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Limited access: The costs of private vehicle dependence in New Brunswick

UCSI Discussion Paper
Yves Bourgeois, PhD, Director
University of New Brunswick's Urban and community studies institute (UNB UCSI)

Saint John, NB, 10 September 2013

New Brunswickers (NBers) are amongst the most dependent on private vehicle transportation in Canada. NBers have the 3rd highest car ownership rate in Canada at 1.55 vehicle per household, compared to 1.47 for all of Canada, and behind only Alberta and Saskatchewan. NBers put 5% more mileage on private vehicles than the Canadian average (Natural Resources Canada, Canadian vehicle survey 2009). NBers also produce 9.4% more CO2 than the Canadian average (Statistics Canada, Environment Accounts and Statistics Division, 2009.

Chart 1 shows the percentage of workers using private vehicles as their primary source of travel to work, for select eastern Canadian towns and cities (2011 Census). It is interesting to note that among NB’s fastest growing communities, Dieppe and Quispamsis also show the highest car dependency ratios. It is unlikely that car dependency rates are lower in NB’s smaller towns, villages and LSDs, where it is typically more common to live and work in different censussubdivisions (CSDs).

Private vehicle dependency is worrisome for a number of reasons.

Chart 1 -- Private vehicle dependency rate, % of all workers, select ciCes

78.3% - Fredericton

81.1% - Moncton

91.9% - Dieppe

91.2% - Riverview

78.1% - Saint John

96.4% - Quispamsis

96.4% - Rothesay

82.7% - St-John's

75.8% - Halifax

80.9% - Charlottetown

56.0% - Montreal

Chart 2 -- Public transit dependency rate, % of all workers, select ciCes

5.0% - Fredericton

4.2% - Moncton

2.3% - Dieppe

1.9% - Riverview

7.6% - Saint John

0.6% - Quispamsis

0.0% - Rothesay

4.9% - St-John's

11.9% - Halifax

1.1% - Charlottetown

32.6% - Montreal

Chart 3 -- Bicycle dependency rate, % of all workers, select ciCes

1.9% - Fredericton

1.4% - Moncton

1.1% - Dieppe

0.6% - Riverview

0.4% - Saint John

0.0% - Quispamsis

0.5% - Rothesay

0.4% - St-John's

1.0% - Halifax

1.6% - Charlottetown

2.2% - Montreal

Converting the majority of private vehicle users to public transportation may be unrealistic and impractical. However, increasing public transportation services can play an important role in addressing social inequities in attaining economic opportunities, accessing public services, as well as accessing cultural, social and leisure activities for all New Brunswickers. Moreover, there are important public finance and economic growth arguments to public transportation, more evident when external costs associated with private vehicle use are internalized. Increased private vehicle dependency translates into lost productivity and leisure, as well as higher public costs associated with road construction and maintenance, increased healthcare costs, environmental remediation costs, etc. Whether through carbon taxes, fuel taxes, tolls or other mechanisms, other jurisdictions are increasingly pricing social costs and publicly-funded infrastructure onto private vehicle use, resetting the balance between private vehicle and public modes of transportation.

There are major sets of challenges that persist in achieving critical mass in public transportation that would make it more sustainable, even when compared to private vehicle use with full social costs added. Namely, we do not know very well how, when and where people need to travel. This owes partly to the fact most transit systems remain hub-and-spoke oriented, focused towards central business districts (CBDs), as they emerged in the last century when CBDs were the primary generators of jobs and retail services. Today, jobs and retail are primarily located in cities’ secondary business districts (SBDs), with CBD workers not being primary transit users, and increased number of residents living in suburbs.

Chart 4 -- Walking dependency rate, % of all workers, select ciCes

12.2% - Fredericton

11.3% - Moncton

3.4% - Dieppe

5.6% - Riverview

11.5% - Saint John

2.1% - Quispamsis

2.1% - Rothesay

9.8% - St-John's

10.1% - Halifax

14.3% - Charlottetown

8.1% - Montreal

In New Brunswick, while close to three quarters of the population work in census areas (CAs) and Census metropolitan areas (CMAs) – communities of 10,000 people or more – closer to half live in communities of 1,000 or less. Hence we have fewer people using mass transit in our larger cities than in other regions, we have a larger percentage of the population travelling rural-urban routes to access work, activities and services, and a large percentage working in small communities with little or no public transportation.

At the same time, we have entered a new era of data analysis where we are uncovering travel patterns that can help improve public transportation routes and uptake. Increasingly, transit authorities elsewhere are abandoning hub-and-spoke models for direct suburb-to-SBDs, since few CBD workers use transit, and total travel time and transfers have been the main obstacles to transit use (Thompson et al 2012). We use remote sensors in vehicles to track travel patterns and we can also use social media data to track where and when people travel. We no longer have to rely on transportation user surveys and what respondents report to be obstacles in adopting public transportation. The opportunity to understand better when and where New Brunswickers need to travel holds great promise in providing improved means to get there.


  1. Adequately cost out all forms of transportation. It is difficult to assess the return to investment on public transportation subsidies because the costs of subsidizing private forms of transportation are typically unaccounted. We need to consider the social costs associated with inefficiencies owing to parking spaces, environmental remediation or degradation, lost productivity etc if we are to compare apples with apples.
  2. Uncover mobility patterns. Total travel time is the key factor why potential transit users drop public transportation (Thompson et al 2012). Improved ambiance, wi-fi and so on are ineffective in luring users if it takes 45 minutes to travel 5km because of connexions and route patterns. Progressive jurisdictions are turning to big data and Census data to rejig transit route to where critical masses of potential users originate and need to go.
  3. Leverage new technologies and improve regulatory framework to improve intermodal and intercommunity transportation. Accessing essential services like hospitals can be an ordeal if you are mobility restrained or without private vehicle owing to disability or age. In New Brunswick, Energy and Utility Board regulations limit how transit authorities can serve outlying regions. Inter-community bus and rail service is precarious at best, let alone connectivity with city transit or ferry services. There are numerous jurisdictions where people can access integrated travel timetables from home computers or mobiles devices, across modes of transportation, private and public.
  4. Better understand the impact of infrastructure location. Cheap real estate in peripheral locations on the periphery of towns can make smart sense in locating public infrastructure like hospitals, schools, sports complexes, government service centres, etc. when considering property development and tax costs. However, when adding social costs accruing from private vehicle use, as well as difficulties accessing remote sites for those with mobility restrictions or lower income, smarter location decisions are needed to reflect the added true costs of transportations.


Canadian Automobile Association (CAA). Car driving costs. 2012.

Natural Resources Canada, Canadian vehicle survey 2009.

Statistics Canada, Environment Accounts and Statistics Division, 2009.

Thompson, Gregory, Jeffrey Brown and Torsha Bhattacharya (2012) What Really Matters for Increasing Transit Ridership: Understanding the Determinants of Transit Ridership Demand in Broward County, Florida. Urban studies. 49(15): 3327-3345.

Turcotte, Martin. 2008. Dependence on cars in urban neighbourhoods. Canadian Social Trends, 85, Statistics Canada Catalogue no. 11-008-XWE.

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