Research in bicycle safety, mode choice, operations, user preference, and more
Calibrating the Wiedemann 99 Car-Following Model for Bicycle Traffic
Heather Kaths, Technische Universitaet MuenchenShow Abstract
Andreas Keler, Technische Universitat Munchen
Klaus Bogenberger, Technische Universitat Munchen
Car-following models are used in microscopic simulation tools to calculate the longitudinal acceleration of a vehicle based on the speed and position of a leading vehicle in the same lane. Bicycle traffic is usually included in microscopic traffic simulations by adjusting and calibrating behavior models developed for motor vehicle traffic. However, very little work has been carried out to examine the following behavior of bicyclists, calibrate following models to fit this observed behavior, and determine the validity of these calibrated models. In this paper, microscopic trajectory data collected in a bicycle simulator study are used to estimate the following parameters of the psycho-physical Wiedemann 99 car-following model implemented in PTV Vissim. The Wiedemann 99 model is selected due to the larger number of assessable parameters and the greater possibility to calibrate the model to fit observed behavior. The calibrated model is validated using the average queue dissipation time at a traffic light on facilities ranging in width between 1.5 m to 2.5 m. Results show that the parameter set derived from the microscopic trajectory data creates more realistic simulated bicycle traffic than a suggested parameter set from a previous study. However, it was not possible to achieve the large variation in average queue dissipation times that was observed in the field with either of the tested parameter sets.
Could There Be Spillover Effects between Recreational and Utilitarian Cycling? A Multivariate Model
Francesco Piras (firstname.lastname@example.org), University of CagliariShow Abstract
Eleonora Sottile, Universita degli Studi Di Cagliari
Giovanni Tuveri, Universita degli Studi Di Cagliari
Italo Meloni, Universita degli Studi Di Cagliari
The current study aims to investigate the impact of socioeconomic and bike infrastructure-related factors on the choice to cycle for different purposes (commuting, errands and leisure/sport) and explores the interplay between these three choices. We developed and estimated a multivariate ordered probit model that accounts for correlation effects among dependent variables and simultaneously controls for the impact of endogenous and exogenous variables. The data used in this study are drawn from a survey conducted by the University of Cagliari in two urban areas in Sardinia (Italy). Model results show that different socio-demographic variables such as gender, age, level of education, household composition and vehicle ownership influence our dependent variables. The results are consistent with previous research, with males and younger individuals more likely to cycle, while people with children show a lower propensity to use the bike for both transportation and recreational purposes. Next, we show that, in the specific context of the study, unobserved effects between the three dependent variables exist and a higher frequency of cycling for leisure leads to a higher frequency of cycling for utilitarian purposes, suggesting the presence of a behavioral spillover effect. Finally, through a policy simulation analysis we find that the adoption of an independent model that ignores the presence of unobserved effects among dependent variables leads to an overprediction of the number of people cycling for utilitarian purposes.
Bicycle Level of Service: Accounting for protected lanes, traffic exposure, and delay
Nicholas Fournier, University of California, BerkeleyShow Abstract
Jiayun Huang, University of California, Berkeley
Alexander Skabardonis, University of California, Berkeley
Motorized traffic exposure and delay are two critical factors for bicycle level of service (LOS). Unfortunately, the current Highway Capacity Manual’s methodology for bicycle LOS fully accounts for neither. At the intersection level, motorized traffic speed and bicycle delay are not considered at all; and at the link level there is no account for one of the most effective traffic-exposure mitigating infrastructure types, separated bicycle lanes. This creates a systemic problem, enabling the design of roadways that ignore bicycle exposure and delay (i.e., comfort and safety), while giving approving LOS grades to otherwise poor roads and intersections. This paper presents several proposed revisions to the existing Highway Capacity Manuals methodology for bicycle LOS. The proposed revisions include methodologies to account for separated bicycle lane buffers along links, estimated bicycle delay from right-turning motorists, estimated bicycle delay when performing one- and two-stage left turns, and the motorized traffic speed exposure of bicycles at intersection. The proposed revisions are largely comprised existing methodologies (e.g., pedestrian delay at two-way stop-controlled intersections) and classical analytical approaches that fall seamlessly into the existing Highway Capacity Manual’s formulaic approach.
The Growing Gap in Pedestrian and Cyclist Fatality Rates between the United States and Western European Countries, 1990-2018
Ralph Buehler (email@example.com), Virginia Polytechnic Institute and State University (Virginia Tech)Show Abstract
John Pucher, Rutgers University
Using official national data for each country, this article calculates trends in walking and cycling fatalities per capita and per km in the USA, the UK, Germany, the Netherlands, and Denmark. From 1990 to 2018, pedestrian fatalities per capita fell by 23% in the USA vs. 55%-80% in the other countries; cyclist fatalities per capita fell by 22% in the USA vs. 55%-68% in the other countries. In 2018, pedestrian fatality rates per km in the USA were 5-10 times higher than in the other four countries; cyclist fatality rates per km in the USA were 4-6 times higher. The gap in walking and cycling fatality rates between the USA and the other countries increased over the entire 28-year period, but especially from 2010 to 2018. Over that 8-year period, per-capita fatality rates in the USA rose by 38% for pedestrians and 30% for cyclists; per-km fatality rates rose by 17% for pedestrians and 33% for cyclists. By comparison, fatality rates either fell or remained stable in the four European countries. We reviewed the relevant literature to identify factors that might help explain the much lower walking and cycling fatality rates in Europe compared to the USA. Possible explanatory factors include better walking and cycling infrastructure; lower urban speed limits; fewer vehicle km traveled; smaller and less powerful personal motor vehicles; and better traffic training, testing, and enforcement of traffic regulations. We recommend that the USA consider implementing an integrated package of mutually reinforcing safety measures such as those that have been successfully implemented in the Netherlands, Denmark, and Germany to reduce pedestrian and cyclist fatality rates.
