Where Are BIXI Bike Stations Located and Why?

ABSTRACT

BIXI, a popular bike sharing company in Montreal, has bicycle sharing stations all across the island. While these bicycle shares have become commonplace in the warmer months of the city, the company is forced to take away stations in colder months, when cycling rates drop. In this case, knowledge on prime locations for bike sharing stations is key for BIXI to continue serving cyclists during off-peak months. This paper embodies the role of the BIXI company, using GIS tools to understand what demographic characteristics correlate to BIXI stations, and how this knowledge can be used to find prime locations if BIXI needs to reduce their 660 stations to 100. First, buffer and overlay tools with BIXI stations and census data finds that characteristics such as population density, proximity to the downtown core, and possibly one-person households, correlate to bike share use. Next, using this knowledge, ArcGIS’s location-allocation tool is used to find 100 prime locations. Three ‘prime location’ maps are thus generated as useful suggestions for the BIXI company to use when deciding what bike stations to keep, remove, or add, during off-peak months. This research hopes to gain a better understanding of how BIXI decides the location of their bicycle stations and what factors may be considered when installing bike

1 “Qui Sommes-Nous?,” BIXI Montréal, accessed March 25, 2021, https:// www.bixi.com/fr/quisommes-nous.

2 “Open Data,” BIXI Montréal, accessed March 25, 2021, https://www.bixi. com/en/page-27. INTRODUCTION

The bike sharing company, BIXI, has become increasingly popular throughout Montreal, with a network of more than 8000 bikes and 660 stations1. With BIXI, people can rent a bike to traverse the city and then park their bike in an equivalent bike dock when done. While BIXI stations can be found all over the island of Montreal, the company does not explain their motive for locating and concentrating bike stations in certain areas of the city over others. Understanding prime locations for bike stations is key for when BIXI is forced to suspend service in colder months, as bike station usage drops.2 This paper thus has two objectives. First, I use buffer and overlay analysis to examine demographic characteristics for Montreal, Laval, and part of Longueuil, to infer what characteristics BIXI may use to decide where they will situate bike stations for widespread use. Second, location allocation analysis is conducted based on

3 Ipek N. Sener, Naveen Eluru, and Chandra R. Bhat, “Who Are Bicyclists? Why and How Much Are They Bicycling?,” Transportation Research Record: Journal of the Transportation Research Board 2134, no. 1 (2009): 63-72, https://doi. org/10.3141/2134-08.

4 Snehanshu Banerjee et al., “Optimal Locations for Bikeshare Stations: A New GIS Based Spatial Approach,” Transportation Research Interdisciplinary Perspectives 4 (2020): 1-11, https:// doi.org/10.1016/j. trip.2020.100101.

5 “Census Profile, 2016 Census Montréal,” Statistics Canada, October 27, 2021, https://www12.statcan.gc. ca/census-recensement/ 2016/dp-pd/prof/details/ page.cfm?Lang=E&Geo1= CMACA&Code1=462&Geo 2=PR&Code2=01&Data= Count&SearchText= Montreal&SearchType =Begins&SearchPR=01 &TABID=1&B1=All. 6 Sener et al., “Who Are Bicyclists?,” 63-71.

7 Ibid, 65.

8 Ibid, 63.

9 Yang Yang, Lan Jiang, and Zili Zhang, “Tourists on Shared Bikes: Can Bike-Sharing Boost Attraction Demand?,” Tourism Management 86 (2021): 1-11, https:// doi.org/10.1016/j. tourman.2021.104328. two of these characteristics in order to visualise the locations of optimal bike stations when BIXI’s 660 stations are reduced to 100. Location allocation determines the best locations to serve a selected clientele. This paper draws on research from Senner and Banerjee who use similar GIS tools to determine common demographic characteristics of cyclists and optimal locations for bike shares, respectively.3 4 This research hopes to gain a better understanding of how BIXI decides the location of their bicycle stations and what factors may be considered when installing bike stations.

