Crashes are important evidence for identifying deficiencies existing in highway systems, but they are random and rare. The investigation of the nature of the problem normally draws on crashes collected over a multiyear period and from different locations to obtain a sizable sample. Hence, the issue of data heterogeneity arises because the pooled data originated from different sources. Data heterogeneity has to be addressed to obtain stable and meaningful estimates for variable coefficients. A desirable method of handling heterogeneous data is quantile regression (QR) because it focuses on depicting the relationship between a family of conditional quantiles of the crash distribution and the covariates. The QR method is appealing because it offers a complete view of how the covariates affect the response variable from the full range of the distribution, which is of particular use for distributions without symmetric or normal forms (i.e., heavy tails, heteroscedasticity, multimodality, etc.). Crash data possess some of the properties that quantile analysis can handle, as demonstrated in an intersection crash study. The compelling results illustrate that conditional quantile estimates are more informative than conditional means. The findings provide information relative to the effect of traffic volume, intersection layout, and traffic control on crash occurrence.

Dynamic message signs (DMS) are used in conjunction with other media to communicate traffic conditions, general information, and recommended diversion strategies to motorists. This study uses loop detector data from Richmond, Virginia, to estimate diversion rates attributable to DMS advisory messages on Interstate 95 where Interstate 295 is available as a comparable alternate route, thus evaluating DMS effectiveness in traffic diversion without major concerns of added trip time or quality of the alternate route. The impacts of existing message strategies are investigated to determine those messages that maximize diversion for specific circumstances. Analysis is performed for various message types for the scenario of an incident on the primary freeway, I-95, encouraging diversion of I-95 traffic to an alternate route (I-295). Results show trends where the usage of particular words in messages is more effective than others in causing diversion, and recommendations are made to create a more effective message that promotes diversion.

This paper deals with the error recovery problem when scanned license plate data are used to predict traffic flows. The aim is to reduce the effects of errors owing to lost plates or mistaken transcription, to improve estimation results. To this end, a method is given and discussed for traffic flow prediction using plate scanning data and taking into account possible errors in plate number recognition. The proposed method uses Bayesian networks because this is an efficient tool for introducing the plate scan error flow as a variable in the model and mending the mistakes in the scan pattern. Several examples are used to illustrate the proposed model. Finally, some conclusions are included.

There is a need to evaluate the damage caused by the new generation of wide-base tires on low-volume secondary roads because of their increased use on trucks. In this study, a three-dimensional (3D) finite-element (FE) model was built to simulate the realistic tire loading on secondary road pavements. The model allows for predicting pavement responses to loading applied by various tire configurations. In addition, the model incorporates the measured 3D tire-pavement contact stresses, models hot-mix asphalt (HMA) as linear viscoelastic material, simulates continuous moving load, and utilizes implicit dynamic analysis. The analyzed pavement structures comprised a 76-mm HMA layer and an aggregate base layer with various thicknesses (203, 305, and 457 mm). The impact of a wide-base tire on secondary road pavement damage was analyzed using available damage models and was compared to that resulting from conventional dual-tire assemblies. It was found that the new wide-base tire (455/55R22.5) caused greater fatigue damage, subgrade rutting, and HMA rutting (densification) but less HMA rutting (shear) and base shear failure compared to the conventional dual-tire assembly when carrying the same load. The findings indicate that wide-base tires’ impact on secondary road pavements depends on the roads’ predominant failure mechanisms. Hence, calculated combined damage ratios can be used for road usage pricing and pavement design practice when wide-base tires are used.

Many aspects influence the skid resistance of a pavement surface including surface texture, tire characteristics, vehicle operations, and environmental factors. The objective of this paper is to quantify the temperature influence on the skid number of asphalt pavement surfaces when measured by the locked-wheel friction tester. Specifically, this study aims to determine an adjustment factor for friction readings to a standard reference temperature, removing the seasonal temperature variations influencing measurements. This allows agencies to improve the comparison of pavement sections and to provide a more objective assessment of pavement conditions for safety. The friction database employed is from the National Center for Asphalt Technology Test Track facility. The data includes friction measurements with a locked-wheel trailer on sections from the 2000 and 2003 research cycles. The approach calculates the temperature adjustment factor, $C_T$, from a grouping of the data by temperature values at the time of the measurements. The results show that it is possible to define a reference temperature to adjust friction measured at any other temperature value. The reference temperature identified is between 19.5°C (67.1°F) and 20.2°C (68.4°F). The study concludes that when testing, if the air temperature is greater than the reference temperature, the friction reading is biased by a positive quantity. Therefore the adjustment factor, $C_T$, reduces the measured friction, whereas for measurements performed at temperatures lower than the reference temperature, $C_T$ increases the measured friction.

This paper integrates the advantages of the analytic hierarchy process (AHP) and fuzzy logic theory and develops a new approach for pavement condition assessment and project prioritization. Roughness, deflection, surface deterioration, rutting, and skid resistance are identified as five performance indicators for evaluating pavement condition. A survey is conducted among experienced professional engineers for establishing fuzzy membership functions of each performance indicator with respect to a fuzzy linguistic evaluation set Very good, Good, Fair, Poor, and Very poor using statistical regression. AHP is applied to determine weight from a paired-comparison matrix. Eventually the fuzzy comprehensive evaluation is carried out using fuzzy relations, which combines a fuzzy evaluation of single performance indicators to the one simultaneously considering all five performance indicators. A maximum grade principle (MGP) and a defuzzified weighted cumulative index (DWCI) are proposed to assign a linguistic assessment result and a numerical assessment result, respectively, to the condition of a road segment. A case study is provided to rank eight road segments using MGP and DWCI. The proposed method offers a promising approach to the accuracy and reliability issues in the data collection and rating of pavement distress condition.

This study focused on the effects of track substructure material nonlinearity on sleeper design pressure for conventional heavy haul railway ballasted railway tracks. The material nonlinearity was applied by using Mohr-Coulomb criteria implemented in finite-element method analysis. By varying the railway track axle load, a sensitivity analysis was carried out considering the different clayey subgrade cohesions, elasticity modulus, and ballast internal friction angles. The comparison of analysis results for the elastic and elastoplastic conditions showed significant differences in the pressure distribution pattern and pressure values. By comparing the sleeper’s average pressure values obtained from the nonlinear analysis with the American Railway Engineering and Maintenance Association and Standards Australia standard sleeper design pressure values, two multipliers were suggested.

Developing an optimal train speed profile for energy-efficient train operation is significant in both theory and applications but very difficult and complex to achieve. An optimization method that minimizes energy consumption by considering track alignment, speed limit, and schedule adherence is proposed. The objective function is total energy consumption, and the decision variables include the timing of train motion regimes. A simulated annealing algorithm (SA) is developed to search for the optimal train operation or “golden run.” The developed model is applied to a segment of the New Haven line of the Metro-North Commuter Railroad, which runs between Woodlawn, New York, and New Haven, Connecticut. A sensitivity analysis is conducted, and the relationship between model parameters and decision variables are explored.