garling, T., D. Eek, P. Loukopoulos, S. Fujii, O. Johansson-Stenman, R. Kitamura, R. Pendyala, and B. Vilhelmson. 2002. A conceptual model of the impact of travel demand management on private car use. Transport Policy 9: 59-70.
Hjorthol, R.J. 2002. The relation between daily travel and use of the home computer. Transportation Research A 36:437-452
Jensen, M. 1999. Passion and heart in transport--a sociological analysis on transport behaviour. Transport Policy 6:19-33.
Koenig, B.E., D.K. Henderson, and P.L. Mokhtarian. 1996. The travel and emissions impacts of telecommuting for the State of California telecommuting pilot project. Transportation Research C 4:13-32.
Loukopoulos, P., C. Joakobsson, T. Garling, C.M. Schneider, and S. Fujii. 2004. Car-user responses to travel demand management measures: goal setting and choice of adaptation alternatives. Transportation Research D 9: 263-280.
Lee-Gosselin, M. 1995. Scope and potential of interactive stated response data collection methods. In Conference on Household Travel Surveys: New Concepts and Research Needs, March 1995, 115-133. Irvine: California.
Lim, C.C. 1997. The status of transportation demand management in Greater Vancouver and energy implications. Energy Policy 25:1193-1202.
Litman, T. 2002. Online TDM Encyclopedia. Victoria Transport Policy Institute. www.vtpi.org
Loo, B.P.Y. 2002. Role of stated preference methods in planning for sustainable urban transportation: state of practice and future prospects. Journal of Urban Planning and Development 128:210-224.
Mackett, R.L. 2001. Policies to attract drivers out of their cars for short trips. Transport Policy 8:295-306.
Miller, E.J., and A. Shalaby. 2003. Evolution of personal travel in Toronto area and policy implications. Journal of Urban Planning and Development 129: 1-26.
Mokhtarian, P.L. 1998. A synthetic approach to estimating the impacts of telecommuting on travel. Urban Studies 35:214-241.
Mokhtarian, P.L. 2000. Telecommunications and travel. In Transportation in the New Millennium. State of the Art and Future Directions. Perspectives from TRB Standing Committee A1C08, Committee on Telecommunications and Travel Behavior. Washington: D.C. NRC.
Mokhtarian, P.L., S.L. Handy, and I. Salomon. 1995. Methodological issues in the estimation of travel, energy and air quality impacts of telecommuting. Transportation Research A 29:283-302.
Mokhtarian, P.L., and R. Meenakshisundaram. 1999. Beyond telesubstitution: disaggregate longitudinal structural equations modeling of communication impacts. Transportation Research C7: 33-52.
Monteiro, L. 2002. Violent crime takes a nosedive. The Record May 9: A1.
Monteiro, L. 2003. Violent crime rate down in region, statistics show. The Record May 15:B1.
Mortimer, K. 2002. Telework and Travel in a Mid-Sized City. Unpublished M.A. Thesis, School of Planning, University of Waterloo. Waterloo: Ontario.
Polak, J., and P. Jones. 1997. Using stated-preference methods to examine traveller preferences and responses. In Understanding Travel Behaviour in an Era of Change, ed. P. Stopher and M. Lee-Gosselin 1997, 177-207. New York: Pergamon.
Pucher, J. 1998. Back on track: eight steps to rejuvenate public transport in Canada. Alternatives Journal 24(1): 26-31.
Region of Waterloo. 1998. Statistical Profile. Population and Households. Regional Municipality of Waterloo, Planning and Culture Department, www.region.waterloo.on.ca
Robinson, R. 1997. Transportation demand management in Canada: an overview. Energy Policy 24:14-15.
Rose, G., and E. Ampt. 2001. Travel blending: an Australian travel awareness initiative. Transportation Research D 6: 95-110
Saxena, S., and P.L. Mokhtarian. 1997. The impact of telecommuting on the activity spaces of participants. Geographical Analysis 29: 124-144.
Shiftan, Y., and J. Suhrbier. 2002. The analysis of travel and emission impacts of travel demand management strategies using activity-based models. Transportation 29:145-168.
Snow, A. 1998. An Auto-mated Existence? Implications of Telecommuting in Canada's Technology Triangle. Unpublished M.A. Thesis, Department of Geography, University of Waterloo, Waterloo, Ontario.
Stradling, S.G., M.L. Meadows and S. Beatty. 2000. Helping drivers out of their cars: integrating transport policy and social psychology for sustainable change. Transport Policy 7:207-215.
Stewart, G., and R. Pringle. 1997. Toronto's tentative approach to TDM. Energy Policy 25:1203-1212.
