Nonparametric Multivariate Adaptive Regression Splines Models for Investigating Lane-Changing Gap Acceptance Behavior Utilizing Strategic Highway Research Program 2 Naturalistic Driving Data

Das, Anik; Khan, Nasim; Ahmed, Mohamed M.

doi:10.1177/0361198120914293

Cited by 29 publications

(20 citation statements)

References 36 publications

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“…The video data used in this study were collected from the SHRP2 NDS. Many previous studies have used this unique data to investigate driver behavior in an attempt to improve the safety of the roadways (13)(14)(15)(16)(17). The present NDS was conducted in six states in the United States from 2010 to 2013.…”

Section: Data Preparationmentioning

confidence: 99%

Development of a Novel Convolutional Neural Network Architecture Named RoadweatherNet for Trajectory-Level Weather Detection using SHRP2 Naturalistic Driving Data

Khan

Ahmed

2021

Transportation Research Record: Journal of the Transportation R

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Driver performances could be significantly impaired in adverse weather because of poor visibility and slippery roadways. Therefore, providing drivers with accurate weather information in real time is vital for safe driving. The state-of-practice of collecting roadway weather information is based on weather stations, which are expensive and cannot provide trajectory-level weather information. Therefore, the primary objective of this study was to develop an affordable detection system capable of providing trajectory-level weather information at the road surface level in real-time. This study utilized the Strategic Highway Research Program 2 Naturalistic Driving Study video data combined with a promising machine learning technique, called convolutional neural network (CNN), to develop a weather detection model with seven weather categories: clear, light rain, heavy rain, light snow, heavy snow, distant fog, and near fog. A novel CNN architecture, named RoadweatherNet, was carefully crafted to achieve the weather detection task. The evaluation results based on a test dataset revealed that RoadweatherNet can provide excellent performance in detecting weather conditions with an overall accuracy of 93%. The performance of RoadweatherNet was also compared with six pre-trained CNN models, namely, AlexNet, ResNet18, ResNet50, GoogLeNet, ShuffleNet, and SqueezeNet, which showed that RoadweatherNet can provide nearly identical performance with a significant reduction in training time. The proposed weather detection model is cost-efficient and requires less computational power; therefore, it can be made widely available mainly owing to the recent thriving of smartphone cameras and can be used to expand and update the current weather-based variable speed limit systems.

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Section: Data Preparationmentioning

confidence: 99%

Development of a Novel Convolutional Neural Network Architecture Named RoadweatherNet for Trajectory-Level Weather Detection using SHRP2 Naturalistic Driving Data

Khan

Ahmed

2021

Transportation Research Record: Journal of the Transportation R

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“…The focus of this study was not to compare which approach is superior but to demonstrate how both methods can provide researchers and transportation practitioners with in-depth understanding of the contributing factors that might affect driver speed selection behavior in adverse weather by leveraging both of their advantages. There are many advantages of using non-parametric models, such as superior performance in relation to prediction accuracy, no predetermined assumption related to data distribution, ability to handle Big Data effectively, and capability to uncover latent relationships among large numbers of variables (24,28). Since the SHRP2 NDS data are massive and the speed selection, especially in adverse weather, is a complex driving behavior, the nonparametric association rules mining method was used as it has no predefined assumptions, unlike parametric models including ordered logistic regression, and it can reveal unclear and complex relationships among variables in Big Data (29).…”

Section: Methodsmentioning

confidence: 99%

“…The RID contains high-quality roadway data, such as curve radius, grade, super-elevation, number of lanes, shoulder information, speed limit, and so forth, of the NDS states (17). Many previous studies have used these datasets to investigate the effect of adverse weather on traffic safety and driver behavior (18)(19)(20)(21)(22)(23)(24)(25).…”

Section: Data Preparationmentioning

confidence: 99%

Non-Parametric Association Rules Mining and Parametric Ordinal Logistic Regression for an In-Depth Investigation of Driver Speed Selection Behavior in Adverse Weather using SHRP2 Naturalistic Driving Study Data

