Calibration of HDM-4 models for Indian conditions of flexible pavement having modified bitumen in wearing course

India is developing her national highway network through widening and rehabilitation of existing highways along with the construction of expressways in different phases, since 1999. Unprecedented growth of road traffic, high variations in pavement temperature and need of long lasting pavements have increased the use of modified bitumen specifically in wearing courses of many flexible pavement road sections of national highway network in entire country. Crumb rubber-modified bitumen (CRMB) and polymer-modified bitumen (PMB) of different grades are mostly used modified binders under different climatic and environmental conditions. During the design life, bituminous road sections show different rates of initiation and propagation of distresses under varying traffic and climatic conditions. In this study, an effort has been made to calibrate the internationally recognised Highway Development & Management (HDM-4) road deterioration models for the selected flexible pavement sections over time with traffic. The different road distresses are modelled using HDM-4 tool for the newly constructed flexible pavement sections of Indian national highway network having modified binder in bituminous concrete (BC) mixes which are located in different regions of the country. Pavement condition data of 23 in-service flexible pavement sections were collected for three consecutive years starting from 2011 to end of the year 2013. Data collected from the study were analysed for calibration and validation of HDM-4 distress models for similar climatic conditions, pavement compositions and traffic loading characteristics. The results of this study are useful for developing pavement maintenance management strategies for Indian national highway network with similar climatic conditions, pavement compositions and traffic characteristics.     

Probabilistic prediction models for crack initiation and progression of spray sealed pavements

Cracking is one of the primary distress modes in spray (chip)-sealed pavement surface performance and its prediction is a major concern for pavement engineers. In order to identify, manage and asses effectively and efficiently cracked pavement at a network level, a probabilistic modelling approach is utilised to develop cracking initiation and progression models. This study aims to predict the probability of pavement cracks occurring using a binary logistic model and cracks progression over time using an ordinal logistic regression model. These models have been developed to take into account the effect of variations among observations, among sections and among highways. Readily available historical time series data (from 2004 to 2011) from 40 highway segments have been collected and prepared for modelling. These time series include surface cracking as a performance parameter and traffic loading, expansion potential of subgrade soil, climate condition, condition of drainage system and pavement strength as predictor parameters. Cracking data include all types of cracking: transverse, longitudinal and crocodile cracking and is reported as a percent of the affected area. The study estimates the probability of crack initiation at a certain time and predicts the probability of a pavement maintaining its current level of cracking. It is found that with the 50% estimated probability, about 82% of the observations are correctly predicted by the crack initiation model and 65% of the observations are correctly predicted by the crack progression model. The study has concluded that the effect of time is stronger than the other variables on crack initiation and progression. Also, the effect of traffic loading is stronger than the effect of initial pavement strength in crack initiation phase. However, the effect of pavement strength at any time is stronger than the effect of traffic loading in crack progression phase. The predicted probabilities have been successfully validated using another data-set from the same network and the results indicate that the developed probability models are well estimating the crack conditions and have the ability to predict future conditions accurately.     

Traffic inputs for mechanistic-empirical pavement design guide using weigh-in-motion systems in Alberta

Mechanistic-empirical pavement design guide (MEPDG) uses axle load spectra and the number of axle applications to characterise traffic loads for pavement design. Alberta Transportation installed weigh-in-motion (WIM) systems at six highway locations to characterise traffic loads in Alberta for MEPDG design. Seasonal and regional trends in traffic characteristics of the six WIM sites were investigated and compared with the default values in the MEPDG for the years 2009 and 2010. Truck traffic classification (TTC) and axle load distribution factor (ALDF) for the WIM sites showed deviations from the MEPDG defaults. Seasonal variations were also evident in the distribution of different classes of truck throughout the year. Differences are attributed to cold climate conditions and special truck traffic in Alberta because of local industries. Influence of the differences between site-specific traffic characteristics and the MEPDG defaults on the performance of both flexible and rigid pavements for Alberta conditions was investigated through a sensitivity analysis. It was found that the flexible pavement performance is sensitive to TTC and ALDF, and the rigid pavement performance is most sensitive to ALDF.     

