A mechanistic response model was recently developed at the University of Illinois to analyze geogrid base reinforced flexible pavements designed for low to moderate traffic volumes with a relatively thin hot-mix asphalt surface layer. This model utilizes the finite element approach and properly considers (1) the nonlinear, stress-dependent behavior of pavement foundation geomaterials, i.e., unbound aggregates and fine-grained soils; (2) anisotropic behavior of the granular base layer; and (3) the compaction and preloading induced unbound aggregate base residual stresses. To validate the developed mechanistic model, field response data were collected from instrumented full-scale pavements constructed with both geogrid reinforced and control test sections. The model predictions using the nonlinear and anisotropic characterizations of the granular base layer moduli were found to better capture the magnitudes and the trends in the measured response data. After trafficking to failure of the pavement test sections, pavement trench studies were conducted to gather constructed layer thicknesses and additional forensic data in an effort to provide refined inputs for the mechanistic response model. An increase in stiffness observed around the geogrid reinforcement in the field was properly modeled by the use of horizontal residual stresses above the geogrid, which resulted in a better match of the predicted with the measured pavement responses of the reinforced sections. As a result, the mechanistic model predictions computed at different locations in the test sections compared reasonably well with a large number of field measured responses under different load levels.

The ability to measure or estimate accurate speed data are of great importance to a large number of transportation system operations applications. Estimating speeds from the widely used single inductive loop sensor has been a difficult, yet important challenge for transportation engineers. Based on empirical evidence observed from sensor data collected in two metropolitan regions in Virginia and California, this research developed a Kalman filter model to perform speed estimation for congested traffic. Taking advantage of the coexistence of dual loop and single loop stations in many freeway management systems, a calibration procedure was developed to seed and initiate the algorithm. Finally, the paper presents an evaluation that illustrates that the proposed algorithm can produce acceptable speed estimates under congested traffic conditions, consistently outperforming the conventional $g$ -factor approach.

Mode choice models used for travel demand forecasting generally include “major” transportation modes of driving, ridesharing, walking, and riding public transit. In the Toronto Area, these make up 96% of all trips. This paper describes the challenge of realistically modeling “minor” modes, while maintaining behavioral realism in the rest of the model. This is critical to public policy since increasing the mode share of bicycling, commuter rail, and school bus has the potential to reduce emissions, save on expensive auto infrastructure, encourage healthier lifestyles, reduce congestion, and support liveable communities. The tour-based model presented in this paper simulates household interactions as part of the mode choice process. Model parameters are estimated using a choice-based sample of tours in the Toronto Area and a genetic algorithm. The model shows very good results for commuter rail and school bus modes, but limited success for the drive access subway, taxi, and bicycle modes. Representation of niche markets through restricted choice sets allows for a parsimonious utility function that includes level of service, land-use, activity, and socioeconomic variables.

By deliberately triggering lateral buckles in a subsea pipeline at a sufficiently low axial load, one can ensure that the distance between lateral buckles is sufficiently small, so that the thermal expansion to be absorbed by each buckle is not excessive. A method of triggering such buckles by a zero-radius bend (ZRB) on a trigger that elevates the pipeline above the seabed is described. This method is more effective than other methods that have been proposed and/or used. The use of triggers designed to work only in the vertical direction by laying the line over a linear support aligned at 90° to the pipeline route, is already known. For the ZRB method, a post is added at one side of the trigger to prevent the pipe of being pulled off the trigger on that side. Once the pipe is firmly seated on the trigger, but not yet touching down onto the seabed on the downlay side of the trigger, the laybarge moves laterally without paying out more pipe, thereby pulling the pipe against the post on the trigger, i.e., the movement of the laybarge is as if it was laying a bend of zero radius, but because of the stiffness of the pipe the radius of curvature remains finite and can be kept well within the elastic range. This paper describes the method, a simple approximate analysis method used to assess the performance of the approach and the pipe stresses due to pulling the pipe around the post, and experiences from application of this method.

Focusing on isolated signalized intersections with four approaches along arterial median busway, a logic rule-based bus rapid transit signal priority (BRTSP), aiming to speed up bus rapid transit (BRT) service with less negative impact on general traffic, is developed based on a phase scheme including two BRT phases, eight vehicle phases, and four pedestrian phases. Two attributes for each vehicle phase, i.e., initial phase and terminal phase, are introduced to analyze the principles of potential phase sequences. Check-in and check-out detectors are placed on the busway to detect BRT vehicle arrivals and departures. Inductive loop detector is placed on each lane of the vehicle phase to monitor time headway(s) on a lane-by-lane basis. All the components of BRTSP can accommodate the variety of phase scheme. The minimum green time of the vehicle phase and the walk interval of the pedestrian phase are calculated cycle by cycle. In case of a BRT vehicle arrival or departure, a priority request either for green extension or for early green may be generated or cancelled by rules for priority request generation. Rules for phase transition can manipulate phase transition durations and avoid intergreen time violations. Using a binary choice approach, rules for green duration adjustment and rules for barrier crossing can serve the priority requests and adjust the green duration of each vehicle phase in an acyclic manner. Simulation experiments conducted by a microscopic, behavior-based multipurpose traffic simulation program indicated that (1) for the phase sequences where the through vehicle phases concurrent with the BRT phases were specified as the terminal phases, the left-turn vehicle phases adjacent to the BRT phases were not recommended to serve early green request in heavy load scenarios; (2) the phase sequence with more through vehicle phases being specified as the initial phases had the potential to enable BRTSP to yield better intersection performance.

