Traffic on a road pavement is characterized by a large number of different vehicle types, and these can be considered in pavement design by using truck factors to transform the damage they apply to the pavement to the damage that would be applied by a standard axle. The truck factors to convert trucks into standard axles or the load equivalent factors to convert axles into standard axles are defined by considering the average loads for each axle. This process includes the vehicles that travel with axle loads above the maximum legal limit. There are also a substantial number of overloaded vehicles in terms of total vehicle weight. These axles/vehicles cause significant damage to the pavements, increasing the pavement construction and rehabilitation cost. Thus, this paper investigates the impact of overloaded vehicles on road pavements by studying the truck factors for different vehicle cases applied to a set of pavements composed of five different asphalt layer thicknesses and five different subgrade stiffness moduli. The study revealed that the presence of overloaded vehicles can increase pavement costs by more than 100% compared to the cost of the same vehicles with legal loads.

An optimization model is designed to guide pavement agencies to make decisions under multicriteria decision circumstances. In this paper, a multiobjective optimization model is illustrated to enable pavement-management agencies to make network-level pavement-preservation decisions based on conflicting objectives, namely, minimizing costs and maximizing pavement smoothness. The optimization model is based on the Pareto optimal concept to simultaneously solve all types of concerns or constraints to obtain the optimum targets, in order to minimize costs and maximize benefits. The simulated constraint boundary method is suggested to find Pareto optimal solutions compared with the genetic algorithm method and a normalized technique for order preference by similarity to ideal solutions was illustrated to assist postoptimization decision making.

The negative binomial (NB) and zero-inflated negative binomial (ZINB) models were employed to simulate the development of pavement transverse cracks on asphalt overlays and to evaluate the influence of different designs of asphalt overlays on crack development. Pavement transverse crack data were collected from 15 long-term pavement performance (LTPP) SPS-5 test sites. Analyzed factors include traffic level, overlay thickness, mixture [using reclaimed asphalt pavement (RAP) or virgin], intensity of surface preparation (mill or no mill) before overlay, total thickness of pavement, and freeze index. Analysis results indicate that the NB and ZINB models were effective in simulating the development of pavement transverse cracks by addressing the overdispersion. In addition, the ZINB model outperformed the NB model by explaining the excess zeros in the cracking count data to capture both the initiation and propagation of cracking. The regression analysis indicates that mill before overlay is effective in retarding the initiation of cracks, but not the propagation of cracks. Thicker overlay appears to reduce transverse cracking. High traffic level or using RAP is likely to increase the number of transverse cracks. Total thickness of pavement and freeze index are not significant for the development of transverse cracks.

At the terminal station, the path headway bias of a trip is linearly correlated to the travel time of the path when it is beyond a critical range, which marks the tendency between successive trips. A tendency-based model for trajectory prediction was proposed in which the path headway bias was assumed to be proportionally amplified at downstream stops with an increase in path travel time, when buses fall into the amplifying tendency, or remain the same, when buses fall into the maintaining tendency. The boundary of the tendency was calibrated by the relative performance between the proportion-based model and the history-based model. The calibrated tendency-based model succeeds in avoiding the amplification of nondirectional path headway bias in the proportion-based model by keeping buses in the maintaining tendency and promoting the amplification of directional path headway bias by allowing buses to fall into the amplifying tendency.

Estimating truck fleet size for winter road maintenance, such as snowplowing, is a challenging task for highway maintenance agencies. To estimate the fleet size for a snowplowing operation, a mathematic model is developed considering road geometry, weather, and traffic speed, subject to service time limit. The complexity of the problem lies in the varying pavement width of road sections, different plowing patterns on the mainline and ramps, weather- and time-dependent traffic speed, and the use of mixed types of plows. With real-world data, the developed model is applied in a case study, and the model demonstrates itself to be considerably flexible in adapting to various circumstances in estimating fleet size for snowplowing operations.