Evaluation of prioritization methods for effective pavement maintenance of urban roads

Priority analysis is a multi-criteria process that determines the best ranking list of candidate sections for maintenance based on several factors. In this paper, two methods for priority ranking of road maintenance, viz. (a) ranking based on subjective rating and (b) ranking based on economic indicator, are evaluated. The subjective ranking was done using maintenance priority index which is a function of road condition index, traffic volume factor, special factor and drainage factor. The second ranking method was based on economic indicator in which NPV/Cost ratio was calculated for each pavement section using the HDM-4 software.     

Development of a post-flood road maintenance strategy: case study Queensland,Australia

Currently, no road authority takes into account flooding in road deterioration (RD) models; as a result, post-flood rehabilitation treatments may be sub-optimal. This paper proposes a new approach to the development of a post-flood maintenance strategy. The recently developed roughness and rutting-based RD models with flooding, by the current authors, are used as input to predict pavement deterioration after a flood (i.e. assuming a flood in year 1). The HDM-4 model has been used to get the post-flood maintenance strategy with constrained and unconstrained budget, where post-flood rehabilitation starts from year 2. The road groups in state road network of Queensland, Australia, are used as the case study. The unconstrained budget solution aims to keep the network in an excellent condition at a cost of $49.7bn with the possible strongest treatments. The constrained budget strategy uses agency cost and pavement performance as constraints in optimisation and provides a reasonable solution. This strategy requires about $26.1bn in life cycle, which is close to the main road authority of Queensland’s post-flood rehabilitation programme. The paper discusses two other strategies on maximise economic benefits and budget optimisation. It is expected that a road authority would properly investigate its flood-damaged roads before implementation. The paper shows pavement performances with the post-flood strategy. The need for a RD model to predict deterioration after a flood and for post-flood treatment selection is also highlighted.     

Rutting progression models for light duty pavements

Deterioration models allow road managers to assess current condition and to predict future conditions of their road networks. Due to heavy vehicle axle repetitions and the effect of environmental factors, permanent deformation (rutting) develops gradually in the wheel paths and impacts on structural and surface performance of flexible pavements. This paper reports the approach adopted to develop absolute deterministic models for permanent deformation of low volume roads. A representative large sample network (23 highways) of light duty pavements was selected. For each section, time series data from four consecutive condition surveys were collected. Multiple regression analysis was carried out to develop models to predict pavement rutting progression over time as a function of a number of contributing variables. They include traffic loading, pavement strength, climate and drainage condition. For more powerful prediction, family group data-fitting approach was utilised to estimate future rutting progression based on the average rut depth curve for a series of pavements with similar characteristics. This study highlighted that separate family deterioration models are preferred and needed for more realistic results. The paper concludes that the analysis approach used for developing the models confirmed their accuracy and reliability by well-fitting to the validation data with low standard error values. Also, study results show that higher traffic loading, lower pavement strength, poor drainage and climates with high seasonal variation contribute to increasing rutting progression rate.     

Modeling traffic accident occurrence and involvement

The Negative Binomial modeling technique was used to model the frequency of accident occurrence and involvement. Accident data over a period of 3 years, accounting for 1,606 accidents on a principal arterial in Central Florida, were used to estimate the model. The model illustrated the significance of the Annual Average Daily Traffic (AADT), degree of horizontal curvature, lane, shoulder and median widths, urban/rural, and the section's length, on the frequency of accident occurrence. Several Negative Binomial models of the frequency of accident involvement were also developed to account for the demographic characteristics of the driver (age and gender). The results showed that heavy traffic volume, speeding, narrow lane width, larger number of lanes, urban roadway sections, narrow shoulder width and reduced median width increase the likelihood for accident involvement. Subsequent elasticity computations identified the relative importance of the variables included in the models. Female drivers experience more accidents than male drivers in heavy traffic volume, reduced median width, narrow lane width, and larger number of lanes. Male drivers have greater tendency to be involved in traffic accidents while speeding. The models also indicated that young and older drivers experience more accidents than middle aged drivers in heavy traffic volume, and reduced shoulder and median widths. Younger drivers have a greater tendency of being involved in accidents on roadway curves and while speeding.     

