Configuration Analysis of Two-Vehicle Rear-End Crashes

2003 ◽  
Vol 1840 (1) ◽  
pp. 140-147 ◽  
Author(s):  
Mohamed A. Abdel-Aty ◽  
Hassan T. Abdelwahab
Keyword(s):  
Statistical Models ◽  
Extreme Value ◽  
Passenger Cars ◽  
Passenger Car ◽  
Traffic Crash ◽  
Crash Data ◽  
Light Duty ◽  
Sight Distance ◽  
Multiple Categories

Light truck vehicles (LTVs), including light-duty trucks, vans, minivans, and sport-utility vehicles, are generally larger than common passenger cars and are able to take on additional tasks. LTVs usually ride higher than other common passenger cars, which likely affects the visibility of passenger car drivers. The role of LTVs in rear-end crashes was investigated. The use of statistical models of unordered multiple categories was attempted, including multinomial logit (MNL), heteroscedastic extreme value (HEV), and bivariate probit (BVP) models. Four different rear-end crash configurations (lead and following vehicles) were defined on the basis of the type of the two vehicles involved (LTV or regular passenger car). General Estimates System (GES 2000) traffic crash data were used to calibrate the three suggested models (the MNL, HEV, and BVP models). Modeling results showed that there are sight distance and discomfort problems when a driver in a regular passenger car is driving behind an LTV. The probability of a rear-end crash involving a regular passenger car striking an LTV increases when the driver of the following vehicle is distracted. The analysis also illustrates that the probability of a regular car striking an LTV increases when the driver of the following vehicle has an obscured view.

Does Separating Trucks from Other Traffic Improve Overall Safety?

2005 ◽  
Vol 1922 (1) ◽  
pp. 156-166 ◽  
Author(s):  
Dominique Lord ◽  
Dan Middleton ◽  
Jeffrey Whitacre
Keyword(s):  
Clear Understanding ◽  
Mixed Traffic ◽  
Contributing Factors ◽  
Truck Traffic ◽  
Passenger Cars ◽  
Construction Costs ◽  
Passenger Car ◽  
Good Opportunity ◽  
Crash Data ◽  
Vehicle Type

Decision makers have long speculated that building separate roads for trucks and passenger cars, or at least separating these into their own lanes, would accomplish two major objectives: (a) roadways would be made safer for passenger cars and (b) roadways designed specifically for a select class of vehicles rather than for all vehicles might represent overall savings in construction costs. This paper addresses the first objective. Recent studies on the evaluation of safety effects of truck traffic levels on general freeway facilities have not provided a clear understanding of how they affect the number of crashes. In some cases, studies have been contradictory. In addition, no studies have specifically compared passenger car–only with mixed-traffic freeway facilities. The research on which this paper is based aimed to assess whether more homogeneous flows of traffic by vehicle type are safer than the current mixed-flow scenario. An exploratory analysis of crash data was conducted on selected freeway sections of the New Jersey Turnpike for 2002. These sections operate as a dual–dual freeway facility: divided inner and outer lanes. At these locations, the inner lanes have the special characteristic of being for passenger cars only (homogeneous traffic). The selected sections, therefore, offer a very good opportunity to compare the crash experience between passenger car–only and mixed-traffic rural freeway facilities. The results of the study show that outer lanes experience more crashes, both when raw numbers are used and when exposure is included in the analysis. It was shown that truck-related crashes contribute significantly to the total number of crashes on the outer lanes. In fact, trucks are overinvolved in crashes given the exposure on these sections. Although the outcome of this study suggests that separating truck traffic from passenger cars for freeway facilities improves safety, further work is needed to understand the contributing factors leading to truck-related crashes in the outer lanes.

scholarly journals Comparative Analysis of China Light-duty Vehicle Test cycle for Passenger Car and Other Typical Driving Cycles

2021 ◽  
Vol 241 ◽  
pp. 02002
Author(s):  
Yu Liu ◽  
Yongkai Liang ◽  
Hanzhengnan Yu ◽  
Xiaopan An ◽  
Jingyuan Li
Keyword(s):  
Automobile Industry ◽  
Driving Cycle ◽  
Third Party ◽  
National Standard ◽  
Test Cycle ◽  
Passenger Cars ◽  
Passenger Car ◽  
Light Duty ◽  
Light Duty Vehicle ◽  
Vehicle Test

