When the internet is not enough: towardan understanding of carpool services for service workers

Ron N. Buliung • Randy Bui • Ryan Lanyon

Published online: 25 November 2011� Springer Science+Business Media, LLC. 2011

Abstract For much of the twentieth century, the economies of Canada and the United

States have increasingly focused on service provision. During this same time period, cities

have grown into expansive urban regions characterized by dispersed workplaces com-

plemented by a wide array of commuting patterns, dominated by single occupancy vehicle

use. This study aims to understand how service worker engagement with an Internet-based

carpool formation software, known as Carpool Zone, and workplace transport policies,

jointly enable carpool formation and use. The piece also explores the question of difference

in carpool formation between female and male service workers. The study area is the

Greater Golden Horseshoe, Canada’s largest metropolitan region. Data were drawn from

Carpool Zone and a 2007 survey of commuter satisfaction. Extending past work, logistic

regression analysis clarifies the importance of specific workplace policies, enacted within

suburban firms, to the carpool formation process, including: provision of carpool spaces

and availability of an emergency ride home service. The findings indicate that the Internet

may not be enough, powerful enabling tools should be situated within expert networks of

human capital developed to ameliorate the negative effects of commuting.

Keywords Carpool � Commuting � Internet � Service economy �Planning � Metrolinx

R. N. Buliung (&) � R. BuiDepartment of Geography, University of Toronto Mississauga, 3359 Mississauga Road,Mississauga, ON L5L 1C6, Canadae-mail: ron.buliung@utoronto.ca

R. Buie-mail: randy.bui@utoronto.ca

R. LanyonSmart Commute, Metrolinx, 20 Bay St. Suite 600, Toronto, ON M5J 2W3, Canadae-mail: ryan.lanyon@metrolinx.com

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Transportation (2012) 39:877–893DOI 10.1007/s11116-011-9384-3

Introduction

One signal of the ongoing global shift in the growth of manufacturing from developed

capitalist economies toward the newly industrializing economies (NIE) of Asia is the

increasing concentration of employment within service industries in Canada and the United

States (Dicken 2007). Although dating to the end of the 19th century, the reversal in the

relative concentration of employment from the goods toward the services producing

industries in Canada and the U.S. has largely been a 20th century phenomenon (Worton

1969). Today, 75% of Canada’s labour force is employed in service industries. In 2008,

services contributed $864 B (CAD) of output or 70% of Canada’s annual gross domestic

product (GDP) (Statistics Canada 2009a). Between 2001 and 2009, Canada’s service

economy grew more quickly (2.4%) than the economy as a whole (1.6%), outpacing the

goods producing industries (-0.3%). Growth of the service industries during that time

period was led by the Retail Trade (3.4%), Administrative and Support, Waste Manage-

ment and Remediation Services (3.1%), Finance and Insurance, Real Estate and Leasing

and Management of Companies and Enterprises (FIRE) (2.8%), and the Professional,

Scientific and Technical (2.8%) sub-sectors (Industry Canada 2010). Much of the growth in

retail trade and services has occurred in Canada’s suburbs (Buliung and Hernandez 2009;

Shearmur and Coffey 2002).

Regionally, and within the group of census metropolitan areas (CMAs: large urban

areas) that comprise the study area, Toronto, Hamilton, and Oshawa, data from the 2006

Census of Canada indicate considerable concentration of labour within service industries.

Service industry participation rates for these three CMAs are 80, 76, and 75%, respectively

Statistics Canada (2009b). While the data for Toronto are not surprising (Toronto is

Canada’s financial hub), Hamilton and Oshawa are major Canadian centres for steel and

automobile production, yet service industries dominate their economies. With the pro-

ductivity cost of road congestion within the Toronto region being measured in billions of

dollars annually OECD (2010), and the orientation of the economy toward service

industries, it is an academically interesting policy relevant endeavour to study the com-

muting behaviours of service workers.

One approach to ameliorating the negative effects of congestion involves development

and implementation of transportation demand management (TDM) strategies. TDM

focuses on shifting transportation demand from the independent use of automobiles (i.e.,

single occupant vehicle (SOV) trips) toward multi-occupant modes such as public transit

and/or active transport modes such as walking and cycling. Broadly defined here as the

sharing of a private vehicle between two or more persons for travel to a pre-arranged

destination like work (BTS 2010), carpooling is another multi-occupant mode often

included in the list of alternatives to SOV commuting. The purpose of this paper is to

report on how some service workers make use of an Internet-based carpool application for

carpool formation and use. The application, Carpool Zone, is maintained by Metrolinx, the

transportation planning authority for the Greater Toronto and Hamilton Area (GTHA),

Canada’s largest metropolitan region.