A crash-prediction model for multilane roads

Considerable research has been carried out in recent years to establish relationships between crashes and traffic flow, geometric infrastructure characteristics and environmental factors for two-lane rural roads. Crash-prediction models focused on multilane rural roads, however, have rarely been investigated. In addition, most research has paid but little attention to the safety effects of variables such as stopping sight distance and pavement surface characteristics. Moreover, the statistical approaches have generally included Poisson and Negative Binomial regression models, whilst Negative Multinomial regression model has been used to a lesser extent. Finally, as far as the authors are aware, prediction models involving all the above-mentioned factors have still not been developed in Italy for multilane roads, such as motorways. Thus, in this paper crash-prediction models for a four-lane median-divided Italian motorway were set up on the basis of accident data observed during a 5-year monitoring period extending between 1999 and 2003. The Poisson, Negative Binomial and Negative Multinomial regression models, applied separately to tangents and curves, were used to model the frequency of accident occurrence. Model parameters were estimated by the Maximum Likelihood Method, and the Generalized Likelihood Ratio Test was applied to detect the significant variables to be included in the model equation. Goodness-of-fit was measured by means of both the explained fraction of total variation and the explained fraction of systematic variation. The Cumulative Residuals Method was also used to test the adequacy of a regression model throughout the range of each variable. The candidate set of explanatory variables was: length (L), curvature (1/R), annual average daily traffic (AADT), sight distance (SD), side friction coefficient (SFC), longitudinal slope (LS) and the presence of a junction (J). Separate prediction models for total crashes and for fatal and injury crashes only were considered. For curves it is shown that significant variables are L, 1/R and AADT, whereas for tangents they are L, AADT and junctions. The effect of rain precipitation was analysed on the basis of hourly rainfall data and assumptions about drying time. It is shown that a wet pavement significantly increases the number of crashes. The models developed in this paper for Italian motorways appear to be useful for many applications such as the detection of critical factors, the estimation of accident reduction due to infrastructure and pavement improvement, and the predictions of accidents counts when comparing different design options. Thus this research may represent a point of reference for engineers in adjusting or designing multilane roads.     

A BP neural network based information fusion method for urban traffic speed estimation

Obtaining comprehensive and accurate information is very important in intelligent traffic system (ITS). In ITS, the GPS floating car system is an very important approach for traffic data acquisition. However, in this system, the GPS blind areas caused by tall buildings and tunnels could affect the acquisition of traffic information and depress the system performance. Aiming at this problem, we developed a novel method employing a back propagation (BP) neural network to estimate the traffic speed in the GPS blind areas. When the speed of one road section is lost, we can use the speed of its related road sections to estimate its speed. The complete historical data of these road sections are used to train the neural network, using Levenberg-Marquardt learning algorithm. Then, the current speed of the related roads is used by the trained neural network to get the speed of the road section without GPS signal. We compare the speed of the road section estimated by our method with the real speed of this road section, and the experimental results show that the speed of this road section estimated by our method is better.     

Empirical approach for estimating the pavement transition probabilities used in non-homogenous Markov chains

An empirical approach is proposed to estimate the transition probabilities associated with non-homogenous Markov chains typically used in developing stochastic-based pavement performance prediction models. A reliable pavement performance prediction model is a key component of any advanced pavement management system. The proposed empirical approach is designed to account for two major factors that cause the transition probabilities (i.e. deterioration rates) to increase over time. The first major factor is the progressive increase in traffic loading as represented by the equivalent single axle load applications. The second major factor is the gradual decline in the pavement structural capacity which can be represented by an appropriate pavement strength indicator such as the structural number. The proposed empirical model can recursively estimate the non-homogenous transition probabilities for an analysis period of (n) transitions by simply multiplying the first-year (i.e. present) transition probabilities by two adjustment factors, namely the load and strength factors. Once the empirical model is calibrated, these two factors can capture the impact of traffic load increases and gradual pavement structural losses on the transition probabilities over time. The calibration process requires the estimation of the model two exponents to be obtained from the minimisation of sum of squared errors wherein the error is defined as the difference between the observed and predicted pavement distress ratings (DRs). The predicted DRs are mainly estimated based on the state probabilities, which are recursively derived from the non-homogenous Markov model. A sample empirical model is presented with results indicating its effectiveness in estimating the pavement non-homogenous transition probabilities.     