Physiological Markers of Traffic-Related Stress during Active Travel
Fajar Ausri, University of British ColumbiaShow Abstract
Alexander Bigazzi (firstname.lastname@example.org), University of British Columbia
Perception of safety and comfort (PSC) while walking or cycling is a key concept for analyzing and promoting active transportation, but current measures, primarily surveys, suffer from validity and reliability issues. Physiological markers of stress like heart rate variability and electrodermal activity have been proposed as alternative, objective measures of PSC. This paper presents a conceptual framework and literature summary examining the relationship between environmental factors, traffic-related psychological stress, and physiological stress markers during active travel. Diverse factors related to the operating facility, motor vehicle traffic, weather, trip and traveler, and vehicle can induce thermal, physical, and psychological stresses during travel, all activating the autonomic nervous system, and manifesting in the same set of physiological markers. The physiological markers of traffic-related psychological stress are indistinguishable in character from those of other stresses, which is a major threat to the internal validity of this approach. The key challenge for on-road active traveller stress studies is to account for the potential confounding effects of (1) non-traffic factors that induce stress (e.g. weather), (2) traffic-related factors that induce stresses other than those associated with PSC (i.e. exertion and psychosocial stress), and (3) personal (e.g. age, sex, weight) and environmental (e.g. weather) factors that can directly influence physiological markers separately from stress. Physiological markers have the potential to provide dynamic, high-resolution, objective information about traffic-related stress, but to ensure accuracy and reliability, further research, particularly controlled experimentation, is needed to address mediating and confounding factors.
Modeling the Level of Service of the Non-motorized Vehicle Crossing the Signalized Intersection
Xiaofei Ye, Ningbo UniversityShow Abstract
Ye Yao, Ningbo University
Tao Wang, Guilin University of Electronic Technology
Jun Chen, Southeast University
Xingchen Yan, Nanjing Forestry University
Xuan Li, Ningbo University
This article aims to identify the factors influencing the LOS of the non-motorized vehicle when crossing the signalized intersection and to develop an appropriate method for evaluating the LOS of the non-motorized vehicle. The key factors influencing the LOS of the non-motorized vehicle were summarized: turning traffic, through traffic, number of the non-motorized vehicles, conflicts and delay. The Highway Capacity Manual method assumes that pure bicycles arrive at a uniform rate and comply with the signal rules at an intersection. However, this assumption is not applicable for the mixed traffic flow combing regular bicycles and e-bikes and high incompliance rate in developing countries, like China. A delay model of the non-motorized vehicle was modified by considering non-uniform arrival rates and signal compliant behavior under mixed traffic condition. The data collected by videos and flied surveys include riders’ perceptions about the comfort and safety level when crossing the signalized intersection. Recognizing the LOS as the dependent variable, Pearson correlation analysis and linear regression methods were applied to identify the key factors affecting the LOS. To overcome the limitations of the linear regression techniques, cumulative logistic regression was brought to develop a model that suits the mixed traffic conditions in China-a model that can predict the probability of the responses within each LOS on the basis of a combination of explanatory variables. The results showed that the cumulative logistic model fit the survey data better than the linear regression model.
Bicycle Longitudinal Motion Modeling
Karim Fadhloun, Virginia Polytechnic Institute and State University (Virginia Tech)Show Abstract
Hesham Rakha (email@example.com), Virginia Polytechnic Institute and State University (Virginia Tech)
Archak Mittal, Ford Motor Company
This research effort uses vehicular traffic flow techniques to model bicyclist longitudinal motion while accounting for bicycle interactions. Specifically, an existing car-following model, the Fadhloun-Rakha (FR) model is re-parametrized to model bicyclists. Initially, the study evaluates the performance of the proposed model formulation using experimental datasets collected from two ring-road bicycle experiments; one conducted in Germany in 2012, and the second in China in 2016. The validation of the model is achieved through investigating and comparing the proposed model outputs against those obtained from two state-of-the-art models, namely: the Necessary Deceleration Model (NDM), which is a model specifically designed to capture the longitudinal motion of bicyclists; and the Intelligent Driver Model, which is a car-following model that was demonstrated to be suitable for single-file bicycle traffic. Through a quantitative and qualitative evaluation, the proposed model formulation is demonstrated to produce modeling errors that are consistent with the other two models. While all three models generate trajectories that are consistent with empirically observed bicycle-following behavior, only the proposed model allows for an explicit and straightforward tuning of the bicyclist physical characteristics and the road environment. A sensitivity analysis, demonstrates the effect of varying the different model parameters on the produced trajectories, highlighting the robustness and generality of the proposed model.
A Bike Count Forecast Model with Multimodal Network Connectivity Measures
Bingqing Liu, New York UniversityShow Abstract
Divya Bade, University of Pennsylvania
Joseph Chow, New York University
With the rise of bicycle mode choice for commuting and short-distance delivery, as well as policy objectives encouraging this trend, bike count models are increasingly critical to transportation planning and investment. Studies have found that network connectivity plays a role in such models, but there remains a lack of measure the connectivity of a link in a multimodal trip context. This study proposes a connectivity measure that captures the importance of a link in connecting the origins of cyclists and nearby subway stations and incorporates it in a negative binomial regression model to forecast bike counts at links. Representative bike trips are generated with bike-friendly consideration using the New York Transit Trip Planner (NYTTP) and used to determine the deviation from the shortest path via the designated link. The measure is shown to improve model fitness with a significance level within 10%. Insights are also drawn for income levels, bike lanes, subway station availability, and average commute time of travelers.
Analyzing transportation equity: Is there unobserved heterogeneity?
Zhiwei Chen, University of South FloridaShow Abstract
Xiaopeng Li (firstname.lastname@example.org), University of South Florida
Assessing the equity impacts of transportation systems/policies has become a crucial component in transportation planning. Existing statistical modeling approaches for transportation equity analysis have typically assumed that parameter estimates are constant across all observations and used data aggregated to certain geographic units for the analysis. Such methods cannot capture unobserved factors that are not contained in the dataset, i.e., unobserved heterogeneity, which is likely to be present in the increasingly popular disaggregated datasets. To investigate whether there is unobserved heterogeneity in transportation equity impacts, this study carries out an empirical study focusing on the distribution of individual accessibility to activity locations via bike-sharing in southern Tampa. A disaggregated dataset containing information on individual bike-sharing accessibility and socio-economic factors is modeled with a random parameters logit model that allows for the investigation of possible unobserved heterogeneity. The model unveils the unobserved heterogeneity in bike-sharing accessibility among populations in different groups defined by different sociodemographic factors in southern Tampa. These results shed insights into how the inconsistent disparity direction of transportation outcomes across individuals in a population group can be measured from the heterogeneity effects. To capture such inconsistency, the use of disaggregated data with heterogeneity models in transportation equity analysis is highly recommended.
