Highway Speeds Research Articles

Repeatability and Agreement of Various High-Speed Macrotexture Measurement Devices

Five high-speed macrotexture measurement devices were tested on a variety of asphalt and Portland cement concrete pavement surfaces to evaluate their repeatability and their pairwise agreement. Experiments were run under three speed conditions: highway speed, varying constant speeds, and various acceleration and deceleration profiles. Data were processed and reduced per current industry standards. A novel approach for outlier removal from line laser devices was developed. The pairwise device agreement was evaluated using a limits of agreement analysis. The results demonstrate good repeatability for each of the devices tested. The agreement analysis showed that not all high-speed distance triangulation devices can be used interchangeably for all pavement surfaces. Data acquisition speed was found to be a factor in macrotexture parameter calculation for two of the five devices. The effect of speed was found to be worse on randomly textured surfaces than on transversely textured surfaces. Finally, acceleration was shown to have an effect on the parameters produced by one of the devices, giving further credence to the fact that care should be taken to gather high-quality datasets for the critical pavement characteristic of macrotexture.

Redundancy-Reducing and Holiday Speed Prediction Based on Highway Traffic Speed Data

The accurate prediction of the highway traffic speed plays an important role in improving the production efficiency and the convenience of lives, and it is also one of the important issues in vehicle social big data analyses. Aiming at the problem of highway traffic speed data preprocessing, this paper proposes the algorithm of Redundant Data Reducing (RDR) that can greatly reduce the amount of data in model training of long short-term memory (LSTM) and improve the training speed under the condition that the influence on prediction accuracy is small and controllable. Aiming at the problem of low prediction accuracy for holiday traffic speeds due to small data volume, this paper proposes the Segment Prediction Algorithm (SPA) based on the speed features, which can effectively improve the prediction accuracy. The experimental results show that RDR can reduce the training data by up to 60% without notably affecting the prediction accuracy, while it improves the training speed by 60%. Compared with the current LSTM algorithm, the prediction accuracy of SPA improves significantly.

Effect of inclined angle on a vehicle-passenger-inclined highway bridge

This paper presents a theoretical analysis of the dynamic interaction of a vehicle-passenger-inclined highway bridge. The mathematical vehicle-passenger model has 12 degrees of freedom: a vehicle model with four masses has six degrees of freedom, and an occupant model with four masses, which is the hinged beam model, has six degrees of freedom. The modal analysis technique using equations of motion are adapted for an inclined highway bridge. By considering both longitudinal and transverse vibrations, the governing differential equations of motion for the bridge are derived. The random roughness of an actual highway surface has been considered in the analysis. During application of the analytical system, the effects of an inclined angle of the highway bridge and speed parameter on vehicle and human dynamic behavior are investigated. The inclined angle is examined in the range of -20° to +20°, and the speed parameter is examined in the range of 0.02-1.5. These factors are found to exert significant influence on the mechanical behavior of vehicles, passenger, and inclined highway bridge. Some results are presented in the form of parametric plots.

A Novel Method to Integrate Speed and Emissions Characteristics in Pavement Marking Design

Pavement marking is a popular method to reduce freeway off-ramp speed. The objective of this study is to develop a methodology for selecting the optimal pavement marking design that will improve traffic safety and reduce emission levels in the freeway off-ramp. Six pavement marking pattern scenarios on a freeway off-ramp were examined through the following sub-tasks. The first step focused on the testing of six selected scenarios for pavement marking design using a VR driving simulator to obtain driving behavior patterns. Next, the driving behavior pattern for each scenario was implemented in VISSIM, a microscopic traffic flow simulation, to extract the speed reduction for each scenario considering vehicle interactions. Then, the vehicle emission level of each scenario was evaluated by a Motor Vehicle Emission Simulator (MOVES). Through a multiple-criteria decision making model, the best pavement marking design scenario that was both eco-friendly and effective in reducing vehicle speed was selected. Study results showed that transverse line type is the optimal design of pavement marking that improves traffic safety while decreasing emission at a freeway off-ramp. The proposed methodology in this study would quantitatively support the selection of optimal pavement marking design that will promote both traffic safety and traffic environment.

