This paper describes, in detail, the concrete operational mechanisms of the Corridor Simulation (CORSIM)–embedded signal transition algorithms. Through detailed analysis of these transition algorithms, this study found that controller configurations, such as minimum green time for main street and recall mode, are important factors affecting plan transition performance. Specifically, this study demonstrated that even when the same transition method is used, the operational result can vary significantly according to these controller configurations. A simulation study was performed to comparatively evaluate delay performance of plan transition algorithms under two operational situations, namely, transition into peak and out of peak traffic demands. Although the shortway method showed the best performance for most simulation scenarios, the three-cycle and immediate methods could be included in the best-performing group in the case of transition into peak through an adjustment of the main street minimum green time. By contrast, these pretimed transition algorithms experienced markedly higher side-street delay during the transition out of peak demand because of the relatively long main street green time required by these transition methods.

Pedestrians walk differently on different types of facilities. Walking speeds are governed not only by the width of the facility but also by age and gender, land uses, temporal variations, cell phone usage, carrying baggage while walking, and movement in groups. This paper discusses development of adjustment factors for effective design of pedestrian facilities on the basis of pedestrian walking speeds under such influences on three types of facilities, e.g., sidewalks, wide-sidewalks, and precincts. Eighteen locations are selected in five cities in India, and data are analyzed considering the influencing parameters. Pedestrians walk faster than the population mean walking speed on sidewalks at $71.22\mathrm{m}/\min$, whereas they walk at a speed of $63.60\mathrm{m}/\min$ on precincts. Exercise and leisure walking speeds of pedestrians are $74.57\mathrm{m}/\min$ and $62.44\mathrm{m}/\min$, respectively. Significant difference is observed in the walking speed of pedestrians when moving alone or in a group. Pedestrians carrying baggage walk slower at $64.26\mathrm{m}/\min$ compared with the population mean walking speed. Similarly, pedestrians using cell phones walk slower at 62.82, 62.19, and $59.29\mathrm{m}/\min$ on sidewalks, wide-sidewalks, and precincts, respectively. Land use also plays a major role in determining the walking speed of pedestrians. Pedestrian speed in an educational area ($85.27\mathrm{m}/\min$) and in shopping areas ($60.21\mathrm{m}/\min$) are 28% faster and 16% slower, respectively, than the population mean walking speed.

Travel time has long been an important performance measure for assessing traffic conditions and the extent of highway congestion. However, recently, more and more attention has been given to understanding the uncertainty regarding the variability in travel time from hour to hour and day to day—variability that is known to be a source of great frustration among road users. In this paper, travel-time variability is studied by collecting travel-time data using probe data on freeway segments in Indianapolis obtained using anonymous Bluetooth sampling techniques. The data show considerable travel-time variability is induced by adverse weather, but also show that variability results from unexpected changes in traffic flow rates and driver behavior. Various statistical models are estimated to understand the effect that traffic-related variables have on variability in individual vehicle travel times as well as average travel times. For individual vehicle travel times, a model is estimated to study how the probability of a vehicle’s duration of time spent on freeway segments changes over time. Interestingly, this model shows that the point where the conditional probability of travel times becoming longer occurs roughly at the onset of level-of-service $F$ conditions—an important finding that supports the traffic-density definitions of level of service $F$ used in the highway capacity manual. Another model, estimated using seemingly unrelated regression estimation, studies 15-min interval average vehicle travel times and the standard deviation of these travel times on the basis of speed and volume (available from remote traffic microwave sensors) and time of day indicators. This model provides interesting insights into the traffic parameters that affect average travel time and its variability.