Khan

Das

Ahmed

2020

Transportation Research Record: Journal of the Transportation R

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Human error is considered to be one of the major causes of crashes, especially in inclement weather. Although many studies have investigated the effect of adverse weather on traffic safety and operations, there is a lack of research into the differences in driving behavior and performance during adverse weather, particularly at a trajectory level. With this research gap in mind, this study presents a novel approach for an in-depth investigation of driver speed selection behavior in adverse weather utilizing trajectory-level data acquired from the SHRP2 Naturalistic Driving Study using a promising association rules data mining technique. The preliminary analysis revealed that drivers reduced their speeds by 3.9% in the presence of light rain, by 10.2% in heavy rain, 15.2% in light snow, 29.8% in heavy snow, 1.8% with distant fog, and 7.4% with near fog. The findings from the association rules mining approach indicated that driving more than 5 mph above the speed limit was closely associated with clear weather as well as young and inexperienced drivers; whereas a reduction in speed to more than 5 mph below the speed limit was closely associated with snowy road surfaces combined with affected visibility. These findings are also in line with the results from the ordered logistic regression, which revealed that drivers were 1.4 times more likely to reduce their speeds in light rain, 1.7 times in heavy rain, 4.3 in light snow, 12.2 in heavy snow, 1.7 with distant fog, and 2.0 with near fog. The findings from this study provide an unprecedented opportunity to develop a Human-in-the-Loop Variable Speed Limit algorithm.

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“…In this study, we assume that the lane change vehicle drivers accept the lead and lag gap when the front center of the lane change vehicle crosses the lane line. The time gaps are used in this paper because they can provide better representation of driver behavior than distance gaps ( 55 , 56 ). Lead gap and lag gap are defined as the time taken to traverse the longitudinal distance between the LCV and LV and between LCV and FV once LCV crosses the lane line.…”

Section: Gap Acceptancementioning

confidence: 99%

Characteristics of Heavy Vehicle Discretionary Lane Changing Based on Trajectory Data

Yang

2021

Transportation Research Record: Journal of the Transportation R

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A comprehensive analysis of the motivations, gap acceptance, duration, and speed adjustment of heavy vehicle lane changes (LC) is conducted in this paper. An rich data set containing 433 discretionary LC trajectories of heavy vehicles is used in this study and the data set is divided into two data sets based on the LC direction (LC to the left lane [LCLL] and LC to the right lane [LCRL]) for comparison. It is seen that LCLL and LCRL have significantly different motivations, which also results in different gap acceptance behavior. However, the LC direction does not significantly influence the LC duration. The navigation speed significantly influences the LC duration of heavy vehicles and the LC duration will decrease with the increase of speed, indicating the substantial impact of traffic conditions on LC duration. An obvious speed synchronization phenomenon is found in the process of LCLL, which is not the case in LCRL. The results of this study highlight the distinct characteristics of the LC of heavy vehicles and produce a better understanding of the lane-changing behaviors of heavy vehicles. The fitted distributions of LC duration and further investigation into gap acceptance behaviors may be used for microscopic traffic simulation and auto driving.

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Nonparametric Multivariate Adaptive Regression Splines Models for Investigating Lane-Changing Gap Acceptance Behavior Utilizing Strategic Highway Research Program 2 Naturalistic Driving Data

Cited by 29 publications

References 36 publications

Development of a Novel Convolutional Neural Network Architecture Named RoadweatherNet for Trajectory-Level Weather Detection using SHRP2 Naturalistic Driving Data

Development of a Novel Convolutional Neural Network Architecture Named RoadweatherNet for Trajectory-Level Weather Detection using SHRP2 Naturalistic Driving Data

Non-Parametric Association Rules Mining and Parametric Ordinal Logistic Regression for an In-Depth Investigation of Driver Speed Selection Behavior in Adverse Weather using SHRP2 Naturalistic Driving Study Data

Characteristics of Heavy Vehicle Discretionary Lane Changing Based on Trajectory Data

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