2018

With an increasing focus on bicycling as a mode of urban transportation, there is a pressing need for improved tools for bicycle travel analysis and modeling. This paper introduces “biking schedules” to represent archetypal urban cycling dynamics, analogous to driving schedules used in vehicle emissions analysis. Three different methods of constructing biking schedules with both speed and road grade attributes are developed from the driving schedule literature. The methods are applied and compared using a demonstration data set of 55 hours of 1-Hz on-road GPS data from three cyclists. Biking schedules are evaluated based on their ability to represent the speed dynamics, power output, and breathing rates of a calibration data set and then validated for different riders. The impact of using coarser 3, 5, and 10 second GPS logging intervals on the accuracy of the schedules is also evaluated. Results indicate that the best biking schedule construction method depends on the volume and resolution of the calibration data set. Overall, the biking schedules successfully represent most of the assessed characteristics of cycling dynamics in the calibration data set (speed, acceleration, grade, power, and breathing) within 5%. Future work will examine the precision of biking schedules constructed from larger data sets in more diverse cycling conditions and explore additional refinements to the construction methods. This research is considered a first step toward adopting biking schedules in bicycle travel analysis and modeling, and potential applications are discussed.

This study presents new trail demand models based on new data from 32 locations throughout all major climatic regions in the U.S., which was collected between January 1, 2014 and February 16, 2016. We contribute fourfold to the practical literature on trail traffic demand analysis. First, we develop a set of econometrics models to predict average daily pedestrian (ADP), average daily bicyclist (ADB), and average daily mixed-mode (ADM) using 5 D’s of the built environment and socio-economic characteristics. Second, we test the performance of trail demand models in predicting ADB, ADP, and ADM using leave-one-out cross-validation technique, and compare the accuracy of the models against one another. Third, we assess the performance of separate bicycle and pedestrian demand models in predicting mixed-mode travel demand. Fourth, we introduce a post-validation technique to advance the prediction accuracy of trail traffic demand models. The results indicate: (1) Not only are ADP and ADB described by nearly distinct characteristics, but also the magnitude of identical variables varies significantly between two modes; (2) The mean relative percentage error (MRPE) for bicyclist, pedestrian, and mixed-mode models equal 65.4%, 85.3%, and 45.9%; (3) Although utilizing multimodal monitoring networks enables us to juxtapose the bicyclist demand with pedestrian demand, there is not a significant improvement in predicting total demand using multimodal monitoring networks; (4) Post-validation mechanism capacitated the demand models to reduce MRPE of bicyclist, pedestrian, and mixed-mode models by 17.7%, 27.9%, and 6.4%. Overall, our models can be used in practical applications where order-of-magnitude estimates suffice.

Bicycling is widely regarded to be a sustainable transport mode. Therefore, policy makers and planners seek to promote bicycling for utilitarian purposes such as commuting and other mandatory activities. The literature has considered numerous potential determinants of utilitarian bicycling, but remains inconclusive about the role of individual factors. Nonetheless, the majority of studies agree that travel distance is a key factor affecting utilitarian bicycling use. This study seeks to advance the understanding of the behavioural determinants of utilitarian bicycle use by analysing distances of home-based mandatory bicycle tours in the Netherlands. We posit that bicycle travel distances are proxies of cycling willingness and apply the principle of hedonic regression to the analysis of bicycling behaviour. Using household travel survey data from the Netherlands, we estimate hierarchical Bayesian generalised linear models to regress distances of i) work and educational tours and ii) other mandatory tours on tour attributes and socio-demographic characteristics. The hierarchical Bayesian approach allows for the flexible estimation of random effects to capture similarities of observations within individuals and households. Tour distances are found to systematically vary as a function of tour attributes and socio-demographic characteristics. Furthermore, the results indicate the presence of household interaction effects. All else being equal, electric bicycle use is found to increase distances of work and educational tours by 68% and distances of other mandatory tours by 39%. Our study underlines that electric bicycles may facilitate sustainable mobility for utilitarian purposes.