The paper contributes to research aimed at understanding complementarities between

information and transport systems and technologies (Salomon 1986). The broad goal of the

work is to understand if and how information and communication technologies (ICTs) can

be directed at partially reducing the negative external costs of work travel. In a previous

study, the authors reported that commuters working at firms engaged in development of

TDM strategies with Metrolinx had greater success than public users with web-enabled

carpool formation (Buliung et al. 2010). Using recently acquired data on employer

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transport policies and programmes, this paper extends past work by unpacking the

workplace transport context, with a view to understanding what it is about an individual

and her/his social, spatial, and now workplace environment that enables carpooling. The

paper also extends the authors’ past research by examining, more closely, gender differ-

ences in carpool formation and use. The study examines the carpool formation and use

process given that an individual service worker has indicated an intention to carpool. The

main hypothesis is that workplace characteristics such as firm size, and transport policies,

will differentially affect carpool formation and use.

Background

Carpooling is not a new mode of transportation. Speculatively, the shared ride (by

automobile) likely dates to the production of the first automobiles. Before WWII,

shared automobility would be tested from time to time, as an alternative to mass transit in

major cities in both Canada and the U.S. (Doucet 1978). The so-called jitney craze of

1914–1915 (Doucet 1978), where private automobile owners offered rides for a fee, is

early evidence of the value, to passenger and driver/owner alike, of an automobile’s

unused capacity. Ferguson (1997) discussed the carpooling practice in the U.S. during

WWII, at a time when resource scarcity spawned government propaganda campaigns

favouring carpooling. Later, the energy crises of the 1970s, i.e., the first and second

OPEC oil crises of 1973–1974 and 1979, focused attention on alternative transport, this

time as an approach to reduce U.S. dependency on ‘‘foreign’’ oil (Brunso and Hartgen

1981). The most recent wave of interest seems to have emerged from concern about

fluctuating and rising commuting costs (i.e., retail fuel prices, commuting times), pro-

ductivity losses associated with road congestion, and perhaps some environmental con-

cerns (Buliung et al. 2010).

Commuter interest in carpooling has ebbed and flowed over time. The economic

stressors and geopolitics of the WWII years, that spawned pro-carpool policies, yielded to

a post-war return to SOV commuting in the U.S. (Ferguson 1997). During the post-war

period, commuters largely returned to their cars, in great numbers, a process that was

reinforced by rising affluence and city growth. The wave of interest following the OPEC

crises of the 1970s would be followed, in the 1980s, with a collapse of interest (Ferguson

1997). U.S. carpool mode share decreased from nearly 20% in 1980 to 13% by 1990

(Benkler 2004; Ferguson 1997). Recent data indicate that, while lagging behind SOV work

travel mode share (76%), carpool mode share (12%) in the U.S. is larger than public transit

(5%). Canadian data indicate the reverse, with, in 2006, 11% of workers taking some form

of public transit, and 7.7% traveling as passengers in private cars (an increase from 6.9% in

2001) (Statistics Canada 2008). Most of these passenger trips are likely facilitated by other

household members (Morency 2007).

Carpooling is conceptualized here as the sharing of a private automobile by two or

more persons for the purpose of reaching a pre-arranged destination (BTS 2010).

The carpool is a multi-occupant mode that may or may not involve not only shared travel,

but also the sharing of space within private vehicles distributed across carpool members.

The compositional and formation characteristics of a carpool can vary considerably

within the context of the rather broad definition used here. Carpools can include per-

mutations of driver, rider, organizer responsibilities, and various combinations of intra-

household and extra-household members (Morency 2007; Richardson and Young 1981;

Teal 1987). Carpool membership does not necessarily require contribution of a private

Transportation (2012) 39:877–893 879

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vehicle. The carpool is not static, with members drifting in and out; the carpool evolves

and devolves through time. The data in this study do not permit analysis of carpool

lifecycle, only the formation process. More work is needed to understand carpool

dynamics in time and space.

Carpool trips are typically longer than work trips by other modes (Levin 1982;

Richardson and Young 1981; Teal 1987). Trip length obscures the added time associated

with drop-offs and pick-ups, with commuters instead focusing on the potential direct cost

savings of shared travel (Richardson and Young 1981). With regard to long commutes,

beyond a certain threshold, residential density and therefore the distance between

potential matches, may decrease to a point that makes carpooling infeasible (Richardson

and Young 1981). The motivation to carpool also declines with the added time associated

with each member. Carpool commute time is therefore partially influenced by the clus-

tering of commuter residences and workplaces. For example, Richardson and Young

(1981) found that residential clustering of matches was more common than the clustering

of workplaces. Buliung et al. (2009, 2010) found that geographical proximity of potential

matches at the home-end was one of the more powerful correlates of achieving a func-

tioning carpool.

The carpool formation process, i.e., negotiating and arranging for shared travel, can be

variously situated within structured or informal settings. The focus of this study, on the

outcome of jointly developed TDM policies between a government agency, Metrolinx, and

regional employers, serves as an example of the formal, structured case. The carpool is

constructed with assistance from a third-party, as an alternative or complement to,

participant based arrangements that leverage pre-existing relationships (i.e., cohabitation,

co-workers, family). Metrolinx seeks to construct networks of trust with regional

employers, with a view to constructing context-sensitive TDM strategies formed around

jointly constructed productivity and sustainability goals. Formation can also emerge within

an informal economy of mobility where riders and drivers make long-term or even

dynamic short-term decisions about shared commuting and other forms of travel. The most

visible example of the casual formation process is the slug-bodysnatcher arrangement

reported in Washington D.C. (Kogan 1997).

