Characteristics of Multimodal Conflicts in Urban On-Street Bicycle Lanes

Abstract

In urban areas, bicycles that travel in bicycle lanes encounter a variety of obstructions, including pedestrians and various types of motor vehicles. Earlier studies focused on the frequency of such events. The goal of this study was to characterize the obstructions. Data were collected in the Manhattan and Brooklyn boroughs of New York City to evaluate specific characteristics (e.g., bicycle lane designs, curb regulations, land use) that might influence the frequency of specific conflict types. A method is described for evaluating the frequency of conflicts between bicycles that travel in on-street bicycle lanes and various other transportation modes and for identifying factors that might affect the frequency of such conflicts.

Different Safety Awareness and Route Choice between Frequent and Infrequent Bicyclists: Findings from Revealed Preference Study Using Bikeshare Data

“…This study found that occasional bicyclists valued bicycle-friendly infrastructures 1.5 times more than regular bicyclists when difference in trip distance was controlled for. Furthermore, both bicycling groups preferred bicyclespecific facilities during peak hours, which can be explained by the reduced conflict with cars, pedestrians, and other road users (39).…”

Truck-bicycle safety: an overview of methods of study, risk factors and research needs

“…These related to road users are young age, as adolescent cyclists have difficulties practicing safe performance in blind spot areas near trucks [37], bbehavioural adaptation to safety measures [34], combination of factors affecting the likelihood of driver errors [18], cyclists' behaviour not conforming to normal expectations [18], driving in unfamiliar locations [18], gender (female cyclists might not correctly differentiate between the risks associated with inside and outside overtaking of trucks compared to male cyclists) [35], reaction time (slower reaction of drivers to objects visible only in mirrors compared to direct viewing through the front windscreen) [39] and time pressure related to delivery time slots for truck drivers [18]. These related to infrastructure are insufficient layout of loading area [40], lack of recognizable and comprehensible intersection design [32], narrow roads and tight corners [18], unseparated signalling phases for turning trucks and straight riding cyclists, particularly when traffic volumes and speeds are high [32] and specific configuration of bicycle lane and parking lane [36]. One factor related to management is related to land use characteristics, that affect the flow of trucks and cyclists [36].…”

“…These related to infrastructure are insufficient layout of loading area [40], lack of recognizable and comprehensible intersection design [32], narrow roads and tight corners [18], unseparated signalling phases for turning trucks and straight riding cyclists, particularly when traffic volumes and speeds are high [32] and specific configuration of bicycle lane and parking lane [36]. One factor related to management is related to land use characteristics, that affect the flow of trucks and cyclists [36].…”

Exploring the Place Dynamics of Roadside Space Conflicts with Street View Images: The Case of Beijing

“…Even though a variety of data collection methods have been explored to address this issue, street view images remain a valuable source of data for large-scale analysis, particularly in extracting static con icts and other street activities in roadside spaces (Conway et al 2013;Kim 2020;). Vehicles inappropriately parking on roadside spaces are not uncommon in street view images, which become a proxy to measure roadside space con icts as the intrusion of automobiles in these shared public spaces increases the chance of con icts and thus poses risks for active mobility (Conway et al 2013;Hara et al 2013). More research is needed to develop tools to effectively capture the frequency of con icts between vehicles, cyclists, and pedestrians from street view images and surveillance footage and identify place-based factors that might affect the distribution of such con icts (Kurnicki 2020).…”