ОСНОВНЫЕ ЭТАПЫ РАЗВИТИЯ НАУЧНОЙ МЫСЛИ ПО ПРОГНОЗИРОВАНИЮ ПАРАМЕТРОВ СЕТИ АВТОМОБИЛЬНЫХ ДОРОГ

In this work there is considered a parameter prediction of highway systems and methods used at the account of traffic intensity. A comprehensive model of the transport system structural diagram of the Republic and the interconnec-tion between its separate elements with the notation of organization environment of a traffic system with its constituents is shown. It allows solving problems of traffic organization and ensuring traffic safety at mini-mum costs scientifically substantiated.
 On the basis of the study of qualitative and quantitative changes between a control system and a controlled one for traffic system efficient functioning there are defined advisable and efficient solutions. The characteristic of traffic system parameters is shown which is presented by three groups including a charac-teristic of highway states, structure and transport ve-hicle speed, traffic intensity and the amount of research works. There are emphasized and described typical peculiarities of methods used at the prediction of traffic intensity, traffic speed, and at the elimination of exist-ing drawbacks. The analysis of considered methods, parameters, models and assessments on a long-term prediction of highway systems allowed defining advantages and dis-advantages of existing methods, revealing a basic tool of all methods – a diagram of extrapolation, offering a suitable method with the purpose of current drawback elimination, presenting a model (formula) for a long-term prediction of a traffic speed, defining the most promising method for a long-term forecast of design loads. The authors of the paper have offered a model for a long-term prediction of a traffic speed, there is considered and characterized an attitude in the system of “man-motor car- traffic environment” in the course of its evolution.

Road Safety Impact of Increased Rural Highway Speed Limits in British Columbia, Canada

Control of vehicle speed is a central tenet of the safe systems approach to road safety. Most research shows that raising speed limits results in more injuries. Advocates of higher speed limits argue that this conclusion is based on older research, that traffic fatalities are decreasing despite higher speed limits, and that modern vehicles are able to safely travel at higher speeds. These arguments were used to justify raising speed limits on rural highways in British Columbia, Canada (July 2014). We used an interrupted time series approach to evaluate the impact of these speed limit increases on fatal crashes, auto-insurance claims, and ambulance dispatches for road trauma. Events were mapped to affected road segments (with increased speed limits) and to nearby road segments (within 5 km of an affected segment). Separate linear regression models were fitted for each outcome and road segment group. Models included gasoline sales to account for changes in vehicle travel. Our main findings were significant increases in (i) total insurance claims (43.0%; 95% Confidence Interval [CI] = 16.0–76.4%), (ii) injury claims (30.0%; 95% CI = 9.5–54.2%), and (iii) fatal crashes (118.0; 95% CI = 10.9–225.1%) on affected road segments. Nearby segments had a 25.7% increase in insurance claims (95% CI = 16.1–36.1%).

Pavement skid resistance as a function of pavement surface and aggregate texture properties

