This paper is aimed at developing a rail transit route optimization model (RTROM) for cost-effective and sustainable rail infrastructure planning and design. Locations of rail transit routes and stations depend on many factors, including potential ridership, costs of land, construction and operation, land use, connecting routes, passenger travel times, and environmental impacts. Suitably located rail transit routes effectively serve the potential demand while also minimizing costs and environmental impacts. Thus, a common problem in all rail infrastructure planning and design projects is to identify the best possible route that satisfies design constraints (such as minimum radius, maximum gradient, and vertical clearance), geographical considerations (such as demand generators and socio-economically sensitive areas) and objectives (such as minimization of associated costs and environmental impacts, or maximization of net benefits). The developed RTROM uses a genetic algorithm for performing optimization, which is integrated to a geographic information system for seamless transfer of land-use, environmental, and topographic data during the optimal search process. The model is tested in a real-world case study from Saint Andrews, Scotland. The lessons learned from the real-world application of the model are discussed. Many extensions of the model remain to be tested in future works.

Train-induced track and ground vibrations accelerate the deterioration of track, cause nuisance to nearby residents, and even damage adjacent structures. Past research has shown that under-sleeper pads (USPs) are effective at improving track performance and insulating ground vibrations generated by moving trains. The present study was the first to investigate the effectiveness of USP at reducing train-induced ground vibrations at both subcritical and supercritical train speeds (critical speed is defined as the Rayleigh wave speed of the ground surface) and was the first to study the effectiveness of USP at reducing ground vibrations with a two-phase poroelastic soil model. A vehicle-track-ground model containing a fully saturated poroelastic half-space was established, and the whole system was solved by a semianalytical method using Fourier transforms and Fourier series. The differences in the performance of the USPs on saturated poroelastic ground and single-phase elastic ground were examined. It was found that the USPs effectively reduced free-field ground vibrations at subcritical train speeds, but the USP insulation effectiveness was minimized at supercritical train speeds. USPs might also reduce excess pore water pressure in the ground during the passage of a train.

This paper provides information about railroad fuel efficiency. Two methods of estimating railroad fuel consumption are demonstrated: (1) an analytical procedure based on train resistance factors, speeds, changes in elevation, and car weights; and (2) a statistical procedure that utilizes observed data. Both procedures are used to estimate the efficiencies of coal, grain, iron ore, and other commodity shipments. The two methods produce similar (but not identical) results. The analytical procedure illustrates the underlying forces that affect fuel efficiency and provides a benchmark for the statistical model.

Automated processing of road surface video clips captured for road condition assessment is necessary to detect the existence of road surface distress in less time and efforts. This paper presents a robust method for automated segmentation of frames with/without distress from road surface video clips captured by existing camera based imaging systems without any artificial lighting systems. The proposed method is based on an adaptive thresholding technique and user defined decision logic for automated detection of road surface distresses out of such video clips. This method has been implemented in a Windows Vista environment with the help of Visual Studio 2008 and OpenCV library and tested on 31 road surface video clips of Indian Highways. The results show that this method can segment a road surface video clip automatically into two categories of video frames, namely frames with distress and frames without distress, with accuracy up to 96% while saving a considerable amount of time and manpower resources.

Asphaltic concrete overlay is a commonly used rehabilitation technology for existing asphaltic or concrete pavements. The existing pavements are often repaired prior to the placement of the overlay. Considering the significant cost associated with this type of repair, it is imperative to understand how preoverlay pavement distresses and preoverlay repair methods can affect the postoverlay performance so that engineers can select the most effective preoverlay repair methods. This study used data that were collected from 449 asphaltic overlays on existing pavements. Statistical analysis results indicated that (1) for asphaltic concrete pavements, milling is more effective in reducing alligator/block cracking, longitudinal cracking, and raveling in asphaltic overlay; (2) for composite pavements, asphaltic concrete base patching is more effective in reducing longitudinal cracking, and doweled concrete base patching is more effective in reducing surface raveling in asphaltic overlay; (3) for joint plain concrete pavements, undoweled concrete base patching is more effective in reducing transverse and longitudinal cracking; and (4) for continuously reinforced concrete pavements, asphaltic concrete base patching is more effective in reducing transverse and longitudinal cracking.

This study presents results of one of the first in situ assessment studies on the use of the Mechanistic-Empirical Pavement Design Guide (M-EPDG) methodology applied to the design of airfield pavements. The M-EPDG’s Enhanced Integrated Climatic Model (EICM) introduces unsaturated soil modeling techniques into the prediction of moisture changes in the unbound materials throughout the life of pavements for highway conditions. Field soil moisture content changes measured at several Air Force bases in the U.S. were collected from historic studies and compared to MEPDG predictions. Details and outcomes from this comparative study are presented herein. Despite significant differences in structural dimensions and geometric design, this study showed that the EICM promises to be a suitable predictive methodology to be adapted for airfield pavement conditions and has the potential to become a tool for the incorporation of unsaturated soil modeling for the design of airfield pavements, provided that several enhancements are made to the model applied to highway design. Also, a discussion on possible sources of uncertainty in the predictions of the MEPDG and a series of alternative revisions and enhancements required to incorporate the EICM with current airfield pavement design methodologies are presented.

