In order to measure the texture depth of asphalt pavement automatically and accurately, a data set generated by a real-time laser pavement texture meter (RLPTM) was realized, and a calculation method was developed. Three groups of experiments were designed and conducted to calibrate the measure results of the electric sand patch method (ESPM) to the ASTM sand patch method and compare the performance of the RLPTM with the ESPM. Experimental results showed that the correlation coefficient of the ESPM and sand patch method have reached up to 0.9830, indicating a good correlation. The RLPTM method had higher resolution (more significant figures) and better repeatability (lower coefficients of variation) than ESPM. The correlation coefficient between RLPTM and ESPM was 0.9207, demonstrating the good repeatability of the overall measurements. The absolute errors of the results obtained by RLPTM and ESPM were within 0.3 mm. Further analysis indicated that results of RLPTM were slightly over predicted (slope value 1.0141) compared to that of ESPM, whereas results of ESPM were slightly under predicted (slope value 0.9823) to that of sand patch. Thus, results of RLPTM may come closer to ASTM sand patch data than ESPM data, indicating the high accuracy of RLPTM.

Stipulating a space headway is a pivotal concern in traffic engineering. Although consideration of a larger headway leads to safer traffic movement, consideration for a smaller headway can serve more traffic volume, which is significant from an economic standpoint. Implementation of a smaller headway, however, could lead to the tailgating phenomenon (short distances between two vehicles), which is perceived as troublesome and dangerous. Evaluating the space headway provides a reasonable approach to understanding the operational benefit for safety and traffic concerns. Using probabilistic analysis to account for uncertainty can be one of the best applicable methods because the headway data are not deterministic and are treated as random variables. More specifically, by emphasizing the reliability analysis, it is feasible to determine the appropriate space headway. The objective of this paper is to present a state-of-the-art approach for the evaluation of the statistical parameters of the headway by utilizing the reliability analysis. Several data sets among entire states were obtained from the Federal Highway Administration (FHWA) in 2012 and utilized in an analysis of a more pragmatic approach.

Roadway lighting systems play a major role in maintaining nighttime traffic safety as they reduce both the number and severity of nighttime traffic accidents. While the design of roadway lighting systems involves multiple objectives, past studies have focused on optimizing only one of the multiple objectives that should be considered. This paper presents a multiobjective optimization model for roadway lighting projects that simultaneously optimizes four design objectives. The incorporated objectives are (1) maximizing the average lighting level on the road surface; (2) maximizing the lighting uniformity along the roadway; (3) minimizing the glare to road users produced by the lighting system; and (4) minimizing the cost of operating the lighting system. The model is designed and developed as a multiobjective genetic algorithm to help decision-makers in their endeavor to provide efficient roadway lighting systems that strike a balance between the four conflicting objectives. The present model considers the following six design variables: type of lighting fixture, mounting height, spacing, fixture offset, fixture’s inclination, and fixture’s rotation angle. An application example is analyzed in this paper to clarify the use of the model and display its significant features in producing better lighting arrangements for roadways.

Conditions at a freeway bottleneck are predicted with a two-dimensional stochastic differential equation (SDE) model of flow and speed. Stochastic behavior is assumed to be standard Brownian motion. To fully describe conditions observed in the field, it is necessary to couple the SDE model with a stochastic capacity model, thus forming a stochastic capacity-differential equation (SCDE) model. The model contains mechanisms to (1) trigger a breakdown event when the traffic flow reaches or exceeds the freeway capacity, and (2) identify when recovery takes place. Measures of freeway performance are derived from SCDE model predictions. They include breakdown probability, expressed as a function of traffic flow and time-of-day, and recovery time, i.e., the length of time the freeway remains in a congested state before returning to a free-flow state. To achieve dependable model forecasts, it is necessary to impose constraints when fitting the stochastic capacity model. The constraint condition consists of matching the breakdown and prebreakdown flow distributions derived from field data and stochastic capacity model forecasts. Sensitivity analyses are performed to test validity of the model-fitting procedure and to test the dependability of the SCDE model forecasts. An empirical study of recurrent workday freeway congestion on I-93 in Salem, NH is used to demonstrate the approach. The approach demonstrates the need to treat breakdown as a random process.

This paper describes a turbo roundabout design procedure based on the rules of design vehicle movement geometry. Previous studies carried out on various junction types have shown that this design approach ensures the usage of optimal roundabout element dimensions as well as safety and comfort during driving. A five-step design procedure is suggested, including a detailed description of relevant influential parameters. They are choosing a design vehicle, implementing the initial roundabout scheme on which the vehicle swept path is analyzed, choosing a method of assigning input parameters for swept path requirements testing procedure, choosing optimal design elements of a standard turbo roundabout, and fastest path vehicle speed analysis.

This paper presents an infinite series solution to predict the time-dependent temperature profile within the pavement surface layer on the basis of measured pavement surface temperature data during falling weight deflectometer (FWD) testing. The infinite series solution was derived using the method of eigenfunction expansions. The model validation was conducted based on the long-term pavement performance (LTPP) program FWD temperature data. The interpolatory trigonometric polynomials were used to approximate a pavement surface temperature history by using temperatures measured at different times during FWD testing. Total of 2,066 and 1,968 pavement subsurface temperatures within surface layers were predicted by using the derived infinite series solution for flexible and rigid pavements, respectively. These temperature profile predictions were compared with the measured pavement subsurface temperatures, showing that the proposed solution can rapidly and accurately predict the transient temperature profile within the pavement surface layer during the short time period of FWD testing with limited inputs.