Inequities in Cycling: A Review of Key Findings and Future Directions
Danial Jahanshahi, University of AucklandShow Abstract
Subeh Chowdhury, University of Auckland
Seosamh Costello, University of Auckland
Bert van Wee, Technische Universiteit Delft
Research studies on mode shift towards sustainable transport, particularly cycling, have become more common in the last decade. Despite some success in increasing cycling usage, there exist many barriers, both environmental, and societal. This study provides a review of the key equity findings to date in cycling usage and identifies knowledge gaps. Barriers to cycling from an equity perspective are examined from three areas: policy and planning, infrastructure and cycling facilities, and population groups. The review includes both peer-reviewed and grey papers. Using a systematic review process, out of 73 documents, 34 which met the scope of the study were carefully examined. The review showed that accessibility is the most common measure for bicycling equity. A key knowledge gap is the lack of robust measures to determine inequities in cycling and evaluate the distribution of benefits across population groups. Other gaps include the limited consideration of health-related equity in policymaking. This is attributed to the lack of measures to effectively evaluate a program or policy from an equity perspective. Consequently, this review emphasises the need to develop and evaluate equity measures for effective policy-making, in order to ensure that the needs of different population groups are met. The paper concludes with recommendations for future research given the identified knowledge gaps.
Auditory Alerts and Safety with Simulated Bicycles and Motor Vehicles
Curtis Craig, University of MinnesotaShow Abstract
Nichole Morris, University of Minnesota
Jacob Achtemeier, General Mills, Inc.
Katelyn Schwieters, University of Minnesota
Bicycling has become an increasingly popular and environmentally friendly active transportation modality for many commuters across the nation. Consequently, as ridership increases so do the rates of bicycle-motor vehicle crashes, of which are often due to a lack of visibility and attention. Therefore, one effective solution to improve bicyclist safety may be through the use of an audible bicycle alarm system to alert both the driver and rider. A study was conducted to determine whether a unique auditory alert would be effective at reducing crash rates and if a localized alert (i.e. an alert presented from the driver’s perspective) would improve drivers’ responsiveness in avoiding a potential collision. A driving simulator study tested car horn sounds, an experimental bike alert, and no auditory alert in different potential collision scenarios to measure collision rates and other collision avoidance metrics. Findings indicated the experimental bike alert contributed to fewer relative crashes compared to the horn sound and no sound on bicycles, motor vehicles were struck more frequently than bicycles, collisions were more likely to occur from the front than the sides, and collisions were more likely for drivers going straight than when making turns. Taken together, the findings suggest an alarm designed to be specifically compatible with bicycles is more effective than auditory alerts from other sources.
Is There Space For Cyclists? Understanding the Impact of Cycle Lanes on Passing Distance
Thiago Louro, Universidade Estadual de LondrinaShow Abstract
Gabriel Gardin, State University of Londrina
Heliana Fontenele, State University of Londrina
Carlos Silva Junior, State University of Londrina
In recent years, cycling has been presented as an alternative to traditional transport systems. However, the lack of adequate infrastructure can lead to accidents, in addition to creating a psychological barrier that can prevent more people from using the bicycle as a mean of transport. The aim of this work was the construction of a prototype to measure the lateral passing distance of motor vehicles in relation to a cyclist, as well as the relative speed of approach. Seven trips were made to collect data. The results indicate that 80.45% of the overtakes had a lateral distance less than 1.5 meters, it was also evident that the presence of a cycle lane avoided very low lateral distances, but it didn’t avoid distances less than 1.5 meters. The prototype was also capable of collecting overtaking relative speed, and it was shown that higher relative overtaking speed only occurred on one avenue, with a higher speed limit than on other roads.
Exposure-Based Models of Trail User Crashes at Roadway Crossings
Robert Schneider (email@example.com), University of Wisconsin, MilwaukeeShow Abstract
Schmitz Andrew, University of Wisconsin, Milwaukee
Greg Lindsey, University of Minnesota
Xiao Qin, University of Wisconsin, Milwaukee
Multi-use trails are popular for transportation and recreation, but pedestrians and bicyclists are exposed to motor vehicle traffic at locations where these facilities cross roadways, creating the risk of crashes, injuries, and fatalities. Many trail crossing design guidelines suggest best practices to make roadway crossings safe, but few studies have quantified the statistical relationship between trail user crashes and a broad set of trail crossing characteristics. Our study develops one of the first trail crossing crash models using trail user crashes reported at 197 crossings in the City of Minneapolis, Minnesota and in the Milwaukee, Wisconsin region between 2011 and 2018. We take advantage of widespread trail counting programs and historic aerial and street-level imagery to create and test more than 30 theoretically-important potential explanatory variables. We address the challenge that many crossings have small numbers of crashes (or zero crashes) during the study period by using a Poisson-lognormal (PLN) model. Our model shows significant associations between trail crossing crashes and trail traffic volume, roadway motor vehicle volume, three-way intersections where the trail crosses perpendicular to the mainline roadway, and total crossing length. While not statistically significant, signalized intersections and limited sight lines between drivers and trail users near crossings may also be associated with more crashes. Future research can build on this study and expand systemic efforts to improve trail crossing safety.
Who are the fastest cyclists? An analysis of speed pedelec users in the Netherlands
Maud van der Salm, Universiteit UtrechtShow Abstract
Zheyan Chen, Universiteit Utrecht
Dea van Lierop (firstname.lastname@example.org), Universiteit Utrecht
In many regions, conventional bicycles and electrically assisted bicycles (e-bikes), are gaining popularity and becoming a commonly used sustainable mode in the urban transportation system. Speed pedelecs are a type of high-speed e-bike with motorized pedal assistance up to a maximum speed of 45 km/h. This relatively new mode is most often used for trips that range from 10 to 40 kilometers making it a promising mode for promoting drivers to switch to using sustainable and active transport. The goal of this study is to assess speed pedelec usage in the Netherlands. We set out to understand who the users are, what motivates them, and whether they experience any barriers to speed pedelec usage. Using a factor-cluster analysis we group users based on their attitudes and preferences regarding the physical environment they cycle in, their socio-demographic characteristics, personal attitudes, and social environments. The analysis revealed four primary user groups: Enthusiastic riders, Efficient riders, Concerned riders and Relaxed riders. Across the groups 85% of the respondents reported using their speed pedelec at least once a week for a trip they would have previously made by car. However, each group has specific travel habits, motivation, and safety concerns. To motivate continued speed pedelec usage each user groups requires specific policy interventions that are aligned with their personal values related to preference, safety, and image. By understanding the specific needs and desires of different segments it becomes possible to develop effective policy interventions aimed at improving the experiences of all speed pedelec users.