Carpool negotiation, particularly in situations involving non-household members, is a

process that can involve reconciliation of group norms and commuter attitudes toward

one another (Brunso and Hartgen 1981; Poulenez-Donovan and Ulberg 1994). In other

words, carpooling is as much a psycho-social phenomenon as it is a practice involving

mechanical elements (cars) and scheduling. Some research suggests that it is the more

qualitative, i.e., commuter attitudes toward carpooling and one another, rather than

pragmatic concerns, that heavily influence carpooling (Benkler 2004; Horowitz and Sheth

1978; Poulenez-Donovan and Ulberg 1994). Qualitative concerns extend to commuter

perceptions about ease of use and perhaps self efficacy, and are influenced by factors that

include: cost, scheduling, proximity and availability of other carpoolers, and geographical

patterns of work and other activity sites (Ozanne and Mollenkopf 2009; Poulenez-

Donovan and Ulberg 1994). There is some debate about the role of familiarity and/or

trust in carpool formation. Some workers might value familiarity, finding greater comfort

in carpooling with workers from the same workplace (even when they might work in

different departments) (Poulenez-Donovan and Ulberg 1994). On the other hand, the

perceived anonymity associated with the casual carpool, or carpooling with previously

unknown individuals could be more appealing to some (Poulenez-Donovan and Ulberg

1994).

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Researchers have also tried to understand how socio-economics, demographics, the

workplace, the geography of commuter travel, and time-use associate with carpool mode

choice, and the related formation and use process. There is limited evidence regarding the

relationship between compositional factors and carpooling. Age, gender/sex, and other

demographic characteristics typically show weak or non-existent associations with

carpooling (Benkler 2004; Brunso and Hartgen 1981; Buliung et al. 2009; Horowitz and

Sheth 1978; Kaufman 2002). In contrast, Teal (1987) reported differences in carpooling by

transport context, economic status, household size, and number of vehicles (Teal 1987).

Buliung et al. (2010) reported a positive association between age and carpool formation

and use, confirming earlier work by Winn (2005).

Beginning with Pratt (1911), there has been considerable research into gender and

transport. Much of the work, until recently (Crane 2007), has been confirmatory—indi-

cating shorter commutes for female workers. Discourses about gender and transport offer

explanations that emphasize the patriarchal organization of households, labour, regional

development, and the spatial organization of economies (England 1993; Johnston-

Anumonwo 1992; Turner and Niemeier 1997). Research suggests that females are more

likely than males to express an intention to switch from SOV use to some form of

‘‘sustainable’’ transport, including carpooling (Tischer and Dobson 1979). Teal (1987)

reported more female carpoolers than male, with a particular bias toward married females.

As suggested by Morency (2007), females appear less likely to participate in external

carpools—the married female carpoolers in Teal’s study were likely engaged in intra-

household rideshare. Gender, along with race, age, and class are also present as selection

criteria in carpool formation (Poulenez-Donovan and Ulberg 1994).

Leading a busy life, full of personal and extra-personal responsibilities also seems to

associate with SOV use, even when an intention to use muti-occupant modes is indicated.

Canadian time-use data indicate that females continue to spend more time on unpaid work

and household responsibilities than males (Statistics Canada 2005). Females might be less

inclined to adopt complicated extra-household carpool arrangements because of the added

daily burden of household responsibilities. Females might value the availability of pro-

grams like emergency ride home (ERH) more than males because they typically have more

to do throughout the day than their male counterparts. While this paper is not focused

entirely on the gender-transport link, close study of the literature suggests that a more

focused, perhaps critical engagement, with the issue of gender and carpooling, is war-

ranted. The literature has adequately pointed to gender differences in carpool adoption; less

is understood about why differences exist.

Workplace transport policy and firm characteristics have strong positive associations

with carpooling. Employer sponsored ride-sharing tends to have some success, particu-

larly when there is a local champion/facilitator (Brunso and Hartgen 1981; Ferguson

1990; Rutherford et al. 1994). Consistent hours of employment tend to aid in carpool

formation and use (Buliung et al. 2010), and larger firms offer sufficiently diverse

employee contexts (e.g., residential locations, work hours, and perhaps even normative

values) to achieve high match rates (Cervero and Griesenbeck 1988; Ferguson 1990; Teal

1987). Occupational and/or class issues can present barriers. The hierarchical structure of

some firms may limit the perceived and/or actual agency of employees in determining

transport outcomes that are compatible with individual needs, tastes and preferences.

Low-wage or hourly workers, for example, could feel disempowered with regard to

the reconciliation of work scheduling in a manner compatible with carpool formation

(Poulenez-Donovan and Ulberg 1994).