Cycling is a legitimate mode of transport for a growing proportion of the population in Australia and North America but the limited segregated cycling infrastructure means that cyclists mainly ride on the road interacting with motor vehicles. Interactions between drivers and cyclists at intersections are complex, particularly left turn negotiations (right turn in North America) when a cyclist is travelling straight and a driver intends to turn left. The aim of this study was to investigate the safe and unsafe interactions involving these types of left turn negotiations between cyclists and drivers in an urban road environment. This study employed a mixed method investigation into the factors involved in driver-cyclist left turn negotiations including desk-based analysis of police reported cyclist crash data and a cross-sectional observational study using data from roadside mounted video cameras. Video analysis included observed driver/vehicle and cyclist behaviour across different infrastructure types. Crash data indicates left turn negotiation incidents are proportionally less severe when compared to all cyclist crashes, gender distributions align with that for work-related bicycle trips, and site traffic control type may influence crash risk. Future studies could extend the methods used across additional sites to gain a representative understanding of the types of road design that maximises safe left turn negotiations between drivers and cyclists.

Very little of the research on campus bicycling has focused on the population most under the policy influence of the university – students living in on-campus housing.  This study seeks to explore the decisions of this population to own and use bicycles.  The results of a survey of 700 campus residents are explored using descriptive statistics and binary logistic regression models of bicycle ownership and use.  The perceived impediments to biking more, satisfaction with existing facilities, and preferred interventions to encourage biking are all presented.  The findings suggest that of this population less than a fifth bikes anywhere, but that interventions as simple as adding bike-share bikes, increasing on-campus bike parking (at destinations and in residential complexes), and restricting vehicle parking permits are all likely to encourage more cycling.  Larger infrastructure investments, such as building out a network of bike paths and boulevards, is critical as well, but will likely need more money, time, project partners, and political will.  In the short term, investments in on campus facilities, disincentives for keeping a car, and active efforts to expand a culture of biking are safer bets for advancing campus cycling.

This study explores the cycling usage and frequency determinants in college campuses located in the Baltimore Metropolitan Area, Maryland. We developed a structural equation model (SEM) using the travel information of 780 individuals, which was collected between December 2014 and June 2015. More precisely, we aim to discern the attitudes of individuals toward the proposed infrastructure and environmental improvements with the goal of promoting biking to campus. The results indicate risk factors have a higher explanatory value on bike-to-campus frequency than campus infrastructure and program. We further examine how and to what extent mix of population in college campuses response to latent factors. The results, for instance, pinpoint that males are less concerned about the risk-related indicators such as risk of theft and road and environment related obstacles such as poor road conditions. However, females have a positive attitude toward campus-related improvements such as pro-bike programs. Students show a negative attitude toward road and environmentally-related obstacles, compared to staff and faculty. Minorities such as African American and Asian show a positive attitude toward campus-related improvements, which was different than the opinion of surveyed whites. The findings can assist planners and advocates in implementing effective policy measures to increase bike-to-campus frequency.

Cycling rates in many North American cities and regions decline significantly in winter months, which is a major challenge in advancing active transportation-related policy, planning, and programs. This research investigates travel behavior of current cyclists who commute to Ryerson University, Toronto, Canada. By combining data from two transportation surveys, this research examines the potential influence of the availability of bicycle infrastructure, the neighbourhood environment, and socio-demographic characteristics on self-reported propensity of cycling throughout the year (including winter months), in comparison to cycling only in fall/spring season. Results from binomial logistic regression indicate that women (OR=0.39) and transit pass holders (OR=0.12) were less likely, while students rather than staff (OR=1.69) were more likely to cycle during the winter. The density of bicycle infrastructure within 500m of the shortest route was positively associated with all-season cycling (OR=1.57). Also, a cyclist living in an older, more stable neighbourhood was more likely to bicycle through winter (OR=4.33), when compared to cycling only in warmer months. This research offers an important case study on a relatively less known topic. The findings support current planning and policy emphasis on improved bicycle infrastructure, and suggest that the universities should utilize their limited policy tools to advocate and further facilitate these ongoing efforts. An improved bicycle network as well as municipal, grass roots and university-based programming focused on specific demographic groups including women could mitigate physical and social barriers to winter cycling, and make cycling more enjoyable for those who already commute by bicycle throughout the year.

Research on bicyclist positioning behavior at signalized intersections is limited. While many cities provide infrastructure such as stencil markings and signage to detect bicyclists at traffic signals, there is much to learn about how bicyclists use this infrastructure. A better understanding of bicyclist behavior would inform more cost-effective treatments that serve bicyclists of different abilities. As part of FHWA Experimentation 4(09)-63 (E) Detector Confirmation Lights – Portland, OR, PBOT has gathered initial data before device installation at select locations throughout the City. Our analysis focuses on observational data using video data collected and reduced by Quality Counts, a local traffic count company. Over four hours at each of the six locations, 670 observations were made regarding bicyclist behavior, compliance, and positioning at six signalized intersections. Initial findings show high compliance of bicyclists with traffic signals. A small segment uses push buttons intended for people walking, but most bicyclists join others queued at signals or place themselves over the stencil.