An Empirical Study of the Impacts of Bicycles on Passenger Car Speeds on Urban Roads without Bicycle Lanes
Jaclyn Schaefer, Portland State UniversityShow Abstract
Miguel Figliozzi, Portland State University
Avinash Unnikrishnan, Portland State University
Increasing the bicycle mode share has been suggested as part of a solution to reduce the burden of additional traffic that continued urbanization and population growth is creating. As strategies to promote bicycling are implemented, concerns have been raised that an increase in the bicycle mode share will lead to reduced vehicle speeds and result in more traffic congestion unless bicycle lanes are provided. This research investigates the effects bicycles may have on motorized vehicle speeds on urban roads without bicycle lanes. A detailed comparative analysis of passenger car speeds was performed using two vehicle scenarios: (i) a passenger car that was preceded by a bicycle, and (ii) a passenger car that was preceded by another passenger car. The mean and 85 th percentile speeds of scenarios (i) and (ii) were analyzed using t -tests. Relationships between speed and gap times with oncoming (opposite direction) traffic were also investigated. The results indicate that at most sites (92%), bicycles do not reduce passenger car mean speeds by more than 1 mph, and are not likely to lead to increased traffic congestion. Significantly reduced speeds were observed less frequently when adequate gaps in oncoming traffic for overtaking were present, and at sites with a lower functional classification or those where sharrows are present.
Integrated Weather Effects on E-cycling in Daily Commuting:
A Longitudinal Evaluation of Weather Effects On E-cycling in the Netherlands
Joost de Kruijf (email@example.com), Universiteit UtrechtShow Abstract
Dick Ettema, Universiteit Utrecht
Dea van Lierop, Universiteit Utrecht
Peter Van Der Waerden, Eindhoven University
Tao Feng, Eindhoven University
Lars Bocker, Universitetet i Oslo
Martin Dijst, Luxembourg Institute of Socio-Economic Research
While in many regions the conventional bicycle has already been regarded as an environmentally friendly and healthy alternative to the car for daily commuting, societal and policy agendas are also increasingly promoting e-bike adoption. Adding to recent research on e-bike safety, satisfaction with travel and behavioral change, this paper reports on the impact of weather circumstances on the use of the e-bike in daily commuting in an e-cycling incentive program in the province of Noord-Brabant, the Netherlands. The impact of this incentive program was analyzed using a longitudinal design, and it combined travel patterns that were derived from individuals’ GPS data over nine months, hourly observed meteorological data, and questionnaires on intended behavior and sociodemographic characteristics. The findings suggest that the presence of snow and ice, precipitation sum, and high windspeed negatively affected the choice of commuting to work by e-bike, in this decreasing order of impact. Although the overall impact of air temperature on e-cycling was positive, the likeliness to commute by e-bike decreased at higher air temperatures. E-cycling under specific weather conditions was more likely if participants’ intention to e-cycle under such weather conditions was stronger. Our study indicates that the benefits of the e-bike in daily commuting are underestimated in relation to adverse weather conditions. For respondents from households with one car who, therefore, have fewer alternatives in poor weather conditions, only precipitation sum and relatively low air temperature impacted e-cycling, where gender and high wind speeds might have been expected.
Examining Crowdsourced Cyclists Near-Miss and Collision Events Using Text Mining and Artificial Neural Networks
Keneth Kwayu (firstname.lastname@example.org), Western Michigan UniversityShow Abstract
Valerian Kwigizile, Western Michigan University
Kevin Lee, Western Michigan University
Jun-Seok Oh, Western Michigan University
Trisalyn Nelson, Arizona State University
Cycling is an eco-friendly and sustainable mode of transportation. Despite its benefits, the cyclists’ risk of collision is still high when interacting with other road users. This study analyzed self-reported near-miss and collision event descriptions for the United States provided by the crowdsourcing platform, BikeMaps.org. Innovative and efficient analytic methods are needed to generate useful information from unstructured textual data sources in transportation safety domain. In this study, explorative text mining, topic modeling, and machine learning are utilized to gain insights from the unstructured textual descriptions of crowdsourced near-miss and collision events. The approaches are used to unveil prevalent words and words associations for near-miss and collision events. Structural Topic Modeling (STM) is deployed to autogenerate latent themes or topics from the event descriptions. The generated topic proportions are used as input in Artificial Neural Networks (ANN) to estimate the cyclist’s propensity to a collision. It was found that cyclists had a higher propensity to a collision in topics that articulated vehicle encroachment to the bike lane, on-street parking close or into the bike lane resulting to dooring, and drivers’ violations at the crosswalk. The results and methodology used in this study can assist engineers, policymakers, and law enforcement officers to proactively reduce potential cyclist collisions, prioritizing areas where cyclist safety improvements are needed and ultimately promoting bicycle ridership in our communities.
Infrequent Bicyclists Have A Different Safety Perception Than Frequent Bicyclists: Findings from Revealed Preference Study Using Bikeshare Data
Nitesh Shah, University of TennesseeShow Abstract
Christopher Cherry, University of Tennessee, Knoxville
Understanding motivating factors associated with bicycling is important to improve educational and built-environment investments to increase cycling. While factors such as physical environment, socio-demographic, and psychology influence bicycling; safety is one of the primary reasons people avoid bicycling. We can observe bicycling safety through objective and subjective (perceived) safety measurements. Although interventions based on objective safety can reduce the number of crashes, injuries, and fatalities, people might still feel uncomfortable bicycling due to their perceived safety. Several studies examined the perceived safety of bicyclists based on stated preference surveys, but these studies have limitations, including response bias. We implemented a revealed preference method by combining 9,101 bicycling trips of Grid Bikeshare in Arizona with transportation network and crash data to identify whether casual bicyclists adopted different route choice behavior compared to regular subscribers. We segmented the Path-Size Correction Logit Model into a “registered” and “casual” user group to evaluate the difference in the safety behavior of regular and occasional bicyclists. We found that these two groups exhibited different behaviors related to crash locations, built environment, and navigation. The significance of different types of bicyclists avoiding historic crash locations or risky infrastructure suggests that crash datasets coupled with route data can be used as one of many indicators for perceived safety. Finally, we made recommendations to increase the perceived safety of occasional bicyclists by expanding bicycle-specific infrastructure, constructing contra-flow bicycle lanes in a one-way street, separating high volume lanes with a bike lane, and improving the education of road users.
