Institute for Transportation

About the research

The researchers at Wichita State University collaborated with the Kansas Department of Transportation’s Traffic Management Center in Wichita, Kansas, to develop an Intelligent Highway Management System (IHMS). The functions of the IHMS were to conveniently extract specific incident-relevant record data from high-dimensional, high-volume time series datasets; autonomously analyze online traffic-related data (e.g., volume and speed) for incident diagnosis/identification; and create autonomous optimization that facilitates traffic control decision making to reduce average incident clearance and traffic recovery time.

The IHMS integrates multiple technologies to improve traffic flow and safety. It also streamlines vehicular operations by managing congested traffic, which has become a major problem, as it leads to issues in safety, productivity, and environmental performance. In this study, the researchers developed a transportation system simulation methodology that could be used to reduce traffic congestion, as well as restore traffic to its normal conditions, by allowing vehicles to reroute and avoid congested roads, in turn dipping the speed profile for a faster and quicker recovery. The created simulation system was customized for the City of Wichita and implemented in Simulation of Urban Mobility (SUMO). Simulation results indicate that this approach reduces traffic congestion, provides for quicker incident recovery, and is a solution to ongoing safety, productivity, and environmental performance issues.

Funding Sources: 
Kansas Department of Transportation
Midwest Transportation Center
USDOT/OST-R ($20,000.00)
Wichita State University ($20,000.00)
Total: $40,000.00

Contract Number: DTRT13-G-UTC37

About the research

The objective of this project is to develop a Highway Incident Management System (HIMS), through collaboration with the Kansas Department of Transportation (KDOT) Traffic Management Center (TMC) in Wichita, Kansas. The anticipated functions of the HIMS are to 1) convenient extraction of specific incident-relevant record data from high-dimensional, high-volume time-series datasets, (2) autonomous analysis of online traffic-related data (e.g., volume and speed) for incident diagnosis/identification, and (3) autonomous optimization that facilitates traffic control decision making, to reduce average incident clearance and traffic recovery time. In this investigation, a total of 182 actively logged incidents, together with the traffic information from multiple online monitoring facility units during the month of April 2015 in Wichita will be used to facilitate the model and technology development.

The outcomes of this research will be the following:

(1)     Analysis results of the online traffic data, for the development of related computational models for modeling the highway incident clearance and recovery times

(2)     A technical tool to analyze the online traffic related data for highway incident diagnosis/identification

(3)     A model with technical tools to facilitate traffic control decision making that can help reduce the average incident clearance and traffic recovery time

About the research

The initial phase of this project was conducted to determine whether adding slag cement to concrete mixtures increases the surface scaling caused by the routine application of deicer salt. A total of 28 field sites that included portland cement concrete pavements and bridge decks containing slag cement were evaluated. Laboratory testing was conducted on 6 in. diameter core samples extracted from 12 field sites and 3 subsites, including 6 pavement sites and 6 bridge decks. The laboratory testing program consisted of scaling tests, rapid chloride permeability tests, surface chloride profile tests, and petrographic examination. The results of this study suggest that construction-related issues played a bigger role in the observed scaling performance than did the amount of slag in the concrete mixture.

About the research

This project was conducted to estimate crash modification factors (CMFs) for lane departure countermeasures in Kansas. Cross-sectional, case-control, and before-and-after empirical Bayes (EB) methods were employed. Results showed that centerline rumble strips on rural two-lane road segments have crash-reduction effects on all lane departure and fatal and injury lane departure crashes on both tangent and curved road segments. Shoulder rumble strips were effective in reducing all lane departure and fatal and injury lane departure crashes on tangent road segments but showed less effectiveness on curved road segments. The combination of centerline and shoulder rumble strips showed significant safety effectiveness on both tangent and curved road segments.

Shoulder rumble strips on four-lane road segments also showed crash-reduction effects on all lane departure and fatal and injury lane departure crashes on both tangent and curved road segments. Cable median barriers showed a crash-reduction effect on all lane departure crashes, and fatal and injury lane departure crashes on four-lane divided road segments. Chevrons and post-mounted delineators also showed effectiveness on both all lane departure crashes and fatal and injury lane departure crashes. The safety edge treatment also showed a crash-reduction effect on all lane departure crashes and fatal and injury lane departure crashes.