Tan, A., H. Timmermans, and B. de Vries. 2001. Interactive computer experiments in virtual reality: issues and propects. In CD-ROM Proceedings of the 80th Annual Meeting of the Transportation Research Board. Washington: D.C.
Tayyaran, M.R. 2000. Impacts of Telecommuting, and Related Aspects of Intelligent Transportation Systems on Residential Location Choice: a combined revealed and stated preference approach. Unpublished PhD Dissertation., Department of Civil and Environmental Engineering, Carleton University, Ottawa.
Transport Canada. 2001. Transportation in Canada Annual Report. www.tc.gc.ca/pol/en/Report/anre2001/tc0105ce.htm
Transport Canada. 2002. Commuter Options: The Complete Guide for Canadian Employers TP13922E www.tc.programs/Environment/ Commuter/
Transportation Climate Change Table. 1999. Transportation and Climate Change: Options for Action. www.tc.gc.ca/envaffairs/subgroups/ toions_Paper/English/
Winters, P.L. 2000. Transportation demand management. In CD-ROM Proceedings of the 79th Annual Meeting of the Transportation Research Board. Washington: D.C.
Wright, C., and J. Egan. 2000. Demarketing the car. Transport Policy 7: 287-294.
Jean C. Andrey
Department of Geography
University of Waterloo
Kyle R. Burns
Local Economic Development Program
University of Waterloo
Sean T. Doherty
Department of Geography and Environmental Studies
Wilfrid Laurier University
You have had one of your vehicles stolen (you may choose which one), and for reasons of your own, you have decided not to replace it, but to meet your travel needs based on having only one vehicle. You may not rent or borrow another vehicle, but you may accept rides and you may use other modes. Based on this scenario, what is the first thing you would most likely change, looking back upon your week?
Table 1 Resolutions to Conflicts Associated with Vehicle-Reduction Scenario Household Day for Resolution Description (T = teleworker; which Came N = non-teleworker) was Played Monday 1 * T changes timing of meeting with client in order to free up car for N during the day. N, who is in sales, comes home early that afternoon so that T can have car for meeting. 1 2 * Both T and N change the timing of their independent trips to an exercise facility so that they can go together. Tuesday 3 * N decides to drop off a courier package early in the morning and then cancel some impromptu sales visits in order to free up the car for T, who has to go out for meetings. Monday No conflict Tuesday 4 * T replaces car trip to work with bicycle trip, and meeting with email, in order to free up car for spouse. 5 * Family joined together for an activity and then the N chauffeurs the T to and from a meeting in between kids' activities. 2 Wednesday No conflict Thursday 6 * T works at home in morning, while N runs errands. Then T goes into work with car while N stays home. 7 * Child walks home from an activity instead of being picked up. 8 * A trip to the video store is cancelled. Friday No conflict Monday 9 * T drops off N and child at work/ school before driving to work. 3 Tuesday 10 * Same as Monday. Wednesday 11 * Same as Monday. Thursday 12 * N cancels leisure activity in order to chauffeur T and child to work/school; T cancels home- based work in afternoon because of lack of car. Monday No conflict Tuesday 13 * T bikes to meeting so that N can have car. Wednesday 14 * N carpools to work so that T can have car for meeting. 4 Thursday 15 * T bikes to meeting and store so that N can have car. Friday No conflict Saturday No conflict Sunday No conflict Monday No conflict Tuesday No conflict Wednesday No conflict 5 Thursday 16 * T chauffeurs N to work so he can have car for squash game; N gets ride home with co-worker. Friday No conflict Saturday No conflict Sunday No conflict Monday No conflict Tuesday No conflict Wednesday 17 * T chauffeurs N and kids to work/ school; spouse walks to lunch- time activity and takes bus home. 6 18 * In the evening, T does errands in neighborhood on foot, allowing N to chauffeur kids to activities. Thursday 19 * N chauffeurs T to work and kids to and from school; T works in office all day, works late, eats in restaurant near office; and carpools home. Friday No conflict Table 2 Work-Related Factors that Facilitated Conflict Resolution Factor Discussed Used in Illustrative Quotations Resolution (T = teleworker; N = non- teleworker; HH = household) Telephone or HH 1, 2, 3 4 "If I can do a credit Email Substitutes approval over the phone, for Travel then I will." [T] "For me it's the same; it's all customer-oriented." [N] [HH1] "Some things I usually do in person I could do electronically. It's not as personal but if that's the only way to get it done ..." [T] [HH2] Flexible Work All HH 1, 9, 10, "Could you have done that Hours 11, 19 Monday night or afternoon when I got home?" [N] "I could've called