Traffic conflicts involving speed-pedelecs (fast electric bicycles): A naturalistic riding study

As electric bicycles (e-bikes) are becoming popular in China, concerns have been raised about their safety conditions. A traffic conflict technique is commonly used in traffic safety analysis, and there are many conflict measures designed for cars. However, e-bikes have high flexibility to change speed and trajectories, which is different from cars, so the conflict measures defined for e-bikes need to be independently explored. Based on e-bike driving characteristics, this paper proposes a new measure, the Integrated Conflict Intensity (ICI), for traffic conflicts involving e-bikes at intersections. It measures the degree of dangerousness of a conflict process, with consideration of both conflict risk and conflict severity. Time to collision is used to measure the conflict risk. Relative kinetic energy is used to measure the conflict severity. ICI can be calculated based on video analysis. The method of determining ICI thresholds for three conflict levels (serious, less serious, and slight) and two conflict types (conflicts between two e-bikes, and conflicts between an e-bike and a car) is put forward based on the questionnaires about safety perception of e-bike riders, which is regarded as the criterion of e-bike safety conditions at intersections. The video recording and a questionnaire survey about conflicts involving e-bikes at intersections have been conducted, and the unified thresholds applicable to different intersections have been determined. It is verified that ICI and its thresholds meet the criterion of e-bike safety conditions. This work is expected to be used in the selection of intersections for safety improvement of e-bike traffic.

Download Full-text

Controller interventions to mitigate potential air traffic conflicts

PsycEXTRA Dataset ◽

10.1037/e578622012-009 ◽

2010 ◽

Author(s):

Deepti Surabattula ◽

Michael Kaplan ◽

Steven J. Landry

Keyword(s):

Air Traffic ◽

Traffic Conflicts

Download Full-text

Propulsion-motor options in battery-powered electric bicycles for pre-industrial nations

10.2514/6.1994-3923 ◽

1994 ◽

Keyword(s):

Electric Bicycles

Download Full-text

Are Older Drivers Safe on Interchanges? Analyzing Driving Errors Causing Crashes

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211031232 ◽

2021 ◽

pp. 036119812110312

Author(s):

Denis Elia Monyo ◽

Henrick J. Haule ◽

Angela E. Kitali ◽

Thobias Sando

Keyword(s):

Latent Class ◽

Time Of Day ◽

Older Drivers ◽

Contributing Factors ◽

Lighting Condition ◽

Crash Data ◽

Traffic Characteristics ◽

Road Users ◽

Traffic Conflicts ◽

Penalized Logistic Regression

Older drivers are prone to driving errors that can lead to crashes. The risk of older drivers making errors increases in locations with complex roadway features and higher traffic conflicts. Interchanges are freeway locations with more driving challenges than other basic segments. Because of the growing population of older drivers, it is vital to understand driving errors that can lead to crashes on interchanges. This knowledge can assist in developing countermeasures that will ensure safety for all road users when navigating through interchanges. The goal of this study was to determine driver, environmental, roadway, and traffic characteristics that influence older drivers’ errors resulting in crashes along interchanges. The analysis was based on three years (2016–2018) of crash data from Florida. A two-step approach involving a latent class clustering analysis and the penalized logistic regression was used to investigate factors that influence driving errors made by older drivers on interchanges. This approach accounted for heterogeneity that exists in the crash data and enhanced the identification of contributing factors. The results revealed patterns that are not obvious without a two-step approach, including variables that were not significant in all crashes, but were significant in specific clusters. These factors included driver gender and interchange type. Results also showed that all other factors, including distracted driving, lighting condition, area type, speed limit, time of day, and horizontal alignment, were significant in all crashes and few specific clusters.

Download Full-text

Real-Time Safety Optimization of Connected Vehicle Trajectories Using Reinforcement Learning

Sensors ◽

10.3390/s21113864 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3864

Author(s):

Tarek Ghoul ◽

Tarek Sayed

Keyword(s):

Reinforcement Learning ◽

Real Time ◽

Low Cost ◽

Safety Evaluation ◽

Traffic Volume ◽

Connected Vehicles ◽

Connected Vehicle ◽

Real World Data ◽

Physical Constraints ◽

Traffic Conflicts

Speed advisories are used on highways to inform vehicles of upcoming changes in traffic conditions and apply a variable speed limit to reduce traffic conflicts and delays. This study applies a similar concept to intersections with respect to connected vehicles to provide dynamic speed advisories in real-time that guide vehicles towards an optimum speed. Real-time safety evaluation models for signalized intersections that depend on dynamic traffic parameters such as traffic volume and shock wave characteristics were used for this purpose. The proposed algorithm incorporates a rule-based approach alongside a Deep Deterministic Policy Gradient reinforcement learning technique (DDPG) to assign ideal speeds for connected vehicles at intersections and improve safety. The system was tested on two intersections using real-world data and yielded an average reduction in traffic conflicts ranging from 9% to 23%. Further analysis was performed to show that the algorithm yields tangible results even at lower market penetration rates (MPR). The algorithm was tested on the same intersection with different traffic volume conditions as well as on another intersection with different physical constraints and characteristics. The proposed algorithm provides a low-cost approach that is not computationally intensive and works towards optimizing for safety by reducing rear-end traffic conflicts.

Download Full-text