Category Archives: bicycles

Exploring Nice Ride job accessibility and station choice

Exploring Nice Ride job accessibility and station choice

Although bike share systems are becoming more popular across the United States, little is known about how people make decisions when integrating these systems into their daily travel. For example, when more than one bike share station is located nearby, how do users choose where to begin their trip, and what factors affect their decision?

Nice Ride station

In a study funded by CTS, researchers from the U of M’s civil engineering department sought to answer this question by investigating how people use the Nice Ride bike share system in Minneapolis and St. Paul. Professor David Levinson and graduate student Jessica Schoner examined how Nice Ride affects accessibility to jobs and developed a model to predict station choice.

In the first part of the study, the researchers created maps showing accessibility to jobs by census block for both Nice Ride and walking—as well as the difference between the two—at time thresholds ranging from 5 to 55 minutes. At lower thresholds, fewer census blocks have job accessibility via Nice Ride because of the time it takes for a person to walk to the Nice Ride station. However, at higher time thresholds, Nice Ride provides an improvement over walking. Overall, in blocks with both Nice Ride and walking job accessibility, Nice Ride provides access to 0.5 to 3.21 times as many jobs as walking.

In 2013, Nice Ride operated 170 stations with about 1550 total bikes in Minneapolis and St. Paul.

By comparing Nice Ride to walking, the study demonstrated that walking can successfully be used as a baseline to show how a bike share system improves job accessibility. The results also pinpointed when and where Nice Ride had the strongest accessibility advantage over walking.

“This type of information can be used by bike share system planners to identify where new stations could be built to maximize their impact on job accessibility,” Schoner says. “They could also look at accessibility to other destinations, like parks, grocery stores, or tourist attractions, depending on the goals of their system.”

Levinson and Schoner also developed a theoretical model for bike share station choice. The model considers users’ choice of a station based on their preference for the amount of time spent walking, deviation from the shortest path (the closest station may not be in the direct path of the person’s destination), and station amenities and neighborhood characteristics.

Nice Ride station

Findings show that people generally prefer to use stations that don’t require long detours to reach, but a station’s surroundings also play an important role. For example, stations located near a park and in neighborhoods with lower crime rates were more likely to be chosen as the starting point of a bike share trip. Results also show that commuters value shorter trips and tend to choose stations that minimize overall travel time, while users making non-work-related trips choose stations that allow them to spend more of their time biking, even if the total travel time is longer.

Understanding people’s station preference can help provide guidance to planners for bike share system expansion, densification, and optimization, Schoner says.

“For instance, even though spacing stations along a route would allow people to walk in the direction of their destination to pick up a bike, people’s strong preference to spend more time biking indicates that clustering stations near where they are starting and ending their trips might make more sense,” Schoner says.

Related Links

Bikes still outsell Cars in US

A recent meme was going around Twitter noting that in Europe, bikes were outselling cars (NPR report) (Part of the problem is the misleading headline in this Time Magazine rehash of the NPR report). This seemed obvious to me, and I am surprised it was news, since it is true in the US as well. I tweeted to the effect:

  • US Bikes: 18.7 Million NBDA
  • US Car sales 8 million, US light truck ~ 8 million  … WSJ

This was widely retweeted. CelloMom commented:

“Even if are children’s bikes, still at parity.
So where ARE all these bikes, why don’t we see them on the roads?”

Bill responded:

“gathering dust in garages”

The meaning of this statistic is clear. Americans like to buy bikes. Just ask Kevin Krizek, who was rumored at one point to have a quantity of bicycles running into the double digits.

Of course many of the bikes are kids bikes (5.7 million of the 18.7 million are below 20 inches wheel), but even so, 13 million are 20 inch and above wheel size, and 13 million is still much bigger than 8 million cars (and near 16 million light vehicles, note also many light vehicles are not for personal use). Even if we just look at specialty bike shops, which sell at the higher end, that’s nearly 3.1 million bikes per year, which while less than cars, is still a pretty big number.

Yet, the number of trips by bike and certainly miles by bike are much lower than by car and are not poised to overtake in the US. We don’t even see 3.1 million bike commutes daily in the US (ACS reports 865,000), so these are more likely for recreational than utilitarian purposes.

Another reason for this statistic is that bikes don’t last as long as cars (The average US car on the road is 11.4
years; I could not find similar data for bicycles, but am sure it is lower, especially given the higher sales — at 18.7 million bikes per year there would be 1 bicycle for every person in the US every 16.7 years, so the average age would be about 8.4 years IF everyone had a bike and there were no losses, and surely that isn’t true). This again is in large part due to the growing up of kids. Reasoning from anecdote and personal experience, (always a bad idea) our garage has 1 “light truck”, 2 striders, and 2 bikes. By next year there will be at least 3 bikes (and maybe 2 more if the adults get them again).

Open Access Article: Spatial modeling of bicycle activity at signalized intersections | Institute of Transportation Studies Library

Open Access Article: Spatial modeling of bicycle activity at signalized intersections

Biking at Grand/Halsted/Milwaukee (3 of 4)

This week is Open Access Week. What’s Open Access? Here is a not very brief overview by Peter Suber. UC Berkeley also has an Open Access Initiative to help open up your research and data. 