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Study design

Metrolinx and smart commute

Metrolinx is the regional transportation authority for the GTHA, Canada’s largest

metropolitan region. As one of Metrolinx’ TDM initiatives, Smart Commute (www.

smartcommute.ca) works with large employers, post-secondary educational institutions

and commercial property managers within the GTHA to develop demand management

strategies. Strategies are developed to address productivity and labour concerns of

employers (employee morale in the face of changing and/or longer commutes) and the

sustainability of work travel in the region. Smart Commute takes a non-coercive approach

to the development, promotion, and implementation of SOV alternatives that include:

carpooling, telework, transit, cycling, walking, or flexible work arrangements. Smart

Commute maintains a decentralized delivery structure, while Metrolinx hosts the coordi-

nating office for Smart Commute, it is the eleven transportation management associations

(TMAs) that today are working closely with 150 of the region’s largest employers and

post-secondary educational institutions that together represent more than 10% of the

region’s workforce. This governance model facilitates integration of the local context into

management plans. Smart Commute resources and tools are also available to the general

public.

One of Smart Commute’s TDM tools is Carpool Zone (www.carpoolzone.ca), a state-

of-the-art online carpool-matching service. It offers commuters working at member and

partner firms, or public users, an opportunity to organize carpools using location and time-

sensitive services constructed in part from the Google Maps application programming

interface (API) (Fig. 1). Commuters can search for others with similar transportation habits

Fig. 1 Carpool Zone interface powered by Google Maps

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through interactive mapping, geocoding, route and schedule matching tools. Carpool Zone

also manages the security, privacy, and administrative processes (i.e., contacting matches

through e-mail) required to facilitate carpool formation among familiar or unfamiliar

parties. While there are no user fees for public users, some participating firms pay an

annual fee for premium services. Carpool Zone leverages the infrastructure of the Internet

as part of a programme targeting congestion reduction.

Study area

While Metrolinx’ mandate extends across the GTHA, this study includes commuters

located within the larger Greater Golden Horseshoe (GGH) because there are commuters

with trip origins outside the GTHA. The GGH contributes approximately 50% of the GDP

of the province of Ontario, Canada; the population of the region is expected to rise to 11.5

million by 2031, an increase of 47.4% over 2001 (MPIR 2006). With a resident population

of 2.5 million, the City of Toronto is the largest urban city in the GGH. Beyond Toronto,

the GGH hosts 25 existing and emerging growth centres. The changing patterns of

employment growth during much of the 20th century, characterized by a gradual decen-

tralization of employment from Toronto, and the post-war influx or growth of migrating

and non-migrating firms to the suburban cities of the GGH (e.g., Mississauga) has pro-

duced a diverse set of commuting patterns (e.g., suburb to suburb, central city to suburb

etc.), that is now typical of many North American urbanized regions (e.g., Pisarski 2006;

Statistics Canada 2001; Statistics Canada 2008). Under a business as usual scenario,

population growth and higher rates of suburban auto-ownership (1.5–2 times as many

vehicles per household than in Toronto), are conditions that lend themselves to ongoing,

and possibly augmented congestions problems (OECD 2010, Transport Canada 2006).

Data and sample characteristics

A non-probability convenience sample was taken from users of Carpool Zone who reg-

istered between November and December 2007. Cases were developed from a database

(developed by the researchers) that combined three separate resources. User profile (e.g.,

demographics) data, and trip data stored on Carpool Zone servers, were linked with data

from the 2007 Carpool Zone User Satisfaction Survey. Carpool Zone users were entered

into a draw for an iPod Touch valued at $375.00 (CAD), or a $50.00 (CAD) iTunes gift

card, as a participation incentive. All users received a personally addressed e-mail; a

reminder message was distributed 6 days prior to the end of the survey period. The survey

was sent to 4,774 registered users, yielding a response of 1,422 cases (29% response rate).

Data about employer transport policy and characteristics (e.g., firm size) extend the

authors’ earlier work (Buliung et al. 2009, 2010). Inclusion of these new data resulted in

the exclusion of all public users. Metrolinx has no knowledge of employer characteristics

beyond the set of employers with whom they work. The motivation to understand the

efficacy of workplace policy in enabling carpool formation, then, resulted in a reduction in

sample size from n = 1,422 cases to n = 358 cases. All variables included in the study are

described in Table 1. Data were not available to model the entire carpool lifecycle; the

paper focuses on the first part of the carpool lifecycle, formation.

One limitation of the study design is that convenience sampling can limit generaliz-

ability of findings to the population level. The study was not designed to infer about the

carpooling behaviour of the commuting labour force writ large; rather, it was developed

as a small exploratory behavioural study that begins to shed light on the carpooling

Transportation (2012) 39:877–893 883

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behaviours of a specific segment of the commuting population who work at employers that

are collaborating with the planning agency. Interpretation and discussion of the research

findings is explicitly limited to the scope of the sample. The sample was nearly evenly split

between female (53%) and male (47%) workers. The study area includes three Census

Metropolitan Areas (CMAs): Toronto, Hamilton, and Oshawa. Workplace destinations are

limited to the suburbs because Toronto firms had not yet started working with Metrolinx.

