Sustainable use of biomass as a renewable source of energy can be an alternative solution to the cost of fossil-based energy and global warming. Production of biofuel from plant biomass results not only in bio-based energy, but also in coproducts containing lignin, modified lignin, and lignin derivatives. This paper discusses the moisture susceptibility of subgrade soil stabilized by bio-based energy coproducts containing lignin, with the aim of establishing a new application for bio-based energy coproducts in soil stabilization. An experimental test program was conducted to compare the moisture susceptibility of lignin coproduct-treated soils and traditional fly ash stabilizer-treated soil samples. Additive combinations were also evaluated. There were two types of laboratory tests for moisture susceptibility evaluation: (1) unconfined compression strength (UCS) tests after “dry” and “wet” conditioning, and (2) visual observation of soaked specimens. Results indicate that the biofuel coproducts have excellent resistance to moisture degradation for the Iowa Class 10 soil classified as CL by the Unified Soil Classification System and as A-6(8) by AASHTO. In particular, Coproduct A with higher lignin content is more effective in providing moisture resistance than Coproduct B with lower lignin content as well as conventional additive (Class C fly ash). Moisture resistance comparable to that of Coproduct A could be obtained through the use of additive combinations (Coproduct A+Class C fly ash, Coproduct A+Coproduct B).

Faulting has traditionally been collected by using manual methods, which are labor intensive, time-consuming, and hazardous to workers and drivers. Therefore, alternative methods for effectively and safely collecting faulting data are needed. With emerging laser technology originally designed for crack detection, high-resolution, full lane-width coverage, three-dimensional (3D) continuous pavement profile data can now be acquired. This paper critically assesses the feasibility of using this 3D continuous pavement profile data for measuring faulting with a special focus on accuracy and repeatability. Controlled field tests were conducted to evaluate the accuracy for faulting in different ranges. Field tests were conducted at highway speeds on I-16 in Georgia to evaluate the repeatability and feasibility of the proposed method. Results show the proposed method can estimate faulting with an average error of less than 1 mm compared with those measured using the Georgia fault meter, and it can achieve reasonable repeatability with a standard deviation less than 1 mm in repeated runs at different highway speeds. Two tests have demonstrated that it is feasible to collect faulting data using 3D continuous pavement profile data. Recommendations for future research are also discussed.

This paper finds that pavement treatment short-term effectiveness in international roughness index (IRI) follows a polynomial relationship with a pretreatment pavement condition. Pretreatment pavement condition is an influential factor of the treatment effectiveness in the short term. This paper finds that (1) hot mill overlay can offer average reductions in IRI of $1.44\mathrm{m}/\mathrm{km}$ IRI; (2) crack sealing can offer average reductions in IRI of $0.27\mathrm{m}/\mathrm{km}$ IRI; (3) aggregate seal can offer average reductions in IRI of $0.31\mathrm{m}/\mathrm{km}$; and (4) chip seal can offer average reductions of 0.72.

This paper presents results of an exploratory study on differences in driving characteristics between normal and emergency situations and builds up the car-following model under the emergency evacuation situation. The simulation scenario has been given to create a driving environment under the emergency evacuation situations. Questionnaire investigations and the electrocardiogram/heart rate monitor are used to verify the validity of the driving environment from both subjective and objective perspectives. Perception reaction time (PRT) and the critical headway are taken as two indicators to describe driving characteristics. The results show that PRT is in accord with normal distribution under both normal and emergency situations. The value of PRT under the emergency situation is lower than that under the normal situation. The results also show that critical headway under the emergency situation is smaller than that under the normal situation. A back propagation (BP) neural network is designed in this study. A combination of the Levenberg-Marquardt BP algorithm and Bayesian regularization is employed to train the network. Gray-correlation analysis is conducted to determine which factors have a great impact on the acceleration of the following car. Simulation of the BP neural network using data collected from driving simulator reveals that the BP neural network has a high precision in the prediction of the car-following model.

The effect of combining horizontal and vertical curves on operational conditions has been the subject of research over the last few years. A number of studies have reported that combining horizontal curves with crest and sag vertical curves would influence the curvature appearance of such combinations. According to these studies, drivers could misperceive the curvature of a horizontal curve when combined with a vertical curve, and this might result in negative operational effects. These negative effects would most notably be in the form of an erroneous driver’s speed choice. This study presents a driving simulation experiment that was planned and conducted to examine the effect of a driver’s misperception of horizontal curvature on the driver’s speed behavior. The simulation data were analyzed statistically and provided support for the hypothesis that drivers’ misperceptions of horizontal curvature affects their choice of speed. The effects of several geometric parameters on speed were also examined, including curve radius, algebraic difference of vertical grades, rate of vertical curvature, turning direction, superelevation rate, and sight distance.

