This article proposes an optimization model for designing the intervals (headways) and sizes of buses circulating on public transport networks by minimizing the system’s operating and user costs. The proposed model takes into account congestion on the public transport system and works by considering elastic demand, allowing the analysis of the repercussions of modifications made to the operational characteristics of the public transport system on changes in users’ mode of transport. An in-depth sensitivity analysis checks the influence the value of time has on the final model results, and these results are compared with those from a model using inelastic demand. The validity of the proposed model is shown by applying it to the city of Santander, Spain. Finally, the consideration of elastic demand is shown to be beneficial in response to proposed operational changes rather than the assumption of a fixed and known public transport trip matrix.

Full equilibrium superelevations are rarely installed on tracks. Consequently, trains run on unbalanced superelevations. Currently, the maximum curving speed is modeled with blanket unbalanced superelevations. Blanket unbalanced superelevations are not desirable for all actual superelevations, regardless of their values. In this paper, the maximum curving speed is modeled to ensure comfort and safety, considering two primary parameters: the curve parameters and the vehicle characteristics. The curve parameters include radius and actual superelevation, and the vehicle characteristics include the height of the center of gravity (c.g.) on top of the rail and the suspension factor. The proposed model has interesting features. It suggests that unbalanced superelevation is proportional to actual superelevation. The maximum curving speed is evaluated against the normal operating speed as the base reference. The model is applied and validated from the point of view of both comfort and safety. The paper suggests a method to divide equilibrium superelevation into actual and unbalanced superelevations.

The prevention of secondary crashes is a high priority task in traffic incident management. However, the limited knowledge regarding the nature of secondary crashes largely impeded the development of established countermeasures. The primary goal of this paper is to improve the literature’s understanding of secondary crashes. This goal is achieved in two steps: first, with an analysis framework that accurately identifies secondary crashes by integrating rich traffic-sensor data with statewide-crash data and, second, by carefully investigating the characteristics of these identified secondary crashes. To that end, secondary crashes within a 27-mile section of a major highway in New Jersey were mined using the developed analysis framework, and a thorough examination of their characteristics has been performed. Empirical findings on the frequency of secondary crashes, their spatio-temporal distributions, clearance time, crash type, severity, and major contributing factors have been highlighted. Taken together, these preliminary results could potentially help transportation agencies make more informed decisions on mitigating secondary crashes and improve their incident management operations. To complement the results, further in-depth investigations using more high-resolution sensor data and high-quality incident records are suggested.

Statistical models that describe the relationship between crash frequency and its influencing factors have been widely studied for the last three decades. Most of the existing methodologies use these models with count data and their variants to study the mean effects of covariates on crash frequency. This study seeks to explore the use of quantile regression for counts as a methodological alternative in analyzing crash frequency. Compared with existing models, the proposed model provides a fuller and more robust analysis of crash data for at least two reasons. First, crash data usually follow typical count distributions with a large proportion of zeros, and the remaining values highly skew toward the right. This nature of crash data makes quantile regression appealing because it can provide more comprehensive information about the effects of covariates on crash frequency rather than just the mean because quantile regression allows various quantiles of a population to be estimated. Second, as a semiparametric technique, quantile regression for counts allows researchers to relax restrictions in the form of the distribution function of the response variable, resulting in more robust estimation. In addition, two prediction methods are proposed to take advantage of such analysis results to yield better point prediction. To illustrate the application of quantile regression, crash data for interstate highways in urban areas in Washington State in 2002 were extracted from the Highway Safety Information System (HSIS) and analyzed with the proposed model. The analysis results and prediction performance were then compared with those from the negative binomial regression model. The numerical case study shows that although the significance and signs of the effects derived from both models are consistent, the proposed quantile regression model reveals more detailed information on the marginal effects of covariates change across the conditional distribution of the response variable and provides more robust and accurate predictions on crash counts.

Two main approaches have been followed to determine the modulus of unbound materials in six asphalt pavement sections tested at the CEDEX test track. The first one consists on back-calculation from the deflection bowls measured under the load of a falling weight deflectometer. In the second approach, a nonlinear constitutive relationship is calibrated from repeated load triaxial testing, and layer modulus is estimated through this relationship for the stresses calculated under traffic loads by using a multilayer linear elastic model. Significant differences were found between the two procedures, with back-calculation resulting in higher modulus. The two approaches were evaluated by comparing model predictions to actual response measured under the load of the traffic simulation vehicles of the test track. The results indicate that back-calculation provided more realistic estimations of the stiffness of unbound materials than the laboratory approach. This last approach significantly underestimated the stiffness of granular materials located at shallow depths in the pavement structure.

