Traffic Safety at Road–Rail Level Crossings Using a Driving Simulator and Traffic Simulation

Kim, Lee-Hyung; Larue, Grégoire S.; Ferreira, Luis; Rakotonirainy, Andry

doi:10.3141/2476-15

Cited by 10 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the critical nature of the EVP problem, it is desired that the proposed scheme should be tested and validated in realistic environments. The proposed scheme is tested in microsimulation software VISSIM, which is widely used for diverse traffic problems [37][38][39]. VISSIM allows simulating complex vehicle interactions realistically on a microscopic level.…”

Section: Simulation Environment Test Bedmentioning

confidence: 99%

A Strategy for Emergency Vehicle Preemption and Route Selection

2019

Self Cite

View full text Add to dashboard Cite

Emergency vehicle preemption (EVP) aims to give right of way to emergency vehicles (EV) heading toward the incident location through a network of signalized intersections by creating a green wave en-route. The design goals of EVP systems are two folds: first, to avoid any hindrance to the passage of EV along the road and at the intersections and second, to reduce the negative impact of preemption on general traffic. The negative impact of EVP on normal traffic can be minimized by selecting appropriate preemption strategy. The EVP schemes proposed earlier aim to minimize the travel time of the EV with no or little consideration to the negative impact of EVP on the normal traffic. In this study, a joint strategy for optimal path selection and EV preemption is developed. The proposed scheme selects the optimal path for the EV before it departs from its origin and then activates the preemption on each intersection en-route at the right time to clear the intersection before the EV reaches. The proposed EVP scheme also aims to minimize the impact of EVP over normal traffic at both stages (i.e., path selection phase and preemption phase). A major advantage of the proposed method is that once the optimal path is selected, the emergency information can be disseminated to other vehicles using vehicle-to-vehicle and vehicle-to-infrastructure communication in the EV path to clear the entire route or the approaching lane. The strategy was tested using a microscopic simulation environment for a real traffic network. The findings indicated a major reduction in the travel time of the EV while minimizing the impact of preemption on the normal traffic. The proposed strategy and evaluation procedure can be helpful for corresponding agencies and practitioners to assess the impact of implementing preemption on existing or proposed arterials.

show abstract

Section: Simulation Environment Test Bedmentioning

confidence: 99%

A Strategy for Emergency Vehicle Preemption and Route Selection

2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…This simulator test was performed at the Centre for Accident Research and Road Safety, Queensland University of Technology. More information regarding the participants can be found in the preliminary work of the authors [21,24].…”

Section: Participantsmentioning

confidence: 99%

Measuring Vehicle Speeds, Compliance Rates, and Braking Reaction Times at Level Crossings Using Fixed and Moving Driving Simulators

Kim¹

2012

(JTTM)

Self Cite

View full text Add to dashboard Cite

Driving simulators are used for many applications including traffic safety. There are several types of driving simulators available for researchers and engineers. This study aims to compare fixed and moving driving simulators in assessing driving behaviors at railway crossings equipped with either typical stop signs or in-vehicle audio safety system. Data used in the comparison included vehicle speed profiles, compliance rates, and braking reaction times. It was found that compliance rates were similar whereas speed profiles and breaking reaction times were slightly different. As the results of the desktop simulator had previously been compared with those from the data collected in the field, the comparison of results between the two simulators reported here can serve as a blueprint to calibrate the use of moving simulators. In summary, the different properties of the simulator can lead drivers to react to warnings differently.

show abstract

“…Many studies have focused on the human factor analysis of using new ITS technologies proposed for increasing safety of RLXs [26,31,44,[50][51][52][53][54][55]. These studies focus on the importance of drivers' acceptance and adoption of new ITS technologies (e.g.…”

mentioning

confidence: 99%

“…Driving simulators (Figure 2.11) are usually used for behavioural analysis of car drivers [31,53,54].…”

mentioning

confidence: 99%

Video Analytics for the Detection of Near-Miss Incidents at Railway Level Crossings and Signal Passed at Danger Events

Aminmansour¹

View full text Add to dashboard Cite

The work contained in this thesis has not been previously submitted to meet requirements for an award at this or any other higher education institution. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made. Currently most of the data for near-miss and SPAD incidents are collected from the reports made by train drivers. There is a doubt that all of the incidents that occurred are actually reported by train drivers, due to the subjectivity of near-miss events, and also disciplinary actions that v might be taken against train drivers, if they are responsible for any incidents.To identify the causal factors of railway collisions, the Cooperative Research Centre for Rail Innovation has initiated a research project to develop an automated data collection system to support railway collision analysis. The system collects data from trains' on-board computers, GPS receivers, and forward facing cameras. This thesis presents computer vision algorithms for automatically detecting near-miss and SPAD incidents from train cameras as part of this project.To automatically detect near-miss events from videos, we localize vehicles in each frame and calculate the distance of the vehicles to the railway. If the distance of a vehicle to the railway is less than a pre-defined distance, the event will be flagged as a near-miss incident for further analysis. Our proposed near-miss detection system consists of railway detection and segmentation, and vehicle detection and localization algorithms. We employ several computer vision techniques including image segmentation, convolutional neural networks, and 3D geometry reconstruction.In order to automatically detect SPAD incidents from videos, we detect railway signals as well as the state of the signals in each frame. An event will be flagged as a SPAD if a train passes a red signal. Our approach for detecting SPAD events consists of using morphological operations, different colour spaces, 3D geometry reconstruction, segmentation algorithms, and machine learning techniques.This thesis introduces algorithms for automatically detecting level crossing near-miss and SPAD incidents from video data obtained by forward facing cameras of trains. The accuracy rate of our near-miss detection system for detecting and segmenting railways on our railway dataset is 96% for day-time, and 93% for night-time videos. The system has 75% of precision and 81% of recall for detecting vehicles on this dataset. We compared our vehicle detector algorithm with the Deformable Part Models (DPM) and Regions with CNN features (R-CNN) object detectors. The recall rate of our algorithm is 29% higher than what can be achieved with DPM or R-CNN detectors on our railway dataset. We also present the results of detecting railway signals on more than 5,000 frames for our SPAD detection system. This thesis opens avenues for helping to identify the causal factors of train collisions.vi

show abstract

Traffic Safety at Road–Rail Level Crossings Using a Driving Simulator and Traffic Simulation

Cited by 10 publications

References 15 publications

A Strategy for Emergency Vehicle Preemption and Route Selection

A Strategy for Emergency Vehicle Preemption and Route Selection

Measuring Vehicle Speeds, Compliance Rates, and Braking Reaction Times at Level Crossings Using Fixed and Moving Driving Simulators

Video Analytics for the Detection of Near-Miss Incidents at Railway Level Crossings and Signal Passed at Danger Events

Contact Info

Product

Resources

About