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

2020 ◽  
Vol 2674 (11) ◽  
pp. 101-119
Author(s):  
Md Nasim Khan ◽  
Anik Das ◽  
Mohamed M. Ahmed
Keyword(s):  
Association Rules ◽  
Heavy Rain ◽  
Speed Limit ◽  
Light Rain ◽  
Adverse Weather ◽  
Selection Behavior ◽  
Naturalistic Driving Study ◽  
Speed Selection ◽  
Naturalistic Driving ◽  
Heavy Snow

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.

Parametric Ordinal Logistic Regression and Non-Parametric Decision Tree Approaches for Assessing the Impact of Weather Conditions on Driver Speed Selection Using Naturalistic Driving Data

2018 ◽  
Vol 2672 (12) ◽  
pp. 137-147 ◽  
Author(s):  
Ali Ghasemzadeh ◽  
Britton E. Hammit ◽  
Mohamed M. Ahmed ◽  
Rhonda Kae Young
Keyword(s):  
Logistic Regression ◽  
Weather Conditions ◽  
Ordinal Logistic Regression ◽  
Speed Limit ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
Speed Selection ◽  
Naturalistic Driving ◽  
The Impact ◽  
Non Parametric

The impact of adverse weather conditions on transportation operation and safety is the focus of many studies; however, comprehensive research detailing the differences in driving behavior and performance during adverse conditions is limited. Many previous studies utilized aggregate traffic and weather data (e.g., average speed, headway, and global weather information) to formulate conclusions about the impact of weather on network operation and safety; however, research into specific factors associated with driver performance and behavior are notably absent. A novel approach, presented in this paper, fills this gap by considering disaggregate trajectory-level data available through the SHRP2 Naturalistic Driving Study and Roadway Information Database. Parametric ordinal logistic regression and non-parametric classification tree modeling were utilized to better understand speed selection behavior in adverse weather conditions. The results indicate that the most important factors impacting driver speed selection are weather conditions, traffic conditions, and the posted speed limit. Moreover, it was found that drivers are more likely to significantly reduce their speed in snowy weather conditions, as compared with other adverse weather conditions (such as rain and fog). The purpose of this study was to gather insights into driver speed preferences in different weather conditions, such that efficient logic can be introduced for a realistic variable speed limit system—aimed at maximizing speed compliance and reducing speed variations. This study provides valuable information related to drivers’ interaction with real-time changes in roadway and weather conditions, leading to a better understanding of the effectiveness of operational countermeasures.

Investigating the Impact of Rainfall on Travel Speed

2014 ◽  
Vol 71 (3) ◽  
Author(s):  
Nordiana Mashros ◽  
Johnnie Ben-Edigbe ◽  
Hashim Mohammed Alhassan ◽  
Sitti Asmah Hassan
Keyword(s):  
Empirical Studies ◽  
Heavy Rain ◽  
Weather Conditions ◽  
Travel Speed ◽  
Operating Conditions ◽  
Speed Reduction ◽  
Light Rain ◽  
Rainfall Condition ◽  
Adverse Weather ◽  
The Impact

The road network is particularly susceptible to adverse weather with a range of impacts when different weather conditions are experienced. Adverse weather often leads to decreases in traffic speed and subsequently affects the service levels. The paper is aimed at investigating the impact of rainfall on travel speed and quantifying the extent to which travel speed reduction occurs. Empirical studies were conducted on principle road in Terengganu and Johor, respectively for three months. Traffic data were collected by way of automatic traffic counter and rainfall data from the nearest raingauge station were supplied by the Department of Irrigation and Drainage supplemented by local survey data. These data were filtered to obtain traffic flow information for both dry and wet operating conditions and then were analyzed to see the effect of rainfall on percentile speeds. The results indicated that travel speed at 15th, 50th and 85th percentiles decrease with increasing rainfall intensities. It was observed that allpercentile speeds decreased from a minimum of 1% during light rain to a maximum of 14% during heavy rain. Based on the hypothesis that travel speed differ significantly between dry and rainfall condition; the study found substantial changes in percentile speeds and concluded that rainfalls irrespective of their intensities have significant impact on the travel speed.

Driver speed selection and crash risk: Insights from the naturalistic driving study

2017 ◽  
Vol 63 ◽  
pp. 187-194 ◽  
Author(s):  
Raha Hamzeie ◽  
Peter T. Savolainen ◽  
Timothy J. Gates
Keyword(s):  
Crash Risk ◽  
Naturalistic Driving Study ◽  
Speed Selection ◽  
Naturalistic Driving

scholarly journals Cloud-Top Temperatures for Precipitating Winter Clouds

2008 ◽  
Vol 47 (1) ◽  
pp. 351-359 ◽  
Author(s):  
Jay W. Hanna ◽  
David M. Schultz ◽  
Antonio R. Irving
Keyword(s):  
Heavy Rain ◽  
Maximum Growth ◽  
Cloud Microphysics ◽  
Infrared Band ◽  
Freezing Rain ◽  
Light Rain ◽  
Brightness Temperatures ◽  
Snow Distribution ◽  
Heavy Snow ◽  
Warm Rain Process