ABSTRACT Skid resistance depends on the macro- and micro-textural characteristics of pavement surface. Mean profile depth (MPD) is a widely used two-dimensional macro-texture indicator calculated from a single pavement surface profile, while pavement micro-texture is primarily affected by the characteristics of aggregates contained within the surface. In this study, twenty-two pavement sites, constructed using six common types of preventive treatments and eight typical sources of aggregates in Oklahoma, are selected as the field test beds. Pavement skid resistance and surface macro-texture data are collected in parallel at highway speeds using a grip tester and a high speed texture profiler, while aggregate properties under 3D are measured using a portable ultra-high resolution 3D laser imaging scanner. MPD values are calculated to represent surface macro-texture. Thirty-one 3D aggregate parameters in four categories (textural, feature, height, and material ratio & volume parameters) are investigated to characterise micro-texture attributes. The Pearson correlation coefficient matrix is computed within each category to exclude the parameters with strong correlations and remove their potential multi-collinearity during friction model development. Multivariate analysis is subsequently performed to examine the relationship between pavement skid resistance, and surface and aggregate texture properties. Eight texture parameters have showed statistical significance on pavement friction. Entropy (Tetp textural parameter) and peak curvature (Spc feature based parameter) present the most significant influences on pavement friction, while MPD demonstrates the least impact. The developed model based on surface and aggregate texture properties can better predict pavement friction performance. The comprehensive analysis of texture properties could assist highway agencies understanding the appropriate characterisation of aggregates and developing aggregate selection guideline for optimised skid resistance.

Development and Implementation of a Pavement Marking Management System in Florida

The visibility of pavement markings is an important aspect of a safe transportation system as the markings convey vital roadway warnings and guidance information to the traveling public. Therefore, it is beneficial to maintain acceptable visibility levels of markings on pavements under all weather and lighting conditions. To ensure the intended in-service visibility level is adequately maintained, the reflectivity must be monitored and quantified accordingly. Historically, visibility or retroreflectivity of in-service pavement markings has been measured with handheld devices and visual inspections. However, visual surveys are considered subjective and the handheld measurements are tedious and potentially hazardous. Consequently, the Florida Department of Transportation (FDOT) has focused on the use of a non-contact technology capable of assessing pavement markings continuously at highway speeds with improved safety and efficiency. The use of mobile technology for measuring reflectivity has allowed FDOT to develop and, subsequently, implement a Pavement Marking Management System (PMMS) to improve the safety and nighttime visibility of its roadways. Implementation of such a system provides an efficient and less subjective methodology to identify conditions that are detrimental to roadway safety, and strategize mitigating solutions including the selection of appropriate materials and application techniques. The system will ultimately result in an effective use of state funds while ensuring the safety of the traveling public. This paper presents a description of the Florida Pavement Markings Management System and its subsequent implementation including FDOT’s effort to ensure the quality, consistency, repeatability, and accessibility of statewide pavement marking retroreflectivity data.

Laser–visual–inertial odometry and mapping with high robustness and low drift

AbstractWe present a data processing pipeline to online estimate ego‐motion and build a map of the traversed environment, leveraging data from a 3D laser scanner, a camera, and an inertial measurement unit (IMU). Different from traditional methods that use a Kalman filter or factor‐graph optimization, the proposed method employs a sequential, multilayer processing pipeline, solving for motion from coarse to fine. Starting with IMU mechanization for motion prediction, a visual–inertial coupled method estimates motion; then, a scan matching method further refines the motion estimates and registers maps. The resulting system enables high‐frequency, low‐latency ego‐motion estimation, along with dense, accurate 3D map registration. Further, the method is capable of handling sensor degradation by automatic reconfiguration bypassing failure modules. Therefore, it can operate in the presence of highly dynamic motion as well as in the dark, texture‐less, and structure‐less environments. During experiments, the method demonstrates 0.22% of relative position drift over 9.3 km of navigation and robustness w.r.t. running, jumping, and even highway speed driving (up to 33 m/s).

B-WIM SYSTEM USING FEWER SENSORS

Bridge Weigh-in-Motion (B-WIM) is the concept of using measured response on a bridge to calculate the static weights of passing traffic loads as they pass overhead at full highway speed. This paper describes an enhancement to the Moving Force Identification (MFI) algorithm by estimating the response of some DOFs using limited number of measurements in order to increase measurements number (Input). The pseudoinverse of the mode shape matrix has been utilized to approximately calculate the modal response using limited measured response. Then the calculated modal response has been used to estimate more DOFs that are different from the measured one. The proper orthogonal decomposition (POD) technique is employed to determine the governing modes that increase the modal response accuracy. Numerical example for quarter car model passing over simply supported bridge has been established to demonstrate the idea.

