Generation of "Biking Schedules" for Bicycle Travel Analysis
Amr Mohamed, University of British Columbia Alexander Bigazzi, University of British Columbia
Show Abstract
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.
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18-02134
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Bicycle, Pedestrian, and Mixed-Mode Trail Traffic: A Performance Assessment of Demand Models
Alireza Ermagun, Northwestern University Greg Lindsey, University of Minnesota Tracy Loh, Brookings Institution
Show Abstract
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.
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18-06499
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What Makes You Cycle This Far? An Analysis of Mandatory Bicycle Tour Distances
Rico Krueger, University of New South Wales Taha Rashidi, University of New South Wales
Show Abstract
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.
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18-03429
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Cyclists and Left-Turning Drivers: A Study of Infrastructure and Behavior
Hugo Nicholls, CARDNO Geoff Rose, Monash University Marilyn Johnson, Monash University Rachel Carlisle, VicRoads
Show Abstract
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.
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18-03892
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Bicycling Among University Campus Residents: A Policy-Oriented Analysis
David Maynard, Kansas State University Gregory Newmark, Kansas State University
Show Abstract
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.
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18-06690
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Cycling Usage and Frequency Determinants in College Campuses
Kaveh Bakhsh Kelarestaghi, Virginia Polytechnic Institute and State University (Virginia Tech) Alireza Ermagun, Northwestern University Kevin Heaslip, Virginia Polytechnic Institute and State University (Virginia Tech)
Show Abstract
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.
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18-06565
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Factors Contributing to Winter Cycling: A Case Study of a Downtown University in Toronto, Canada
Tamara Nahal, Ryerson University Raktim Mitra, Ryerson University
Show Abstract
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.
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18-02858
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Bicyclist Positioning Behavior at Signalized Intersections in Portland, Oregon
Oliver Smith, City of Portland, Oregon Peter Koonce, City of Portland (OR) David Soto Padin, Portland State University
Show Abstract
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.
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18-05609
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Social Media Hashtags Associated with Bike Commuting: Applying Natural Language Processing Tools
Subasish Das, Texas A&M University Gabriella Kolodzy, Texas A&M Transportation Institute Lisa Minjares-Kyle, Texas A&M Transportation Institute Zachary Elgart, Texas A&M Transportation Institute
Show Abstract
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.
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18-03545
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Motivational Factors Underlying E-Bike Users’ Intention to Run the Red Light
Hongtai Yang, Southwest Jiaotong University Fan Su, Southwest Jiaotong University Xiaohan Liu, Southwest Jiaotong University Christopher Cherry, University of Tennessee, Knoxville Yugang Liu, Southwest Jiaotong University
Show Abstract
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.
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18-00893
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Cyclists' Preferences for Bike Lanes Versus Road-Adjacent Shared-Use Paths Using a Year-Long Bikeshare GPS Data Set
Benjamin Burmester, Auburn University Jeffrey LaMondia, Auburn University
Show Abstract
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.
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18-00826
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Consideration of Energy Expenditure, Air Quality, and Safety in Cyclist Routing Decisions
Alexander Bigazzi, University of British Columbia Steven Gehrke, Northern Arizona University
Show Abstract
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.
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18-04947
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