Modeling the Impacts of Electric Bicycle Purchase Incentives
Alexander Bigazzi (email@example.com), University of British ColumbiaShow Abstract
Elmira Berjisian, University of British Columbia
Governments are interested in incentivizing e-bike adoption, due to potential benefits from increasing physical activity and displacing travel by private automobile. In developing purchase incentive programs, a key question is how to use available resources most effectively. To inform e-bike purchase incentive program designs, the objective of this paper is to determine how key elements of program design (in particular rebate amounts and structure) affect e-bike purchases and relevant impacts such as bike shop revenue. An aggregate demand model is developed to estimate the effects of e-bike purchase incentives, and then applied to realistic rebate scenarios to examine incentive effectiveness. Results show that e-bike rebate incentive programs are expected to be bound by number of rebates and not e-bike demand. Additional bike shop revenue is expected to exceed rebate costs at any rebate amount. Incentive programs improve access to e-bikes for lower-income residents, but may not overcome disparities in baseline demand. At a fixed budget, fewer rebates at higher amounts generally yield fewer additional sales and lower additional bike shop revenues, but a larger share of rebates go to low-income and new purchasers. Flat and proportional rebate structures yield similar results, although flat (or capped) rebates yield better income equity. Estimated effects are robust to uncertainty in baseline e-bike demand. Based on these findings, flat rebates of $400 to $800 (CAD) are a reasonable starting point for initiating an e-bike rebate incentive program, with the strong recommendation of a robust evaluation plan to better inform future program designs.
Quantifying the effect of signage on bicycle ridership
Ali Al-Ramini (firstname.lastname@example.org), University of Nebraska, LincolnShow Abstract
Mohammad Ali Takallou, University of Nebraska, Lincoln
Daniel Piatkowski, University of Nebraska, Lincoln
Fadi Alsaleem, University of Nebraska, Lincoln
Cities rarely have comprehensive networks of bicycle infrastructure, or the funds available to create such infrastructure. Instead, many communities have disjointed and disparate bicycle infrastructure, and limited funds to create a connected system. Signage offers an inexpensive means for cities to connect existing bicycle infrastructure. However, the effect of signage on ridership is unclear. This research takes advantage of a natural experiment to quantify the effect of adding signage to existing infrastructure, and as a means to connect disparate bicycle infrastructure. We compare bicycling rates, drawn from the Strava fitness app, over three years (2017-2019) in the city of Omaha, Nebraska. In 2019, the city added signage connecting existing bicycle infrastructure. In some cases, the signage replaced sharrows, and in others, it was in addition to existing sharrows and bike lanes. Using a machine-learning approach, we predict existing and expected bicycle traffic along corridors in which signage was added, using the difference as evidence of the likely impact of signage. After controlling for weather, demographics, and street characteristics, results demonstrate a 24% increase in bicycling after sharrows were replaced by signage. This suggests that signage may be an inexpensive and effective means of connecting existing bicycle infrastructure.
Assessment of Barriers to Adoption of Electric Bicycles in Centre Region, Pennsylvania
Emmeline Evans, Pennsylvania State University, University ParkShow Abstract
Reilly Smith, Pennsylvania State University, University Park
Elizabeth Traut (email@example.com), Pennsylvania State University, University Park
In the search for lower-emissions alternatives to internal combustion engine (ICE) vehicles, electric bicycles, or e-bikes, have emerged as a popular option, due to their ease of use, comparatively low cost, and extended range when compared with manual bicycles. This study develops a framework for performing a community level assessment of potential barriers for the adoption and use of e-bikes, particularly as a mode shift away from ICE vehicles. This work assesses the factors of local and state policy, cyclist safety, available infrastructure, costs to the local municipalities, and household factors, including personal costs. We present a case study for Centre Region, Pennsylvania, which includes the Penn State University Park Campus and immediately surrounding municipalities. Findings suggest that the Centre Region’s main barrier to adoption is lack of infrastructure, which in turn hinders the real and perceived safety of riding e-bikes.
Assessing the Impact of Bicycle Infrastructure Treatment Type on the Frequency of Right-Hook Conflicts Between Bicyclists and Motorized Vehicles at Signalized intersections
Katerina Deliali (firstname.lastname@example.org), National Technical University of Athens (NTUA)Show Abstract
Chengbo Ai, University of Massachusetts, Amherst
Eleni Christofa, University of Massachusetts, Amherst
Bicycle infrastructure treatments are implemented to enhance bicyclist mobility and safety. However, crashes between motorized vehicles and bicyclists still occur at locations where these treatments are present, indicating the need to further investigate their safety impact. This paper aims to assess the impact of three bicycle treatments, namely: 1) conventional bike lanes, 2) protected bike lanes, and 3) sharrows (i.e., shared vehicle-bicycle traffic lanes) on right-hook conflicts, which is a common unsafe interaction between right-turning vehicles and through—bicyclists at signalized intersections. Video data were collected from six intersections in Cambridge, Massachusetts. The videos were manually processed to identify interactions between right-turning vehicles and through-bicyclists that corresponded to a Post Encroachment Time (PET) of less or equal to four seconds. Negative Binomial models were developed to relate the number of traffic conflicts with the number of right-turning vehicles, through-bicyclists, and the treatment type however, the latter was not found to affect the conflict frequency. Further analysis of the PET thresholds showed that there is a significant difference in the recorded PET values depending on the user sequence in the conflict area. Specifically, when a motorized vehicle was followed by a bicyclist, PETs of 1 second were more frequent compared to the opposite user sequence. This observation may motivate research regarding different thresholds for bicycle-leading or bicycle-following PET, which in turn may advance the conflict-based bicycle safety approaches. Overall, conflict—based methods stand as an efficient approach to assess user interactions.