Sunday night and rearranged that. So if you could have been home by ..." [T] "I could have easily been home by one." [N] [HH1] "What we have to do first is decide what I have to do during the day for my car." [N] "Yep, let's look at you first 'cause my job's a lot more flexible." [T] "In general, T would try to reschedule all her appointments at night while I'm away during the day." [N] [HH1] "I have extremely flexible hours, and no routine." [N] [HH3] Flexible Work HH l, 2, 3, 6, 12, "Cause I didn't have Location 3, 6 19 anything going on in the morning, so you could've gone to Kitchener." [T] "Nope. I wouldn't have. Rather than rush home and get you the car, I'd rather just work at home." [N] [HH1] "Ok, so that would have meant that I couldn't have had coffee that day ... or you could have worked at home." [N] [HH2] "You'd have to do that at the office." [N] [HH6] Table 3 Travel Options that Facilitated Conflict Resolution Factor Discussed Used in Illustrative Quotations Resolution (T = teleworker; N = non- teleworker; HH = household) Excess Auto All HH 5, 9, 10 "When I get home on Friday, Capacity 11, 16 my car is parked till Sunday or Monday." [N] [HH1] "Mondays and Tuesdays aren't so bad ... because neither of us work Monday, and you don't work on Tuesday ..." [T] [HH6] "Monday I didn't get out of the house until 4:45." [T] "That was the first time you needed it? ... I probably could've been back from school by that time. Yes, I could've been home easily at that stage." [N] [HH4] "Then we went out for dinner at night together, so there's no time when two cars need to be used at the same time." [N] "I guess we could've easily handled having one car this week. That was pretty easy. Wow. The thing is--it is easy this week, but like I said, this week was a little atypical because I didn't go into the office at all. But you know what? If I only went into the office one day, like tomorrow, then you could carpool one day." [T] [HH5] "And the way I do it with regards to getting to work is I commute with a girlfriend of mine who lives in the area ... she would drive one month and I would drive one month." [N] But [your friend] could probably carpool for the whole year. Let's say we only had one car. You could probably just give her money." [T] Exactly, because when she didn't have a car that's what she did with me." [N] [HH6] Table 4 Factors Related to Non-Work Activities Factor Discussed Used in Illustrative Quotations Resolution (T = teleworker; N = non- teleworker; HH = household) Cancel Non-Work All HH HH 8, 12 "We'll leave the Y for now Activities except 5 until we work on your day and see if we can fit it in." [T] [HH1] "Okay, so that would have meant that I couldn't have had coffee that day." [N] [HH2] "... and you were fishing ...you would have had to give that up with one car." [N] [HH3] "Tuesday coffee." [T] "Is this something you would have really done?" [N] [HH4] "I can't help our daughter with her homework." [T] [HH6] Spatial HH 3, 4 "Saturday was interesting Flexibility because we did a whole lot of things separately. Because I went back to work, I visited a friend and I had a medical appointment. So I went my way ... you played with our son, and you went up and picked up some friends ... and took them to Kidsworld." [T] "But that was more of a lack of creative thinking. But if we had one vehicle and T had needed to do those errands, then we would have made do right here." [N] [HH3] Temporal HH 1,2,3 2 "I would challenge the Flexibility: need for certain trips Non-Work and activities. They can Activities be postponed until they correspond with more necessary ..." [N] [HH3] "... if I had wanted to go to do an errand, I'll often wait for T, if T needed the car, and do it when he got home." [N] [HH2] Table 5 Travel Patterns Before and After Game Average Daily Travel Time in a Vehicle (minutes) Teleworker Non-teleworker Household 1 82.5 [right arrow] 72.5 107.5 [right arrow] 92.5 Household 2 43.8 [right arrow] 33.8 47.5 [right arrow] 45.0 Household 3 41.3 [right arrow] 35.0 88.8 [right arrow] 98.5 Household 4 76.4 [right arrow] 72.9 96.4 [right arrow] 97.1 Household 5 37.9 [right arrow] 40.0 33.6 [right arrow] 33.6 Household 6 76.0 [right arrow] 78.0 92.8 [right arrow] 93.8 Average 59.6 [right arrow] 55.4 77.8 [right arrow] 76.8 Household-Level Trip Counts Average Daily # Trips Average Daily # Single- Occupant Trips Household 1 13.0 [right arrow] 11.5 13.5 [right arrow] 5.0 Household 2 13.4 [right arrow] 13.5 4.8 [right arrow] 3.3 Household 3 10.4 [right arrow] 10.5 4.0 [right arrow] 3.0 Household 4 7.9 [right arrow] 7.7 2.9 [right arrow] 2.0 Household 5 7.1 [right arrow] 7.3 3.7 [right arrow] 3.5 Household 6 11.0 [right arrow] 11.4 8.4 [right arrow] 7.6 Average 10.5 [right arrow] 10.3 6.2 [right arrow] 4.0
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