This paper explains a modeling approach that offers better understanding of the routing strategies taken by evacuees to reach a safe destination during hurricane evacuation. Route choice during evacuation is a complex process because evacuees may prefer to take the usual or familiar route on the way to the destination, or they might follow the routes recommended by the emergency officials. Depending on the condition of the traffic stream, sometimes they might switch to a different route to obtain better travel time from the one initially attempted, i.e., the routing behavior is random. By using data from Hurricane Ivan, a mixed (random parameters) logit model is estimated which captures the decision making process on what type of route to select while accounting for the existence of unobserved heterogeneity across households. Estimation findings indicate that the choices of evacuation routing strategy involve a complex interaction of variables related to household location, evacuation characteristics, and socioeconomic characteristics. The findings of this study are useful to determine the manner in which different factions of people select a type of route for a given sociodemographic profile during an evacuation.

The likelihood of the occurrence of secondary incidents is usually linked to primary incidents by using predefined spatial and temporal criteria. This paper extends past research on secondary incident detection by defining the dynamic thresholds of the influence area of a primary incident, using detailed freeway traffic data from upstream loop detectors. The dynamic thresholds are defined by using both analytical and empirical approaches. Results that use analytical approaches to track the spatiotemporal boundaries of the influence upstream of a primary incident offer influence curves of different characteristics (influence duration and maximum spatial extent) with respect to the prevailing traffic conditions. To accurately detect secondary incidents, an empirical method based on spatiotemporal speed evolution is applied that imprints influence areas with respect to their dissipation patterns. The results of the proposed approach are compared to those of five commonly used static and dynamic methods for detecting secondary incidents. Their differences are identified and discussed.

Passenger car units (PCU) of different types of vehicles are required to convert a mixed traffic stream into a homogeneous equivalent, and thereby to express the mixed traffic flow in terms of equivalent number of passenger cars. Earlier studies have reported that PCU for a vehicle is dynamic in nature and changes with traffic volume and proportional share of a vehicle type in the traffic stream. The present study provides PCU values for different types of vehicles typically found on interurban multilane highways in India at different levels of service (LOS). Traffic simulation model VISSIM is used to generate the traffic flow and speed data for conditions that are difficult to obtain from field observations. Important VISSIM parameters are first calibrated to reflect mixed traffic flow behavior and then the software is used to draw the speed-volume relationships for cars and one of the remaining four categories of vehicles in the traffic stream. The proportion of second category of the vehicle was also varied to observe its effects on PCU values. Finally, PCU values are suggested for different type of vehicles at different LOS and for different traffic composition on four-lane and six-lane divided highways.

The evaluation of dynamic message signs (DMSs) has been challenging to undertake because of the difficulty of identifying the effects of DMSs from traffic patterns. The primary contribution of this study is the comprehensive evaluation of DMSs in a rural area during a full freeway closure by using field data, driver surveys, and simulation. A full freeway closure scenario offers an opportunity to identify the effects of DMSs, because there is a network-wide shift in traffic demand from the closed route to the alternate route(s). A unique data collection and analysis approach was used to capture regional traffic patterns, including the processing of all traffic movements from four full interchanges. A significant increase in traffic flow on the detour route and a corresponding decrease on the affected route were observed during the freeway closure. Simulation was used to isolate the benefits of DMSs from other traveler information modes such as newspaper, TV, and radio, and to explore the sensitivity of the network to different levels of driver compliance. Depending on the proportion of drivers who chose to divert based on DMSs, monetary benefits were estimated to range between US$2,394 and $65,643 for the three-day freeway closure. Driver surveys conducted during the freeway closure revealed that 57% of the drivers utilized DMS information and 41% of drivers relied solely on DMSs to obtain information related to the closure and the detour route.

This paper demonstrates the application of particle swarm optimization (PSO) to the programming of pavement maintenance activities at the network level. Furthermore, the application of the PSO technique and its relevance to solving the programming problem in a pavement management system (PMS) is discussed. The robustness and quick search capability of PSO enables it to effectively handle the highly constrained problem of pavement management activities programming, which has an extremely large solution space of astronomical scale. Examples are presented to highlight the versatility of PSO in accommodating different forms of objective functions and comparing the results with the genetic algorithm (GA). This paper compares PSO and GA with respect to rate of convergence and accuracy of modeling PMS using an example problem. The results of this paper confirmed the potential of PSO to successfully model the PMS.