Prediction of Non-Motorized Vehicle’s Overtaking Trajectory on Shared Bicycle Lane
Ailing Yin, Tongji UniversityShow Abstract
Xiaohong Chen, Tongji University
Lishengsa Yue, University of Central Florida
Current research on non-motorized vehicles is not as extensive as motor vehicles, particularly in regard to the prediction of non-motorized vehicle trajectory. The accurate prediction of non-motorized vehicle trajectory is essential for many roadway safety applications such as the autonomous vehicle’s path planning in road space shared with non-motorized vehicles and developing non-motorized vehicle related crash avoidance systems. Specifically, this study aims to develop models to predict a non-motorized vehicle’s overtaking trajectory. Trajectory data of 1179 non-motorized vehicles from 168 overtaking events were collected to develop the model. A LSTM model was established based on variables including the average distance, average speed difference and other indicators that reflect the relationships between non-motorized vehicles. Its results were compared with a RandomForest model which was used as a base model. The results demonstrated that the LSTM neural network obtained more accurate results with a 98% of accuracy for the lateral position, compared with a 91% of accuracy of the RandomForest model. Further segmentation of trajectories and independent modeling of trajectories after overtaking begins obtained more accurate results on future location prediction, and the prediction accuracy of the lateral coordinates after 0.5s was increased from 95% to 97%. Using two-second historical information, the mean absolute error of the trajectory position prediction after 1 / 12s and 0.5s was respectively 0.208m and 0.429m. The findings of this study showed the promising of the LSTM on the prediction of non-motorized vehicle trajectory.
Cyclist Behaviour Towards Stop Signs. A Before-After Study on Stop-Controlled Intersections Using Video Trajectory and Surrogate Methods
Bismarck Ledezma-Navarro, McGill UniversityShow Abstract
Nicolas Saunier, Ecole Polytechnique de Montreal
Luis Miranda-Moreno, McGill University
The installation of stop-signs in residential areas converting them from minor-approach-only stop (MAS) intersections to all-way-stops (AWS) intersections brings a positive perception by the general population. Although there is little research that has looked at the impact of AWS on cyclist behaviour and their safety effects. This paper aims at investigating the safety effect of converting MAS to AWS intersections using an observational before and after approach and surrogate measures of safety (SMoS). More specifically, the impact of AWS conversion is investigated using multiple indicators including cyclist speed measures, and the postencroachment time of cyclist-pedestrian, cyclist-cyclist and cyclist-vehicle interactions. A multilevel linear models for site and approach variance, which was also used for the safety analysis, along with an ordered logit model where all the models were controlled for behavior variables, built environment features, approach and intersection geometry. The speed of the cyclist on the major approaches shows little change, while in the minor approach a systematic speed increase is presented in all the different evaluated indicators. Whereas the minor approaches have a speed increase, this is not translated to a decrease on PET or an increase of number of very dangerous interactions.
Examining the Use of Microsimulation Modeling to Assess Bicycle-Vehicle Conflicts at Intersections: A Case Study Incorporating Field-Observed Conflict Data
David Lemcke, Northern Arizona UniversityShow Abstract
Katherine Riffle, Northern Arizona University
Brendan Russo, Northern Arizona University
Edward Smaglik, Northern Arizona University
Microsimulation software has become an invaluable tool for analysis of operational performance at signalized intersections and can also examine safety performance through analysis of surrogate measures of safety, such as conflicts identified using Post Encroachment Time (PET) or Time-To-Collision (TTC). Surrogate safety parameters can be extracted using vehicle, bicycle, and/or pedestrian trajectories obtained from microsimulation software (e.g. VISSIM) using the Surrogate Safety Assessment Model (SSAM) available from the United States Federal Highway Administration (FHWA). To progress knowledge on the use of VISSIM and SSAM to calibrate microscopic simulations to field observed bicycle-vehicle conflicts, this study performed a quantitative analysis on the impacts of changeable behavioral parameters in microsimulation on the frequency of bicycle-vehicle conflict outputs in an attempt to understand the steps and procedures required to calibrate microsimulation models to field observed bicycle-vehicle conflicts. After analysis, it was found that that the default driving behavior parameters within VISSIM underestimated bicycle-vehicle conflicts as compared with field-observed data. Various parameters and combinations of parameters within VISSIM were changed in an attempt to match the number of field observed conflicts with the VISSIM / SSAM output. Additionally, the results of this study indicate that the Random Seed value can significantly impact the occurrence of bicycle-vehicle conflicts in microsimulation. Future research is needed in this area to further understand the complex interaction of VISSIM user behavior parameters and their impact on the number of reported user conflicts.
Modeling and Simulation of High-density Through Bicycle Flow at Mixed-traffic Intersections
Yixin Li, Tongji UniversityShow Abstract
Ying Ni (email@example.com), Tongji University
Jian Sun, Tongji University
Mixed-traffic intersections with high-density bicycle traffic flow are one of the most common bottlenecks in the urban cycling network. Due to the shared space and frequent interactions with other traffic users, through bicycle flow shows the obvious tendency of lateral dispersion, resulting in severe influences of traffic efficiency and safety. However, existing microscopic simulation models of through bicycles disregard the dynamic variations of the dispersion extent under changing traffic context, and they also simplify cyclists’ behaviors and interactions, which has limitations in showing a good representation of reality. This paper presents a microscopic simulation model of through bicycles by embedding a dynamic boundary model and behavior decision models into the original social force model. The dynamic boundary model determines the boundary of the actual cycling area in each signal cycle to capture the dynamic characteristics of the lateral dispersion. The decision module uses the rule-based method to select a suitable behavior in four alternatives: freely moving, following, overtaking, and merging. Finally, behavior decisions and boundary constraints are exerted by corresponding behavior forces. The proposed model is tested by comparing simulation results with the original social force model and with the empirical data which contains 743 through bicycle trajectories collected in Shanghai, China. The comparison results show the proposed model is capable of reproducing the realistic motion features of through bicycles. This new microscopic model can be used to simulate the through bicycle flow to fulfill the need for traffic efficiency evaluation, safety assessments, and infrastructure designs of mixed-traffic intersections.
