Roadway Conditions Research Articles

Quantifying Roughness of Unpaved Roads by Terrestrial Laser Scanning

Unpaved roads represent a major component of the roadway network in Iowa and require regular surface grading to control roughness (e.g., corrugation). This paper presents a study that shows how terrestrial laser scanning can characterize road roughness in relation to ( a) a spatially analyzed international roughness index (IRI), ( b) a fast Fourier transform (FFT) spectral analysis, and ( c) surface texture characterization that uses statistical analysis. Algorithms are presented for each of the roughness calculations. The spatial nature of terrestrial laser scanning makes possible the determination of IRI values along any selected path within the scan area. In this study, median IRI values were used to characterize overall road roughness. To characterize the nature of road roughness, FFT was used to decompose the laser scan height spectrum. Examples are presented for unpaved road sections with a relatively smooth surface, a smooth surface with corrugations, an unsystematically rough surface, and an unsystematically rough surface with corrugations. On the basis of the observation that the surface aggregate materials were segregated, selected small regions were studied with a scan with spacing of 0.5-mm points. These scans were filtered and statistically analyzed for variations in height by using root mean square, skewness, and kurtosis parameters. These parameters were studied in relation to the values for particle size index for the surface materials of the three distinct gradations. The algorithms presented will be of value to terrestrial laser scanning users interested in characterizing unpaved roadway conditions.

Driver behavior in use of guide and logo signs under distraction and complex roadway conditions

White-on-blue logo signs on the sides of highways are typically used to notify drivers of food, gas, and lodging at an upcoming interchange. The current research assessed driver performance and attention allocation in a simulated freeway driving task when exposed to six-panel logo signs, nine-panel logo signs, mileage guide signs, and roadway work zones both with and without an in-car navigation device. The objective was to identify the impact of signage types on driver behavior under realistic driving conditions. Results revealed glance durations and fixation frequencies to guide signs to be significantly lower than with six-panel and nine-panel logo signs, but no differences were found between six-panel and nine-panel logo signs. There were also statistical differences among the independent variables for speed deviation and lane deviation, but magnitudes were not large enough to be considered practically significant in terms of driving safety. Overall, there were minor differences in sign processing time between logo signs and mileage guide signs, but such differences did not translate to degradations in vehicle control.

Crash frequency analysis of left-side merging and diverging areas on urban freeway segments – A case study of I-75 through downtown Dayton, Ohio

This paper analyzes the effect of left- and right-side merging and diverging areas and other variables such as light condition, roadway pavement condition, drivers’ age and presence of construction work zones on the occurrence frequency of crashes. A 6.5-mile (10.5-km) section of I-75 that passes through downtown Dayton, Ohio was considered. The area of interest has a high traffic volume and consists of different geometric design challenges including closely spaced merging and diverging ramps. A four-year record of crash data (2005–2008) and a statistical modeling technique that assumes a negative binomial distribution on generalized linear models (GLMs) were used to develop separate models for merging and diverging areas. The model results show that left-side merging and diverging areas are critical elements in crash frequency in the vicinity of ramps on freeways. In addition, pavement condition, light condition, and work zones were found to be significant predictors of crash frequency. Specifically, the results indicate that crashes are about 7.88 times more likely to occur on merging areas located on the left side of the freeway lanes compared to those on the right. For diverging areas, about 2.26 times more crashes are likely to occur near diverging areas on the left compared to those diverging on the right side of the freeway. In addition, adverse pavement conditions (such as wet pavement, snow, and ice), adverse light conditions (such as darkness and glare), and presence of work zone were found to be significant variables in the occurrence of crashes.

Effect of driver, roadway, collision, and vehicle characteristics on crash severity: a conditional logistic regression approach

The aim of the study is to examine the factors that appear to have a higher potential for serious injury or death of drivers in traffic accidents in Turkey, such as collision type, roadway surface, vehicle speed, alcohol/drug use, and restraint use. Driver crash severity is the dependent variable of this study with two categories, fatal and non-fatal. Due to the binary nature of the dependent variable, a conditional logistic regression analysis was found suitable. Of the 16 independent variables obtained from Turkish police accident reports, 11 variables were found most significantly associated with driver crash severity. They are age, education level, restraint use, roadway condition, roadway type, time of day, collision location, collision type, number and direction of vehicles, vehicle speed, and alcohol/drug use. This study found that belted drivers aged 18–25 years involving two vehicles travelling in the same direction, in an urban area, during the daytime, and on an avenue or a street have better chances of survival in traffic accidents.