Trucking industry growth increases loads on existing roads and may raise the possibility of overloaded vehicles, which consequently cause significant damage to pavement. Weigh-in-motion (WIM) technology improves weight enforcement activities by increasing the capacity of static weigh stations and provides traffic monitoring and traffic data for pavement research and design. However, there is still a lack of knowledge about the behavior of WIM sensors installed in different environments, which affects reliable and precise data gathering. Therefore, different types of piezoelectric WIM systems, including quartz, polymer, and ceramic sensors, were installed at the Center for Pavement and Transportation Technology (CPATT) test sites of the University of Waterloo in Canada. The objective of this research is to explore the behavior of piezoelectric WIM sensors under different load conditions specifically for the polarized sensors, which illustrated heteroscedasticity in weight estimations caused by inconsistent variance of estimations. The issue was solved by transformation of variables, which resulted in constructing a compensation curve. The investigated effect of the weight factor will assist in the recommendation of improved benefits of the least expensive WIM technology.

Previous research has found that distracted driving measurably increases driver response times to unanticipated roadway hazards. These instances are of particular consequence as they tend to be highly correlated with vehicle crashes resulting in property damage and/or injury. However, comparatively little attention has been allocated to quantifying the negative impacts of distracted driving on driver reaction to anticipated stimuli. This study empirically determined the impact of distracted driving on queue discharge rates at signalized intersections with protected left-turn phases for dual left-turn lanes. Observational studies were conducted at 11 intersection approaches at six signalized intersections in three states resulting in the observation of 844 distracted and 3,726 undistracted left-turning drivers. A statistical model was developed for the start-up lost time resulting from the average headways of the samples in each state. The difference in start-up lost times between queues comprised entirely of distracted or undistracted drivers ranged from 3.36 to 4.06 s in Kansas, 2.97 to 4.41 s in Oregon, and 2.25 to 5.14 s in Utah.

The term breakdown of flow at a freeway bottleneck location has been used to describe the transition from relatively free-flowing traffic to congestion, often called stop-and-go traffic, but more generally experienced as slow-and-go. The focus of the research presented in this paper was to develop probabilistic models to predict breakdown of flow by using data from five freeway-ramp merging segments. The development of the probabilistic models allows exploration of several issues: the identification of breakdown, the performance measures for the identification of the breakdown, and the breakdown location. The breakdown probability model (BPM) is based on lifetime data analysis statistics. The breakdown probability models can be very useful tools in establishing capacity values, which in turn can be part of the freeway traffic management process.

This paper proposes a multiobjective optimization approach for planning railway ballast, rail, and sleeper renewal operations. The objective is to support an informed decision that considers not only the railway track life-cycle cost (LCC) but also the track occupation times required to perform interventions. Two objective functions were minimized, as follows: (1) railway track unavailability caused by railway track maintenance and renewal operations, and (2) railway track components’ LCC. Furthermore, to rationalize the renewal strategy, the model considers a multicomponent formulation that assesses, in time and space, the opportunistic combination of railway track renewal activities. A numerical application of the model on a real case study (Lisbon-Oporto line) is developed and discussed. The results show the interest of using this simple multiobjective optimization approach to obtain a decision-making process to support the scheduling of major railway track renewal works with an informed LCC-unavailability trade-off.

The number of motorcycle crashes in Alabama more than doubled from 1999 to 2008, while the number of fatal motorcycle crashes tripled during the same period. Most work on motorcycle crash severity has been based on analysis of all crashes involving motorcycles. The majority of motorcycle crashes in Alabama are cause by the motorcyclist. An analysis of factors affecting the injury severity outcome of motorcycle causal crashes is presented. The analysis uses a multinomial logit (MNL) regression model to examine 5 years (2006 to 2010) of crash data. The variables affecting motorcycle crashes were grouped by common characteristics into four categories: motorcyclist, crash, environment, and roadway. Average direct pseudoelasticities were obtained to interpret the factors influencing motorcyclist-caused crashes (MCCs) severity. With some 70% of motorcycles crashes in Alabama resulting in some type of injury, there is potential for positive impact on safety from policies and programs that address the behavior-related crashes identified in this study. In addition to reducing behaviors considered as aggressive, it would appear that considerable safety benefit could be derived from efforts to alter motorcyclist behavior in the vicinity of large vehicles, around roadway curves, and in rural areas.