Advanced map-matching algorithms resolve spatial ambiguities between differential global positioning system (DGPS) and roadway centerline data. Most of these algorithms need further research to assess their performances with respect to their controlling parameters and their relationships with spatial data and temporal resolution. This paper presents an analysis of the effects of three parameters controlled by the user and two variables dominated externally through simulated data on the performance of a postprocessing decision-rule map-matching algorithm previously developed by the writers. The algorithm is tested against three different digital roadway map scales from counties in Wisconsin and Iowa, and two automatic vehicle location (AVL)/DGPS technologies mounted on intelligent winter maintenance vehicles. Sensitivity analyses indicate that the algorithm is sensitive to controlling parameter values depending on the data being tested. The algorithm satisfactorily resolves spatial ambiguities given different spatial data qualities, AVL/DGPS technologies, and temporal resolutions. Statistical analysis suggests a direct relationship between data collection frequency and spatial mismatch resolution. Parameter values are presented for minimizing false negatives and maximizing solved cases, thus enhancing the performance of the map-matching algorithm.

The mechanistic-empirical (ME) design procedures utilize axle load spectra to characterize the individual traffic loadings for a site. These loading characteristics are employed to calculate pavement response and for subsequent damage computations. Generally, these axle load distributions exhibit a bimodal shape and a combination of two continuous statistical distributions can be used to model them. In this paper, closed-form solutions are developed to estimate the parameters of the bimodal distribution from data. A combination of two normal distributions is shown to reasonably fit observed axle spectra. Since it is anticipated that the AASHTO equivalent single-axle load (ESAL) concept will continue to be used by pavement engineers even after the full adoption of ME design methods, a closed-form statistical relationship between ESALs and axle load spectra is proposed. Such a relationship will be useful in estimating a traffic level index from an axle distribution. In addition, the relationship can provide an estimate of the relative pavement damage caused by axle distributions, and be used to rank axle load spectra within a geographical region, or between regions in order to identify heavier traffic loading corridors.

Red light running causes more than 100,000 crashes and 1,000 fatalities annually and results in an estimated economic loss of over $14 billion per year in the United States. Red light running is a significant safety issue facing communities which rarely have the resources to place additional law enforcement in the field. As a result, communities are increasingly turning to automated red light running camera-enforcement systems to address the problem. The effectiveness of red light running cameras in reducing the number of drivers who run the red light (violations) in an Iowa community was evaluated. The number of red light running violations at camera-enforced intersection approaches were compared to violations at approaches at intersections where cameras were not used within the same metropolitan area using a cross-sectional analysis. A Poisson lognormal regression was used to evaluate the effectiveness of the cameras in reducing violations. Results indicated that red light running cameras substantially reduced the number of violations at camera-enforced approaches as compared to control approaches.

The increasing commuting population often fuels the need for new highways. As the demand for new highways increases, many low-income neighborhoods are often considered for placement of new highways. While this issue has long been recognized as part of environmental justice and context-sensitive solution initiatives, no quantitative comparisons of highway construction impacts on low-income families have been reported in the literature. The objective of this research is to develop a general methodology to model the effects of socioeconomic factors in highway construction and expansion, to examine impacts on low-income families. The general methodology developed can be applied to various income classes to determine the extent of impact with new highway construction. Penalty costs are chosen and used to penalize highway alignments crossing low-income areas. Multiple values for penalty costs are used to test the sensitivity of generated alignments. Tests are performed to examine the robustness of the developed methodology. The results lead to the need for developing appropriate penalty costs that are suitable for avoiding low-income areas. This research is a first step in quantifying the impacts of highway construction and expansion on low-income families.

Initial implementation of a road management system will typically face the challenge of a lack of time-series data for performance modeling. This paper presents one approach for developing initial performance prediction models that are required to support trade-off and optimization analyses in a road management system. The paper demonstrates that starting performance models can be formulated based on as little as 2 years’ network-level data on condition and traffic. The paper builds a locally calibrated pavement condition index and subsequently uses it for network-level strategic planning and programming of works. The paper demonstrates a method of extracting initial estimates of treatments’ effectiveness from the condition data. The case study is based on the Costa Rican national road network. The model uses commercial software to allocate resources and optimize decisions. Several investment strategies were tested to investigate the sustainability of the road network value over time. The results demonstrate that optimization improves road conditions in a sustainable manner, which in the long run releases funds for other necessities.