This study assesses the estimated crashes per bicyclist and per vehicle as a function of bicyclist and vehicle traffic, and tests whether greater traffic reduces the per-car crash rate. We present a framework for comprehensive bicyclist risk assessment modeling, using estimated bicyclist flow per intersection, observed vehicle flow, and crash records. Using a two-part model of crashes, we reveal that both the annual average daily traffic and daily bicyclist traffic have a diminishing return to scale in crashes. This accentuates the positive role of safety in numbers. Increasing the number of vehicles and cyclists decelerates not only the probability of crashes, but the number of crashes as well. Measuring the elasticity of the variables, it is found that a 1% increase in the annual average daily motor vehicle traffic increases the probability of crashes by 0.14% and the number of crashes by 0.80%. However, a 1% increase in the average daily bicyclist traffic increases the probability of crashes by 0.09% and the number of crashes by 0.50%. The saturation point of the safety in numbers for bicyclists is notably less than for motor vehicles. Extracting the vertex point of the parabola functions examines that the number of crashes starts decreasing when daily vehicle and bicyclist traffic per intersection exceed 29,568 and 1,532, respectively.
Congratulations to soon-to-be Dr. Jessica Schoner for successfully defending her dissertation: ‘Mutually Reinforcing Relationships Between Bicycling Infrastructure’ before a standing room only crowd at the University of Minnesota campus on 21 August 2017.
Researchers have long sought evidence about whether dedicated bicycling infrastructure induces people to cycle, based on a supply-driven assumption that providing infrastructure causes the behavior change. However, supply inducing demand is only one of four theoretical relationships between bicycling and infrastructure. The aims of this research are twofold:
Develop a theoretical framework to identify and evaluate all of the possible relation- ships between bicycling and infrastructure and describe how these factors reinforce one another to shape diffusion of bicycling and infrastructure in cities; and
Develop and execute a research plan to empirically model selected hypotheses within the theoretical framework.
The empirical portion of the dissertation tests the hypotheses that (1) bicycling infrastructure supply induces bicycling demand, and (2) bicycling demand induces additional demand. The research uses a series of cross-sectional tests at multiple points in time as well as lagged variable models to add a layer of temporal precedence to our otherwise cross-sectional understanding of associations between bicycling and infrastructure. The findings show persistent associations between infrastructure and bicycling over time, across geographies, and at both the individual and aggregate level. The association between bicycling and additional bicycling holds over time at the individual household level and for bike share membership. However, the tests failed to find evidence of bike share stations and activity affecting general population cycling rates.
This dissertation provides a roadmap for future research into feedback loops between bicycling and infrastructure. It additionally provides practitioners with guidance on both the strengths and limitations of both infrastructure provision and socially-focused bicycling initiatives.
Like most bicycling research, this dissertation is limited by the quality of data available for both bicycling behavior and infrastructure supply. Neither the data nor the tests performed are rigorous enough to infer causality; instead, the findings add strength and nuance to the existing body of literature.
Papers related to the dissertation are available at:
Schoner, Jessica and David Levinson (2014) The Missing Link: Bicycle Infrastructure Networks and Ridership in 74 US Cities. Transportation 41(6) 1187-1204. [doi]
Schoner, Jessica, Greg Lindsey, and David Levinson (2016) Is Bikesharing Contagious? Modeling its effects on System Membership and General Population Cycling. Transportation Research Record: Journal of the Transportation Research Board. 2587 pp. 125-132. [doi]
Schoner, J., & Lindsey, G. (2015). Differences Between Walking and Bicycling over Time: Implications for Performance Management. Transportation Research Record: Journal of the Transportation Research Board, (2519), 116-127. [doi]
The final dissertation will be posted online soon.
As every good dissertation should, it raises as many questions as it answers, and if you are looking for a topic, there are strong research opportunities available to test Hypotheses 2 and 4 on the effect of bicycling demand on infrastructure, and infrastructure momentum. There are also opportunities to examine the new stationless bike sharing systems that are emerging in China (and Australia, and elsewhere) regarding Hypothesis 3 and social diffusion.
