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.

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.

Measuring the Passenger Car Equivalent of Small Cars and SUVs on Rainy and Sunny Days

2018 ◽  
Vol 2672 (31) ◽  
pp. 110-119 ◽  
Author(s):  
Majid Zahiri ◽  
Xiqun (Michael) Chen
Keyword(s):  
Field Investigation ◽  
Urban Traffic ◽  
Heterogeneous Traffic ◽  
Motor Vehicles ◽  
Mixed Traffic ◽  
Passenger Cars ◽  
Passenger Car ◽  
Passenger Car Equivalent ◽  
The Government ◽  
Rainy Days

Traffic volume is a fundamental measurement in traffic analyses. In mixed traffic, vehicles vary in size, length, headway, spacing, and acceleration/deceleration. Therefore, if we can categorize the vehicles in mixed traffic in greater detail, the estimated passenger car equivalent (PCE) number will be more accurate. Practical and appropriate methods that convert different vehicles into the equivalent number of passenger cars need to be employed to determine PCE factors for heterogeneous traffic. Following economic growth and increased use of motor vehicles in developing countries, the purchase of sports utility vehicles (SUVs) continues to grow, though the government encourages people to buy small cars because of the limited road capacity, as well as air pollution problems. In this research, we categorize passenger cars into three subsets: small cars (hatchback cars without a trunk), SUVs, and standard cars (taxis and typical family cars). A field investigation shows that the penetration rates of these passenger cars are 12%, 23%, and 55%, respectively (10% are other vehicles) in Hangzhou, China. We also measure the PCE value for small cars and SUVs using the mean time headway method. Because different countries have different weather conditions, we continue to measure the PCE values for sunny days and moderate rainy days. The results show that PCE values for small cars and SUVs are 0.87 and 1.26 on sunny days, and 0.87 and 1.31 on rainy days, respectively. By using the PCE with high precision, urban managers can accomplish the analysis of urban traffic with greater accuracy.

scholarly journals Analysis of Vehicle-Following Heterogeneity Using Self-Organizing Feature Maps

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Jie Yang ◽  
Ruey Long Cheu ◽  
Xiucheng Guo ◽  
Alicia Romo
Keyword(s):  
Relative Velocity ◽  
Data Sets ◽  
Feature Maps ◽  
Passenger Cars ◽  
Passenger Car ◽  
Feature Map ◽  
Car Following ◽  
Vehicle Type ◽  
Vehicle Trajectories ◽  
Self Organizing

A self-organizing feature map (SOM) was used to represent vehicle-following and to analyze the heterogeneities in vehicle-following behavior. The SOM was constructed in such a way that the prototype vectors represented vehicle-following stimuli (the follower’s velocity, relative velocity, and gap) while the output signals represented the response (the follower’s acceleration). Vehicle trajectories collected at a northbound segment of Interstate 80 Freeway at Emeryville, CA, were used to train the SOM. The trajectory information of two selected pairs of passenger cars was then fed into the trained SOM to identify similar stimuli experienced by the followers. The observed responses, when the stimuli were classified by the SOM into the same category, were compared to discover the interdriver heterogeneity. The acceleration profile of another passenger car was analyzed in the same fashion to observe the interdriver heterogeneity. The distribution of responses derived from data sets of car-following-car and car-following-truck, respectively, was compared to ascertain inter-vehicle-type heterogeneity.

scholarly journals Two-Lane Highways Crest Curve Design. The Case Study of Italian Guidelines

2020 ◽  
Vol 10 (22) ◽  
pp. 8182
Author(s):  
Marco Guerrieri
Keyword(s):  
Body Height ◽  
Vehicle Body ◽  
Height Distribution ◽  
Passenger Cars ◽  
Construction Costs ◽  
Passenger Car ◽  
Curve Design ◽  
Statistical Sample

The main purpose of the research is to evaluate the crest vertical curves radii Rv, not considering a conventional value of the opposing vehicle height h2, but the average vehicle heights h2(m) and the value of the 15th percentile of the height distribution h2(15) of the passenger car population. The study only considered car models with more than 20,000 registered vehicles in Italy. One hundred and fifteen car models belonging to different brands were taken into consideration, for a total of over 9 million vehicles. For the statistical sample analyzed, the following vehicle heights were estimated: h2(m) = 1.48 m and h2(15) = 1.39 m. The deviations between the crest radii calculated with the Italian standard (h2 = 1.10 m), and those obtained for h2(m) = 1.48 m and h2(15) = 1.39 m are up to 12%. The differences ΔHv between the values of the visible vehicle body height Hv = Hv(t) calculated using, respectively, h2(15) = 1.39 m and h2(m) = 1.48 m are modest. The value h2(m) = 1.48 m could be adopted in order to reduce the highways construction costs. In fact, the research shows that the value h2 = 1.10 m is too conservative and leads to oversizing of the crest vertical curves. Therefore, it would be necessary to make an appropriate choice of h2 value in order to take into account the current heights of passenger cars.