Travel speed ties directly to travel time, so it is an important measure for quantifying arterial performance. However, accurately estimating link travel speed for urban arterials is difficult because of traffic fluctuations and stop-and-go conditions caused by signal control. This research proposes a two-step empirical approach to effectively estimate the link-journey speeds using only advance loop detector outputs. The first step is to estimate the spot speed on the basis of advance loop measurements using Athol’s algorithm. The robust regression technique can be used to calibrate the speed estimation parameter (or $g$-factor) in Athol’s algorithm. The second step is to use the proposed simplified speed estimation model to estimate the link speed using only the calculated loop-spot speed without any knowledge of signal timing plans. Traffic operations in the central business district of the City of Bellevue, Washington, are simulated in the VISSIM traffic simulation model. The test results show that only 50 cycles of data are needed to calibrate the $g$-factor in loop-speed estimation and the same datasets can be used to calibrate the proposed link-speed model. Using this model, the average mean absolute error over the study links is reduced from 4.24 to 1.51 mph. With proper calibration, this average error can be further reduced to 0.91 mph. The results are encouraging and satisfactory. The results also show that the accuracy of speed estimation may be further increased when more data are applied for calibration.

The simulation of U-turns is currently of great interest to many transportation professionals due to the widespread use of indirect driveway left-turn treatments. In this study, a procedure was proposed to help transportation professionals model U-turn movements at unsignalized intersections with nontraversable median cross sections in the traffic simulation program VISSIM. The behavioral features of U-turning drivers, such as the priority rule, lane selection, and turning speed of U-turns, were determined using data collected from 13 different locations. A procedure based on a genetic algorithm was proposed to calibrate and validate the VISSIM simulation model. The simulation results of the calibrated VISSIM model were compared to field measured capacity and to the capacity estimated using gap acceptance models developed in previous studies. The calibrated VISSIM simulation model yields mean absolute percent errors of 17.6 and 20.7% for four-lane and six-lane streets, respectively. The results show that VISSIM provides reasonable capacity estimates for U-turns at unsignalized intersections with raised median cross sections, after crucial parameters are properly defined and calibrated.

The goals of this paper are to (1) provide a review of the recently developed measures for assessing the quality of origin-destination (O-D) trip tables estimated from traffic counts and (2) propose a new generalized demand scale (GDS) measure. This GDS quality measure generalizes the total demand scale (TDS) quality measure by allowing the assessment of the intrinsic underdeterminant problem of O-D estimation from traffic counts at various spatial levels. Numerical examples are provided to compare the proposed GDS measure with the TDS measure, the maximal possible relative error, and the expected relative error and to illustrate the features of the GDS measure.

A toll-lane selection process at a toll plaza can be looked at as an outcome of the choice processes of individual drivers. In this paper, an attempt is made to develop a random utility-based discrete multinomial choice model for the behavior of automobile drivers while selecting toll lanes at a toll plaza. Specifically, a multinomial logit model is developed and calibrated using disaggregate level choice data from three toll plazas with different geometry and rates of arrival of vehicles. The calibrated logit models from the different sites when statistically compared show that a generic model, applicable to all the sites, is possible. Such a generic model is also developed. The use of the proposed model can improve the analysis of traffic flow at toll plazas and ultimately lead to more effective designs of these facilities.

Traffic flow forecasting, especially the short-term case, is an important topic in intelligent transportation systems (ITS). This paper researches network-scale modeling and forecasting of short-term traffic flows. First, the concepts of single-link and multilink models of traffic flow forecasting are proposed. Secondly, four prediction models are constructed by combining the two models with single-task learning (STL) and multitask learning (MTL). The combination of the multilink model and multitask learning not only improves the experimental efficiency but also improves the prediction accuracy. Moreover, a new multilink, single-task approach that combines graphical lasso (GL) with neural network (NN) is proposed. GL provides a general methodology for solving problems involving lots of variables. Using L1 regularization, GL builds a sparse graphical model, making use of the sparse inverse covariance matrix. Gaussian process regression (GPR) is a classic regression algorithm in Bayesian machine learning. Although there is wide research on GPR, there are few applications of GPR in traffic flow forecasting. In this paper, GPR is applied to traffic flow forecasting, and its potential is shown. Through sufficient experiments, all of the proposed approaches are compared, and an overall assessment is made.

