Studies and research
We are conducting our own research, commissioning cross-tabulated census datasets and reporting on peer-reviewed studies.
CloseCommute has prepared "The Effects of Long Commutes
and What To Do About Them - An Annotated Bibliography" which presents peer-reviewed research into the effects of long commuting, mechanisms for quantifying the direct and external costs, and previous commute trip reduction programs.
[ download as a 1.3Mb PDF file ]
An excerpt from the summary of Part 1 of the annotated bibliography…
The studies identified correlations between long commutes and these outcomes:
- obesity or adiposity [severe or morbidly overweight] (Jacobson et al., 2011) (Jilcott et al., 2010) (Lopez-Zetina et al., 2006) (Hoehner et al., 2012) (Sacker et al., 2014) (Sugiyama et al., 2016) (Tsuji et al., 2015)
- higher daily exposure to particulate matter and black carbon (Karanasiou et al., 2014) (Shekarrizfard et al., 2016)
- more visits to general practitioner (Künn-Nelen, 2016)
- lower cardiorespiratory fitness (Hoehner et al., 2012) and higher cardio-metabolic risk (Hoehner et al., 2012) (Sugiyama et al., 2016)
- higher blood pressure (Novaco et al., 1979)
- diabetes (Tsuji et al., 2015)
- chronic fatigue (Kageyama et al., 1998) and hypertension (Tsuji et al., 2015)
- self-reported poor health, serious backache, headaches, sleep disorders and fatigue (Hämmig et al., 2009)
- anxiety and depression, lack of energy and optimism (Hämmig et al., 2009)
- chronic stress, being in a sympathodominant state (Kageyama et al., 1998)
- increased stress and anxiety (Pohanka et al., 2004)
- lower sense of well-being (Stutzer et al., 2008)
- reduction in sleeping, physical activity and food preparation which over time may contribute to obesity and other poor health outcomes (Christian, 2012)
- less physical exercise (Künn-Nelen, 2016) (Hoehner et al., 2012) (Nomoto et al., 2015)
- fewer sleeping hours (Nomoto et al., 2015)
- increased sickness absence (Ala-Mursula et al., 2006) (Künn-Nelen, 2016)
- longer average paid time loss days due to work-related injury (Fan et al., 2013)
- fewer working hours (Nomoto et al., 2015)
- more accidents (Pohanka et al., 2004)
- lower job satisfaction and decreased intention to stay with same employer (Steinmetz et al., 2014)
- less access to social capital (Besser et al, 2008); less time with friends (Sandow, 2011); and social isolation (Pohanka et al., 2004)
- higher time- and strain-based work-life conflict [WLC] (Hämmig et al., 2009)
- strain on relationships and likelihood of divorce (Sandow, 2011)
- low social participation and low general trust (Mattisson et al., 2015)
RBC bank employee commuting study
During the summer of 2016, our sister company, Trelawny Consulting Group Ltd., was granted confidential access by RBC Royal Bank of Canada to the home and work addresses, and job classifications for all their 241 employees working on Southern Vancouver Island [Greater Victoria] for the purposes of studying their commutes, including the environmental burden and the potential for improvement.
Using Google Maps APIs, we calculated the commute distance and duration of every employee from their home to all 16 branches. We could then determine that fewer than 25% of the employees were working at the closest branch to home. This could also be stated as, "for over 75% of the employees, switching to work at a different location would reduce commuting time and distance."
Having the employees' current commute distances allowed us to calculate the associated greenhouse gas burden (a "baseline" for measuring improvement). This bank (as is common with all other major banks) does not include GHGs from employee commuting in its annual summary of corporate environmental footprint (as Vancity Credit Union does), even though it appears that employee commuting might be as large a contributor as all of the reported sources combined.
It was demonstrated by Mullins & Associates (Proximate Commuting,1995) that a multi-worksite employer can significantly reduce commute distances over time by considering home/work proximity during hiring and internal transfer processes. Doing so will significantly reduce commute effects within a year - likely by an amount similar to the 17% decrease in total vehicle miles traveled recorded by Gene Mullins in the Key Bank initiative.
We next quantified the potential for a more immediate and proactive program to reduce employee commuting distance and duration, a program whereby the employer identifies reciprocal, lateral transfer pairings – what we call a “swap match.” Essentially, two people in the same job role would be offered the opportunity to voluntarily switch locations for mutual advantage. (Other benefits would accrue to the employer, society and the environment, as identified in the annotated bibliography.)
We identified at least one potential swap match pairing for over 50% of the employees, and multiple swap options for many of these employees. Not all these employees could have swapped at once, however, since implementing one swap would remove its participants from other potential pairings. Presumably an employer would initially focus on those pairings that produce the largest benefits. Some swap pairings would produce large improvements; for example, encouraging 14 of the longer commuters to swap would reduce the bank's regional environmental burden by 8.6%.
In one example swap, two financial planners would save 52.0 km and 52.8 km respectively daily by swapping locations. That would eliminate about 25,200 km of commuting annually in total. The CO2 equivalent reduction from this one swap would be almost 3.5 tonnes, over 1.9% of the bank's regional total. Some of the benefits for those two individuals: The first person would save about 170 hours a year (42.4 minutes a day) and about $4,160 in after-tax expenses (assuming 33 cents per km vehicle operating costs). The second person would save about 150 hours a year (37.2 minutes a day) and perhaps $4,220 if continuing to drive. Given that the new commute would be less than 3 km, he or she might consider walking or biking for even greater financial savings.
This study clearly demonstrated that the scope for potential swaps is quite large (over 50% of all employees for this bank), and that there could be very significant savings in time, money and greenhouse gas emissions.
We commissioned from Statistics Canada tens of thousands of cross-tabulations of the national census data, so we can conduct definitive and detailed analyses about commuting flows and intensities. The data reveal answers to questions such as:
- how many teachers (or 30 other professions), in any given census subdivision, commute by car alone (or 9 other modes), taking less than 10 minutes (or 12 other time intervals)?
- how many bank tellers work in Surrey (or another census subdivision) yet work in Burnaby, and vice versa?
We've recruited over a dozen professors from across Canada to analyze the data, producing what we expect will be sobering and statistically-sound statements about the current, grossly-inefficient situation. We will release these statements along with the datasets themselves – at no charge – to the media, school boards, municipalities, banks and other large employers in March. If you want to analyze this cross-tabulated dataset, contact Bruce at firstname.lastname@example.org to be put on the list.
Your home, work and play only a short hop apart …
We've started gathering media partners, sponsors and endorsers. If you have contacts to suggest or expertise to offer, please email Bruce at email@example.com or phone 250-380-0998 (Pacific time).