2287 - Speed Limit Policy in Michigan: The Effects on Air Pollution and Human Health

Highest Scoring Abstract: Doctoral Background In 2017, Michigan raised the speed limit from 70 to 75 mph for passenger vehicles on 614 miles of freeways and raised the truck speed limit from 60 to 65 mph on all freeways statewide. While most of the focus of this policy change has focused on weighing the cost of safety and fuel consumption with the benefit of reduced travel times, little attention has been paid to the impact this policy will have on air pollution and human health. Methods Speed data were collected using LIDAR speed detectors from a vehicle not readily visible to motorists. At each site, it was attempted to collect 100 readings for passenger vehicles and 10 readings for trucks in each direction, alongside volume counts. Before data consisted of 85 sites (59 study sites and 26 control sites); after data were collected for 42 sites (19 control sites and 23 increase sites). Average speed data were weighted by volume, which was used to estimate fuel economy of vehicles. Exposure data was then used to estimate the increase in fuel consumption and literature values were used to estimate the impacts to human health and air pollution. Results A t-test demonstrated that there was a significant increase (p Conclusions Operating speeds increased for passenger cars along study corridors, and truck operating speeds increased statewide after freeway speed limits increased. This resulted in fuel economy declining, and consequently, emissions increasing. The increase in PM2.5 emissions is the most concerning emissions increase due to the large cost to human health.

2287 - Speed Limit Policy in Michigan: The Effects on Air Pollution and Human Health

In 2017, Michigan raised the speed limit from 70 to 75 mph for passenger vehicles on 614 miles of freeways and raised the truck speed limit from 60 to 65 mph on all freeways statewide. While most of the focus of this policy change has focused on weighing the cost of safety and fuel consumption with the benefit of reduced travel times, little attention has been paid to the impact this policy will have on air pollution and human health. Speed data were collected using LIDAR speed detectors from a vehicle not readily visible to motorists. At each site, it was attempted to collect 100 readings for passenger vehicles and 10 readings for trucks in each direction, alongside volume counts. Before data consisted of 85 sites (59 study sites and 26 control sites); after data were collected for 42 sites (19 control sites and 23 increase sites). Average speed data were weighted by volume, which was used to estimate fuel economy of vehicles. Exposure data was then used to estimate the increase in fuel consumption and literature values were used to estimate the impacts to human health and air pollution. A t-test demonstrated that there was a significant increase (p<0.0001) in passenger car speeds on study segments, while speeds on control segments did not change (p=0.997). Speeds for trucks, however, significantly increased for both control and study segments (p<0.0001), which is to be expected, as the speed limit for trucks increased statewide. Passenger car speeds increased from an average of 72.0 to 74.0 mph while truck speeds increased from 61.1 to 62.7 mph along study corridors and from 61.0 to 62.5 mph along control corridors. Passenger car fuel economy did not change along control corridors and declined from 24.2 to 23.4 mpgUS along study corridors. Truck fuel economy declined from 6.4 to 6.2 mpgUS statewide. Ultimately, it was estimated that CO emissions will increase by 1,490 tonsUS, NOx by 213 tonsUS, PM2.5 by 3.32 tonsUS, and CO2 by 77,100 tonsUS annually. The greatest concern for human health is PM2.5, with costs to human health of $88,000-$130,000/metric ton. Operating speeds increased for passenger cars along study corridors, and truck operating speeds increased statewide after freeway speed limits increased. This resulted in fuel economy declining, and consequently, emissions increasing. The increase in PM2.5 emissions is the most concerning emissions increase due to the large cost to human health.