Mean profile depth analysis of field and laboratory traffic-loaded chip seal surface treatments

This paper presents a laboratory and field study to evaluate the mean profile depth (MPD) parameter that represents the surface texture of chip seal pavements. A three-dimensional laser profiler is used to determine the MPD values from both field pavement sections and field samples that have been tested in the laboratory using the third-scale model mobile loading simulator (MMLS3). Data obtained from five different field-constructed chip seal sections are used to evaluate the effects of different factors on the MPD of chip seal pavements. These factors include aggregate type, emulsion application rate, field versus MMLS3 traffic loading and traffic volume. The results presented in this paper suggest that: (1) chip seal pavements constructed using lightweight aggregate have larger initial MPD values and faster reduction in MPD as a function of the number of wheel passes than those constructed using granite 78M aggregate; (2) MPD values obtained from a drier section (with drier indicating a lower emulsion-to-aggregate ratio than that of the sections it is being compared against) initially drop quickly and significantly, resulting in a much smaller ultimate MPD value; (3) in general, the MPD values obtained under MMLS3 loading are similar to those obtained from field traffic loading, thus allowing the translation of the laboratory MMLS3 data to the field response data; (4) a short rest period in a high-traffic volume road retards the recovery of the binder and, therefore, leads to more permanent changes in the MPD and (5) the initial measured MPD value can help predict aggregate loss performance.     

Risk based probabilistic pavement deterioration prediction models for low volume roads

Accurate prediction of pavement performance is important for efficient management of road infrastructure. Pavement performance prediction models developed for low-volume roads are mainly based on deterministic approach. The deterministic prediction models are inadequate to completely capture the deterioration mechanism. Uncertainties may occur in pavement behaviour under changing traffic loads and environment conditions, which may not be realistically represented by deterministic model. The objective of this paper is to develop pavement deterioration prediction models by probabilistic approach, for various distresses observed on low-volume roads in the state of Kerala in India, with the help of existing deterministic models. The major distresses observed on low-volume roads were ravelling, pothole and edge failure. Load-associated distresses were rarely observed on these roads as the maximum cumulative standard axle observed was only one million standard axle (msa). Hence, lack of proper drainage and construction quality (CQ) could be attributed as the major reasons for the pavement deterioration. Progression of deterioration of pavements with age has been studied and the intensity of distresses along with corresponding probabilities was arrived at. The distresses predicted by probabilistic models were compared with those predicted by deterministic models and the actual distress values observed in the field. The prediction models were validated using Mean Absolute Percentage Error, a statistical method for accuracy measurement of forecasting models. A risk analysis was then conducted to select the critical percentile value for each type of distress corresponding to varying pavement age. A sensitivity analysis was also carried out to study the effect of pavement age and CQ on the progression of pavement deterioration.     

Health burden of road traffic accidents,an analysis of clinical data on disability and mortality exposure rates in Flanders and Brussels

Statistics on road traffic accidents (RTAs) mainly come from police records. The police reported RTA statistics however are known to have a large degree of under-registration, underestimating the true risk of being injured in traffic accidents. The use of medical based datasets can provide a more accurate estimate of the actual traffic accident health risk. Exposure-based rates of the actual burden from Flanders and Brussels were calculated, comparing differences between road user, age, gender and type of injury sustained. Minimal Clinical Data (MCD) was selected for the years 2003–2007, as well as data from the mortality statistics. Disability Adjusted Life Years (DALY) were calculated and put into perspective with the passenger kilometres travelled.     

考虑路网交通流状态的单变量交通噪声预测

为实现路网区域交通噪声预测,克服传统预测模型中路段间交通特性相互独立以及路段内流量与速度相互独立的缺陷,借助Van Aerde交通流模型,在不同道路等级、设计速度约束下,结合道路线声源噪声排放,构建基于速度的单变量交通噪声预测模型.分别对比4种常见城市道路的交通噪声实测值,模型预测值平均偏差为1.63 dB,满足精度需...     