METHODOLOGY

Part 1: Overlay of demographic information

The demographic factors considered come from the 2016 Census and include: Population Density Per Square Kilometre, Average Age, Median Total Income in 2015 Among Recipients ($), One-Person Households, Bicycle Use as Main Mode of Commuting to Work.5 The five choropleth maps below (figure 1-5) show each respective characteristic overlaid with BIXI’s bike sharing stations. Through visual analysis, I observed whether any of these considered variables correlate with the placement of BIXI stations. The rationale for examining these five characteristics comes from Senner’s study, which similarly looked at specific characteristics of cyclists in cities in Texas, USA.6 Population density was chosen with the assumption that the more densely populated an area, the more likely people will use active transportation. For average age, I expected that census areas with a younger average age are more likely to use bike stations as younger groups tend to be more environmentally conscious, able-bodied and use biking for daily activities over merely exercise.7 Income level was found to be an important factor in bike share usage with higher-income people biking more than their lower-income counterparts.8 Further, I hypothesise that higher concentrations of one-person households are more likely to correlate with bike share locations because those who live alone may not need to rely on a car to transport multiple people. Moreover, tourism may be another factor influencing the location of bike sharing stations. Yang et al. found that bike-sharing stations were positively correlated to tourism in Chicago, Illinois,9 and due BIXI’s drastic dip in bike sharing uses from the summer months to the fall,10 it seems as though BIXI similarly caters on tourism. Therefore, proximity to the downtown core may affect where BIXI stations are located. To determine whether BIXI stations cater to the downtown core, I created a buffer around two major points within downtown Montreal:

Sherbrooke Street & Frontenac Street, and Sherbrooke Street & Aylmer Street. In order to determine how far the buffer should be, I relied on a report by Savage from Statistics Canada which looked at commuting distances from places of residence to places of work in the largest census metropolitan areas in Canada.11 Savage found that the median distance to commute to work in Montreal is 8.6 km in 2016, which was rounded up to 10 km for this analysis.12

Part 2: Location Allocation based on demographic data

According to the BIXI Open Data page,13 the number of purchases and short term access to bike stations dropped from 83,901 to 15,525 from July to November 2021– about an 80% drop. Using the main demographic predictors of bike station location from Part 1, I use the location allocation tool in ArcGIS–a tool to find optimal locations for facilities based on service demand– to find the prime locations for 100 stations. This could be relevant during a transitional month period, such as the fall, where fewer tourists are in the city and weather conditions discourage some from cycling. The rationale for this section is to embody the role of the BIXI company, using one or two of the characteristics that appear to correlate with bike station usage to determine prime locations for BIXI stations in off-seasons. In order to conduct this location allocation I used bike lanes as the analysis network with a search tolerance of 10,000 metres. I used the centroid of census tracts as the demand points.

MAPS AND DISCUSSION

(See pages 46-55)

Projection: UTM 1983, 18N.

10 “Open Data.”

11 Katherine Savage, “Results from the 2016 Census: Commuting within Canada’s Largest Cities,” Government of Canada, Statistics Canada, May 29, 2019, https://www150. statcan.gc.ca/n1/ pub/75-006-x/2019001/ article/00008-eng.htm.

12 Ibid.

13 “Open Data.”

DISCUSSION

Part 1

The main driver of BIXI station locations appears to be proximity to the downtown core. Nearly all of the bike stations are within these two 10 km buffers on Sherbrooke street with the exception of five in Laval and six in the eastern end of the island. Population density (figure 5) and those who bike to work (figure 4) also appear to be directly and strongly aligned with BIXI bike stations. These two results support my initial hypotheses. While the densest areas of one-person households do appear to be concentrated near BIXI stations, there are census tracts that have a high concentration of one-person households and no bike sharing stations, so the relationship between this variable and BIXI locations is less clear (figure 1). It is interesting, however, that the stations in Laval and the eastern end of the island that fall outside the 10 km Sherbrooke buffer are also within the census areas that have high concentrations of one-person households. This further suggests that perhaps one-person households are using bike stations or are living in areas more conducive to bike shares. Total median income appears to be a moderately strong predictor of BIXI stations, however this finding goes against my inference that bike stations are located in higher income areas. In fact, it appears that the majority of bike stations are located in lower-to-mid income areas (figure 3). Lower average age groups (26-37; 37-40) appear to be correlated to bike shares as well (figure 2).