The conditional probability of travel speed and its application to short-term prediction

Over the past a few decades, a large number of researchers have worked on embedding mathematical methods into the prediction of traffic conditions. However, a lot of previous work only considered the temporal and spatial covariates as static explanatory variables. Different from previous studies, this paper investigates the conditional probability of freeway travel speed based on its lag values. Such conditional probability can be regarded as an attribute for each road segment. Based on this attribute, this paper proposes a conditional cumulative distribution function (CCDF)-based travel speed prediction approach. A real-world case study is conducted to predict short-term freeway speeds. The proposed CCDF-based model is compared with the Neural Networks (NN) model and the Support Vector Regression (SVR) model. The proposed model is faster in model training. Moreover, it can significantly improve the prediction accuracy, with 7.11% accuracy increase, compared to traditional NN and SVR prediction approaches.

Aerodynamic Analysis of Cooling Airflow for Different Front-End Designs of a Heavy-Duty Cab-Over-Engine Truck

Improving the aerodynamics of heavy trucks is an important consideration in the strive for more energy-efficient vehicles. Cooling drag is one part of the total aerodynamic resistance acting on a vehicle, which arises as a consequence of air flowing through the grille area, the heat exchangers, and the irregular under-hood area. Today cooling packages of heavy trucks are dimensioned for a critical cooling case, typically when the vehicle is driving fully laden, at low speed up a steep hill. However, for long-haul trucks, mostly operating at highway speeds on mostly level roads, it may not be necessary to have all the cooling airflow from an open-grille configuration. It can therefore be desirable for fuel consumption purposes, to shut off the entire cooling airflow, or a portion of it, under certain driving conditions dictated by the cooling demands. In Europe, most trucks operating on the roads are of cab-over-engine type, as a consequence of the length legislations present. However, there are new directions from the European Union, which would permit slightly longer vehicles to improve aerodynamics and also to allow for a safer, more environmentally friendly vehicle. The truck design, where a cab-over-engine cab has an elongated front, is often referred to as a Soft Nose, where, as the name implies, the nose should be “soft” to improve the safety for pedestrians and also for car occupants in the event of a collision. This paper deals with the analysis of cooling airflow for two different front-end designs of a heavy truck. The first design is a cab-over-engine cab; the second is a Soft Nose cab, which in this case is basically an elongation of the grille area of the cab-over-engine cab to obtain a smoother shape of the cab. The Soft Nose model used in this investigation was extended 200 mm from the cab-over-engine front. Computational Fluid Dynamics was used as the tool for examining the aerodynamic properties of the vehicle models. A steady RANS-based approach was conducted, based on the method evaluated in previous work performed by the authors. The cab-over-engine and Soft Nose models were evaluated in an open-road environment. The configurations were evaluated both with inactive and active heat exchangers, in order to examine the effect of heating the air on the drag co-efficient and also to determine the cooling capacity of the different models. A sub- study was performed where different opening percentages of the grille area was investigated to determine the minimum percentage opening that would be needed to achieve a radiator Top Tank Temperature value below a target limit of 100 °C. The results show that there was potential for drag reductions for the Soft Nose model used. The cooling airflow was different for the cab-over-engine and Soft Nose models; as a consequence of the longer distance between the grille and cooling package, less air entered the cooling module for the Soft Nose model. A large portion of the airflow entering the grille leaked around the cooling module for the Soft Nose model. Also, following on from the reduced airflow through the cooling package, the radiator Top Tank Temperature values were considerably increased with the Soft Nose model. It was also shown that for the specific driving condition simulated here, an opening of 17.5% of the grille area was required to ensure sufficient cooling capacity. An interesting continuation of the cooling airflow analysis of the Soft Nose model would be to add ducts, guiding the air from the grille to the cooling module, to investigate if leakage could be reduced and cooling capacity increased.