Examine Factors Associated with Motorcycle Injury and Fatality

The objective of this paper is to examine contributory factors of motorcycle-related injury and fatal crashes and provide safety countermeasures and interventions based on modelling results. Motorcycle crashes, from 2007 to 2009, in Kentucky State were utilized as data source and two binary logistic models, for motorcycle-related injuries and fatalities, were developed respectively. Roadway conditions, roadway type, roadway alignment, gender, age, crash avoidance behaviors, airbags were found to be significant factors. Speed, curves, and crash avoidance maneuvers of braking and steering were found to be strongly correlated with motorcycle injuries and fatalities. According to modelling results, control of motorcycle access on interstate/state highways, roll stability systems, educational and training programs, usage of helmets, and deployment of airbags and anti-lock brake systems are suggested for motorcycle safety improvement.

A finite mixture model of vehicle-to-vehicle and day-to-day variability of traffic network travel times

This study proposes an approach to modeling the effects of daily roadway conditions on travel time variability using a finite mixture model based on the Gamma–Gamma (GG) distribution. The GG distribution is a compound distribution derived from the product of two Gamma random variates, which represent vehicle-to-vehicle and day-to-day variability, respectively. It provides a systematic way of investigating different variability dimensions reflected in travel time data. To identify the underlying distribution of each type of variability, this study first decomposes a mixture of Gamma–Gamma models into two separate Gamma mixture modeling problems and estimates the respective parameters using the Expectation–Maximization (EM) algorithm. The proposed methodology is demonstrated using simulated vehicle trajectories produced under daily scenarios constructed from historical weather and accident data. The parameter estimation results suggest that day-to-day variability exhibits clear heterogeneity under different weather conditions: clear versus rainy or snowy days, whereas the same weather conditions have little impact on vehicle-to-vehicle variability. Next, a two-component Gamma–Gamma mixture model is specified. The results of the distribution fitting show that the mixture model provides better fits to travel delay observations than the standard (one-component) Gamma–Gamma model. The proposed method, the application of the compound Gamma distribution combined with a mixture modeling approach, provides a powerful and flexible tool to capture not only different types of variability—vehicle-to-vehicle and day-to-day variability—but also the unobserved heterogeneity within these variability types, thereby allowing the modeling of the underlying distributions of individual travel delays across different days with varying roadway disruption levels in a more effective and systematic way.

Development of a Prototype Web GIS-Based Disaster Management System for Safe Operation of the Next Generation Bimodal Tram, South Korea—Focused Flooding and Snowfall

The Korea Railroad Research Institute (KRRI) has developed a bimodal tram and advanced bus rapid transit (BRT) system which is an optimized public transit system created by mixing the railway’s punctual operation and the bus’ easy and convenient access. The bimodal tram system provides mass-transportation service with an eco-friendly and human-centered approach. Natural disasters have been increasing worldwide in recent years, including floods, snow, and typhoons disasters. Flooding is the most frequent natural disaster in many countries and is increasingly a concern with climate change; it seriously affects people’s lives and productivity, causing considerable economic loss and significant damage. Enhanced conventional disaster management systems are needed to support comprehensive actions to secure safety and convenience. The objective of this study is to develop a prototype version of a Web GIS-based bimodal tram disaster management system (BTDMS) using the Storm Water Management Model (SWMM) 5.0 to enhance on-time operation and safety of the bimodal tram system. The BTDMS was tested at the bimodal tram test railroad by simulating probable maximum flood (PMF) and snow melting for forecasting flooding and snow covered roads. This result could provide the basis for plans to protect against flooding disasters and snow covered roads in operating the bimodal tram system. The BTDMS will be used to assess and predict weather impacts on roadway conditions and operations and thus has the potential to influence economic growth. The methodology presented in this paper makes it possible to manage impacts of flooding and snowfall on urban transportation and enhance operation of the bimodal tram system. Such a methodology based on modeling could be created for most metropolitan areas in Korea and in many other countries.