In the spirit of Open Access Week, here’s an interesting article from an open access journal - The Journal of Transport and Land Use. Go check it out and peruse the articles. No need to depend on your institution’s sibscription because it’s free to the public! (Thanks open access!)

In “Spatial modeling of bicycle activity at signalized intersections“, Jillian Strauss and Luis F Miranda-Moreno look at the built-environment and cycling. 

This paper presents a methodology to investigate the link between bicycle activity and built environment, road and transit network characteristics, and bicycle facilities while also accounting for spatial autocorrelation between intersections. The methodology includes the normalization of manual cyclist counts to average seasonal daily volumes (ASDV), taking into account temporal variations and using hourly, daily, and monthly expansion factors obtained from automatic bicycle count data. To correct for weather conditions, two approaches were used. In the first approach, a relative weather ridership model was generated using the automatic bicycle count and weather data. In the second approach, weather variables were introduced directly into the model. For each approach, the effects of built environment, road and transit characteristics, and bicycle facilities on cyclist volumes were determined. It was found that employment, schools, metro stations, bus stops, parks, land mix, mean income, bicycle facility type (bicycle lanes and cycle tracks), length of bicycle facilities, average street length, and presence of parking entrances were associated with bicycle activity. From these, it was found that the main factors associated with bicycle activity were land-use mix, cycle track presence, and employment density. For instance, intersections with cycle tracks have on average 61 percent more cyclists than intersections without. An increase of 10 percent in land-use mix or employment density would cause an increase of 8 percent or 5.3 percent, respectively, in bicycle flows. The methods and results proposed in this research are helpful for planning bicycle facilities and analyzing cyclist safety. Limitations and future work are discussed at the end of this paper.

The full article can be found here

Who’s Out There On The Roads? The 4 Types Of Cyclists – Forbes

Forbes cites Nexus alumnus and McGill Professor Ahmed El-Geneidy on the 4 Types of Cyclists … Who’s Out There On The Roads? The 4 Types Of Cyclists:

“Path-using cyclists (36 percent) are motivated by the fun of riding, its convenience, and the identity that cycling gives them. They’d rather use a continuous route, rather than dodge cars. They were actively encouraged by their parents to ride for fitness and to get places.

Dedicated cyclists (24 percent) are motivated by speed, predictability and flexibility that bike trips offer. These cyclists are the least likely to be deterred by the weather. They aren’t as interested in bike paths, and actually enjoy riding in traffic. The researchers say these cyclists consider riding to be an important part of their identity.

Fairweather utilitarians (23 percent) are just that. They like to ride in good weather, and they’ll take another form of transportation in rain or snow. These are also bike path users, and they don’t necessarily see themselves as cyclists.

Leisure cyclists (17 percent) ride because it is fun, and not as much for commuting. They prefer bike paths, don’t like to deal with traffic, and want to feel safe, especially when riding with family members.”

Which Station? Access Trips and Bike Share Route Choice

StationChoice

Recent working paper:

Bike share systems are an emerging technology in the United States and worldwide, but little is known about how people integrate bike share trip segments into their daily travel. Through this research, we attempt to fill this knowledge gap by studying how people navigate from place to place using the Nice Ride Minnesota bike share system in Minneapolis and St. Paul. We develop a theoretical model for bike share station choice inspired by research on transit route choice literature. We then model people’s choice of origin station using a conditional logit model to evaluate their sensitivity to time spent walking, deviation from the shortest path, and a set of station amenity and neighborhood control variables. As expected, people prefer to use stations that do not require long detours out of the way to access. However, commuters and non-work travelers differ in how they value the walking portion of their trip, and what station amenities and neighborhood features increase a station’s utility. The results from this study will be important for planners who need a better understanding of bike share user behavior in order to design or optimize their system. The findings also provide a strong foundation for future study about comprehensive route choice analysis of this new bicycling technology.

Catalysts And Magnets: Built Environment Effects On Bicycle Commuting

Catalysts

Recent working paper:

What effects do bicycle infrastructure and the built environment have on people’s decisions to commute by bicycle? While many studies have considered this question, commonly employed methodologies fail to address the unique statistical challenge of modeling such a low mode share. Additionally, self-selection effects that are not adequately accounted for may lead to overestimation of built environment impacts. This study addresses these two key issues by using a zero-inflated negative binomial model to jointly estimate participation in and frequency of commuting by bicycle, controlling for demographics, residential preferences, and travel attitudes. The findings suggest a strong self selection effect and modest contributions of bicycle accessibility: that bicycle lanes act as “magnets” to attract bicyclists to a neighborhood, rather than being the “catalyst” that encourages non-bikers to shift modes. The results have implications for planners and policymakers attempting to increase bicycling mode share via the strategic infrastructure development.

This is based on Jessica Schoner’s Master’s Thesis.

Shifting Gears: A cross-regional analysis of bicycle facility networks and ridership

Jessica Schoner just received an honorable mention from APA’s Transportation Planning Division for her paper (which was a class term paper (technically 2 term papers), not a thesis or dissertation!): Shifting Gears: A cross-regional analysis of bicycle facility networks and ridership. A Reviewer said: “Of all the years doing this contest this is by far the best on bicycling I’ve seen.” If you care about network structure, or about travel behavior, or about bicycles, read it.