Table 1 Variable descriptions

Variable name Definition Univariatedescription*

(1) Demographics

Age Age in years 35.6 (32.0)

Gender Female, Male (reference category) 0.53

Median household income Median household income in Canadian dollars in thepostal code zone of residence

67,844.00(71,393.00)

(2) Motivation to carpool

Cost Savings Reference category 0.35

Don’t drive, or have accessto a car

Primary motivation is absence of auto-mobility 0.20

Environmental concern Primary motivation is concern for the environment 0.45

(3) Household auto-mobility

Number of householdautomobiles

Vehicle to worker ratio could not be constructed; dataon number of household workers was not collected

1.4 (1.0)

(4) Scheduling of work

Typical vs. atypical workschedule

Typical work hours (Monday to Friday, between 8 and 9a.m. and 4–5 p.m. Atypical work hours (referencecategory, with a work schedule deviating from above)

0.33

(5) Commute distance

Network distance (km) Shortest path network distance between residence andfixed place of work

30.0 (27.8)

(6) Employer characteristics

Firm size Number of employees at a respondent’s workplace 9,230 (2,000)

(7) Workplace transport policy

Carpool spaces Number of carpool spaces 6.6 (3.0)

Emergency ride home (ERH) The employer does or does not (reference category)offer an emergency ride home (ERH) program

0.70

Gender and ERH Assumes gender moderates effect of ERH on carpoolformation (reference category: male at a firm withoutan ERH)

0.36

(8) Geographical Proximity

Proximity to nearest carpoollot (km)

Network distance to closest government-managedcarpool lot. These lots were typically located athighway interchanges and serve as congregationpoints for SOV drivers to transfer to a carpool

13.2 (10.8)

Proximity to users Number of respondents within a 2.5 km radial distanceof each case

11.6 (10.0)

* Unless otherwise indicated, mean is followed by median in brackets. For categorical variables, reportedvalue indicates proportion of cases in non-reference category

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The median personal income within the census zone of sampled workers (71,393.00 CAD)

was nearly three times larger than the national median (25,615 CAD), and at least double

the median personal income reported for the Toronto (32,005 CAD), Hamilton (28,416

CAD), and Oshawa (26,754 CAD) CMAs (Statistics Canada 2006). The median commute

for the sample (27.8 km) is larger than the national median (7.5 km) and larger than the

median commute reported for the Toronto (9.4 km), Oshawa (11.0 km), and Hamilton

(8.3 km) CMAs (Statistics Canada 2008). Approximately 97% of the sample had com-

mutes greater than the smallest median commute for study area CMAs (8.3 km, Hamilton).

With a growing but relatively small percentage of commuters in each of the Toronto,

Hamilton, and Oshawa CMAs travelling at least 25 km to work: 13.8, 19.4, and 32.6%

(Statistics Canada 2008), sampled commuters are members of the region’s long-distance

commuting subpopulation, working at suburban firms in the study area.

Origin by destination matrices were developed for all registered sample commutes, and

separately for commutes involving operational carpools (data not reported). Trip origins

were concentrated in Toronto (28.2%), the suburban City of Mississauga (15.6%), and the

City of Hamilton (11.2%). Destinations were located in the suburban GGH: Mississauga

(31.3%), Oakville (15.4%), and Brampton (11.7%). With regard to operational carpools,

the dominant commuting pattern for this sample appears to be an urban to suburban, or

reverse commute, rather than a cross-commute between suburban cities. Commutes

between Toronto (the largest and oldest urban city in the GGH), and Mississauga (Can-

ada’s six largest city located in the suburban GGH) dominated the set of sampled origins

and destinations (14.1%). The observed commute pattern and distributions of trip origin

and destinations is partially the product of an institutional issue. At the time of sampling,

there was no active TMA in Toronto—in other words, Toronto firms were not yet involved

with the Smart Commute project.

Firm size ranged from 75 to 60,000 employees, 88% of respondents were drawn from

firms ranging in size from 75 to 11,000 employees. Sampled workers are employed pri-

marily at small- to medium-sized firms. The median household auto-ownership of one

vehicle likely reflects the concentration of trip origins within Toronto, which has an auto-

ownership rate of 1.1 vehicles per household (Data Management Group 2008). Overall,

then, the typical commuter examined in this study is a relatively well-paid reverse com-

muter, who is relatively young (B40 years) and working within the service industries. The

typical commuter is also working non-standard hours at a small- to medium-sized firm, and

has access to few household vehicles. With respect to the service economy, classified using

the North American Industrial Classification Standard (NAICS), the sample included

workers from the following industry divisions: Public Administration (n = 138); Profes-

sional, Scientific and Technical Services (n = 78); Finance and Insurance (n = 33);

Information and Cultural Industries (n = 28); Other Services (n = 13); and Health Care

and Social Assistance (n = 12).