The (In)Equitable Distribution of Bicycling Facilities & the Causality Dilemma with Socioeconomic Change
Nick Ferenchak (firstname.lastname@example.org), University of New MexicoShow Abstract
Wesley Marshall, University of Colorado, Denver
Interstates and arterial highways have historically been installed though lower-income and/or minority neighborhoods, leading to transportation equity issues. The 21st century has seen both a reduction in the construction of such urban infrastructure and increased attention on active transportation in cities, particularly bicycling facilities. But how well has the expansion of bicycling facilities been distributed across the socioeconomic and sociodemographic spectrum? Furthermore, does the installation of bicycling facilities lead to socioeconomic/sociodemographic changes in a neighborhood or vice versa? To answer these questions, we longitudinally assess 17,966 bicycling facilities over nine years in 11,293 block groups across 29 U.S. cities. We then examine the installation of different bike facility types (i.e. protected, buffered, and standard bike lanes, shared lane markings, and off-road trails) against variables representing socioeconomic/sociodemographic change. The findings suggest an inequitable distribution of bike facilities. Lower-income White neighborhoods saw 60% more bike facilities installed than lower-income non-White neighborhoods. In terms of income disparities, higher-income non-White neighborhoods saw 42% more bike facilities installed than lower-income non-White ones. These results may speak more to differences in demand for bike facilities than a problem with equitable supply, but that issue warrants further research. With respect to the causality dilemma, we found evidence of a bi-directional relationship between bike facilities and socioeconomic/sociodemographic change. However, the relationship is 22% stronger and more statistically significant when bike facilities is the preceding variable to socioeconomic/sociodemographic change. This result suggests that cities should consider policies to protect residents against displacement when installing bike facilities.
Regional Bicycle Network Evaluation and Strategic Planning - A Quantitative Methodological Approach Despite Limited Data Sources for Cycling
Alex van Dulmen, Technische Universitat GrazShow Abstract
Martin Fellendorf, Technische Universitat Graz
In cases where budgets and space are limited, the realization of new bicycle infrastructure is often hard, as a proper evaluation of the existing network or the benefits of new investments are hardly possible. Travel demand models can offer a tool to support decision makers, but because of limited data availability for cycling, the validity of the demand estimation and trip assignment are often questionable. This paper presents a quantitative method to evaluate a regional bicycle network and plan strategic improvements, despite limited data sources for cycling. The proposed strategic network planning method is based on a multimodal aggregate travel demand model. Instead of evaluating the effects of network improvements on the modal split as well as link and flow volumes, this method works the other way around. A desired modal share for cycling is set and the resulting link and flow volumes, are the basis for a hypothetical bicycle network that is able to satisfy this demand. The current bicycle network is compared with the new hypothetical network, resulting in preferable actions and a ranking based on the importance and potentials to improve the modal share for cycling. Necessary accompanying measures for other transport modes can also be derived using this method. Our test case Graz showed for example that a shift of short trips in the inner city towards cycling would without countermeasures likely provide capacity for new longer car trips. The proposed method can be applied to existing travel models that already contain a mode choice model.
Limitations of Recursive Logit for Inverse Reinforcement Learning of Bicycle Route Choice Behavior in Amsterdam
Thomas Koch, Vrije UniversiteitShow Abstract
Elenna Dugundji, Vrije Universiteit, Amsterdam
Used for route choice modelling by the transportation research community, recursive logit is a form of inverse reinforcement learning. By solving a large-scale system of linear equations recursive logit allows estimation of an optimal (negative) reward function in a computationally efficient way that performs for large networks and a large number of observations. In this paper we review examples of recursive logit and inverse reinforcement learning models applied to real world GPS travel trajectories and explore some of the challenges in modeling bicycle route choice in the city of Amsterdam using recursive logit as compared to a simple baseline multinomial logit model with environmental variables. We discuss conceptual, computational, numerical and statistical issues that we encountered and conclude with recommendation for further research.
Bike Boxes: A Review of Design Guidelines Across the US and Impacts on Safety
Katerina Deliali (email@example.com), National Technical University of Athens (NTUA)Show Abstract
Eleni Christofa, University of Massachusetts, Amherst
Chengbo Ai, University of Massachusetts, Amherst
Bike boxes or advanced stop lines are a traffic control device aimed to improve intersection safety. Bike boxes provide a designated space for people biking to gather at the front of the traffic queue and are intended to improve bicyclist visibility and predictability of their positions. While several bike boxes have been installed over the past few years in the United States, this traffic control device is still considered experimental and requires approval from the Federal Highway Administration before installation. This paper summarizes guideline documents published by federal agencies, municipal entities as well as other organizations in the United States in an effort to determine whether there is agreement on where and how bike boxes should be designed. A comprehensive review of research studies across the globe is also summarized with a focus on the safety performance of bike boxes and its correlation with bike box design characteristics. This research concludes with a list of research questions related to bike box design and safety that warrant additional investigation. Overall, the majority of the guideline documents suggest the use of a “NO TURN ON RED” sign and a bike depth between 8-14 ft to limit crosswalk encroachment. So far, research has been inconclusive on the impact of pavement coloring. Overall, bike boxes motivate high driver comprehension and compliance rates, which are associated with improved safety at intersections. Yet, a comprehensive understanding of how bike box design and user comprehension affects the safety performance of bike boxes is missing.
Creating a Road Environment where People on Foot and on Bike Are as Safe as People in Cars
Ge Shi, University of ConnecticutShow Abstract
Vannesa Methoxha, Howard/Stein-Hudson Associates, Inc.
