This paper analyzed the effectiveness and cost-effectiveness of several asphalt pavement rehabilitations by investigating the long-term pavement performance (LTPP) database. The multiple regression method was employed to evaluate the influence of overlay thickness, pavement thickness, traffic volume, and preoverlay pavement conditions on the effectiveness and cost-effectiveness. The international roughness index (IRI) was selected as an indicator of pavement performance. The postrehabilitation IRI, IRI drop, roughness increase after rehabilitation, and the “benefit” were used as measures of treatment effectiveness. The results of the presented analyses indicated that thicker overlay and milling reduced the roughness after rehabilitation. Thin overlay, high traffic level, and poor prerehabilitation pavement condition increased the deterioration rate of new overlay. Using reclaimed asphalt material did not influence the treatment performance but was cost-effective in reducing the roughness of new overlay. For a certain deterioration rate, there was an optimized prerehabilitation roughness value or time for applying maintenance treatment.

In the present study, three strain gauge installation methods (mound, trench-cut, and block-out) are evaluated to propose a set of methods that are appropriate for different layers of hot mix asphalt (HMA) pavements. The block-out method is also improved to minimize strain gauge mislocations (i.e., installation offsets). Field tests show that the improved block-out method is suitable for most layers, except for a thin surface, for which the mound should be used. To validate the proposed methods, 374 strain gauges are installed at different layers of thirty-three HMA pavement sections (48 in surface, 157 in intermediate, and 169 in base) at the Korea Highway Corporation Test Road (KHCTR) and their initial and long-term performances are monitored at different times. In addition, the failure rates of the embedded strain gauges are correlated with the evolution of the overall pavement conditions and key distresses at selected sections from 2003 to 2007.

Transverse cracks are distresses whose initiation and propagation affect the stability and structural integrity of the highway pavement. They are caused by load, moisture, temperature, construction defects, or a combination of these. Cracking leads to inherent pavement defects that are exacerbated by moisture infiltration as well as the formation of other pavement distresses like roughness with their attendant problems to roadway agencies and users. One feature of transverse cracks is the spontaneity of their initiation and the uncertainty of their propagation. As a result, modeling the formation and spread of transverse cracks requires a framework within which uncertainty can be expressed explicitly. This research seeks to use a statistical approach to model the propagation of transverse cracks on road pavements in Delaware. The aim is to investigate how hierarchical Markov-chain Monte Carlo (MCMC) simulation performs in estimating and predicting the spread of transverse cracks without neglecting their associated uncertainty. Hierarchical MCMC models use the Bayesian approach, which accounts for uncertainty in pavement distresses.

Rebuilding and maintaining the nation’s highway infrastructure will require very large capital outlays for many years to come. While the expenditures involved in the maintenance and construction of highway facilities are large, current methods of pavement design used in common engineering practice do not routinely take advantage of design optimization methodologies. This paper presents an optimization formulation for mechanistic-empirical pavement design that minimizes life-cycle costs associated with the construction and maintenance of flexible pavements. Sensitivity analysis is performed on the model to understand how the optimal design changes with respect to variations in the critical design inputs. Using typical values for the costs associated with the construction of each pavement layer and the reconstruction of failed pavement sections, it is determined that extended-life flexible pavements may provide significant life-cycle cost savings despite their higher initial construction cost. However, perpetual pavements that control critical strains to levels near the fatigue and endurance limits for the hot mix asphalt (HMA) and subgrade soil should be designed only when traffic levels are sufficiently high to warrant them or when sufficient uncertainty exists in the mean values of design input probability distributions. Optimization studies performed under uncertainty have showed that designs for extended-life pavements are robust with respect to physical variability in material properties, but are significantly impacted by a lack of knowledge of probability distributions.

Traditionally, it has been a common practice to apply priority weights to selected parameters in the process of optimal programming of pavement maintenance. However, there are several issues associated with this approach of incorporating priority preferences into pavement maintenance programming. For instance, applying priority weights to selected problem parameters will lead to a suboptimal solution with respect to the original objective function (such as minimal total maintenance cost or maximum pavement condition). The decision makers may not be aware of this consequence and the magnitude of loss in optimality caused by their choice of priority scheme. This paper proposes an improved methodology of incorporating priority preferences into pavement maintenance programming to overcome these problems. Instead of applying priority weights directly into the mathematical formulation of maintenance programming, priority preferences are handled in two stages of postprocessing of the optimal programming process, namely, a tie-breaking analysis and a trade-off analysis. The optimal programming problem is first solved without applying priority weights to any parameters of the problem. This ensures that the optimality of the solution is not disturbed. In the tie-breaking postprocessing, prioritized maintenance activities are identified to replace lower-priority activities in the solution without affecting the optimality of the solution. Finally, a trade-off analysis is performed to introduce more prioritized activities into the solution on the basis of the willingness of the highway agency to accept some loss in optimality.

