Contextual Guidance at Intersectionsfor Protected Bicycle Lanes

Monsere, Christopher M.; McNeil, Nathan; Wang, Yi; Sanders, Rebecca

doi:10.15760/trec.241

Cited by 2 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Overall, previous research with respect to mixing zones has generally indicated improvements in safety, though there have been some mixed results and further research is warranted. It should be noted that recent research utilizing survey data has found that bicyclists tended to be uncomfortable when traversing mixing zones as compared with other treatment types (19).…”

Section: Safety Impacts Of Mixing Zonesmentioning

confidence: 97%

Analyzing the Impacts of Intersection Treatments and Traffic Characteristics on Bicyclist Safety: Development of Data-Driven Guidance on the Application of Bike Boxes, Mixing Zones, and Bicycle Signals

Russo

Kothuri

Smaglik

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

Transportation modes converge at intersections resulting in conflicts between bicyclists and motor vehicles. A common conflict and crash type involving bicycles at intersections is the right-hook, where a right-turning vehicle collides with a through bicyclist. While various geometric and signal control treatments have been used in attempts to mitigate bicycle–vehicle crashes and conflicts, agencies often face questions about optimal treatment selection. To date, limited research has been conducted to analyze how certain treatments (bike boxes, mixing zones, and bicycle signals) along with traffic characteristics (e.g., vehicle, bicycle, and pedestrian volumes) affect the frequency of bicycle–vehicle conflicts, as well as the severity of such conflicts. To address this research need, this study provides an analysis of bicycle–vehicle conflicts extracted from field-collected videos at 12 intersections; three with bike boxes, three with mixing zones, three with bicycle signals, and three control sites with no specific bicycle treatment. From these videos, bicycle–vehicle conflicts were identified and their severity measured using post-encroachment time (PET), along with conflict-involved road user speeds and hourly road user volumes. From these data, a series of Poisson regression models were estimated to assess hourly predicted conflicts as a function of bicycle and vehicle volumes across different treatment types. Additionally, differences in conflict severity across treatments were analyzed, including use of a novel measure which incorporates both PET and vehicle speed. Ultimately, several differences in conflict frequency and severity were observed between the treatment types, providing new data-driven guidance to assist practitioners in treatment selection.

show abstract

Section: Safety Impacts Of Mixing Zonesmentioning

confidence: 97%

Analyzing the Impacts of Intersection Treatments and Traffic Characteristics on Bicyclist Safety: Development of Data-Driven Guidance on the Application of Bike Boxes, Mixing Zones, and Bicycle Signals

Russo

Kothuri

Smaglik

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…Various models, such as those based on artificial neural networks and machine learning [28,34,35], logistic regression [28], MCDM and analytical hierarchy processes (AHP) [28,[36][37][38] and metaheuristic optimisation techniques [38,39], have introduced approaches to identify spatial associations between socioeconomic and built environment factors and risks such as PTAs in urban areas. For example, logistic regression has been used to determine whether or not there was a relationship between the spatial distribution of PTAs and various predictors in Dhaka, Bangladesh [40], Changsha, China [41], Hong Kong [42], Phoenix, Arizona, United States (US) [43], Iran [28] and a number of other cities, particularly in the US [44]. In some studies [27,35], GISbased models have been used to identify the most significant effective factors involving pedestrian-vehicle accidents as well as APLs in urban areas.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Road Traffic Accidents in Metropolitan Areas: GIS-Based Prediction Modelling of Cases in Mashhad, Iran

et al. 2023

View full text Add to dashboard Cite

This study utilised multi-year data from 5354 incidents to predict pedestrian–road traffic accidents (PTAs) based on twelve socioeconomic and built-environment factors. The research employed the logistic regression model (LRM) and the fuzzy-analytical hierarchy process (FAHP) techniques to evaluate and assign weights to each factor. The susceptibility map for PTAs is generated using the “Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)”. Subsequently, the probability of accidents in 2020 was predicted using real multi-year accident data and the Markov chain (MC) and cellular automata Markov chain (CA-MC) models, with the prediction accuracy assessed using the Kappa index. Building upon promising results, the model was extrapolated to forecast the probability of accidents in 2023. The findings of the LRM demonstrated the significance of the selected variables as predictors of accident likelihood. The prediction approaches identified areas prone to high-risk accidents. Additionally, the Kappa for no information (KNO) statistical value was calculated for both the MC and CA-MC models, which yielded values of 0.94 and 0.88, respectively, signifying a high level of accuracy. The proposed methodology is generalizable, and the identification of high-risk locations can aid urban planners in devising appropriate preventive measures.

show abstract

Contextual Guidance at Intersectionsfor Protected Bicycle Lanes

Cited by 2 publications

References 25 publications

Analyzing the Impacts of Intersection Treatments and Traffic Characteristics on Bicyclist Safety: Development of Data-Driven Guidance on the Application of Bike Boxes, Mixing Zones, and Bicycle Signals

Analyzing the Impacts of Intersection Treatments and Traffic Characteristics on Bicyclist Safety: Development of Data-Driven Guidance on the Application of Bike Boxes, Mixing Zones, and Bicycle Signals

Pedestrian Road Traffic Accidents in Metropolitan Areas: GIS-Based Prediction Modelling of Cases in Mashhad, Iran

Contact Info

Product

Resources

About