Investigating the effects of the fixed and varying dispersion parameters of Poisson-gamma models on empirical Bayes estimates

Traditionally, transportation safety analysts have used the empirical Bayes (EB) method to improve the estimate of the long-term mean of individual sites; to correct for the regression-to-the-mean (RTM) bias in before-after studies; and to identify hotspots or high risk locations. The EB method combines two different sources of information: (1) the expected number of crashes estimated via crash prediction models, and (2) the observed number of crashes at individual sites. Crash prediction models have traditionally been estimated using a negative binomial (NB) (or Poisson-gamma) modeling framework due to the over-dispersion commonly found in crash data. A weight factor is used to assign the relative influence of each source of information on the EB estimate. This factor is estimated using the mean and variance functions of the NB model. With recent trends that illustrated the dispersion parameter to be dependent upon the covariates of NB models, especially for traffic flow-only models, as well as varying as a function of different time-periods, there is a need to determine how these models may affect EB estimates. The objectives of this study are to examine how commonly used functional forms as well as fixed and time-varying dispersion parameters affect the EB estimates. To accomplish the study objectives, several traffic flow-only crash prediction models were estimated using a sample of rural three-legged intersections located in California. Two types of aggregated and time-specific models were produced: (1) the traditional NB model with a fixed dispersion parameter and (2) the generalized NB model (GNB) with a time-varying dispersion parameter, which is also dependent upon the covariates of the model. Several statistical methods were used to compare the fitting performance of the various functional forms. The results of the study show that the selection of the functional form of NB models has an important effect on EB estimates both in terms of estimated values, weight factors, and dispersion parameters. Time-specific models with a varying dispersion parameter provide better statistical performance in terms of goodness-of-fit (GOF) than aggregated multi-year models. Furthermore, the identification of hazardous sites, using the EB method, can be significantly affected when a GNB model with a time-varying dispersion parameter is used. Thus, erroneously selecting a functional form may lead to select the wrong sites for treatment. The study concludes that transportation safety analysts should not automatically use an existing functional form for modeling motor vehicle crashes without conducting rigorous analyses to estimate the most appropriate functional form linking crashes with traffic flow.     

Assessment of freeway traffic parameters leading to lane-change related collisions

This study aims at 'predicting' the occurrence of lane-change related freeway crashes using the traffic surveillance data collected from a pair of dual loop detectors. The approach adopted here involves developing classification models using the historical crash data and corresponding information on real-time traffic parameters obtained from loop detectors. The historical crash and loop detector data to calibrate the neural network models (corresponding to crash and non-crash cases to set up a binary classification problem) were collected from the Interstate-4 corridor in Orlando (FL) metropolitan area. Through a careful examination of crash data, it was concluded that all sideswipe collisions and the angle crashes that occur on the inner lanes (left most and center lanes) of the freeway may be attributed to lane-changing maneuvers. These crashes are referred to as lane-change related crashes in this study. The factors explored as independent variables include the parameters formulated to capture the overall measure of lane-changing and between-lane variations of speed, volume and occupancy at the station located upstream of crash locations. Classification tree based variable selection procedure showed that average speeds upstream and downstream of crash location, difference in occupancy on adjacent lanes and standard deviation of volume and speed downstream of the crash location were found to be significantly associated with the binary variable (crash versus non-crash). The classification models based on data mining approach achieved satisfactory classification accuracy over the validation dataset. The results indicate that these models may be applied for identifying real-time traffic conditions prone to lane-change related crashes.     

INFLUENCE OF INTERMEDIATE LOADING RATES AND TEMPERATURE ON THE FRACTURE TOUGHNESS OF ORDINARY CARBON–MANGANESE STRUCTURAL STEELS

Three-point bend and compact tension specimens, taken from beam sections of modern and older ordinary C–Mn structural steels, were tested at intermediate loading rates at room temperature and −30 °C. The experimental work, except the loading rates used, was performed according to ASTM E-813. In order to investigate transferability of data, full-scale beam sections were also tested at intermediate loading rates. The fracture toughness of C–Mn structural steels depends strongly on the loading rate, and decreases rapidly with increasing loading rate at and just above the maximum prescribed in ASTM E-813. Fracture toughness data for structures exposed to intermediate loading rates indicate the requirement for testing at appropriate loading rates. The behaviour of full-scale structural elements subjected to intermediate loading rates can, provided certain conditions are fulfilled, be predicted from data obtained from small laboratory specimens.     