Methods

Once registered, a user of Carpool Zone and his/her carpool can occupy one position in a

set of exhaustive and mutually exclusive carpool states. These states include: (1) waiting

for a carpool match; (2) waiting for a better carpool match; (3) waiting for a response to a

carpool request; (4) formed a carpool without starting to travel, or (5) formed and started

carpooling. Identification of state membership was made possible through the satisfaction

survey. Logistic regression analysis is used to provide insight into the relative influences of

individual, household, spatial, temporal, and workplace characteristics on the carpool

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formation and use process. The categorical response variable was constructed for each

respondent from the Satisfaction Survey data, with the value 1 indicating carpool formation

and use, and the value 0 indicating membership in one of the four remaining states

described previously. The logistic regression model assumed the form:

logit(p) ¼ logp

1� p

� �¼ aþ b1x1 þ b2x2 þ . . .þ bkxk þ e ;

where p is the probability of having formed and started carpooling at the time of survey,

log(p/1-p) is the log odds, a is a regression constant, the b1… bk are the parameter

estimates for k independent variables x, and e is a random error term. The logged odds of

having formed and started carpooling is modelled as a linear predictor that includes the

eight blocks of variables shown in Table 1. A researcher controlled block variable entry

method was selected to allow for an examination of the differential effect of thematically

linked sets of variables on the carpool formation and use process (Buliung et al. 2010;

Meyers et al. 2006).

Results

As a preface to the introduction of the results (Table 2), it is important to reiterate that the

model was specified to investigate the process of carpool formation and use, the model

does not look at the mode choice process that would have preceded the decision to engage

with carpool formation. For this sample, and in the adjusted model, there were no statis-

tically significant demographic effects. Separate from the multivariate analysis, gender

differences were observed. Females had shorter commutes, on average, than males

( �Xfemale ¼ 27:2 km \ �Xmale ¼ 33:1 km, p \ 0.01), an effect that remained significant, but

marginally weaker, when statistical comparisons were made between males and females

who had formed carpools ( �Xfemale ¼ 28:5 km \ �Xmale ¼ 36:5 km; p \ 0:05), and for

males and females who had formed carpools at firms offering ERH programmes

( �Xfemale ¼ 27:2 km \ �Xmale ¼ 33:1 km, p \ 0.05). No between group differences were

found for age or income. There could be gender differences in individual income, but these

data were not available. Commute distances of females in each comparison group

remained, on average, 2.5 to nearly 4 times larger than the national and regional commutes

reported earlier.

Qualitative assessment of commuter motivations produced no statistical association

with carpool formation and use, although there is perhaps preliminary evidence that rel-

ative to the desire to reduce costs, workers without vehicle access were less likely to have

established an operational carpool than others. The number of household cars has a positive

correlation with carpool formation, on average; each additional household vehicle

increases the odds of carpool formation by 33% (p B 0.10). Although the direction of the

relationships emerged as expected, the scheduling of work, and commute distance were not

statistically significant.

Importantly, and related to the specific contribution of this article, the strongest pre-

dictor is a workplace transport policy variable. Firm size produced a negligible positive

effect while, on average, carpool odds was shown to increase by 3% (p \ 0.05), with each

additional preferential designated carpool parking space. Workers at employers with an

ERH program were 3.2 times (p \ 0.01) more likely to be carpooling than others at the

time of survey. With respect to gender and carpool formation, relatively more females

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(16%) than males (13%), employed at firms with ERH, had formed carpools at the time of

sampling. While contingency analysis suggested that gender moderates the impact of ERH

on carpool formation (p \ 0.01) the effect did not persist into the adjusted model.

With regard to carpool geography, proximity to potential matches had a positive and

significant association with carpooling. On average, the addition of a potential match

within 2.5 km of the place of residence increased the odds of carpooling by 2% (p \ 0.05).

Access to carpool lots had a significant but neutral positive effect (p \ 0.01). Of the

household/individual variables, household cars and residential proximity to potential

matches had the strongest effects. With regard to workplace characteristics, policies

favouring the development of carpool infrastructure (e.g., carpool spaces), and the

Table 2 Logistic regression results

Fixed effects ! p value OR OR ±95%CI

Lower Upper

Constant -4.082 0.000 0.017

(1) Demographics

Age 0.005 0.673 1.006 0.980 1.031

Median household income 0.000 0.183 1.000 1.000 1.000

Gender (reference: male) -0.231 0.676 0.794 0.268 2.349

(2) Motivation to carpool (reference: cost savings)

Don’t drive, or don’t have access to a car -0.200 0.624 0.819 0.368 1.823

Environmental concern -0.166 0.583 0.847 0.469 1.531

(3) Household auto-mobility

Number of household automobiles 0.287 0.075 1.333 0.972 1.827

(4) Scheduling of work

Typical vs. atypical work schedule (reference: atypical) 0.153 0.580 1.165 0.678 2.000