Acceleration Testing and Modeling of Vehicle Kinematics Under Idle Conditions

Determination of vehicle speed at the time of impact is frequently an important factor in accident reconstruction. In many cases some evidence may indicate that the brake pedal of a striking vehicle was disengaged, and the vehicle was permitted to idle forward prior to impacting the target vehicle. This study was undertaken to analyze the kinematic response of various vehicles equipped with automatic transmissions while idling, with the transmissions in drive and the brake pedals disengaged. An array of sedans, SUV's and pickup trucks were tested under 3 roadway conditions (flat, medium slope and high slope). The vehicle responses are reported and mathematical relationships were developed to model the idle velocity profiles for flat and sloped roadway surfaces. Language: en

Impact of Dynamic Message Signs on Speeds Observed on a Rural Interstate

Dynamic message signs (DMSs) are a component of intelligent transportation systems (ITSs) used to convey real-time travel information to motorists, enabling them to make better decisions in response to real-time roadway conditions such as congestion, crashes, and adverse weather. The purpose of this paper is to analyze the existing DMS system implemented by the Wyoming Department of Transportation (WYDOT) along a rural segment of Interstate 80 in southeastern Wyoming between the cities of Laramie and Cheyenne. The research uses speed, weather, and DMS message data collected from the winter season of 2009 to 2010 along the corridor to determine how driver speed behavior is affected by DMS signs. A linear regression model utilizing four different weather severity categories was estimated and the results indicate that the DMS message signs are effective at reducing drivers’ speeds along rural interstate corridors from 8 km/h to 32 km/h (5 mi/h to 20 mi/h) above the speed reductions that can be accounted for by weather conditions alone.

IDENTIFICATION OF HAZARDOUS RURAL HIGHWAY LOCATIONS

An effective procedure was developed for identifying hazardous rural highway locations based on accident statistics. Indicators of accident experience that are necessary include the number of fatal accidents, total number of accidents, number of equivalent-property-damage- only accidents, and the nature of the local safety improvement program, local traffic and roadway conditions, and prevailing attitudes toward highway safety. Specific recommendations are given for use of the procedure in Kentucky. Critical accident rates are established by using quality control procedures. In identification of hazardous highway locations, distinction is made between short highway segments (spots) and large segments (sections), and spots are further classified as intersection and nonintersection locations. Intersection spots should include a distance of 0.15 mile (0.24KM) along all approaches; nonintersection spots should be 0.3-mile (0.48-km) floating segments; and sections should be 3-mile (4.8-km) floating segments. Both spots and sections should be classified by highway type and location. The use of 1- and 2-year intervals for accumulating and evaluating accident statistics was found to be desirable.

Optimization of Roadheader Crawler Parameters Based on the Theory of Driving

The heading machine is one of the key equipment of coal mine roadway drivage, and cantilever machine applied most widel, A lot of coal roadway floor condition is very poor in our country, so its travel mechanism performance also have a higher requirements. Especially when driving on slopes, frequently, caterpillar not firm grip, and backward. This article is based on crawler and the ground interaction theory optimization model is established, the crawler structure parameters are optimized in order to obtain maximum tractive force,thereby improving the performance of walking.

Applications of Electromagnetic Waves for Void and Anomaly Detections

Abstract Knowledge of the existing roadway condition is vital to transportation government agencies for management and safety purposes. Verifying the presence and extent of subsurface defects/voids is vital to prevent failures and accidents from occurring. A nondestructive and noninvasive geophysical method, ground penetrating radar (GPR), which utilizes electromagnetic waves, was employed in this study. Four field projects are presented in this study to demonstrate the application of electromagnetic waves for detection of voids and anomalies. Auguring and excavation were used to verify the suspicious areas that were located by the electromagnetic waves. Voids and different subsurface features, like rebar, geo-grid, and geo-textile, were successfully detected. A large negative peak that appears in the wave form is an indication of a dry void. Void sizes, ranging from a few centimeters to several meters, were successfully detected. Early identification of concealed subsurface voids under the roadway structure can prevent major failures from occurring. The methodologies presented in this study are applicable to many other projects.

Use of Structural Equation Modeling to Measure Severity of Single-Vehicle Crashes

Injury severity and vehicle damage are two of the main indicators of the level of crash severity. Other factors, such as driver characteristics, roadway conditions, highway geometry, environmental factors, vehicle type, and roadside objects, may also be directly or indirectly related to crash severity. All these factors interact in such complicated ways that it is often difficult to identify their interrelationships. The aim of this study was to examine the relationships between these contributors and the severity of single-vehicle crashes. Structural equation modeling (SEM) offers the opportunity to explore the complex relationships between variables by handling endogenous variables and exogenous variables simultaneously. Furthermore, SEM allows latent variables to be included in the model and bridges the gap between dependent and explanatory variables. In this study, the number of latent variables was defined by the understanding of collision force, kinetic energy, and mechanical process of a collision, as well as statistical goodness of fit that was based on available data. Three SEM models (one with one latent variable, one with two, and one with three) representing the hypothesized relationships between collision force, speed of a vehicle, and severity of a crash were developed and evaluated in an attempt to unravel the relationships between exogenous factors and severity of single-vehicle crashes. On the basis of goodness of fit and model predictive power, the model with two latent variables outperformed the other two. Additional insights about model selection were provided through the development and comparison of the three models.