An insight of World Health Organization (WHO) accident database by cluster analysis with self-organizing map (SOM)

ABSTRACTObjective: Road crashes are increasing every year in low- and middle-income countries compared to high-income countries, which show decreasing trends. One theory may be because of differences in enforcement of laws, vehicle safety, road standards, and many other factors. A detailed review was made of 5 death trends (total number of deaths/100,000 population and percentages of 4-wheeler, pedestrian, motorized 2/3-wheeler, bicyclist) and the underlying patterns in different countries and regions across the world. This review was done to understand the main reasons for the variances to focus on efficient improvement strategies related to future vehicle and road safety issues.Methods: A self-organizing map (SOM) technique is used to map the nonlinear relationships among different attributes. Overall, 176 countries with 44 attributes were considered from the World Health Organization's (WHO) Global Status Report on Road Traffic Crashes database. Of the 44 attributes, 5 related to accident deaths were considered as response attributes.Results: Very distinct and unique cause–effect patterns for 3 clusters were observed from SOM results. High-income countries were found to have a lower total number of deaths/100,000 population. One theory espouses that this was due to those countries maintaining high vehicle standards and policies, whereas it was quite a different situation for low-income countries. Even though helmet laws were available in Association of South East Asian Nations + 6 (ASEAN + 6) countries, the percentage of 2/3-wheeler deaths may be higher due a lack of enforcement of those laws. Percentage of deaths involving 4-wheeler vehicles was higher in certain countries in the Persian Gulf, the United States, Australia, and New Zealand. This may be due to the fact that these countries have a number of rural areas where drivers drive at highway speeds versus some lower income countries with more urban areas where drivers operate vehicles at slower speeds. Countries with a lack of laws protecting bicyclists saw higher death percentages among bicyclists. The percentage of bicyclist deaths was also higher in areas with no helmet requirement and no investment in infrastructure improvements. The percentage of pedestrian deaths was high when there was no policy to separate road users, especially in low-income African countries. Deaths can be reduced by enforcement of laws and practicing good safety standards related to road traffic.Conclusions: Future vehicle and road safety strategies should consider using advanced statistical tools like SOM to advance safety. Based on a triple-layer (vehicle, infrastructure, and society) safety approach, strict regulations and enforcement are effective measures to reduce fatalities in low- and middle-income countries. On the other hand, introduction of more advanced vehicle technologies will be useful in countries with high gross national incomes (GNIs). Hence, a proper balance of different countermeasures based on economic zones could be effective to reduce total world traffic casualties.

Computation of road geometry parameters using mobile LiDAR system

The continuous assessment of road geometry parameters is important for road condition analysis and the required maintenance work. Mobile LiDAR system (MLS) is being frequently used for roadway data collection due to its capability of accurate and dense three-dimensional data acquisition at highway speed. The high-precision road terrain obtained in terms of the point cloud is used as primary data for road geometry parameters computation. An automatic method is proposed in this research paper for computing road width, road centerline, longitudinal slope and cross slope. Initially, an equation of the line as a best-fit polynomial is used to represent road boundary as connected piecewise linear segments. Further road width and road centerline are computed. MLS data points at road boundary and road centerline are used to compute road slopes. The root mean square error of 2.1%, 2.5%, and 2.2% for longitudinal slope, cross slope (left lane), and cross slope (right lane), respectively were obtained. Proposed method works efficiently on the MLS data points of complex and heterogeneous roadway scene.

Physiological Information for Pavement Ride Quality Verification

Abstract Heart rate variability (HRV) refers to the beat-to-beat fluctuations in heart rate, which is one of the most promising physiological indicators reflecting mental stress of humans. It has different spectral components in the high and low frequency ranges that are 0.15–0.4 Hz and 0.04–0.15 Hz, respectively. One of the interesting features of HRV is that mental stress inactivates the high frequency (HF) spectral oscillations depending on its severity. The purpose of this study was to verify pavement ride quality ratings based on HRV. For this purpose, a sophisticated driving simulator (KITDS: Kitami Institute of Driving Simulator) was employed and was confirmed to be applicable to physiological testing for pavement ride quality. The result of the simulator experiment showed that the HF oscillations in the time-frequency plane analyzed by continuous wavelet transform decreased with the increase of surface roughness in terms of the international roughness index (IRI). This phenomenon was validated by the on-site experiment using an actual vehicle prior to the ride quality verification study using the KITDS. In the verification study, we observed a particular case of which the subjective ratings were inconsistent with physiological stress signs in terms of the HF oscillations. Although the traditional questionnaire-based testing allows direct measurement of the ride quality of road users, in such a case, the participants of the experiment are unconsciously exposed to mental stress regarding ride quality. The experiment result indicated that 70 % of the participants suffered the unconscious mental stress. To improve this inconsistency, we categorized subjective ratings into three categories depending on the HF oscillations of HRV. According to the result, we suggested that the IRI of 3 mm/m was reasonable as a threshold of pavement ride quality acceptance at the highway speed of 80 km/h.