This study compared the performance of three aggregate layers, commonly used in the construction of unbound forest roads in Ireland, when they were subjected to repeated loading in a new large-scale test rig. These layers comprised: (1) a layer of uncrushed, granitic, sandy gravel-a good quality road aggregate, (2) a layer of shale-a poor quality aggregate, and (3) a layer of crushed limestone–an excellent quality aggregate with a wet mix macadam (WMM) grading–on top of a poor quality shale subbase layer. The repeated load testing rig was designed and constructed to test different surface or completion layering thicknesses of the aggregates over a common formation or subgrade material of silty sandy soil. This testing was achieved by surface loading the aggregates through a 200-mm-diameter rubber pad-attached to a hydraulic actuator on the test rig-for up to 150,000 load applications. The subgrade pressures and surface deflections were measured at applied stresses of 500, 750, and 1,000 kPa. The good quality granitic aggregate performed much better than the poor quality shale aggregate under the repeated loading and is suitable as a completion material for use in unbound forest roads. The shale aggregate can be used in unbound forest roads as a subbase material.

Ramp-metering, which controls the onramp flow into the freeway, is successful in increasing freeway throughput and reducing overall travel-time. The maximum flow allowed from the onramp during ramp-metering is typically estimated so that the sum of flows from the onramp and mainline do not exceed a predetermined threshold (either the capacity of the downstream section or a threshold based on occupancy at capacity). Recent research has shown that this threshold is probabilistic and the transition from noncongested to congested conditions (i.e., breakdown) occurs stochastically. Also, research has shown that the contribution of the ramp and freeway demands on breakdown is different; 100 additional vehicles arriving from the ramp increase the probability of breakdown more than 100 additional vehicles from the freeway. This fluctuation has been studied through the development of breakdown probability models, which provide the probability of breakdown as a function of the combination of the mainline and ramp flows. The writers’ objective was to develop suitable site-specific probability of breakdown models and use them within existing ramp-metering algorithms to evaluate their ability to postpone the breakdown and reduce congestion at freeway facilities with recurring congestion. The writers first develop a process for obtaining breakdown-probability models for existing critical ramps (i.e., those where breakdown starts). Next, the writers propose specific enhancements to existing ramp-metering algorithms that incorporate probability-of-breakdown models. Proposed enhancements are presented for two algorithms, as follows: (1) the Minnesota stratified ramp-metering algorithm (SZM), and (2) the Ontario COMPASS algorithm. Simulation was used to replicate these algorithms and evaluate the proposed enhancements. The results of these experiments showed that the enhancements are effective in postponing congestion at the two sites by 17–35 min.

The recently developed mechanistic-empirical pavement design guide (M-E PDG) represents a significant improvement over the AASHTO design guide. One of the major improvements to the M-E PDG is traffic characterization. The M-E PDG does not use the equivalent single axle load (ESAL) approach that is employed in the AASHTO design guide. Instead, it follows a more rational approach that is based on describing traffic in terms of number of axles by axle type and axle load distributions. In recent years, several research studies have been conducted to evaluate the sensitivity of the M-E PDG to the required traffic inputs. It was determined that vehicle class distribution (VCD) has a significant influence on the design of pavement structures. This study investigated various methods for estimating the VCD factors based on functional classification (FC), truck traffic classification (TTC), cluster analysis, and short-term counts. An extensive traffic monitoring data set that included information from 143 permanent traffic monitoring sites, composed of 93 automated vehicle classifier (AVC) and 50 weigh-in-motion (WIM) sites distributed throughout the state of Ohio, was used to evaluate these methods. A sensitivity analysis was also conducted to determine the effect of these methods on the predicted pavement service life. It was found that the functional classification, M-E PDG TTC grouping system and cluster analysis methods may significantly underestimate or overestimate the predicted pavement service life, whereas the short-term count method produces small variations in the predicted pavement service life. This suggests that the short-term count method is the most accurate method for estimating the VCD factors to be used in pavement design.

Researchers are still seeking a better understanding of the parameters that affect safety in horizontal curves. Curves are one of the most critical sections of the road network contributing to a high percentage of serious runoff accidents and lane-changing crashes. Moreover, driving in curves requires combined control of both steering and speed, taking into account the dynamic response and limits of the car. The objectives of this study were to evaluate the safety performance of horizontal curves by analyzing vehicle dynamic signals, such as lateral acceleration and speed, as well as quantitative analysis of lane-changing maneuvers. The study uses real traffic environments where driver behavior and vehicle response data were recorded and stored during regular operations without subjecting the driver to any experimental controls. A total of 96 curves, equally distributed for left and right turn directions, have been collected and grouped according to their radii. The analysis identified frequent overtaking and lane-change maneuvers on the curves, of which 20% more lane changes occurred on right curves than on left curves. Lane-change maneuvers also increased significantly with increasing curve radius. The curve entrance was found to be the most dangerous segment of a curve. Current design practice assumes the safety risk is constant when driving along horizontal curves. The results also showed that drivers consider curve radius in choosing their driving speed rather than the posted speed limit of the curves. The study showed how road design influences the driver’s strategy by establishing links between curve features, vehicle dynamic responses, and the driver’s behavior. Analyzing road characteristics gave insight into how road geometry affects the vehicle dynamics relevant to safety and driving strategy through curves. The findings are useful inputs for reviewing curve design, selecting appropriate countermeasures, and improving active safety devices.