Some other bicycling research led by the newly minted Dr. Schoner includes:
Nice Ride Minnesota Program Evaluation. [Twin Cities] [Bemidji]. Blue Cross/Blue Shield of Minnesota.
“Driverless cars appear unstoppable – except of course you can simply walk in front of one and force it to brake. Could this conundrum eventually mean a return to a dystopian world of segregated urban highways?”
I was interviewed, my quotes below …
Or how about prosecuting pedestrians or cyclists who get in the way of driverless cars? David Levinson, a professor at the School of Civil Engineering at the University of Sydney, is broadly supportive of AVs, but says: “It’s very big brother like, there’s a question of safety v freedom. How much risk to endanger yourself are we going to let you take?”
Thinking back to the kids stopping driverless cars on our imaginary future street, Levinson sees a future where blocking a driverless car could even be criminalised. “The car has a camera and the picture will be sent to the police department, and the police department will come and arrest you for annoying an autonomous vehicle.”
Given these challenges, experts including Hickman and Levinson believe segregation and AV-only roads are inevitable. But wouldn’t that risk a return to the urban dystopia of the 1960s and 70s, when planners crisscrossed cities with elevated highways and erected barriers around roads with the aim of improving safety? The unintended consequences were fast, aggressive driving, and the splitting in two of countless communities.
“I think there will be some roads that will be transformed to higher speed roads,” says Levinson. “I’d be sceptical of someone who says we will not do any of that. But if you can move traffic away from the lower speed streets that pedestrians and cyclists want to use, that’s an improvement.”
Hickman believes “the case is overwhelming against AVs” but fears the powerful motor industry lobby means there is so much private and government money already at stake that the rise of driverless cars would be hard to stop.
This paper presents new evidence about the role of bike share systems in travel behavior using a diffusion of innovation framework. We hypothesize that bike share systems have a contagion or spillover effect on (𝐻1) propensity to start using the system and (𝐻2) propensity to bicycle among the general population. We test the first hypothesis by modeling membership growth as a function of both system expansion and the existing membership base. We test the second hypothesis by using bike share activity levels near one’s home in a model of household-level bicycle participation and trip frequency. Our study shows mixed results. Bike share membership growth appears to be driven, in a small part, by a contagion effect of existing bike share members nearby. However, we did not identify a significant relationship between proximity to bike share and cycling participation or frequency among the general population. The findings hold implications for marketing, infrastructure investments, and future research about bike share innovation diffusion and spillover effects.
KEYWORDS: Bike Share; Diffusion of Innovation; Travel Behavior
So I was intrigued to come across a study this week that examined how far cyclists in three large U.S. metropolitan areas are willing to ride to catch a bus or train that will take them the rest of the way to work.
One of those metro areas was Minneapolis-St. Paul. The other two were Los Angeles and Atlanta.
The study, which used data collected through mass-transit ridership surveys, found that while only a small percentage of people in the three metro areas ride their bike to a bus or train to commute to work, those who do tend to cycle an average of three miles or less — one to two miles in Minneapolis-St. Paul and Atlanta and a slightly longer three miles in Los Angeles (probably because that city’s weather is more conducive to biking).
Mobility hubs are major transit access points and an integrated part of multi-modal transportation planning efforts. For the implementation of bicycle infrastructure improvements around mobility hubs a better understanding of bicycle access distances is needed. Using responses from on-board travel surveys in three U.S. metropolitan areas, this study found that median bicycle access distances to transit stations are within the buffer radii suggested for community hubs (1 mile) and gateway hubs (2 miles) in long-range transportation plans. Multiple regression analysis identified several street and transit network characteristics affecting bicycle access distance, which should be considered when planning infrastructure improvements.
The researchers turned up three especially interesting findings. The first is that in single-bicyclist homes, men are roughly twice as likely as women to ride. But when you’ve got two or more cyclists living together, that gap disappears. That could be because living with a cyclist encourages people of any gender to starting biking, or because people who enjoy cycling end up in the same home through marriage or friendship. “I don’t know what direction causality goes,” Schoner says.