(5) Commute distance

Network distance between origin and destination (km) 0.012 0.119 1.012 0.997 1.027

(6) Firm characteristics

Firm size 0.000 0.001 1.000 1.000 1.000

(7) Firm transport context

Carpool spaces 0.028 0.055 1.029 0.999 1.058

ERH 1.170 0.011 3.221 1.312 7.904

(8) Proximity

Proximity to user (2.5 km) 0.035 0.036 1.036 1.002 1.071

Proximity to nearest carpool lot 0.000 0.005 1.000 1.000 1.000

(9) Interaction variable

Gender (female) X ERH (ERH available at firm) 0.526 0.398 1.692 0.500 5.731

Summary statistics

Number of Cases 358

-2[L(0)-L(B)] 375.843

X2 82.177

OR Odds Ratio, 95% CI Confidence Interval

* p \ 0.01, ** p \ 0.05, *** p \ 0.1

Transportation (2012) 39:877–893 887

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development of programmes to manage the uncertainty associated with carpooling (ERH),

had the strongest effects. The adjusted models suggest then, that for this sample, once

service workers select themselves into a carpool formation process, proximity to one

another at the residential end, and workplace transport policy, are relatively more influ-

ential than individual/compositional characteristics.

Among the commute pattern possibilities for this sample (internal commuting within a

suburban city, cross-commutes, reverse commuting), the reverse commute emerged as the

dominant commute pattern, and was the dominant type of commute for operational car-

pools. The commute patterns of workers having predicted odds greater than or equal to two

standard deviations above the mean odds for the entire sample were mapped (Fig. 2).

Mapping at two-plus standard deviations revealed the location of cases particularly suited

to the construction of carpools using the web-based tool. In other words, the map begins to

help with the identification of hot-spots, where the Carpool Zone programme, at the time of

sampling, may be prone to achieving success over the long-term.

Discussion and conclusion

Weak associations between demographic variables and carpool formation were expected

(Benkler 2004; Buliung et al. 2009, 2010; Kaufman 2002). An important caveat is that the

models are looking at the carpool formation process, once a service worker has selected

her/himself into the process. In other words, ‘‘who’’ an individual ‘‘is’’ could be less

important to carpool formation because Carpool Zone members have already agreed to

share in the experience of attempting to form a carpool, this shared experience/familiarity

might temper concerns about difference. In a confirmatory sense, gender differences in

carpool prevalence and distance were observed (e.g., Teal 1987). It was interesting to note

that the shorter commutes of females reported in the literature, persist within this spe-

cialized sample of female service workers. A more interesting direction for future inquiry

involves understanding why these differences occur. Gender differences in non-instru-

mental/psycho-social concerns and carpool preference/participation should be studied

(e.g., Ellaway et al. 2003; Steg 2005).

Number of household cars associates with carpool formation. The ratio of vehicles to

drivers would have been a preferred predictor; the necessary data were not available. Short-

term changes in the economics of auto-ownership and use could encourage some com-

muters, such as the long-distance commuters in this sample, to consider shared mobility. In

2007, on average, 47.5% of the vehicle operating budget of Canadian households was spent

on fuel, an increase from 41.9% a decade earlier (Statistics Canada 2010a). At the time of

survey (November–December 2007), and in Toronto, the average retail fuel price for

regular unleaded gasoline was on an upward trend, following a decline in retail prices

during the months of June through October. There have been 12 months since January of

1979 where the average retail fuel price in Toronto was at least $1.00 (CAD) per litre—

seven of these months occurred between January and December of 2007 (Statistics Canada

2010b). This study was situated within a period of relatively high retail fuel prices.

Perception regarding ease of scheduling, and the actual scheduling of work have been

shown to influence carpooling (Buliung et al. 2010; Ozanne and Mollenkopf 2009;

Poulenez-Donovan and Ulberg 1994). Scheduling had the expected sign in the models, but

was a non-significant predictor. In this sample, there was a larger share of workers engaged

in work during non-standard hours (outside of the 8:00 a.m.–5 p.m. window) that had

started carpooling (22%), than carpooling workers with standard hours (12%). The ‘‘white

888 Transportation (2012) 39:877–893

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Fig. 2 First Panel, work trip origins for respondents with predicted carpool odds C 2 standard deviations ofthe mean predicted sample odds. Second Panel, work trip destinations for work trip origins from panel one.Third Panel, desire lines connecting work trip origin and destinations (panels 1 and 2) of respondents withpredicted odds C 2 standard deviations of the mean predicted sample odds. Commute patterns of operationalcarpools dominated by cross- (suburb to suburb) and reverse commutes (from Toronto to elsewhere)

Transportation (2012) 39:877–893 889

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collar’’ service sector orientation of the sample, and above-average total personal incomes

perhaps indicate greater agency in scheduling decisions. Carpoolers tend to have above-

average commute trip lengths than workers traveling by other modes (Ferguson 1997;

Levin 1982; Richardson and Young 1981; Teal 1987). Univariate analysis revealed long-

distance service workers. Distance may have a greater role in a preceding mode choice

decision, with long-distance commuters selecting themselves into carpooling. Because

carpoolers share in the experience of long-distance commuting, the role of distance in

formation is likely less relevant than other factors (e.g., Buliung et al. 2010; Richardson

and Young 1981).