Model calibration: A hierarchical Bayesian approach

Many geotechnical engineering models are empirical and calibrated based on data gathered from various sites/projects, using optimisation algorithms with criteria like least squared errors or minimising the coefficient of variation of method bias with the constraint of mean bias equal to unity. This paper discusses the use of hierarchical Bayesian regression models for the same purpose. A database of axial capacity of piles in predominantly clay sites and a CPT-based design model, compiled and developed as part of a Joint Industry Project (JIP) led by the Norwegian Geotechnical Institute (NGI), is used for demonstration. The analyses focus on two related areas that the traditional approaches overlook: (i) quantification of uncertainty in the estimated parameters of the model, and (ii) modelling site-dependency of the model parameters (i.e., between-group variation). The former is important in the context of reliability-based design and contributes to establishing confidence in estimated reliability indices, particularly when only limited data are available. The latter expands our understanding regarding the domain of applicability of a model; that is, if a model is broadly applicable or highly site-dependent. The benefits of the proposed Bayesian approach are highlighted with a prediction exercise where the calibrated models are used in conjunction with limited site or project-specific data.     

Safety impact of Gateway Monuments

Gateway Monuments are free standing roadside structures or signage that communicate the name of a city, country or township to motorists. The placement of such monuments within state-controlled right-of-way is a relatively recent occurrence in California. As a result, the California Department of Transportation (Caltrans) initiated research to quantify the impacts that this type of signage may or may not have on crashes in their vicinity. To date, no specific research has examined the impact such features have on crashes. To determine whether these features impacted safety, the before–after study method using the Empirical Bayes technique was used, with reference groups and Safety Performance Functions adapted from existing studies, eliminating the need to calibrate new models. Results indicated that, on an individual basis, no deterioration in safety was observed at any monument site. When all sites were examined collectively (using two different scenarios), the calculated index of effectiveness values were 0.978 and 0.680, respectively, corresponding to 2.2% and 32.0% reductions in crashes. In addition to the EB method, naïve study methods (with and without AADT taken into account) were applied to the study data. Results (crash reductions) from these methods also showed that the presence of Gateway Monuments did not have negative impact on traffic safety. However, the use of EB technique should be very careful employed when adopting reference groups from different jurisdictions, as these may affect the validity of EB results. In light of these results, Caltrans may continue to participate in the Gateway Monument Program at its discretion with the knowledge that roadway safety is not impacted by monuments.     

Surrogate safety measure for evaluating rear-end collision risk related to kinematic waves near freeway recurrent bottlenecks

This study presents a surrogate safety measure for evaluating the rear-end collision risk related to kinematic waves near freeway recurrent bottlenecks using aggregated traffic data from ordinary loop detectors. The attributes of kinematic waves that accompany rear-end collisions and the traffic conditions at detector stations spanning the collision locations were examined to develop the rear-end collision risk index (RCRI). Together with RCRI, standard deviations in occupancy were used to develop a logistic regression model for estimating rear-end collision likelihood near freeway recurrent bottlenecks in real-time. The parameters in the logistic regression models were calibrated using collision data gathered from the 6-mile study site between 2006 and 2007. Findings indicated that an additional unit increase in RCRI results in increasing the odds of rear-end collision by 21.1%, a unit increase in standard deviation of upstream occupancy increases the odds by 19.5%, and a unit increase in standard deviation of downstream occupancy increases the odds by 18.7%. The likelihood of rear-end collisions is highest when the traffic approaching from upstream is near capacity state while downstream traffic is highly congested. The paper also reports on the findings from comparing the predicted number of rear-end collisions at the study site using the proposed model with the observed traffic collision data from 2008. The proposed model's true positive rates were higher than those of existing real-time crash prediction models.