Agent-Based Simulation of Ecospeed-Controlled Vehicles at Signalized Intersections

Ecospeed control systems attempt to reduce vehicle fuel consumption levels by optimization of vehicle trajectories in the vicinity of signalized intersections while these systems account for traffic signal timing constraints. The proposed algorithm uses dynamic programming to compute the vehicle trajectory needed to navigate through the intersection with the amount of minimum fuel subject to several constraints, including vehicular interactions, traffic signal timing changes, and vehicle and roadway constraints. The proposed application uses infrastructure-to-vehicle and vehicle-to-vehicle communication to receive traffic signal and vehicle data. The research presented in the paper developed an agent-based modeling tool to simulate and test the system under various traffic volume and market penetration levels. The simulation model used a variety of microscopic inputs, such as the roadway vertical profile, roadway surface condition, traffic volumes, and traffic signal timing information. The system was tested on a sample signalized intersection and produced fuel consumption reductions of 30% and travel speed increases of 200%, on average, within the vicinity of the intersection. Actual savings in total trip time, average speed, and the amount of fuel consumed depend on the trip profile, including the number of intersections and total trip length.

Designing Traffic Signal Yellow and Change Intervals considering Truck Impacts

This research quantifies the effect of heavy vehicles on the design of yellow and all-red intervals. With empirical data from previous studies and truck performance characteristics, models are developed to simulate driver perception–reaction times and truck deceleration levels at signalized intersections. These models consider roadway surface and weather conditions (dry, fog, or rain); driver attributes (age and gender); roadway grade; approach speed; and time and distance to the intersection at the onset of the yellow indication. An agent-based approach is used to develop driver- and vehicle-specific yellow times. The yellow timings for light-duty and heavy-duty vehicles are then combined and consider different percentages of heavy vehicles in the traffic stream. Lookup tables are developed for grades, precipitation types and levels, and percentages of trucks to provide practical guidelines for the design of yellow and all-red signal timings. In general, the research demonstrates that adding trucks to the traffic stream increases yellow times by 2% to 20%, depending on approach speed, grade, and weather conditions, with the most significant increases occurring on dry roadways with high positive gradients at lower speeds. In absolute terms, the yellow time is increased from approximately 0.1 to 0.6 s when trucks constitute 5% of the traffic stream to 0.6 to 1.4 s when trucks are 30% of the traffic stream. The study also demonstrates a need to increase the all-red time by up to 0.5 s when trucks comprise 30% of the traffic stream.

Evaluation of Truck Impact Hazards for Interstate Overpasses

Increased freight activities on Interstate highways escalate the risk of collision between trucks and overpass bridges. Although such events are rare, the consequences of such a collision may be catastrophic. Many overpass bridges were designed and constructed in the early years of development of the Interstate highway system and therefore did not meet the requirements specified in AASHTO's Load and Resistance Factor Design Bridge Design Specifications. In this study, 5-year truck run-off-the-road crash data, information on Interstate highway geometric characteristics, and traffic information were collected to develop crash prediction models for bridge bents. The bent-based predictive methods improved the collision risk estimate by evaluating the individual bents of a bridge on the basis of roadway geometrics and traffic conditions. The detour costs subsequently measured on the basis of the additional road user costs caused by the failure of a bridge were used to assess the importance of a bridge quantitatively. As a result of this study, a bridge index was developed from both the collision risk and detour costs. This study could provide guidance to departments of transportation to develop mitigation plans for bridges with high collision risk indexes.

How Many Runs? Analytical Method for Optimal Scenario Sampling to Estimate Travel Time Variance in Traffic Networks

Scenario-based approaches provide an effective and practical approach to capturing the probabilistic nature of travel time in a traffic network. Scenarios that represent daily roadway conditions are generated by identifying various demand- and supply-side factors that affect travel time variability and by sampling a set of mutually consistent combinations of the associated events. The sampled scenarios are then evaluated with traffic simulation models to obtain travel time distributions to provide a basis for extracting a wide range of reliability performance metrics. A key question under this framework pertains to the number of input scenarios needed to achieve the best estimators of reliability measures of interest, given a limited computation budget. Given a stratification of the entire domain of daily scenarios into distinct scenario categories (or strata), the study addressed the optimal sample size allocation problem in connection with stratified sampling. Existing sample allocation schemes (e.g., Neyman's) were optimized for estimating the mean. However, dispersion measures such as variance and standard deviation were of greater interest in travel time reliability studies. Thus, this study explicitly specified the optimal allocation scheme for estimation of variance. With a specific characteristic observed in travel time data, namely, a strong positive correlation between standard deviation and mean, an analytical formula that approximated the variance of the sample was developed, and an analytical approximate solution for the optimal allocation for estimating the variance was derived. The proposed method was validated with a simulation study and was compared with other allocation methods in regard to estimation of various reliability measures.