This paper presents a continuous approximation model for the vehicle routing problem for emissions minimization (EVRP) and demonstrates the model’s applicability and usefulness with numerical studies based on implementations of the Solomon test instances. The EVRP is a variant of the time-dependent vehicle routing problem (TDVRP) in which minimizing emissions is an additional objective of the model. The continuous approximation model presented in this paper will facilitate strategic planning of one-to-many distribution systems and evaluate the effects of emissions costs. Furthermore, results from the continuous model can provide guidelines for constructing solutions for the discrete EVRP. Results from a sensitivity analysis indicated that the optimal number of dispatches during the peak period is smaller than the optimal number of dispatches during the off-peak period when considering the temporal effects of congestion. Results revealed that the potential cost savings attributable to incorporating emissions in distribution problems are considerable. Incorporating emissions costs in distribution problems will contribute toward having more environmentally sustainable distribution systems. The proposed continuous approximation model can be extended for other specific conditions (e.g., when considering pipeline inventory, storage restrictions, different customers, random demand, discriminating strategies, etc.).

The Hong Kong Transport Department initiated a trial of the spiral-marking system on four roundabouts in different phases between January 2004 and August 2008. In this study, observational and questionnaire surveys were conducted to assess how the proposed roundabout marking system affected driver behavior, level of service, and safety performance. The vehicle travel time, number of conflicts, crash incidence, and weaving movements were assessed before and after the implementation in the observation surveys, and the perceived safety, congestion level, and lane-changing difficulties were assessed through questionnaire surveys, respectively. Generally, drivers preferred the spiral-marking roundabout to conventional roundabouts, especially after trialing the new marking system. Inexperienced drivers, in particular, appreciated the spiral-marking system because of the increased safety level, reduced congestion level, and increased ease of navigation. On the other hand, the results of the observational survey indicated that there was no unfavorable change in the average approach lane choice, circulating time, and lane-crossing rate following the implementation of the spiral-marking roundabouts. The confounding effects of possible factors, including circulating lane width, entry and exit width, entry and exit curvature, entry angle, time period, and roundabout flow were taken into account in the multiple regression model. Nonetheless, there were marginal decreases in potential conflicts, with no significant reduction in crash frequency at the spiral-marking roundabouts.

Designing baseline or vertical alignment in railways greatly depends on the designer’s experience. An inaccurate design imposes unreasonable costs during construction and operations. This paper presents a program to find the best vertical alignment for a track with a given horizontal layout. First, the vertical alignment is formulated using mathematical equations, and then an algorithm is developed to produce its geometric configuration. Construction and operating costs, which are sensitive to gradient changes, are expressed as vertical alignment variables and included in the model. A direct search method is employed to solve the objective function, and a genetic algorithm encodes solution algorithms in a program, whereas certain heuristic operators are utilized to cover operational situations. The program also provides railway engineers and planners with practical information on the resulting alignment such as time and speed graph, total traction force, and block capacity. Moreover, the model is evaluated using a real example to verify the algorithms’ ability to find the optimal alignment.

The bottleneck section of a railway passenger station is very complex, and many frequent technical and shunting operations are performed over a long time. The connection of station tracks, locomotive depots, and passenger train stock servicing yards also have great effects on the carrying capacity of a railway passenger station. Therefore, there have been many optimization problems in engineering projects and practices. This paper proposes a bottleneck optimization model to enhance the carrying capacity by reasonably arranging routes and groups of turnouts with the consideration of proportionality and minimized total occupation time. The simulated annealing algorithm is provided to solve this programming, and the optimized strategy is given. A case study focused on the bottleneck section optimization in the passenger station of China illustrates the application of the proposed model.

People, industries, and public agencies all face the possibility of sudden events that disrupt their normal activities. Disasters on the transportation network, whether attributable to nature, human error, or human intent, raise awareness of the need for plans of action to quickly restore mobility. Resilience of the transportation network is the capacity to absorb the effects of a disruption and to quickly return to normal operating levels. Resilience measures are useful to evaluate and predict disruptions and recovery and to guide infrastructure investments that protect against those disruptions or that accelerate recovery after a disruption. This paper presents a methodology for estimating two composite resilience measures—reduction and recovery. The method is illustrated by using resilience triangles derived from sampled truck speeds and counts along the Interstate $90/94$ corridor from Hudson to Beloit, Wisconsin during two significant weather events in 2008. This paper presents a set of criteria, on the basis of observed effects on various sections of the highway corridor, to qualify the computed resilience measures. In this paper, the focus is on generalizing the characteristics of the resilience response rather than characteristics of the corridor sections that lead to patterns of the response for each section. The criteria and the measures together are a useful tool to evaluate resilience.