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SAE International Journal of Transportation Safety
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Windshield Glare from Bus Interiors: Potential Impact on City Transit Drivers at Night

SAE International Journal of Transportation Safety

OSHTECH Incorporated, Canada-Peter Pityn, Sue Clouse-Jensen
  • Journal Article
  • 09-07-02-0008
Published 2019-11-15 by SAE International in United States
Windshield glare at night is a safety concern for all drivers. Public transit bus drivers also face another concern about glare caused by interior lighting sources originally designed for passenger safety. The extent to which interior light reflections contribute to glare is unknown. Unique methods for measuring discomfort and disability glare during bus driving were developed. An initial simulation study measured windshield luminance inside of a New Flyer D40LF diesel bus parked in a controlled, artificial, totally darkened test environment. Findings indicated significant disability glare (from elevated luminance) in the drivers’ primary field of view due to interior reflections. Any reduction in contrast would result in less prominent glare if actual driving conditions differ. To assess this, levels of windshield glare were also measured with the bus parked on the roadside under the “background glow” of the urban environment. Findings reveal that under road conditions the extent of disability glare from interior reflections is much less, but not negligible, when contrast is reduced. The information gathered in these studies may be useful to manufacturers and…
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Analysis of Driving Performance Based on Driver Experience and Vehicle Familiarity: A UTDrive/Mobile-UTDrive App Study

SAE International Journal of Transportation Safety

University of Texas at Dallas, USA-Yongkang Liu, John Hansen
  • Journal Article
  • 09-07-02-0010
Published 2019-11-21 by SAE International in United States
A number of studies have shown that driving an unfamiliar vehicle has the potential to introduce additional risk, especially for novice drivers. However, such studies have generally used statistical methods based on analyzing crash and near-crash data from a range of driver groups, and therefore the evaluation has the potential to be subjective and limited. For a more objective perspective, this study suggests that it would be worthwhile to consider vehicle dynamic signals obtained from the Controller Area Network (CAN-Bus) and smartphones. This study, therefore, is focused on the effect of driver experience and vehicle familiarity for issues in driver modeling and distraction. Here, a group of 20 drivers participated in our experiment, with 13 of them having participated again after a one-year time lapse in order for analysis of their change in driving performance. A clustering-based, outlier detection grading method was used to grade individual driver behavior, as well as discrepancy score, which is measured by the Euclidean distance in the vehicle dynamical feature space, to evaluate driving performance. Results show that the variation…
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A Personalized Lane-Changing Model for Advanced Driver Assistance System Based on Deep Learning and Spatial-Temporal Modeling

SAE International Journal of Transportation Safety

Jianghan University, China-Jun Gao, Jiangang Yi
University of Michigan-Dearborn, USA-Yi Lu Murphey
  • Journal Article
  • 09-07-02-0009
Published 2019-11-14 by SAE International in United States
Lane changes are stressful maneuvers for drivers, particularly during high-speed traffic flows. However, modeling driver’s lane-changing decision and implementation process is challenging due to the complexity and uncertainty of driving behaviors. To address this issue, this article presents a personalized Lane-Changing Model (LCM) for Advanced Driver Assistance System (ADAS) based on deep learning method. The LCM contains three major computational components. Firstly, with abundant inputs of Root Residual Network (Root-ResNet), LCM is able to exploit more local information from the front view video data. Secondly, the LCM has an ability of learning the global spatial-temporal information via Temporal Modeling Blocks (TMBs). Finally, a two-layer Long Short-Term Memory (LSTM) network is used to learn video contextual features combined with lane boundary based distance features in lane change events. The experimental results on a -world driving dataset show that the LCM is capable of learning the latent features of lane-changing behaviors and achieving significantly better performance than other prevalent models.
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A Novel Metaheuristic for Adaptive Signal Timing Optimization Considering Emergency Vehicle Preemption and Tram Priority

SAE International Journal of Transportation Safety

Universite Mohammed V de Rabat Ecole Mohammadia d’Ingenieurs, Morocco-Maryam Alami Chentoufi, Rachid Ellaia
  • Journal Article
  • 09-07-02-0007
Published 2019-09-24 by SAE International in United States
In this article, a novel hybrid metaheuristic based on passing vehicle search (PVS) cultural algorithm (CA) is proposed. This contribution has a twofold aim: First is to present the new hybrid PVS-CA. Second is to prove the effectiveness of the proposed algorithm for adaptive signal timing optimization. For this, a system that can adapt efficiently to the real-time traffic situation based on priority signal control is developed. Hence, Transit Signal Priority (TSP) techniques have been used to adjust signal phasing in order to serve emergency vehicles (EVs) and manage the tram priority in a coordinated tram intersection. The system used in this study provides cyclic signal operation based on a real-time control approach, including an optimization process and a database to manage the sensor data from detectors for real-time predictions of EV and tram arrival time. Then, a simulation model is developed using Arena Simulation Software to evaluate best timing plans at the intersection.
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Factors Affecting the Severity of Motor Vehicle Traffic Crashes in Tunisia

SAE International Journal of Transportation Safety

Najran University, Saudi Arabia-Mounir Belloumi
University of Sousse, Tunisia-Fedy Ouni
  • Journal Article
  • 09-07-02-0006
Published 2019-08-19 by SAE International in United States
We investigate the contribution of several variables concerning the severity of accidents involving vehicle occupant and pedestrian victims in Tunisia. In order to investigate the effect of various explanatory variables, Odds Ratio (OR) effects are considered for both serious injury accidents and fatal accidents. The empirical results are of great variety. The vehicle-occupant severity model indicates that male drivers are associated with higher severity levels as compared to female drivers. Added to that, accidents occurring in rainy conditions increase the likelihood of fatal injuries but have no significant effect on other injury severity levels. Among driver contributory factors, a driver under the influence of alcohol or drug is associated with an increased risk of sustaining fatal injuries compared to other driver contributory factors. The season factor shows that accident severity during the summer season is high. Among time of accident, daytime periods indicate a high likelihood of severe injuries as compared to nighttime periods. Another finding of the study is that the day of accident and region of accident increases the probability of severe injury.…
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