Analysis of Effects of Manhole Covers on Motorcycle Driver Maneuvers: A Nonparametric Classification Tree Approach

Objective: A manhole cover is a removable plate forming the lid over the opening of a manhole to allow traffic to pass over the manhole and to prevent people from falling in. Because most manhole covers are placed in roadway traffic lanes, if these manhole covers are not appropriately installed or maintained, they can represent unexpected hazards on the road, especially for motorcycle drivers. The objective of this study is to identify the effects of manhole cover characteristics as well as driver factors and traffic and roadway conditions on motorcycle driver maneuvers. Method: A video camera was used to record motorcycle drivers’ maneuvers when they encountered an inappropriately installed or maintained manhole cover. Information on 3059 drivers’ maneuver decisions was recorded. Classification and regression tree (CART) models were applied to explore factors that can significantly affect motorcycle driver maneuvers when passing a manhole cover. Results: Nearly 50 percent of the motorcycle drivers decelerated or changed their driving path to reduce the effects of the manhole cover. The manhole cover characteristics including the level difference between manhole cover and pavement, the pavement condition over the manhole cover, and the size of the manhole cover can significantly affect motorcycle driver maneuvers. Other factors, including traffic conditions, lane width, motorcycle speed, and loading conditions, also have significant effects on motorcycle driver maneuvers. Conclusions: To reduce the effects and potential risks from the manhole covers, highway authorities not only need to make sure that any newly installed manhole covers are as level as possible but also need to regularly maintain all the manhole covers to ensure that they are in good condition. In the long run, the size of manhole covers should be kept as small as possible so that the impact of manhole covers on motorcycle drivers can be effectively reduced. Supplemental materials are available for this article. Go to the publisher's online edition of Traffic Injury Prevention to view the supplemental file.

A Methodology for Estimating Exposure-controlled Crash Risk Using Traffic Police Crash Data

Exposure control or case-control methodologies are common techniques for estimating crash risks, however they require either observational data on control cases or exogenous exposure data, such as vehicle-kilometres travelled. This study proposes an alternative methodology for estimating crash risk of road user groups, whilst controlling for exposure under a variety of roadway, traffic and environmental factors by using readily available police-reported crash data. In particular, the proposed method employs a combination of a log-linear model and quasi-induced exposure technique to identify significant interactions among a range of roadway, environmental and traffic conditions to estimate associated crash risks. The proposed methodology is illustrated using a set of police-reported crash data from January 2004 to June 2009 on roadways in Queensland, Australia. Exposure-controlled crash risks of motorcyclists—involved in multi-vehicle crashes at intersections— were estimated under various combinations of variables like posted speed limit, intersection control type, intersection configuration, and lighting condition. Results show that the crash risk of motorcycles at three-legged intersections is high if the posted speed limits along the approaches are greater than 60km/h. The crash risk at three-legged intersections is also high when they are unsignalized. Dark lighting conditions appear to increase the crash risk of motorcycles at signalized intersections, but the problem of night time conspicuity of motorcyclists at intersections is lessened on approaches with lower speed limits. This study demonstrates that this combined methodology is a promising tool for gaining new insights into the crash risks of road user groups, and is transferrable to other road users.

Effects of Information Filtering on Driving

As Connected Vehicle (CV) communicates information to the driver using safety and non-safety applications, problems can occur when these individual CV applications fail to communicate effectively, or when several CV technologies attempt to communicate with the driver simultaneously. Too much information would increase the driver's workload and levels of distraction resulting in growing threat to driving safety. Connected Vehicle Systems (CVS) afford opportunities to collect and share information about the driving environment, including vehicle state, traffic dynamics, and roadway conditions to infer real time workload demands on the driver and then CVS applications filter the complexity of information presented to that driver.This filter strategy strives to maintain driving performance under high workload traffic conditions by protecting the driver's cognitive resources. The primary objective of the study is to analyze critically how interaction with CVS within different levels of traffic condition influences proxy measures for crash risk in the following variables: i) lane position variability; ii) steering wheel input variability. Analysis results showed that information filtering did have positive safety effect on driving, especially under volatile traffic condition.