To predict the pavement response correctly at any condition, the constitutive relation between stress and strain will require the use of a damaged modulus. Several asphalt concrete slabs constructed in the laboratory using vibratory roller compactor and instrumented with strain gauges and thermocouples were tested under the one-third scaled accelerated loading device called MMLS3. A frequency domain approach has been used to analyze the continuous strain data. The constitutive equation in time domain has been transformed to frequency domain using the Fourier transform technique, and the complex modulus values have been determined continuously during the loading. The results indicated that complex modulus is affected by the combined effect of loading and temperature. A general trend of reduction of complex modulus attributable to these factors has been observed in the study. The analysis results indicate that the frequency domain approach is an efficient and better way to handle large amount of data generated in long-term pavement studies and it can be used to determine the change in the complex modulus attributable to loading.

This study presents an advanced model for calculating water levels on impervious areas. The approach combines information about rain intensity and flow condition to dynamically determine overland roughness values. When water levels on profiled surfaces of less than 500 m are calculated, the full dynamic wave approximation is necessary because the transition of flow differs compared to kinetic wave theory at the beginning of the runoff event. The application of the model to a civil airport demonstrates how the frequency of critical water levels changes when the cutting of grooves into the runways is considered. The local storm water characteristics are defined by heavy convective precipitation during summer. A critical event was presumed when the water level may cause aquaplaning. The results present a reduction of the number of critical events during a period of 10 years by 68%, even without recognizing the additional friction by the grooves. If increasing friction was included, the remaining frequency would be 0.2 times per year.

Much research has been carried out in recent years as to the most appropriate manner in which to obtain suitable transition probabilities for use in Markovian-based infrastructural deterioration models. Although many optimization algorithms have been applied in the process, a review of the literature has shown that the methods in practice may benefit from the use of more sophisticated search algorithms. Consequently, this paper focuses on the applicability of cross-entropy, a previously developed iterative method, as a robust, efficient solver for use in the derivation of these probabilities. An advantage of this method is that through the use of a smoothing parameter, it tends to avoid becoming stuck in the bound constraints of the problem. This value is usually taken to be arbitrarily between 0.6 and 0.9 but was included in this paper as another variable to be optimized, thereby adding to the accuracy of the method. Two different objective functions were compared and the use of stationary versus nonstationary Markov chains was also investigated.

Driver behavior while approaching high-speed signalized intersections at the onset of a yellow indication varies as a function of many parameters including age, gender, perception-reaction time, and acceptable deceleration levels. This paper develops a novel approach for computing the clearance interval duration that explicitly accounts for the reliability of the design (probability that drivers do not encounter a dilemma zone). Lookup tables are developed to assist practitioners in the design of yellow timings. The approach is demonstrated using data gathered along the Smart Road test facility for dry and clear weather conditions for two approach speeds: $72.4\mathrm{km}/\mathrm{h}$ and $88.5\mathrm{km}/\mathrm{h}$. Each data set includes a complete tracking of the vehicle every decisecond within 150 m before and after the intersection. A total of 2016 signal stopping records were gathered. Using these data, models that characterize driver perception-reaction times and deceleration levels are developed. Based on these models, lookup table are developed to assist practitioners in the design of yellow timings that reflects the stochastic nature of driver perception-reaction time (PRT) and deceleration levels.

Civil engineers plan, design, manage, and guide investments in surface transportation. Truck weight limits are inextricably linked to the consideration of pavements, bridges, safety, asset management, sustainability, jobs, and economic productivity. The U.S. 36.3 t cap for gross vehicle weight (GVW), and the Federal Bridge Formula B (FBF B) constrain truck movement of 32.5 t full ISO (International Organization for Standardization) containers. This study identifies the options—and associated implications—for trucks to transport full containers notwithstanding FBF B and the cap. We conducted a national investigation of truck weight regulations across 50 states to identify options, with 28 cases involving telephone interviews. Results show the options are complex, vary by state, result in a patchwork of routes, and include: (1) nondivisible permits, (2) special haul routes, (3) grandfather rights, (4) state roads, (5) tare reduction, and (6) operation outside formal regulations. Containerships, railroads, ports, and highways of U.S. trading partners each accommodate full containers. Conditions identified in this paper can result in underutilization of the productivity and efficiency benefits afforded by international standardization.

General freeway design practice assumes deceleration for a vehicle exiting the freeway occurs between the point when the vehicle clears the through-traffic lane and the point of limiting design speed for the ramp proper. The length provided between these points should be at least as great as the distance needed to accomplish the appropriate deceleration, which is governed by the speed of traffic on the through lane and the speed to be attained on the ramp. The deceleration length values used in design are based on assumed running speed for the limited-access highway and the ramp, along with deceleration rates based on 1930s studies. The need to update the speed assumption for the highway and the ramp curve is clear, although determining appropriate deceleration rates is not as simple. Previous research has demonstrated that drivers select speeds at or above the design speed on horizontal curves, rather than the much lower average running speed that previously had been assumed for several design elements, including exit ramps. Although the assumption that drivers choose speeds less than the design speed in a free-flow situation is certainly questionable, there is some evidence that drivers do decelerate in the travel lane before moving into the deceleration lane. Updates for the current assumptions were identified on the basis of research, including studies on speed behavior on highways and detailed decelerating behavior for 27 exit maneuvers at two ramps. The paper includes suggested deceleration lengths.