In 2019, China issued the first national standard for vehicle driving cycle, in which China light-duty vehicle test cycle for passenger car (CLTC-P) is the driving cycle for light-duty passenger cars. CLTC-P is of great significance to the development of China’s automobile industry, and has a great impact on the development and calibration of vehicles of automobile enterprises. In this paper, firstly, the driving characteristics of CLTC-P are analyzed systematically. Then it is compared with the third-party navigation big data to prove the rationality and effectiveness. Finally, CLTC-P is compared with other legal cycles in terms of time, distance, speed, and acceleration characteristics. The result shows that by comparing the characteristics of CLTC-P with other typical cycles and the GIS weighted results, the CLTC-P is more in line with Chinese reality and is significantly different from other typical cycles.

Driver Eye and Vehicle Heights for Use in Geometric Design

1998 ◽  
Vol 1612 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Kay Fitzpatrick ◽  
Torsten Lienau ◽  
Daniel B. Fambro
Keyword(s):  
Geometric Design ◽  
Design Criteria ◽  
Passenger Cars ◽  
Passenger Car ◽  
Sight Distance ◽  
Total Vehicle ◽  
Vehicle Fleet ◽  
Stopping Sight Distance

Driver eye, headlight, taillight, and vehicle heights are important elements for determining passing and intersection sight distances and horizontal and vertical curve lengths to provide required stopping sight distance. Driver eye and object heights have varied significantly since their inception in the 1920s, when their values were suggested as 1676 mm. The objective of this study was to determine appropriate driver eye, headlight, taillight, and vehicle heights for use in developing geometric design criteria. The results of this research were used to recommend a driver eye height of 1080 mm for design purposes. This value represents 90 percent of the passenger car driver eye height values and an even higher percentage of the total vehicle fleet, because passenger cars have the lowest driver eye height values and represent fewer than two-thirds of the total vehicle fleet. Headlight and taillight heights of 600 mm are recommended for design. These values represent over 90 and 95 percent of the passenger cars observed in this study, respectively. The vehicle height recommendation for sight distance was 1315 mm, which represents the 10th percentile passenger car height values measured in the research.

scholarly journals To LDT or Not to LDT: Assessment of Principal Impacts of Light-Duty Trucks

2000 ◽  
Vol 1738 (1) ◽  
pp. 3-10 ◽  
Author(s):  
Kara Maria Kockelman
Keyword(s):  
Fuel Economy ◽  
Sport Utility Vehicle ◽  
Passenger Cars ◽  
Passenger Car ◽  
True Cost ◽  
Safety Standards ◽  
Light Duty ◽  
Cost Differences ◽  
Utility Vehicle ◽  
Light Duty Truck

Light-duty truck classification allows manufacturers and owners to avoid a host of passenger-car regulations, including gas-guzzler taxes, safety standards, and more stringent emissions and fuel-economy standards. The distinct policies that govern light-duty trucks and passenger cars are described; the emissions, safety, and fuel economy differences that have resulted are evaluated; and the household use differences across such vehicles are investigated. The result is that when the average new pickup truck or sport-utility vehicle is compared with a passenger car, there appears to be an implicit subsidy of roughly $4,400 favoring the light-duty truck. When minivans are compared with passenger cars, this subsidy is estimated to be around $2,800. With more equitable vehicle regulations, it is likely that prices would more accurately reflect the true cost differences resulting from the use of these vehicles, causing light-duty trucks to lose some of their popularity or clean up their act.

Integrating Crime and Traffic Crash Data in Nashville

2010 ◽  
Author(s):  
Jason Wyatt ◽  
Michael Alexander
Keyword(s):  
Traffic Crash ◽  
Crash Data

scholarly journals Analysis of Severe Injuries in Crashes Involving Large Trucks Using K-Prototypes Clustering-Based GBDT Model

Safety ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 32
Author(s):  
Syed As-Sadeq Tahfim ◽  
Chen Yan
Keyword(s):  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
The United States ◽  
Clustering Methods ◽  
Severe Injuries ◽  
Traffic Crash ◽  
Crash Data ◽  
Boosted Decision Trees ◽  
The Individual ◽  
Crash Characteristics