Finally, all models were validated to check for accuracy. Models developed for the cross-sectional method were validated using mean square error and mean of the residuals. Case-control models were validated using the specificity, accuracy, and sensitivity of the models. The significance of the CMFs developed using the before-and-after EB method was realized using the method given in the Highway Safety Manual.

Funding Sources:
Kansas Department of Transportation ($50,000.00)
Midwest Transportation Center
USDOT/OST-R ($50,000.00)
Total: $100,000.00

Contract Number: DTRT13-G-UTC37

About the research

The structural deterioration of aging infrastructure systems and the costs of repairing these systems is an increasingly important issue worldwide. Structural health monitoring (SHM), most commonly visual inspection and condition rating, has proven to be a cost-effective method for detecting and evaluating damage. However, the effectiveness varies depending on the availability and experience of personnel performing the largely qualitative damage evaluations.

The artificial neural network (ANN) approach presented in this study attempts to augment visual inspection through a crack-induced damage quantification model for reinforced concrete bridge girders that requires only the results of limited field measurements to operate.

Using Abaqus finite element (FE) analysis software, the researchers modeled simply supported three-dimensional concrete T-beams with varying geometric, material, and cracking properties. The ANNs achieved excellent prediction accuracies, with coefficients of determination exceeding 0.99 for both networks. Additionally, the ANNs displayed good predictions accuracies when predicting damage levels in beams not included in the database. Results indicate promise for this application of ANNs.

Utilizing the two top-performing network architectures, the researchers developed a touch-enabled software application for use as an on-site bridge member damage evaluation tool in the field. The application was given the acronym BRIDGES, for Bridge Rating for Induced Damage in Girders: Evaluation Software. The application’s outputs were validated as matching the ANN predictions.

The researchers developed a similar software application for the reverse problem/damage detection and use as an on-site damage prediction tool. This application tries to predict the crack configurations using ANN, based on the geometrical and material parameters, as well as the nine nodal stiffness ratios. This touch-enabled application was given the acronym DRY BEAM, for Damage Recognition Yielding Bridge Evaluation After Monitoring.

Funding Sources:
Kansas Department of Transportation ($34,153.00)
Kansas State University ($15,784.00)
Midwest Transportation Center
USDOT/OST-R ($49,937.00)
Total: $99,874.00

Contract Number: DTRT13-G-UTC37

About the research

The objectives of this five-year Transportation Pooled Fund study are to evaluate conventional and new technologies and procedures for testing concrete and concrete materials to prevent material and construction problems that could lead to premature concrete pavement distress, and to develop a suite of tests that provides a comprehensive method of ensuring long-term pavement performance. A preliminary suite of tests to ensure long-term pavement performance has been developed. Shadow construction projects are being conducted to evaluate the preliminary suite of tests. A mobile concrete testing laboratory has been designed and equipped to facilitate the shadow projects. The results of the project are being compiled in a user-friendly field manual, which will be available by summer 2006.

About the research

Over-consolidation is often visible as longitudinal vibrator trails in the surface of concrete pavements constructed using slip-form paving. Concrete research and practice have shown that concrete material selection and mix design can be tailored to provide a good compaction without the need for vibration. However, a challenge in developing self-consolidating concrete for slip-form paving (SF SCC) is that the new SF SCC needs to possess not only excellent self-compactibility and stability before extrusion, but also sufficient “green” strength after extrusion, while the concrete is still in a plastic state. The SF SCC to be developed will not be as fluid as the conventional SCC, but it will (1) be workable enough for machine placement, (2) be self-compacting with minimum segregation, (3) hold shape after extrusion from a paver, and (4) have performance properties (strength and durability) compatible to current pavement concrete.

The overall objective of this project is to develop a new type of SCC for slip-form paving to produce more workable concrete and smoother pavements, better consolidation of the plastic concrete, and higher rates of production. Phase I demonstrated the feasibility of designing a new type of SF SCC that can not only self-consolidate, but also have sufficient green strength. In this phase, a good balance between flowability and shape stability was achieved by adopting and modifying the mix design of self-consolidating concrete to provide a high content of fine materials in the fresh concrete. It was shown that both the addition of fine particles and the modification of the type of plasticizer significantly improve fresh concrete flowability. The mixes used in this phase were also found to have very good shape stability in the fresh state. Phase II will focus on developing a SF SCC mix design in the lab and a performing a trial of the SF SCC in the field. Phase III will include field study, performance monitoring, and technology transfer.