Safety analysis of urban signalized intersections under mixed traffic

Anjana, S.; M.V.L.R., Anjaneyulu

doi:10.1016/j.jsr.2014.11.001

Cited by 31 publications

(5 citation statements)

References 15 publications

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“…In addition to suggestions for road infrastructure, several traffic improvements have been suggested based on traffic-light analysis. Anjana et al [22] presented a method based on different traffic volumes at intersections to evaluate the safety of green traffic lights.…”

Section: Related Workmentioning

confidence: 99%

Image Data Extraction and Driving Behavior Analysis Based on Geographic Information and Driving Data

Lin¹,

Zhang²,

Chang³

2023

Preprint

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This paper proposes an approach for image data extraction and driving behavior analysis using geographic information and driving data. Information derived from geographic and global positioning systems was used for image data extraction. In addition, we used an onboard diagnostic II and a controller area network bus logger to record driving data for driving behavior analysis. Driving behavior was analyzed using sparse automatic encoders and data exploration to detect abnormal and aggressive behavior. A regression analysis was used to derive the relationship between aggressive driving behavior and road facilities. Several traffic improvements were proposed for specific intersections and roads. The results indicate that lane ratios without lane markings and with straight lane markings are important features that affect aggressive driving behaviors.

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Section: Related Workmentioning

confidence: 99%

Image Data Extraction and Driving Behavior Analysis Based on Geographic Information and Driving Data

Lin¹,

Zhang²,

Chang³

2023

Preprint

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“…Dotzauer et al (2015) investigated the behavior adaption of younger and older drivers to an intersection crossing advisory system, and the findings indicated that as the intersection time and percentage of stops decreased, the effects of the advisory system were significantly positive. Some prior studies have also examined the crash causative factors of signalized intersections under mixed traffic, and the results have shown that the factors that influence the traffic safety of signalized intersections are control measures, as well as geometric, traffic, driver, vehicle, and environmental factors (Abdel-Aty and Keller, 2005;Anjana and Anjaneyulu, 2015). In addition, recent literature has focused on the safety evaluation of intersections with advanced systems.…”

Section: The Study On the Safety Of Crossroadsmentioning

confidence: 99%

Pro-active warning system for the crossroads at construction sites based on computer vision

Zhu

Wen

Deng

2020

ECAM

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Purpose To improve insufficient management by artificial management, especially for traffic accidents that occur at crossroads, the purpose of this paper is to develop a pro-active warning system for crossroads at construction sites. Although prior studies have made efforts to develop warning systems for construction sites, most of them paid attention to the construction process, while the accidents that occur at crossroads were probably overlooked. Design/methodology/approach By summarizing the main reasons resulting for those accidents occurring at crossroads, a pro-active warning system that could provide six functions for countermeasures was designed. Several approaches relating to computer vision and a prediction algorithm were applied and proposed to realize the setting functions. Findings One 12-hour video that films a crossroad at a construction site was selected as the original data. The test results show that all designed functions could operate normally, several predicted dangerous situations could be detected and corresponding proper warnings could be given. To validate the applicability of this system, another 36-hour video data were chosen for a performance test, and the findings indicate that all applied algorithms show a significant fitness of the data. Originality/value Computer vision algorithms have been widely used in previous studies to address video data or monitoring information; however, few of them have demonstrated the high applicability of identification and classification of the different participants at construction sites. In addition, none of these studies attempted to use a dynamic prediction algorithm to predict risky events, which could provide significant information for relevant active warnings.

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“…Conventional transportation research has extensively studied the safety effects associated to signal timing. In particular, previous works (Anjana and Anjaneyulu 2015;Wong, Sze, and Li 2007) analyzed the contributory factors for traffic crashes and found red-light violations, yellowlight time changes, and left-turn phases are critical for traffic safety. Other works (Essa and Sayed 2019;Persaud, Lord, and Palmisano 2002) predicted the safety risk through regression models and Bayesian methods and provided authoritative equations and rules (Urbanik et al 2015) as domain knowledge to help traffic engineers determine the signal timing in practice.…”

Section: Introductionmentioning

confidence: 99%

SafeLight: A Reinforcement Learning Method toward Collision-Free Traffic Signal Control

Du,

Ye,

et al. 2023

AAAI

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Traffic signal control is safety-critical for our daily life. Roughly one-quarter of road accidents in the U.S. happen at intersections due to problematic signal timing, urging the development of safety-oriented intersection control. However, existing studies on adaptive traffic signal control using reinforcement learning technologies have focused mainly on minimizing traffic delay but neglecting the potential exposure to unsafe conditions. We, for the first time, incorporate road safety standards as enforcement to ensure the safety of existing reinforcement learning methods, aiming toward operating intersections with zero collisions. We have proposed a safety-enhanced residual reinforcement learning method (SafeLight) and employed multiple optimization techniques, such as multi-objective loss function and reward shaping for better knowledge integration. Extensive experiments are conducted using both synthetic and real-world benchmark datasets. Results show that our method can significantly reduce collisions while increasing traffic mobility.

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Safety analysis of urban signalized intersections under mixed traffic

Cited by 31 publications

References 15 publications

Image Data Extraction and Driving Behavior Analysis Based on Geographic Information and Driving Data

Image Data Extraction and Driving Behavior Analysis Based on Geographic Information and Driving Data

Pro-active warning system for the crossroads at construction sites based on computer vision

SafeLight: A Reinforcement Learning Method toward Collision-Free Traffic Signal Control

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