Are Older Drivers Safe on Interchanges? Analyzing Driving Errors Causing Crashes

2021 ◽  
pp. 036119812110312
Author(s):  
Denis Elia Monyo ◽  
Henrick J. Haule ◽  
Angela E. Kitali ◽  
Thobias Sando
Keyword(s):  
Latent Class ◽  
Time Of Day ◽  
Older Drivers ◽  
Contributing Factors ◽  
Lighting Condition ◽  
Crash Data ◽  
Traffic Characteristics ◽  
Road Users ◽  
Traffic Conflicts ◽  
Penalized Logistic Regression

Older drivers are prone to driving errors that can lead to crashes. The risk of older drivers making errors increases in locations with complex roadway features and higher traffic conflicts. Interchanges are freeway locations with more driving challenges than other basic segments. Because of the growing population of older drivers, it is vital to understand driving errors that can lead to crashes on interchanges. This knowledge can assist in developing countermeasures that will ensure safety for all road users when navigating through interchanges. The goal of this study was to determine driver, environmental, roadway, and traffic characteristics that influence older drivers’ errors resulting in crashes along interchanges. The analysis was based on three years (2016–2018) of crash data from Florida. A two-step approach involving a latent class clustering analysis and the penalized logistic regression was used to investigate factors that influence driving errors made by older drivers on interchanges. This approach accounted for heterogeneity that exists in the crash data and enhanced the identification of contributing factors. The results revealed patterns that are not obvious without a two-step approach, including variables that were not significant in all crashes, but were significant in specific clusters. These factors included driver gender and interchange type. Results also showed that all other factors, including distracted driving, lighting condition, area type, speed limit, time of day, and horizontal alignment, were significant in all crashes and few specific clusters.

Measurement of reverberation time of a passenger car utilizing the wavelet filter bank

2005 ◽  
Vol 219 (3) ◽  
pp. 329-336 ◽  
Author(s):  
Sang-Kwon Lee ◽  
Dong-June Yu
Keyword(s):  
Filter Bank ◽  
Bandpass Filter ◽  
Wavelet Filter ◽  
Reverberation Time ◽  
Passenger Cars ◽  
Passenger Car ◽  
Concert Hall ◽  
Low Level ◽  
Wavelet Filter Bank

A few researchers have tried to find the measurement of the reverberation time of a passenger car. However, this has proved to be extremely difficult because the reverberation time of a passenger car is too short to measure using the traditional bandpass filter. If the reverberation time is very short, the product of the reverberation time ( T) and the bandwidth ( B) of the traditional bandpass filter is very small. The low level of the product BT required for the measurement of the reverberation time using the traditional bandpass filter is 16. In order to overcome this problem, the wavelet filter bank has been developed. In the paper, this new wavelet filter is employed to measure the reverberation times of five different classes of passenger car. The low level of the product BT required for the measurement of reverberation time using the wavelet filter is 4. Therefore, it was possible to measure the reverberation times of five passenger cars successfully using the new wavelet filter bank. It is found that the reverberation times measured in most passenger cars are around 0.04. It is a very short reverberation time compared with those of general acoustic rooms like a concert hall.

Cab Conceptual Design for the Equipment System Used for Passenger Car Ergonomic Design and Evaluation

2012 ◽  
Vol 215-216 ◽  
pp. 399-406
Author(s):  
Xiao Ming Du ◽  
Jin Dong Ren ◽  
Yong Qing Liu ◽  
Zhong Xian Chen ◽  
Shi Hai Li ◽  
...  
Keyword(s):  
Product Development ◽  
Cycle Time ◽  
Evaluation Method ◽  
Target Population ◽  
Steering Wheel ◽  
Passenger Cars ◽  
Passenger Car ◽  
Packaging Design ◽  
Development Cycle ◽  
Equipment System

Ergonomics is an important validation content in vehicle product development. The traditional evaluation method of ergonomics adopts physical mockups or prototypes, which is very costly, and is inconvenient to modify, and usually cause prolonged development cycle-time. In this paper, in conjunction with enterprise’s requirements, an ergonomic validation equipment system, which has adjustable main parts, was developed. Firstly, requirements were analyzed, and target model cars ranges were determined, and ergonomic contents which can be validated using this equipment were clarified. The packaging characteristic of the passenger car was analyzed, and the reference fiducial marks system used for packaging design were determined. Based on the requirement of occupant accommodation, anthropometries of target population were statistically analyzed. By using revised SAE J1517 H-Point curves models, the drivers’ seating space was designed, as well as the requirement of the driver seat track travel. Based on the analysis of packaging data of passenger cars, and considering the possible changes, controls such as steering wheel, pedals, sticks, etc., were packaged.