The second finding is that among people who rode at least once on the day they kept their travel diary, there is no gender gap when it comes to the number of trips taken that day. In other words, women who ride do so just as frequently as men. “This suggests,” Schoner and Lindsey write, “that much of the remaining gender gap can be attributed to a participation gap, not an intensity gap.”
Finally, the 2010 data shows that having kids doesn’t lead to people biking less. That’s a change: In 2000, a parent was only half as likely to be a cyclist as a non-parent. There’s no gender difference here, but because women bear the greater burden when it comes to childcare, it’s encouraging news for those working to shrink the gender gap. “The relationship between having children and bicycling is complex and unclear,” Schoner says, but “having children may be becoming less of a barrier to bicycling over time.”
Bicycling has grown in popularity over the past decade, but the gap in rates of bicycling between men and women in the United States (US) persists. This paper uses regional travel behavior study data from the Minneapolis-St. Paul Metropolitan Region in 2000 and 2010 to measure and model the gender gap in bicycling over time.
Findings from a series of statistical tests show that in aggregate, women bike less than men, and that growth in bicycling has been slower for women than for men over the past decade. However, stratifying the sample shows that women who live with at least one other adult bicyclist participate in bicycling at an equal rate as men. Similarly, frequency of bicycle trips among people who participate in bicycling differed by gender only slightly in 2000, and not at all in 2010. Binary logistic modeling results show that several factors, such as age and trip purpose, are associated with different bicycling outcomes for men and women, but some commonly hypothesized explanations, such as having children, were declining in effect or altogether insignificant.
These findings and conclusions are important for practice and research because understanding the nuances of the gender gap, such as the apparent gap in participation but not in frequency or the contagion effect of living with a cyclist, is essential for targeting programs effectively. This paper also identifies several travel behavior data collection limitations that complicate studying the gender gap, and offers recommendations for further study.
Transportation policies and plans encourage non-motorized transportation and the establishment of performance measures to assess progress towards multi-modal system goals. Challenges in fostering walking and bicycling include the lack of data for measuring rates of walking and bicycling over time and differences in pedestrians and bicyclists and the trips they make. This paper analyzes travel behavior inventories conducted by the Metropolitan Council in the Minneapolis-St. Paul Metropolitan Area in 2001 and 2010 to illuminate differences walking and bicycling over time and illustrate the implications for performance measurement. We focus on the who, what, where, when, and why of non-motorized transportation: who pedestrians and bicyclists are, where they go and why, when they travel, and what factors are associated with the trips they make. Measured by summer mode share, walking and bicycling both increased during the decade, but the differences between the modes overshadow their similarities. Using descriptive statistics, hypothesis testing, and multinomial logistic models, we show that walkers are different than bicyclists, that walking trips are shorter and made for different purposes, that walking and bicycling trips differ seasonally, and that different factors are associated with the likelihoods of walking or bicycling. While the increase in mode share was greater for walking than bicycling, the percentage increase relative to 2001 share was greater for bicycling than walking. Both walking and bicycling remain mainly urban transportation options. Older age reduces the likelihood of biking trips more than walking trips, and biking remains gendered while walking is not. These differences call into question the common practice of treating nonmotorized transportation as a single mode. Managers can use these results to develop performance measures for tracking progress towards system goals in a way that addresses the unique and different needs of pedestrians and bicyclists.
Abstract: Cities promote strong bicycle networks to support and encourage bicycle commuting. However, the application of network science to bicycle facilities is not very well studied. Previous work has found relationships between the amount of bicycle infrastructure in a city and aggregate bicycle ridership, and between microscopic network structure and individual tripmaking patterns. This study fills the missing link between these two bodies of literature by developing a standard methodology for measuring bicycle facility network quality at the macroscopic level and testing its association with bicycle commuting. Bicycle infrastructure maps were collected for 74 United States cities and systematically analyzed to evaluate their network structure. Linear regression models revealed that connectivity and directness are important factors in predicting bicycle commuting after controlling for demographic variables and the size of the city. These findings provide a framework for transportation planners and policymakers to evaluate their local bicycle facility networks and set regional priorities that support nonmotorized travel behavior, and for continued research on the structure and quality of bicycle infrastructure and behavior.
Keywords Bicycling · Travel Behavior · Networks