Introducing naturalistic cycling data: What factors influence bicyclists’ safety in the real world?

Dozza, Marco; Werneke, Julia

doi:10.1016/j.trf.2014.04.001

Cited by 126 publications

(79 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The use of new 'naturalistic' monitoring technologies and innovative methods of analysis has been used recently to estimate the factors associated with risk at the local level (Dozza and Werneke, 2014). We believe this methodology, and the simpler approach of asking participants where they feel most in danger (Chataway et al, 2014), has great potential for improving understanding of the social and geographical factors associated with both perceived risk and risk inadequately indexed by STATS19.…”

Section: When Are Bicycle Crashes Most Likely?mentioning

confidence: 99%

Who, where, when: the demographic and geographic distribution of bicycle crashes in West Yorkshire

Lovelace

Roberts

Kellar

2016

Transportation Research Part F: Traffic Psychology and Behaviou

View full text Add to dashboard Cite

Factors associated with cycle safety, including international differences in injury and mortality rates, protective equipment and bicycle training, have been subject to increasing academic interest. Environmental variables associated with cycle safety have also been scrutinised, but few studies have focussed on geographical factors at the local level. This paper addresses this research gap by analysing a geo-referenced dataset of road traffic incidents, taken from the UK's STATS19 dataset (2005 -2012). We investigate incidents involving cyclists within West Yorkshire. This is an interesting case study area as it has an historically low cycling rate but very ambitions cycling plans following investment from the Department of Transport. West Yorkshire is found to be an unusually risky area for cyclists, with an estimated 53 deaths and 1372 serious injuries per billion kilometres cycled, based on census commuting statistics. This is roughly double the national average. This riskiness varies spatially and temporally, broadly in line with expectations from the previous literature. An unexpected result was that cycling seems to be disproportionately risky for young people in West Yorkshire compared with young people nationally. The case study raises the issue of potential negative health impacts of promoting cycling amongst vulnerable groups in dangerous areas. We conclude by highlighting opportunities for increasing cycling uptake via measures designed primarily to improve safety. The analysis underlying this research is reproducible, based on code stored at github.com/Robinlovelace/bikeR.

show abstract

Section: When Are Bicycle Crashes Most Likely?mentioning

confidence: 99%

Who, where, when: the demographic and geographic distribution of bicycle crashes in West Yorkshire

Lovelace

Roberts

Kellar

2016

Transportation Research Part F: Traffic Psychology and Behaviou

View full text Add to dashboard Cite

show abstract

“…For instance in the UK, where this study was conducted, drivers and cyclists are often required to share the roads, and 84% of all serious or fatal cycle crashes involve collision with another vehicle, mostly motor vehicles (The Royal Society for the Prevention of Accidents, 2012). A recent study by Dozza and Werneke (2014) collected naturalistic cycling data using sensors, GPS and cameras installed on bicycles in Sweden. They found that 33% of the conflicts and critical events (such as crashes, or a car crossing the bicycle lane) that cyclists experienced involved a motorised vehicle.…”

Section: Introductionmentioning

confidence: 99%

Car driver attitudes, perceptions of social norms and aggressive driving behaviour towards cyclists

Fruhen

Flin

2015

Accident Analysis & Prevention

View full text Add to dashboard Cite

“…2) Cyclist and pedestrian safety: A separate branch of safety research leverages GPS, Wi-Fi and Bluetooth trajectory data to provide insight into cyclist and pedestrian safety. For example, [30] investigates bicycle risk by analyzing GPS traces, calculating incident rates through simple odds ratios, and concluding that crash risk is greatest at intersections and on roads that are in poor condition. A related research combines GPS traces with bicycle count data to infer highrisk areas for cycling injuries [31].…”

Section: F Safetymentioning

confidence: 99%

Applications of Trajectory Data From the Perspective of a Road Transportation Agency: Literature Review and Maryland Case Study

Marković

Sekula

Laan

et al. 2019

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

Transportation agencies have an opportunity to leverage increasingly-available trajectory datasets to improve their analyses and decision-making processes. However, this data is typically purchased from vendors, which means agencies must understand its potential benefits beforehand in order to properly assess its value relative to the cost of acquisition. While the literature concerned with trajectory data is rich, it is naturally fragmented and focused on technical contributions in niche areas, which makes it difficult for government agencies to assess its value across different transportation domains. To overcome this issue, the current paper explores trajectory data from the perspective of a road transportation agency interested in acquiring trajectories to enhance its analyses. The paper provides a literature review illustrating applications of trajectory data in six areas of road transportation systems analysis: demand estimation, modeling human behavior, designing public transit, traffic performance measurement and prediction, environment and safety. In addition, it visually explores 20 million GPS traces in Maryland, illustrating existing and suggesting new applications of trajectory data.

show abstract

Introducing naturalistic cycling data: What factors influence bicyclists’ safety in the real world?

Cited by 126 publications

References 20 publications

Who, where, when: the demographic and geographic distribution of bicycle crashes in West Yorkshire

Who, where, when: the demographic and geographic distribution of bicycle crashes in West Yorkshire

Car driver attitudes, perceptions of social norms and aggressive driving behaviour towards cyclists

Applications of Trajectory Data From the Perspective of a Road Transportation Agency: Literature Review and Maryland Case Study

Contact Info

Product

Resources

About