Abstract To explore the role of cloud microphysics in a large dataset of precipitating clouds, a 6-month dataset of satellite-derived cloud-top brightness temperatures from the longwave infrared band (channel 4) on the Geostationary Operational Environmental Satellite (GOES) is constructed over precipitation-reporting surface observation stations, producing 144 738 observations of snow, rain, freezing rain, and sleet. The distributions of cloud-top brightness temperatures were constructed for each precipitation type, as well as light, moderate, and heavy snow and rain. The light-snow distribution has a maximum at −16°C, whereas the moderate- and heavy-snow distributions have a bimodal distribution with a primary maximum around −16° to −23°C and a secondary maximum at −35° to −45°C. The light, moderate, and heavy rain, as well as the freezing rain and sleet, distributions are also bimodal with roughly the same temperature maxima, although the colder mode dominates when compared with the snow distributions. The colder of the bimodal peaks trends to lower temperatures with increasing rainfall intensity: −45°C for light rain, −47°C for moderate rain, and −50°C for heavy rain. Like the distributions for snow, the colder peak increases in amplitude relative to the warmer peak at heavier rainfall intensities. The steep slope in the snow distribution for cloud-tops warmer than −15°C is likely due to the combined effects of above-freezing cloud-top temperatures not producing snow, the activation of ice nuclei, the maximum growth rate for ice crystals at temperatures near −15°C, and ice multiplication processes from −3° to −8°C. In contrast, the rain distributions have a gentle slope toward higher cloud-top brightness temperatures (−5° to 0°C), likely due to the warm-rain process. Last, satellite-derived cloud-top brightness temperatures are compared with coincident radiosonde-derived cloud-top temperatures. Although most differences between these two are small, some are as large as ±60°C. The cause of these differences remains unclear, and several hypotheses are offered.

scholarly journals Speed Characteristics in Relation to Speed Limit Increase and Its Influence on Driver’s Speed Selection Behavior

2020 ◽  
Vol 12 (4) ◽  
pp. 1369
Author(s):  
Reza S. Shirazinejad ◽  
Sunanda Dissanayake
Keyword(s):  
Traffic Safety ◽  
Quality Measurement ◽  
Speed Limit ◽  
Test Results ◽  
Significant Difference ◽  
Selection Behavior ◽  
Before And After ◽  
Kolmogorov Smirnov ◽  
Speed Selection ◽  
And Control

Speed is a quality measurement for travel, since it is related to traffic, safety, time, and economics. The speed limit on selected freeways in Kansas changed from 70 mph to 75 mph in the summer of 2011. In this study, the driver’s speed selection behavior was analyzed by considering average speed and 85th percentile speed in the before and after periods. Data from Automatic Traffic Recorders (ATRs) on the sections affected by speed limit increase and sections with no speed limit increase were analyzed. The t-test was applied to investigate if there was any significant difference in the speed of drivers on both treated and control sections. The Kolmogorov-Smirnov (K-S) test was also conducted to see if the distribution of speed data in the before period was different than after the period. The results showed that for the majority of the sections affected by speed limit change, there was a statistically significant difference in the 85th percentile speed of drivers during after period. Additionally, the K-S test results showed that the distribution of speed data in the before period was different than after the period for the majority of treated sections. The results indicated how drivers’ behavior was influenced by the speed limit increase.

Using Naturalistic Driving Data to Develop a Typology of Speeding Episodes

2017 ◽  
Vol 2659 (1) ◽  
pp. 91-97 ◽  
Author(s):  
Christian M. Richard ◽  
James L. Brown ◽  
Randolph Atkins ◽  
Gautam Divekar
Keyword(s):  
United States ◽  
The United States ◽  
Speed Limit ◽  
Data Set ◽  
Slowing Down ◽  
Situational Characteristics ◽  
Duration Magnitude ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
Study Participants

Speeding-related crashes continue to be a serious problem in the United States. A recently completed NHTSA project, Motivations for Speeding, collected data to address questions about driver speeding behavior. This naturalistic driving study used 1-Hz GPS units to collect data from 88 drivers in Seattle, Washington, to record how fast vehicles traveled on different roadways. The current project further developed this data set to redefine speeding in terms of speeding episodes, which were continuous periods in which drivers exceeded the posted speed limit by at least 10 mph. More than half of all study participants averaged less than one speeding episode per trip taken. Various characteristics of speeding episodes representing aspects such as duration, magnitude, variability, and overall form of speeding were examined. Cluster analyses conducted using these characteristics of speeding episodes identified six types of speeding. These included two types of speeding that occurred around speed-zone transitions (speeding up and slowing down), incidental speeding, casual speeding, cruising speeding, and aggressive speeding. Qualitative examination of the speeding types indicated that these types also differed in terms of the prevalence of additional risky situational characteristics.

scholarly journals WeatherEye-Proposal of an Algorithm Able to Classify Weather Conditions from Traffic Camera Images

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 717
Author(s):  
Khouloud Dahmane ◽  
Pierre Duthon ◽  
Frédéric Bernardin ◽  
Michèle Colomb ◽  
Frédéric Chausse ◽  
...  
Keyword(s):  
Real Time ◽  
Road Traffic ◽  
Heavy Rain ◽  
Weather Conditions ◽  
Full Spectrum ◽  
Light Rain ◽  
Large Numbers ◽  
Adverse Weather ◽  
Essential Prerequisite ◽  
Local Weather

In road environments, real-time knowledge of local weather conditions is an essential prerequisite for addressing the twin challenges of enhancing road safety and avoiding congestions. Currently, the main means of quantifying weather conditions along a road network requires the installation of meteorological stations. Such stations are costly and must be maintained; however, large numbers of cameras are already installed on the roadside. A new artificial intelligence method that uses road traffic cameras and a convolution neural network to detect weather conditions has, therefore, been proposed. It addresses a clearly defined set of constraints relating to the ability to operate in real-time and to classify the full spectrum of meteorological conditions and order them according to their intensity. The method can differentiate between five weather conditions such as normal (no precipitation), heavy rain, light rain, heavy fog and light fog. The deep-learning method’s training and testing phases were conducted using a new database called the Cerema-AWH (Adverse Weather Highway) database. After several optimisation steps, the proposed method obtained an accuracy of 0.99 for classification.

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