Carol Atkinson-Palombo, University of Connecticut
Norman Garrick, University of Connecticut
Road crashes take millions of lives in each year worldwide, overwhelmingly in low- and middle-income countries. A handful of higher income countries have made great progress in reducing traffic fatalities and are moving towards the vision zero goal. Understanding how one such country, the Netherlands, have cut traffic fatalities by over 90% is the goal of this study. The results show that the Dutch have virtually eliminated the concept of “vulnerable road users” in that the risk of fatality for pedestrian, bikers and vehicle occupants all converged at a low level. This is an amazing achievement especially when compared to countries like the U.S. where risk of fatality for non-vehicle occupants are 5 to 8 times greater than for vehicle occupants. In this paper, we assess the evolution of the risk for different types of road users in the Netherlands since 1970. We also review critical events, advocacy, policies, and programs that were implemented in the Netherlands over the last four decades to address the issue of traffic safety. This analysis demonstrates that historically the Dutch made use of protests and advocacy campaigns to gain supports for policies and programs that supported non-motorized transportation as routine mobility choice. Furthermore, in the Netherlands the governing body for safety were early adopters (in the 1990s) of a systems approach to traffic safety called Sustainable Safety. A 2020 FHWA webinar highlights the fact that this systems approach is now beginning to take hold in the U.S.
The influence of the Ecobici Bike Share Program on Drivers’ Perceptions towards Cyclists
Yazmin Valdez-Torres, Florida State UniversityShow Abstract
Michael Duncan, Florida State University
One of the main barriers for cycling is the fear of being hurt by a motor vehicle. However, most of the research on cycling has been focused on cyclists’ perceptions, leaving aside drivers’ perception. This paper focuses on identify whether people who live, work, study, or drive consistently within the Ecobici service area have a better perception towards cyclists compared to those that mostly drive outside the service area. The research was carried out in Mexico City, a large metropolitan area with an increasing number of cyclists, which makes it a good case study. The evaluation method was an online survey with a sample of 710 participants. The findings show important similarities for both areas: drivers reported a good perception towards seeing more cyclists in the city as well as using public funding to build more bicycle infrastructure. On the other hand, participants also show a slightly negative perception about the way cyclists performed while cycling. The differences suggest that drivers within the bike share area are less supportive on reducing parking to building bicycle infrastructure, but they are more supportive about encouraging close friends and family to cycle. Finally, participants within the service area reported less frustration when overtaking cyclists. These findings may be due to the fact that the presence of Ecobici helps to normalize cycling. This research serves to further the understanding of the perception drivers have towards cyclists, especially from those who drive as their primary mode of transportation.
Modeling Bicyclists’ Speed Using GPS Data
Muntahith Orvin, University of British ColumbiaShow Abstract
Mahmudur Fatmi, University of British Columbia
This study develops a model for bicyclists’ speed using trip-trajectory GPS data of the dockless bikeshare service (DBS) users. An advanced machine learning (ML) algorithm is adopted for improved trip-trajectory identification using the GPS records. Following the data processing, a latent segmentation-based linear regression (LSLR) model is developed for bicycle speed analysis. A flexible segment allocation model is formulated within the LSLR framework to capture heterogeneity. This study tests the effects of attributes related to the trips, weather, built environment, and accessibility on bicycle speed. The goodness-of-fit measures suggest that the proposed ML algorithm outperforms the traditional rule-based trip identification technique. The segment allocation results reveal that segment 1 is more likely to include weekend suburban trips made in the off-peak hours of summer. In contrast, urban peak hour weekday trips made in the fall and winter seasons are more likely to be allocated in segment 2. Model results suggest that bicyclists’ speed might be higher in infrastructure with lower elevation, higher bike index, longer shared path, and locations far from road intersections and CBD. The model confirms heterogeneity across the segments. For instance, speed is more likely to increase with an increase in temperature in winter. In contrast, speed is likely to decrease for a higher temperature in summer. Similarly, bike index, cycle track, AADT, and bike lane length reveal heterogeneity across the segments. The findings of this study reveal important insights to develop bicycle-friendly plans and policies, as well as will assist in effective bicycle infrastructure investment decision-making.
Impacts of Cyclability Features on Optimal Cycling Route
Axel Grante (firstname.lastname@example.org), Ecole Polytechnique de MontrealShow Abstract
Catherine Morency, Ecole Polytechnique de Montreal
Jean-Simon Bourdeau, Ecole Polytechnique de Montreal
More and more cities around the world are trying to promote active transport as a good alternative to private car. Decreasing the share of motorised trips in cities, namely those by private cars, contributes to reduce air pollution, GHG emissions, congestion in addition to increasing road safety and public health through increased level of physical activities. This paper proposes a sensitivity analysis of optimal cycling routes by varying the generalized cost of the road segments to account for the travel conditions proposed to cyclists. As reported in the literature, many factors can change the perception cyclists have of the route quality and, therefore, they may select a longer but most fitted route for their travel. By changing the generalized cost of each road segment to account for these factors, it is possible to measure how variable is the optimal cycling route. The sensitivity analysis is conducted in the city of Montreal, Canada and relies on coefficients (of the cost function) that were found in the literature. The proposed analysis can also help assess whether there is a lack of bicycle infrastructures in an area. Results of this paper give an estimation of the average detour a cyclist may accept to maximize his uses of bicycle infrastructures and to limit exposure to risky situations. The value of this detour is 17% of the shortest distance.
A Method to Define the Influence Area of Transferred Shared Bicycle in Rail Transit Based on Multi-source Data
Yu Zeng, Southeast UniversityShow Abstract
Jun Chen, Southeast University
Jun Hao, Southeast University
Bike sharing plays an important role in the transfer and connection of metro, and also extends the passenger flow attraction range. Therefore, it is of great significance to carry out the quantitative definition of the impact area of rail stations, which has important guiding significance for the scientific allocation of shared bicycles within the attractive scope. This paper takes Nanjing, China as the research site. Through the space-time characteristics of riding, the influence area can be defined as the core layer, radiation layer and peripheral layer. Based on the transfer data of shared bicycles around the stations, the stations are classified into five types: morning inflow-evening outflow type, morning outflow-evening inflow type, morning and evening double peak type, all day equilibrium type and sparse transfer type, which is convenient to analyze the difference in the influence area. In addition, this paper defines the area of bicycle transfer station by multi-source data mining, including urban road network data, riding order data, land use POI facilities and other data sources. Through the Voronoi diagram preliminary division, adding the space-time range, establishing the amendatory index system, and using ArcGIS spatial overlay to define the area scope, this paper puts forward a set of division method of rail transit station transfer influence area which can be defined by quantitative indicators. The research will provide decision support for the layout planning, design and construction of transfer facilities, promote the transformation of combined travel, and effectively promote the micro-circulation of the public transport system.
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