Carpool studies conducted years before web-browsers and e-mail showed that the

presence of in-house carpool coordinators could increase employee engagement with

carpooling (Brunso and Hartgen 1981). An enabling environment characterized by pro-

motion, and facilitation through the development of supportive infrastructures (e.g., car-

pool parking spaces) might have a positive affect on carpooling. Past work by the authors

suggests that indeed, the Internet is not enough, public users of Carpool Zone did not have

the same carpool prevalence as employees of firms working jointly with Metrolinx to

develop context-sensitive TDM solutions (Buliung et al. 2010). What could not be

determined in the earlier work was exactly how different workplace carpool supportive

policies affect employee commuting. This study suggests that, for service workers with

suburban destinations, it is likely important to situate carpool-enabling technologies within

a broader set of supportive workplace policies. The models suggest that carpool spaces and

implementation of an ERH program enable carpool formation.

ERH is offered to the employees of firms who are members or partners of Smart

Commute, either by the local TMA or by the employer. ERH is a risk-management

strategy, where a participating employer or TMA assumes the role of managing the risk

associated with an employee’s decision to rely on a multi-occupant mode like carpooling

or transit. ERH guarantees that non-driving carpool members will have access to trans-

portation if an unplanned activity or event affects the availability of the carpool driver. The

emergency ride is typically provided through a local taxi service at no cost to the com-

muter, up to a maximum allotment. Under certain conditions, rides may also be provided

through a daily automobile rental arrangement, or public transit. Tools like Carpool Zone,

then, can facilitate the front-end spatiotemporal query and communication required for

carpooling, but by making additional investments in carpool spaces, and by helping to

manage the risk associated with shared mobility, employers will likely have greater success

in facilitating carpooling.

With regard to the geography of the service economy and model results, all workers

with predicted odds exceeding one standard deviation above the mean of the predicted

odds for the entire sample were located in two sub-sectors: Public Administration; and

Professional, Scientific and Technical Services. Despite sampling bias toward these sub-

sectors, the results motivated additional thinking about the spatial structure of the service

economy and carpooling. These workers are part of the broad class of service workers and

are the so-called knowledge workers or producer service workers of the ‘‘new’’ economy;

their jobs are increasingly located in the suburbs (Shearmur and Coffey 2002). Employees

from these two sectors were particularly likely to have started carpooling before other

members at the time of sampling.

For this sample of workers, the commutes most likely to be organized into carpools

were reverse commutes (compared with suburban internal commutes or cross-commutes)

from Toronto to elsewhere. It is useful to observe that a regional trend toward reverse

commuting (Statistics Canada 2001), reinforced by employment and residential

890 Transportation (2012) 39:877–893

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decentralization (Statistics Canada 2008), can be met by a TDM bundle that includes

carpool formation tools. Given the qualities of the respondents, situating the results within

the context of these broader economic and transport trends could suggest that the web-

based carpool experiment is well matched to the tastes, preferences and technological

literacy of a particular service worker: the relatively young white collar worker, who trades

transport costs to well-paid employment in a growing peripheral service economy, against

the costs of locating his/her residence within a high amenity urban area.

Conversely, it is not always by choice that a worker takes on a long commute.

Firms seeking the competitive advantage associated with lower land values and corporate

tax rates, commonplace within the suburban parts of the study area, may relocate.

Employees may find themselves lost in space, left with a long commute to a recently

suburbanized office (of course there will be workers for whom this suburban shift is

advantageous because they live in the suburbs). The geographical transformation of the

workplace location might or might not be followed by an adjustment in the place of

residence of the employee. Nevertheless, and even for a household considering reloca-

tion, there will likely be some lag between the firm and residential relocation processes.

The tension between sustainability/productivity outcomes associated with development

and implementation of ICT- enabled TDM strategies, and how these same tools may

enable, or reinforce consumer and commercial processes that give rise to expansive

development and lengthy, arguably unsustainable patterns of commuting, is an issue

worthy of future exploration.

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Author Biographies

Ron N. Buliung is an Associate Professor of Transport Geography and Spatial Analysis in the Departmentof Geography at the University of Toronto Mississauga. He is also a research associate of the Cities Centreat the University of Toronto. He received his Ph.D. in Geography at McMaster University in 2004. Hisresearch interests broadly cover the relationship between the technologies of mobility (ICTs, transport),travel behaviour, and the development of cities and regions.

Randy Bui earned his Masters degree from the Department of Geography & Program in Planning at theUniversity of Toronto. His interests lie at the intersection of GIS and transport studies.

Ryan Lanyon is the Team Lead for the Smart Commute and TDM sections of Metrolinx. He has worked intransportation demand management since 1999, in both the non-profit sector and government.

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