Safety Evaluation for Expressways: A Comparative Study for Macroscopic and Microscopic Indicators

Qu, Xiaobo; Kuang, Yan; Oh, Erwin; Jin, Sheng

doi:10.1080/15389588.2013.782400

Cited by 83 publications

(39 citation statements)

References 19 publications

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“…Note that the proposed test platform has a very high flexibility in simulating real world traffic operations, including those with purely traditional vehicles. For example, it can be used for conducting traffic capacity analysis [28][29][30], Long Distance Commuter lane , traffic oscillations [31,32], traffic safety analysis [33][34][35], and others. At the end of this paper, we use a simple case study of high-speed U-turn to build the tracking control function.…”

Section: Resultsmentioning

confidence: 99%

PaTAVTT: A Hardware-in-the-Loop Scaled Platform for Testing Autonomous Vehicle Trajectory Tracking

Wang

Zhang³

et al. 2017

Journal of Advanced Transportation

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With the advent of autonomous vehicles, in particular its adaptability to harsh conditions, the research and development of autonomous vehicles attract significant attention by not only academia but also practitioners. Due to the high risk, high cost, and difficulty to test autonomous vehicles under harsh conditions, the hardware-in-the-loop (HIL) scaled platform has been proposed as it is a safe, inexpensive, and effective test method. This platform system consists of scaled autonomous vehicle, scaled roadway, monitoring center, transmission device, positioning device, and computers. This paper uses a case of the development process of tracking control for high-speed U-turn to build the tracking control function. Further, a simplified vehicle dynamics model and a trajectory tracking algorithm have been considered to build the simulation test. The experiment results demonstrate the effectiveness of the HIL scaled platform.

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Section: Resultsmentioning

confidence: 99%

PaTAVTT: A Hardware-in-the-Loop Scaled Platform for Testing Autonomous Vehicle Trajectory Tracking

Wang

Zhang³

et al. 2017

Journal of Advanced Transportation

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“…The Gaussian distribution model has been applied to many areas including the traffic management system [5,8,11,25] and outlier detection [26], because of its simplicity and effectiveness. In this paper, the Gaussian distribution model is applied to traffic anomaly detection.…”

Section: Gaussian Distribution Modelmentioning

confidence: 99%

Traffic anomaly detection based on image descriptor in videos

Liu

Huang

2015

Multimed Tools Appl

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The huge and ever growing volume of traffic video poses a compelling demand for efficient automatic detection of traffic anomaly. In this paper, a new traffic anomaly detection algorithm is introduced. It firstly divides a traffic video into several video cubes in temporal domain, and each video cube is divided into video blocks in spatial domain. Each image block of a video block is described using the local invariant features and the visual codebook approach. Based on the descriptor of the image block, we count the category number of the block (CNB) of a video block. Then, a Gaussian distribution model for estimating the probability of normal traffic with respect to the CNB is learned. The learned Gaussian distribution model is then used to detect the traffic anomaly from the test traffic video. Eventually, the results of all video blocks are fused to achieve the final decision. Experimental results show that the proposed algorithm performs better than two existing algorithms on both the intersection traffic videos and main road traffic videos.

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“…9,10 Different driving tasks and roadway conditions may influence the stress perceived by drivers. 11,12 So how can the traffic congestion inflect on driver's emotion and behavior? It is a viable way to build a new grade division standard for road traffic congestion based on the driver's feeling.…”

Section: Introductionmentioning

confidence: 99%

Method and application of grade division for road traffic congestion based on driver’s feeling

Wen

2015

Advances in Mechanical Engineering

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Although road traffic congestion is an objective state of traffic flow, drivers have different feelings about road traffic congestion. First of all, in order to reveal the law that driver's mental state changes along with congested degree of road traffic, the targeted questionnaire was designed to analyze the driver's psychological feeling under different state of traffic flow. Then, from the perspectives of the driver's subjective feeling, the new definition of road traffic congestion was put forward, so the pressure coefficient of traffic congestion, which is written as x t 0 Àt n press , is defined to measure traffic congestion pressure. Furthermore, by adopting statistical methods to associate the driver's subjective feeling toward traffic congestion with objective parameters of traffic flow, the grading thresholds of the unblocked state, mild congested state, moderate congested state, and severe congested state were calculated, which are x t 0 Àt n press = 0:37, x t 0 Àt n press = 0:51, and x t 0 Àt n press = 0:65, respectively. And in the field of application for the grading thresholds, Fourier Transform theory was introduced to calculate domain frequency indexes of the driver's heart rate variability in the four states of traffic flow, respectively. The results show that the domain frequency indexes of the driver's heart rate variability present obvious differences in the four states, which illustrates the solving rationality and applied value of the new grading thresholds for traffic congestion based on driver's feeling. On one hand, the new grading method for traffic congestion, which combines the subjectivity with objectivity, can reflect the driver's actual feeling; on the other hand, it lays the foundation for the study about the influence of traffic congestion on the driver's physiological and psychological characteristics.

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Safety Evaluation for Expressways: A Comparative Study for Macroscopic and Microscopic Indicators

Abstract: Speed dispersion is recommended to proactively assess road safety because (1) it provides consistent risk evaluation with microscopic potential risks and (2) it makes data collection easier.

Cited by 83 publications

References 19 publications

PaTAVTT: A Hardware-in-the-Loop Scaled Platform for Testing Autonomous Vehicle Trajectory Tracking

PaTAVTT: A Hardware-in-the-Loop Scaled Platform for Testing Autonomous Vehicle Trajectory Tracking

Traffic anomaly detection based on image descriptor in videos

Method and application of grade division for road traffic congestion based on driver’s feeling

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