Impact of road-surface condition on rural highway safety: A multivariate random parameters negative binomial approach

Chen, Sikai; Saeed, Tariq Usman; Labi, Samuel

doi:10.1016/j.amar.2017.09.001

Cited by 100 publications

(34 citation statements)

References 86 publications

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“…Highway safety is an issue of great concern to transportation professionals all over the world [1][2][3][4][5][6][7][8][9][10][11][12]. Despite the decline in highway fatalities between 20012014, the National Highway Traffic Safety Administration's (NHTSA) Fatality Analysis Reporting System (FARS) reported that fatal accidents increased between 2015-2016 [13].…”

Section: Introductionmentioning

confidence: 99%

The Influence of Road Geometry on Vehicle Rollover and Skidding

Yin

Wen

Sun

et al. 2020

IJERPH

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This paper analyzes the influence of single and combined unfavorable road geometry on rollover and skidding risks of D-class mid-sized sport utility vehicles (SUVs) with front-wheel drive for roads with design speeds at 80 km/h. A closed-loop simulation model of human-vehicle-road interactions is established to examine the systematic influence of road geometry on vehicle rollover and skidding. The effects of different road geometry on rollover and skidding on SUVs are studied for pavement surface with good and poor friction when vehicles are in the action of steady state cornering. The rollover and skidding risks of the most unfavorable road segments are assessed. The critical wheel is defined by the threshold of skidding during curve negotiation. The results found that SUVs are not easy to rollover on the most unfavorable roads, regardless of good or poor friction of pavement surface. The safety margin of rollover is greater than that of skidding. The safety margin of skidding is minimal on poor friction roads. Therefore, for the sake of driving safety, it is not recommended to design the roads with these unfavorable road geometry combinations

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Section: Introductionmentioning

confidence: 99%

The Influence of Road Geometry on Vehicle Rollover and Skidding

Yin

Wen

Sun

et al. 2020

IJERPH

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“…At the current time, several nations are investing heavily in safety initiatives including Vision Zero, a multi-national road traffic safety that seeks to eliminate all road traffic fatalities or serious injuries. However, safety-inspired initiatives and improvements in highway policy and design (such as wider lanes and shoulders) and vehicle technology advances (such as antilock brakes, airbags, and collision-avoidance systems) seem to have limited effects due to the effect of human risk-compensation behaviors (Chen, Saeed, Alqadhi, & Labi, 2019;Chen, Saeed, & Labi, 2017;Labi, 2016;Labi, Chen, Preckel, Qiao, & Woldemariam, 2017;Winston, Maheshri, & Mannering, 2006). Therefore, as long as human drivers operate vehicles, it is difficult to see how such safety initiatives can reach their targets.…”

Section: The Persistence Of the Highway Safety Problem And The Promismentioning

confidence: 99%

A deep learning algorithm for simulating autonomous driving considering prior knowledge and temporal information

Chen

Leng

Labi

2019

Computer aided Civil Eng

Self Cite

105

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Autonomous vehicle (AV) stakeholders continue to seek assurance of the safety performance of this new technology through AV testing on in‐service roads, AV‐dedicated road networks, and AV test tracks. However, recent AV‐related fatalities on in‐service roads have exacerbated public skepticism and eroded some public trust in the safety of AV operations. Further, test tracks are unable to characterize adequately the real‐world driving environment. For this reason, driving simulators continue to serve as an attractive means of AV testing. However, in most AV driving simulators, the AV operation is based on commands external to the vehicle and embedded in the code for the driving environment. To address the simulation shortfalls associated with this approach, this paper develops a deep convolutional neural network–long short‐term memory (CNN–LSTM) algorithm for self‐driving simulation. This algorithm observes and characterizes the AV's driving environment, and controls the AV movement in the driving simulation. The CNN part extracts features that use transfer learning to introduce human prior knowledge, and the LSTM part uses temporal information to process the extracted features, and incorporates temporal dynamics to predict driving decisions. The AV may also use an external server with a database containing road environment data as an additional source of information. It is acknowledged that different driving simulators differ in their functions and their capabilities to access driving‐environment data. Therefore, to make it sufficiently flexible to facilitate replication by other researchers that use driving simulators, the algorithm has been designed and demonstrated using only image data of the driving environment as input. This is because roadway image data are easily and readily accessible from the screen of any driving simulator. The proposed algorithm was tested using the open racing car simulator test track platform and was found to be able to mimic human driving decisions with a high degree of accuracy.

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“…Bai et al (2010) presented a framework for comprehensive estimation of user cost for bridge management, synthesizing the existing state of practice of user cost estimation and techniques to address a number of considerations in such estimation. Chen et al (2017) recently calibrated multivariate models able to quantify accident rates in relation to road-surface condition state. In case of the occurrence of natural hazards like earthquakes, tornadoes, etc., damage can affect multiple bridge structures, thus influencing the functionality of the transportation network itself.…”

Section: Social Performance Indicatorsmentioning

confidence: 99%

State-Of-Research on Performance Indicators for Bridge Quality Control and Management

Zanini

Faleschini

Casas

2019

Front. Built Environ.

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The present study provides a review of the most diffused technical and non-technical performance indicators adopted worldwide by infrastructure owners. This work, developed within the European COST Action TU 1406-"Quality specifications for roadway bridges, standardization at a European level," aims to summarize the state-of-art maintenance scheduling practices adopted by bridge owners, mainly focusing on the identification and classification of the most diffused performance indicators (PIs). PIs are subdivided in technical and non-technical ones: for this latter subclass, PIs are classified in environmental, social and economic-targeted. The study aims to be a reference for researchers dealing with performance-based assessments and bridge maintenance and management practices.

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Impact of road-surface condition on rural highway safety: A multivariate random parameters negative binomial approach

Cited by 100 publications

References 86 publications

The Influence of Road Geometry on Vehicle Rollover and Skidding

The Influence of Road Geometry on Vehicle Rollover and Skidding

A deep learning algorithm for simulating autonomous driving considering prior knowledge and temporal information

State-Of-Research on Performance Indicators for Bridge Quality Control and Management

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