The unobserved heterogeneity in traffic crash data hides certain relationships between the contributory factors and injury severity. The literature has been limited in exploring different types of clustering methods for the analysis of the injury severity in crashes involving large trucks. Additionally, the variability of data type in traffic crash data has rarely been addressed. This study explored the application of the k-prototypes clustering method to countermeasure the unobserved heterogeneity in large truck-involved crashes that had occurred in the United States between the period of 2016 to 2019. The study segmented the entire dataset (EDS) into three homogeneous clusters. Four gradient boosted decision trees (GBDT) models were developed on the EDS and individual clusters to predict the injury severity in crashes involving large trucks. The list of input features included crash characteristics, truck characteristics, roadway attributes, time and location of the crash, and environmental factors. Each cluster-based GBDT model was compared with the EDS-based model. Two of the three cluster-based models showed significant improvement in their predicting performances. Additionally, feature analysis using the SHAP (Shapley additive explanations) method identified few new important features in each cluster and showed that some features have a different degree of effects on severe injuries in the individual clusters. The current study concluded that the k-prototypes clustering-based GBDT model is a promising approach to reveal hidden insights, which can be used to improve safety measures, roadway conditions and policies for the prevention of severe injuries in crashes involving large trucks.

Revised Method for Calculating Departure Sight Distance at Two-Way Stop-Controlled (TWSC) Intersections

2021 ◽  
pp. 036119812110315
Author(s):  
Essam Dabbour ◽  
Said M. Easa
Keyword(s):  
Traffic Safety ◽  
Major Road ◽  
Light Duty ◽  
Sight Distance ◽  
Data Loggers ◽  
Global Positioning ◽  
Actual Field ◽  
System Data ◽  
Negative Impacts ◽  
Light Duty Vehicles

This paper introduces realistic acceleration profiles for light-duty vehicles departing from rest at two-way stop-controlled (TWSC) intersections where minor roads (controlled by stop signs) intersect with uncontrolled major roads. The new profiles are based on current vehicle characteristics and driver behavior patterns. They are established based on actual field data collected using global positioning system data loggers that recorded the positional and speed data of various experimental vehicles starting from rest at TWSC intersections. Acceleration profiles are established in this paper and are used to develop a revised method for calculating the departure sight distance at TWSC intersections. Design tables were created to provide realistic sight distance values at TWSC intersections for different design speeds and number of lanes on the major road. It was found that the current values of intersection sight distance suggested by the design guides may be inadequate. Such values may force some approaching drivers on the major road to reduce their speeds or move to different traffic lanes to avoid conflicting with the departing vehicles. These maneuvers may have negative impacts on traffic safety. Therefore, implementing the revised method for calculating intersection sight distance, as presented in this paper, may ultimately reduce traffic collisions at TWSC intersections.

ASYMMETRIES IN UNIVERSAL GRAMMAR The Role of Method and Statistics

2000 ◽  
Vol 22 (2) ◽  
pp. 209-228 ◽  
Author(s):  
John C. Paolillo
Keyword(s):  
Logistic Regression ◽  
Statistical Analysis ◽  
Statistical Models ◽  
Logistic Regression Model ◽  
Universal Grammar ◽  
Nonnative Speakers ◽  
Potential Explanation ◽  
Differential Learning ◽  
Grammaticality Judgments

Felix (1988) claimed to demonstrate that UG-based knowledge of grammaticality causes nonnative speakers (NNSs) to have more accurate grammaticality judgments on sentences that are ungrammatical according to UG than on those that are grammatical. Birdsong (1994) criticized the methodology employed, noting that it ignores “response bias” (a propensity to judge sentences as ungrammatical) as a potential explanation. Felix and Zobl (1994) dismissed this criticism as merely methodological. In this paper, Birdsong's criticism is upheld by considering a statistical model of the data. At the same time, a more complete logistic regression model allows a fuller statistical analysis, revealing tentative support for the asymmetry claim, as well as differential learning states for different constructions and a tendency toward transfer avoidance. These theoretically significant effects were unnoticed in the earlier discussion of this research. For SLA research on grammaticality judgments to proceed fruitfully, appropriate statistical models need to be considered in designing the research.

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