The objective of this paper is to present an improved paint pavement-marking retroreflectivity degradation model. Previous degradation-modeling methods were reviewed and studied. The paint-marking retroreflectivity data were collected on 25 two-lane rural highways in North Carolina from November 2007 to May 2010. A linear mixed-effects model (LMEM) was selected as most appropriate for the paint-marking retroreflectivity data. The LMEM took into account the correlation among repeated measurements of the data set and produced more accurate predictions than other methods. LMEMs were established for white edge and yellow center paint pavement markings. The LMEM results showed that the estimated white edge pavement marking life cycle is 37.5 months and yellow center marking life cycle is 38.9 months on average when using 100 and $65\mathrm{mcd}/{\mathrm{m}}^2/\mathrm{lx}$ as the minimum acceptable retroreflectivity values for white and yellow paint markings. A model comparison indicated that the LMEM provided a more accurate prediction than linear-regression models when multiple retroreflectivity measurements are collected on a specific road.

An accurate estimate of temperature stress in asphalt pavements is an important consideration for colder regions. In asphalt, a viscoelastic material, the developed stress depends on the trend of temperature variation and the relaxation modulus of the asphalt mix. This paper presents a comparative study of the temperature stresses by considering different temperature profiles for the same temperature differential and different relaxation models for the same asphalt mix. A nonlinear viscoelastic approach was used for the stress calculation. From this study, it was concluded that both the temperature profile and the relaxation model significantly affect the stress profile. Also, for cases in which the rate of change of the temperature was nonlinear, the computed maximum stress value for a given temperature profile seemed to be different within few cycle periods.

Curling stresses of concrete pavement can be very damaging, and reducing the temperature swings would be very beneficial. This study includes a field instrumentation effort with pavement temperature sensors to quantify the thermal behavior of concrete pavement with and without an open-graded asphalt rubber friction course. The study shows a nonlinear temperature profile across slab thickness, with a large change in temperature between day and night at the top of the concrete slab, and little change at the bottom of the slab. Adding an open-graded friction course over the concrete pavement reduces the temperature fluctuation between day and night as a result of the aeration effect, which is increased by traffic. A three-dimensional (3D) finite-element analysis with a nonlinear temperature gradient shows that adding the friction course reduces the curling stresses in the summer. Furthermore, since traffic increases the aeration effect, sections without traffic show lower effect of friction course on reducing the temperature differentials between the top and bottom of the slab.

Vehicle derailment is difficult to explore because accidents are caused by many factors. In this paper, derailment behaviors are divided into three categories: friction-type derailment, structure-type derailment, and nonlinear-type derailment. Each category has unique characteristics. Wheel climb is a frequent form of friction-type derailment, resulting from mechanical and generally dynamical behavior between wheel and rail. Dynamic simulation is an important tool for prediction of friction-type derailment. There exists a critical point during wheel climb, and lateral normal force primarily prevents the wheel’s moving up. Structure-type derailment is a result of component failure, appearing as local, then developing into general derailment. Factors affecting the development process, such as failure modes, front wheelset derailment, and vehicle speed, are discussed. Nonlinear-type derailment commonly occurs as impact derailment when the vehicle loses stability beyond a nonlinear critical speed. The critical speed for derailment is derived, and the case of a bifurcation diagram is studied. Although nonlinear-type derailment is sensitive to initial conditions, the possibility of accurate prediction is discussed.

The potential growth of railway traffic is expected to be substantial worldwide; hence, railway companies and agencies are looking for better tools to allocate their capital investments to capacity planning in the best possible way. This paper presents a comprehensive decision support framework with three stand-alone tools for strategic railway capacity planning: (1) “Alternatives Generator,” which enumerates the possible expansion options along with their cost and capacity effects; (2) “Investment Selection Model,” which determines the portions of the network that need to be upgraded with certain capacity improvement alternatives; and (3) “Impact Analysis Module,” which evaluates the trade-off between capital investment and delay cost. This research also developed and implemented a specific decision support framework for North American class 1 railroads. On the basis of the network characteristics, estimated future demand, and available budget, this decision support framework can successfully determine the optimal investment plan. This tool can significantly help railroads maximize the return on investment from capacity expansion projects, thus enhancing their ability to provide reliable service to customers.