This paper presents an investigation of the accurate methodology of motorcycle equivalent units (MEUs) in mixed traffic flow by considering the characteristics of moving vehicles, such as velocity and effective space. In addition, the effective space of each kind of vehicle is computed with consideration of the influences of velocity, physical size of the subject vehicle, and the surrounding motorcycles in the proposed methodology. The field data were collected in Hanoi, Vietnam in the case study. The results indicated that the MEU values of cars, buses, minibuses, and bicycles are 3.4, 10.5, 8.3 and 1.4, respectively. On the other hand, the collection of the field data of vehicle flow and speed passing through road segments was conducted in three groups of divided roads with raised medians. The fundamental diagrams of vehicle speed-flow-density relationships, ranging from stable traffic flow to unstable conditions, were plotted in this paper. The values of capacity, maximum motorcycle flow, critical mean stream speed, and critical density of traffic flow were computed. Results indicated that the capacity values of urban roads with two, three, and four lanes per direction are 13,358, 21,725 and 24,335 (motorcycles per hour) respectively. The capacity, therefore, increases with the number of lanes of urban roads.

Multivariate analysis can be used to identify the effects of several factors on the causes of a crash compared with univariate analysis. This paper uses a multivariate-analysis technique, the multinomial logistic regression (MLR) model, to examine the differences in crash-contributing factors for six collision types for both divided- and undivided-highway nonjunctions, given that a crash has occurred. Multinomial logistic regression was used to investigate (1) single-vehicle and (2) multivehicle collisions, which included (1) angular, (2) head-on, (3) rear-end, (4) sideswipe-same-direction, and (5) sideswipe-opposite-direction collisions. The risks associated with different collision types were found to be significantly influenced by various vehicle actions. The risk of sideswipe-same-direction collisions was higher while changing lanes and merging on undivided and divided highways. Similarly, while merging, drivers were prone to angular collisions, and when slowing down to rear-end collisions on undivided and divided highways. On weekdays, there was higher risk of multivehicle collisions, whereas on weekends, single-vehicle collisions were found to be statistically significant. The risk of single-vehicle collisions attributable to drivers negotiating a curve, driving on a wet road surface, during nighttime, when vision was obscured, avoiding objects on the roadway, and driving under the influence of alcohol was higher compared with that of other collision types. On the basis of the results of the analysis, it was found that the risk of single-vehicle collisions was higher on divided and undivided highways compared with other collision types. Further, binary logistic regression model was used to identify the factors that contribute to crash-injury severity, given that a crash has occurred. Drivers and passengers who did not wear lap and shoulder belts, and drove under the influence of alcohol were involved in serious crash injuries. Drivers involved in a crash on horizontal and vertical curves were prone to severe crash injuries compared with crashes on straight and level roadways. Head-on and single-vehicle collisions were found to be at a higher risk for severe injuries compared with other collision types. Additionally, collision types were strongly related to driver behavior (decision making) parameters, such as merging, changing lanes, and slowing/stopping, compared with parameters such as roadway geometry, atmospheric conditions, and surface conditions. From these results, the importance of different statistical techniques is evident, as the significant variables varied for crash severity and different collision types.

Investment decision making in transportation asset management is typically characterized by a wide diversity of asset types for purposes of optimization at overall system level. To enhance investment analysis and decision making for these multiobjective problem types, the analysis of trade-offs associated with different performance measures can be illuminating and informative. This paper provides techniques for efficient trade-off analysis as part of multiobjective-optimization for transportation asset management. The multiobjective-optimization problem is first formulated by establishing the objectives expressed in terms of network-level performance measures underlying the analysis of trade-offs. Then, the Extreme Points Nondominated Sorting Genetic Algorithm II (NSGA II) technique, an improvement over traditional NSGA II, is applied to generate Pareto frontiers that illustrate the trade-offs. Using candidate projects from a varied range of asset types as a case study, the paper successfully conducts the trade-offs between performance objectives and cost, and then among the performance objectives. The paper also shows that Extreme Points NSGA II has a faster convergence speed and yields a distribution that is superior to the traditional NSGA II.

Work zone capacity is a key variable in scheduling construction activity on roadways. Understanding the effect of different definitions of capacity on the values computed from field data will result in better planning and scheduling of lane closures. Capacity values obtained using three different definitions (and methods) of capacity: (1) maximum sustained flow, (2) rescaled cumulative flow curves, and (3) 85th percentile flow were compared using field data from short-term work zones in Missouri. The queue discharge flow (QDF) values were found to be the most conservative estimates of capacity. The 85th percentile flows were lower than the 15-min sustained flow values in all but one location. The prequeue flow (PQF) values, indicative of near-constant flow prior to breakdown, did not occur in any of the four work zones. The four-site average capacities were 1,149, 1,267 and 1,301 vphpl for QDF, 85th percentile flow, and 15-min sustained flow, respectively. These capacity values were close to the 1,240 vphpl Highway Capacity Manual (HCM)-based capacity value used in Missouri, but lower than most values found in the nationwide survey.