AI-Based Malicious Network Traffic Detection in VANETs

Inherent unreliability of wireless communications may have crucial consequences when safety-critical C-ITS applications enabled by VANETs are concerned. Although natural sources of packet losses in VANETs such as network traffic congestion are handled by decentralized congestion control (DCC), losses caused by malicious interference need to be controlled too. For example, jamming DoS attacks on CAMs may endanger vehicular safety, and first and foremost are to be detected in real time. Our first goal is to discuss key literature on jamming modeling in VANETs and revisit some existing detection methods. Our second goal is to present and evaluate our own recent results on how to address the real-time jamming detection problem in V2X safety-critical scenarios with the use of AI. We conclude that our hybrid jamming detector, which combines statistical network traffic analysis with data mining methods, allows the achievement of acceptable performance even when random jitter accompanies the generation of CAMs, which complicates the analysis of the reasons for their losses in VANETs. The use case of the study is a challenging platooning C-ITS application, where V2X-enabled vehicles move together at highway speeds with short inter-vehicle gaps.

Network-wide traffic state estimation using a mixture Gaussian graphical model and graphical lasso

This study proposes a model that estimates unobserved highway link speeds by a machine learning technique using historical probe vehicle data. For highway traffic monitoring, probe vehicle data is one of the most promising data source. However, since such data do not always cover an entire study area, we cannot measure traffic speeds on all links in a time-dependent manner; quite a few links are unobserved. To continuously monitor speeds on all links, it is necessary to develop a technique that estimates speeds on unobserved links from historical observed link speeds. For this purpose, we extend the current Gaussian graphical model so as to use two or more multivariate normal distributions to accurately estimate unobserved link speeds. In general, since the number of unknown model parameters (mean parameters and covariance matrices) is enormous and also unobserved links always exist, the EM algorithm and the graphical lasso technique are employed to determine the model parameters. Our proposed model was applied to the Bangkok city center in Thailand as well as to the Fujisawa city in Japan. We confirmed that the model can estimate the unobserved link speeds quite reasonably.

Full vehicle CFD investigations on the influence of front-end configuration on radiator performance and cooling drag

Proper underhood airflow management of road vehicles has two distinct objectives, viz. ensure the required cooling performance of the heat exchangers, and minimize the cooling drag component of the total vehicle drag. From the radiator performance perspective, there are distinct concerns according to the vehicle speeds. At highway speeds, the flow rate and flow-distributions over the radiator surface are the major operating parameters influencing the performance of a radiator. However, at low vehicle speeds, a phenomenon detrimental to the A/C system performance, called the underhood hot air recirculation, may occur. The cooling airflow not only causes drag directly, the interaction of under-body flow with other vehicle components may lead to more interference drag. This paper presents computational studies to investigate underhood airflow features associated with radiator performance and cooling drag. Additionally, analysis of the impact of the front grille opening size and underhood passive aerodynamic devices on the cooling drag and radiator performance are presented based on full vehicle CFD simulations carried out using a model of Hyundai Veloster. It is demonstrated in this study that by properly manipulating the cooling airflow pattern, simultaneous improvement of radiator performance and total vehicle drag can be achieved.