Emphasis on non-motorized travel modes (for example, biking) reduces motorized trips and provides positive effects on the environment and the quality of human life. Understanding factors that influence people to biking or bike commuting can help in decision-making procedures for the city planners and transportation professionals. The conventional methods include survey and crash data analysis to understand the relevant factors. Survey and crash data analysis are difficult to perform in broad scale due to data availability and efforts. Understanding sentiments or motivations of the bike commuters could be an alternative way to determine the most relevant factors. People use terms (with hashtag in the beginning of the term) in Twitter, a popular social media network, to express their thoughts, activities or information. To perform this analysis, Twitter data associated with bike commuting hashtags were obtained for eight years (2009-2016). This study employed different natural language processing (NLP) tools to perform knowledge discovery from the unstructured text data. Exploratory text mining was performed to understand most frequent words and temporal patterns. Sentiment analysis was performed to understand people’s opinion or sentiments over the years. Additionally, the findings from network analysis helped to visualize information sharing patterns of the Twitter users while sharing posts on bike commuting. The findings show that the positive sentiments on bike commuting are stronger than negative sentiments. The significant factors are weather, traffic condition, and distance to workplace. The networks show small group effects rather than a broader influence pattern.

Electric bike (e-bike) users in China have a high red light running (RLR) rate, contributing to a large number of accidents. This paper aims to examine the psychological motivation of e-bike users’ RLR behaviors. A survey questionnaire was designed employing the construct of theory of planned behavior (TPB). The survey was performed in Chengdu, China in November, 2016. We found that both married e-bike users and users older than 35 have more negative attitude towards RLR behavior and identify themselves as more cautious riders. Riders younger than 35 years old have higher intention to run the red light. E-bike users with car drivers’ licenses regard running the red light as a more difficult task to perform, and regard this behavior as more morally wrong. Hierarchical regression was used to analyze the data. The results showed that both TPB variables (attitude and perceived behavioral control) and extended variables (moral norm and self-identity) are significant predictors for the intention of RLR behavior. The results could provide reference for designing more effective interventions and safety education programs for reducing e-bike users’ RLR rate.

The presence of bicycle infrastructure has been shown to influence cycling levels and mode choice. To reach more potential users and increase ridership, much effort has been done to improve cycling facilities with recent emphasis on providing protected bicycle facilities separated from vehicular traffic. It has been hypothesized that less confident cyclists prefer protected or off-street facilities compared to traditional bicycle lanes that only separate motor vehicle traffic and cyclists with striping. It has also been documented through national design guidance that off-street shared-use paths do not substitute for the need for on-street bicycle facilities. This study uses one full year of GPS route data from the Auburn University’s bike share program to model facility choice between an ideal on-street bicycle lane and off-street shared-use path. By selecting a site where both bicycle facility types are present, the analysis found that slightly more than half of the almost 900 trips were taken on the bicycle lane. A binary logic regression model was run using calculated user demographics, annual travel patterns and trip characteristics to determine what variables influence the facility choice. The modeling and analysis validates the difference in bicycle users where confident, regular cyclists overall prefer direct, on-street bicycle facilities while less confident and less experienced cyclists would rather ride on a protected facility away from traffic even if it requires sharing the path with pedestrians and is a slightly less direct route.

Understanding cyclist route perceptions and preferences is important for planning, designing, and managing sustainable transportation networks. Current literature focuses on proxy variables for the factors influencing route choices (hills, traffic) rather than primary motivators (energy, safety). The objective of this paper is to investigate the joint consideration of energy expenditure, air quality, and safety concerns in cyclist routing decisions, and their relationship with cycling activity. A structural equation model is developed based on data from a survey of 625 intercepted real-world cyclists. Air quality and energy expenditure were considered in routing decisions by 51% and 73% of the cyclists, respectively. Model results show that traffic safety and air pollution risks are perceived differently by cyclists, which has implications for modeling urban cycling behavior in the context of evolving motor vehicle fleets. Safety concerns were associated with less frequent cycling, but not air quality concerns. Consideration of energy expenditure varies significantly among individuals and trip types, which will emerge as different preferences related to hills, stops, speeds, and electric-assistance. Energy and air quality concerns were significantly associated, suggesting health-conscious cyclists who tended to be older, more educated, more physically active, and cycle more recreationally. Less utilitarian cycle trips were more impacted by health-related considerations and more associated with weekly physical activity.