scholarly journals Hydrogen Mobility Europe (H2ME): Vehicle and Hydrogen Refuelling Station Deployment Results

2018 ◽  
Vol 9 (1) ◽  
pp. 2 ◽  
Author(s):  
Peter Speers
Keyword(s):  
Fuel Cells ◽  
Fuel Cell ◽  
Electric Vehicles ◽  
First Year ◽  
Passenger Cars ◽  
Passenger Car ◽  
Cell Range ◽  
Fuel Cell Electric Vehicles ◽  
Light Vehicle

Hydrogen Mobility Europe (H2ME, 2015–2022) is the largest European Fuel Cells and Hydrogen Joint Undertaking (EU FCH JU)-funded hydrogen light vehicle and infrastructure demonstration. Up until April 2017, the 40 Daimler passenger car fuel cell electric vehicles (FCEVs) and 62 Symbio Fuel Cell-Range Extended Electric Vans (FC-REEV)-vans deployed by the project drove 625,300 km and consumed a total of 7900 kg of hydrogen with no safety incidents. During its first year of operation (to April 2017), the NEL Hydrogen Fueling HRS (hydrogen refuelling station) in Kolding, Denmark dispensed 900 kg of hydrogen, and demonstrated excellent reliability (98.2% availability) with no safety incidents. The average hydrogen refuelling time for passenger cars is comparable to that for conventional vehicles (2–3 min).

scholarly journals Ford and the New Auto Industry

2019 ◽  
Author(s):  
Jose Daniel Arroyo
Keyword(s):  
Market Share ◽  
Mainland China ◽  
Ford Motor Company ◽  
Passenger Cars ◽  
Passenger Car ◽  
Market Expansion ◽  
Car Market ◽  
New Vision ◽  
Development Approach ◽  
The Masses

The iconic Ford Motor Company is considered one of the pillars of the automotive industry. Its first vehicle, the Model T, introduced the concept of mass production and automobile affordability to the masses. Today, the market is full of domestic and foreign competitors, yet, Ford has struggled to remain competitive, even though it promotes itself as America’s best-selling brand. Its stock was recently downgraded, and recently, it announced its intentions to only sell sports utility vehicles, trucks, Mustangs and Focus, in other words, it will stop selling most of its passenger cars. Conversely, Toyota continues to lead the market in passenger car sales, while newcomer Tesla struggles to deliver on its backlog of 500,000 electric vehicle orders. Ford has strong brand equity, has automated production facilities and a large franchised dealer network across the globe. Yet, it is rapidly losing passenger car market share, its profits are eroding, and its new vision is confusing. Furthermore, the company is facing a significant threat from well capitalized new competitors entering from the tech industry. The company, on the other hand, has enjoyed significant success in mainland China, where the demand for American automobiles continuous to be strong. Yet, the company needs to assume a cost leadership position and attempt to use this strategy to increase market share. The contention in this paper is that an adequate approach for the company appears to be a combination of market penetration in the domestic front, market expansion in Asia and other parts of the globe, and a product development approach that ushers cost reductions.

scholarly journals 1 DESIGN OF MODULAR ARCHITECTURE FOR EV AND HEV FOR PASSENGER CARS

2021 ◽  
Author(s):  
Midhun Muraleedharan ◽  
◽  
Amitabh Das ◽  
Dr. Mohammad Rafiq Agrewale ◽  
Dr. K.C. Vora ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Fuel Consumption ◽  
Modular Architecture ◽  
Passenger Cars ◽  
Passenger Car ◽  
Vehicle Performance ◽  
Matlab Simulink ◽  
System Designer ◽  
Battery Energy

Hybridization is important to obtain the advantages of both the engine and motor as the sources of propulsion. This paper discusses the effect of hybridization of powertrain on vehicle performance. The Hybrid architectures are differentiated on the basis percentage of power dependency on the engine and motor. Passenger car with hybridization ratios of 20%, 40%, 60%, 80% and 100% are modelled on MATLAB/Simulink using the backward facing approach with the engine and motor specifications remaining constant. The hybridizations ratios and the energy consumption in terms of fuel and battery energy are obtained from the model and compared. Neural network is implemented to determine the fuel consumption. The outputs can be used by a system designer to determine a desirable hybridization factor based on the requirements dictated by the specific application.

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