Using Bicycle Level of Service for Decision Making

LaMondia, Jeffrey J.; Moore, Nathan

doi:10.3141/2520-14

Cited by 6 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fortunately, the widespread use of smartphones enables high-resolution spatial and temporal data collection from a large number of active travelers with a relatively low burden [ 1 ]. These rich crowd-sourced data are valuable in various types of walking and cycling analyses such as estimating volumes [ 2 , 3 ], studying air pollution exposure [ 4 ], identifying network improvements [ 5 , 6 ], and studying routes, accessibility, and traffic flow [ 7 – 9 ].…”

Section: Introductionmentioning

confidence: 99%

By cyclists, for cyclists: Road grade and elevation estimation from crowd-sourced fitness application data

Berjisian,

Bigazzi,

Barkh

2023

PLoS ONE

View full text Add to dashboard Cite

Road grade or slope is a key factor for walking and cycling behavior and outcomes (influencing route, speed, energy, etc.). For this reason, the scarcity of precise road grade data presents a challenge for travel information and analysis. This paper examines the accuracy of using crowd-sourced GPS data from a fitness application to estimate roadway grade profiles, which can then be used to develop network-wide road grade datasets. We externally validate an elevation estimation method described by McKenzie and Janowicz using field surveying data, and then propose and evaluate modifications for estimation of road grade (which is more directly relevant than elevation for walking and cycling analysis). We find that a modest amount of crowd-sourced GPS data can be used to generate relatively accurate road grade estimates: better than commonly-used low-resolution elevation models, but not as accurate as high-resolution data derived from LiDAR (Light Detection and Ranging). We also find that the grade estimates are more reliable than the elevation estimates, relative to alternative data sources. The most accurate method to aggregate crowd-sourced GPS data builds a composite roadway grade profile using partition-around-medoid clustering of individual grade sequences, first smoothed with a Savitzky-Golay filter and cleaned with Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Implementing this method with an average of 150 GPS traces per location yields a root mean square error (RMSE) of 1% road grade. The findings in this paper can be used to incorporate precise road grade information into street network datasets over a wide spatial scale, which is necessary for walking and cycling analysis that fully considers the physiological aspects of active transportation.

show abstract

Section: Introductionmentioning

confidence: 99%

By cyclists, for cyclists: Road grade and elevation estimation from crowd-sourced fitness application data

Berjisian,

Bigazzi,

Barkh

2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Another important safety factor is the condition of the cycling infrastructure and whether it is protected from the main traffic or not. Bicycle users tend to feel safer and more satisfied when they cycle through protected bike lanes, and the cycling infrastructure is adequately maintained [40,41].…”

Section: Introductionmentioning

confidence: 99%

Identifying and Modeling the Factors That Affect Bicycle Users’ Satisfaction

Ketikidis,

Papagiannakis,

Basbas

2023

Sustainability

View full text Add to dashboard Cite

The parameters that affect bicyclists’ satisfaction are of vital importance when it comes to determining the reasons that endure bicycle usage in an urban environment. This research refers to the factors that affect bicycle users’ satisfaction with the existing infrastructure in the Municipality of Thessaloniki, Greece. Bicycle dependence in Thessaloniki is relatively limited when it is compared to other European cities with similar topological and demographic characteristics. This article aims to determine the most suitable measures that policymakers should implement to enhance bicycle infrastructure. The data collection process was realized through an online questionnaire survey addressed to the bicycle organizations and groups that are situated in Thessaloniki. Inferential statistical analysis investigated the dependency between the perceived satisfaction and the perceived level of safety of cyclists. Ordinal and multinomial logistic regressions were applied to identify the significant problems that influenced cyclists’ satisfaction, as well as the most important improvement measures suggested by the survey participants. The findings revealed that the lack of safety and urban integration of bicycle infrastructures were the statistically significant issues affecting users’ satisfaction. The development of a safe, integrated, and interconnected bicycle network area emerges as a priority to increase cyclists’ satisfaction and daily bicycle trips in cities with low bicycle culture.

show abstract