Part 2

To determine the prime 100 bike stations, I conducted two location allocations: one with predictors that clearly dictate BIXI’s bike share location decisions, and one with predictors that may not be considered by BIXI but potentially should be. Since population density aligned strongly with BIXI bike stations, this characteristic was used in reducing 660 BIXI bike stations to 100. Figure 6 shows the prime locations for BIXI stations which would serve the most populated census tracts. The second location allocation I conducted (figure 7) was based on one-person households, as bike stations were less correlated with this demographic. When reduced to 100 bike stations, the demand weight is 310224 households, serving 450 census tracts out of 673. Figure 8 illustrates two alternative bike station locations that I proposed, based on one-person household density. After running the location allocation analysis, both my candidate facilities were chosen and improved the demand weight from 310224 to 313000 households and added four

additional census tracts that were able to access bike stations. These two proposed bike stations may be effective for the BIXI company if they decided to use one-person households as a factor for choosing station locations.

CONCLUSION AND FUTURE RESEARCH

This project sought to understand what drivers are at play when the BIXI bike company chooses the locations of their bike stations. I found that high population density, high concentrations of people who bike to work, younger age groups, low median income, and one-person households all seem to align with BIXI bike sharing stations at various levels. The strongest correlation, through visual analysis, is proximity to the downtown core. This paper only touched on five demographic variables in relation to BIXI stations. Future branches from this study could examine several other demographic variables such as education, gender, as well as perceptions of biking and bike sharing systems, quality of bike routes in areas and level of traffic. There are clearly several factors at play that determine where companies such as BIXI decide to locate their bike stations. Through this research, my hypothesis that BIXI caters to stations for professionals working downtown and tourists visiting the city, appears to be supported. Future research questioning the effectiveness of locating BIXI stations so close to the downtown core could be another branch of research from this paper. What communities are being left out from BIXI’s vision? What communities could benefit from BIXI stations and are they being served? While this paper looked at what demographics BIXI is catering towards, with this knowledge, future research could build and question whether these communities are the most in need of bike sharing stations in Montreal. 

DATA

All data was downloaded in a .csv form but presented in google sheets for the purpose of readability.

BIXI Bike station point data

https://docs.google.com/spreadsheets/d/1yU56aB2t0qTYENffAolZlR-xToMwCfZDk1xP8z1kOs U/edit?usp=sharing Demographic Choropleth data:

https://docs.google.com/spreadsheets/d/1aTAvkP9-0AbyytphoXPnEm8GQgPJ0Bt81nO_MK48QDA/edit#gid=285064734

BIBLIOGRAPHY

Banerjee, S., Kabir, M. M., Khadem, N. K., & Chavis, C. (2020). Optimal locations for bikeshare stations: A new GIS based spatial approach. Transportation

Research Interdisciplinary Perspectives, 4, 100101. https://doi.org/10.1016/j. trip.2020.100101

Government of Canada, S. C. (2019, August 9). Census Profile, 2016 Census

Montréal [Census metropolitan area], Quebec and Quebec [Province]. Census Profile, 2016 Census - Montréal [Census metropolitan area], Quebec and Quebec [Province]. https://www12.statcan.gc.ca/census-recensement/2016/ dp-pd/prof/details/page.cfm?Lang=E&Ge o1=CMACA&Code1=462&Geo2=PR&Code2=24&SearchText=Montreal&SearchType=Begins &SearchPR=01&B1=All&TABID=1&type=0.

Mueller, N., Rojas-Rueda, D., Cole-Hunter, T., de Nazelle, A., Dons, E., Gerike, R., Götschi, T., Int Panis, L., Kahlmeier, S., & Nieuwenhuijsen, M. (2015). Health impact assessment of active transportation: A systematic review. Preventive medicine, 76, 103–114. https://doi.org/10.1016/j.ypmed.2015.04.010

Open Data. BIXI Montréal. (n.d.). https://www.bixi.com/en/page-27.

Qui sommes-nous? BIXI Montréal. (n.d.). https://www.bixi.com/fr/qui-sommes-nous.

Savage, K. (2019, May 29). Results from the 2016 Census: Commuting within Canada’s largest cities. Statistics Canada. https://www150.statcan.gc.ca/n1/ pub/75-006-x/2019001/article/00008-eng.htm.

Senner, I. N., Eluru, N., & Bhat, C. R. (2009). Who are Bicyclists? Why and how much are they Bicycling? Transportation Research Record: Journal of the Transportation Research Board, 2134(1), 63–72. https://doi.org/10.3141/2134-08

Yang, Y., Jiang, L., & Zhang, Z. (2021). Tourists on shared bikes: Can bike-sharing boost attraction demand? Tourism Management, 86, 104328. https://doi. org/10.1016/j.tourman.2021.104328