Institute for Transportation

About the research

The transportation system is crucial for transferring people and goods from point A to point B. However, its reliability can be decreased by unanticipated congestion resulting from planned special events. For example, sporting events collect large crowds of people at specific venues on game days and disrupt normal traffic patterns.

The goal of this study was to understand issues related to road traffic management during major sporting events by using widely available INRIX data to compare travel patterns and behaviors on game days against those on normal days. A comprehensive analysis was conducted on the impact of all Nebraska Cornhuskers football games over five years on traffic congestion on five major routes in Nebraska. We attempted to identify hotspots, the unusually high-risk zones in a spatiotemporal space containing traffic congestion that occur on almost all game days. For hotspot detection, we utilized a method called Multi-EigenSpot, which is able to detect multiple hotspots in a spatiotemporal space. With this algorithm, we were able to detect traffic hotspot clusters on the five chosen routes in Nebraska. After detecting the hotspots, we identified the factors affecting the sizes of hotspots and other parameters. The start time of the game and the Cornhuskers’ opponent for a given game are two important factors affecting the number of people coming to Lincoln, Nebraska, on game days. Finally, the Dynamic Bayesian Networks (DBN) approach was applied to forecast the start times and locations of hotspot clusters in 2018 with a weighted mean absolute percentage error (WMAPE) of 13.8%.

About the research

This study developed a systematic approach for using data from multiple sources to provide active traffic management solutions. The feasibility of two active traffic management solutions is analyzed in this report: ramp-metering and real-time crash risk estimation and prediction. Using a combined dataset containing traffic, weather, and crash data, this study assessed crash likelihood on urban freeways and evaluated the economic feasibility of providing a ramp metering solution.

A case study of freeway segments in Omaha, Nebraska, was conducted. The impact of rain, snow, congestion, and other factors on crash risk was analyzed using a binary probit model, and one of the major findings from the sensitivity analysis was that a one-mile-per-hour increase in speed is associated with a 7.5% decrease in crash risk.

FREEVAL was used to analyze the economic feasibility of the ramp metering implementation strategy. A case study of a 6.3 mile segment on I-80 near downtown Omaha showed that, after applying ramp metering, travel time decreased from 9.3 minutes to 8.1 minutes and crash risk decreased by 37.5% during the rush hours. The benefits of reducing travel time and crash cost can easily offset the cost of implementing ramp metering for this road section.

The results from the real-time crash risk prediction models developed for the studied road section are promising. A sensitivity analysis was conducted on different models and different temporal and spatial windows to estimate/predict crash risk. An adaptive boosting (AdaBoost) model using a 10 minute historical window of speeds obtained from 0.25 miles downstream and 0.75 miles upstream was found to be the most accurate estimator of crash risk.

About the research

In recent years there has been a growing desire for the use of probe vehicle technology for congestion detection and general infrastructure performance assessment. Unlike costly traditional data collection by loop detectors, wide-area detection using probe-sourced traffic data is significantly different in terms of measurement technique, pricing, coverage, etc. This affects how the new technology is applied and used to solve current traffic problems such as traffic incident management and roadway performance assessment. This report summarizes the experiences and lessons learned while using probe data for traffic operations and safety management in the state of Nebraska and makes recommendations for opportunities to maximize the use of probe data in light of its limitations. A detailed analysis of performance monitoring and historical trend analysis, including identification of the top 10 congested segments, congestion per mile across metro areas, congested hour(s) during summer and winter months, and yearly travel time reliability, for Interstate 80 segments in Nebraska were performed. Two main conclusions can be drawn from this study. First, there is almost always a speed bias between data streaming from probes and traditional infrastructure-mounted sensors. It is important to understand the factors that influence these biases and how to cope with them. Second, lack of confidence score 30 (real-time) probe data is a critical issue that should be considered precisely for incident detection, roadway performance assessment, travel time estimation, and other traffic analyses. Ultimately, the authors present several recommendations that will help transportation agencies gain the best value from their probe data.

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.

About the research

None available for this project