Institute for Transportation

About the research

The purpose of this research project was to develop visualization techniques and methods that display pavement performance data and information in a communication-friendly format for a variety of end users. Several possible scenarios of end users and possible questions that they might use visual analyses to answer were envisioned. The researchers believed that the two primary types of end users were county office administrators examining the status of the roads in their counties and the state and engineers working with the data sets. Accordingly, several visualizations are presented that would be useful for both of these end-user groups. For each, a short description is given along with a sample question that the visualizations could be used to answer. The data used to generate these interactive visuals were provided by the Iowa Department of Transportation (DOT) and outline numerous indicators of pavement performance for the year 2013. Software programs used to produce the visual analyses were Microsoft Excel 2010 and Tableau 9.0.

The visual analyses are interactive and are designed to provide a dashboard at the county level. They can also be modified to display information and visuals at other levels of county groupings.

To further develop visualizations in a clear and insightful way, the researchers generated interactive visuals using data provided by the Iowa DOT and covered the period from 2013 to 2015. This data set contains numerous indicators of pavement performance by road segment, county, and district. Tableau 10.3 was used conduct the visual analysis.

This work focused on identifying information about the different types of cracks, relationships between them, relationships between condition and distress data, and performance in Iowa districts and counties. Counties were divided into five county types based on their size and proximity to major cities, as described by U.S. Census data from 1970 to 2010. These five county types included central city metropolitan, outlying metropolitan, regional center, small urban, and rural.

Funding Sources:
Midwest Transportation Center
USDOT/OST-R ($42,536.00)
Total: $42,536.00

Contract Number: DTRT13-G-UTC37

About the research

Missouri, Iowa, and Illinois drivers often share each other’s roadways; therefore, it is important to examine similarities and differences in the causes of motor vehicle crashes among the three states. This is especially true in light of a recent National Highway Transportation Safety Administration (NHTSA) report stating that the monetary cost of highway crashes in the US is approximately $900 per person (NHTSA 2014). In order to lower this high cost, it is necessary to have an understanding of the factors that contribute to these crashes. Systematic differences between states’ crash causes can shed light on the effectiveness of these states’ various driver training programs and driver policies, which, in turn, has the potential to make the roadways safer and reduce crash-related expenses.

This study theorizes that Missouri, Iowa, and Illinois have similar crash factors and that crash contributing factors differ as a function of gender, despite the varying size of the states’ populations. Therefore, the purpose of this study is to examine circumstances contributing to crashes for each state by gender in order to uncover differences and similarities that may provide policy implications.

Funding Sources:
Midwest Transportation Center
University of Missouri – Saint Louis ($20,000.00)
USDOT/OST-R ($27,000.00)
Total: $47,000.00

Contract Number: DTRT13-G-UTC37

About the research

Because crashes at rural intersections frequently result from failure to yield, agencies attempt to find countermeasures that encourage drivers to stop and yield appropriately. In this research, two promising low-cost rural intersection countermeasures were selected and evaluated for their impact on safety: post-mounted beacons and retroreflective strips on stop sign posts. The post-mounted beacons were set to activate only when an approaching vehicle’s speed surpassed a predetermined threshold.

High-crash rural stop-controlled intersections were identified using in-house crash and roadway data and then filtered for suitability via site visits. The retroreflective strips were installed on stop signs at 14 intersections on both minor street approaches. The post-mounted beacons were installed on stop signs at 10 approaches at 6 intersections. Driver behavior was used to assess the countermeasures. Because the post-mounted beacon was expected to noticeably impact driver behavior while the retroreflective strips were not, driver behavior data were only collected at locations where post-mounted beacons were installed. Video data were collected using trailer-mounted camerasat all 10 approaches where post-mounted beacons were installed 1 month before and 1 month after installation. For 6 of the 10 approaches, data were also collected 12 months after installation. Several driver behavior metrics, including type of stop, stopping position, braking point, and number of times braking, were reduced for a random sample of vehicles for each approach in each evaluation period and were compared before and after installation.

Overall, the post-mounted beacon had an overwhelmingly positive safety benefit, as measured by several changes in driver behavior. Most approaches where the countermeasure was installed experienced increases in the number of drivers making full stops, braking within 450 to 500 ft of the intersection, stopping at or before the stop bar, and braking only once. Ideally, these improvements in driver behavior will result in reduced crashes at the study intersections. Because the retroreflective strips were not evaluated, the researchers propose to conduct a crash analysis when at least three years have elapsed after installation.

Funding Sources:
Iowa Department of Transportation ($50,000.00)
Iowa Highway Research Board ($80,000.00)
Midwest Transportation Center
USDOT/OST-R ($81,728.00)
Total: $211,728.00

Contract Number: DTRT13-G-UTC37

About the research

Connected and autonomous vehicle (CAV) technologies are likely to be gradually implemented over time and in a traffic environment consisting of a significant share of alternative fuel vehicles, such as flexible-fuel, plug-in electric, and fuel cell vehicles. This work proposes the use of rule-based ecological adaptive cruise control strategies—the ecological smart driver model (Eco-SDM) for gasoline CAVs and the energy-efficient electric driving model (E³DM) for electric CAVs (e-CAVs)—to improve the energy efficiency of individual vehicles and traffic flow.

By adjusting the spacing between the leading and the following vehicles, the Eco-SDM provides smoother deceleration and acceleration than the adaptive cruise control strategies based on intelligent driver model-adaptive cruise control (IDM-ACC) and the Nissan model (Nissan-ACC). The E³DM is able to maintain high energy efficiency of regenerative braking by adjusting the spacing between the leading and the following vehicles.

To estimate vehicle energy consumption in a mixed traffic stream, the Virginia Polytechnic Institute and State University (Virginia Tech) microscopic energy and emission (VT-Micro) model was calibrated for gasoline vehicles and a power-based electricity consumption model that considers the impact of ambient temperature on auxiliary load was proposed for battery electric vehicles (BEVs). Single-lane vehicle dynamics in a traffic stream with a mix of CAVs and human-driven vehicles were simulated.

Results showed that the Eco-SDM and E³DM outperform IDM-ACC and Nissan-ACC in terms of energy consumption. For Eco-SDM-based CAVs, the fuel saving benefit was greatest when a CAV is at the front of a platoon. For E³DM-based e-CAVs, higher market penetration of e-CAVs may not result in higher energy efficiency of the entire fleet. Considering mixed traffic streams with BEVs and gasoline vehicles, the energy consumption of the entire fleet decreased when the market penetration of BEVs (which contains both e-CAVs and manual BEVs) increased. A higher ratio of e-CAVs to manual BEVs resulted in higher energy efficiency.

Funding Sources:
Iowa Department of Transportation
Iowa Highway Research Board
Midwest Transportation Center
USDOT/OST-R ($45,000.00)
Total: $45,000.00

Contract Number: DTRT13-G-UTC37

About the research

Large truck transport is vital for freight shipping in the United States; yet, it can prove to be a dangerous mode of transportation. From 2002 to 2012, 91,145 crashes involving large trucks occurred in Missouri, resulting in 1,156 fatalities and 18,457 injuries. Many factors contribute to large-truck crash severity, and it is theorized that these factors and their effect on injury severity vary as a function of gender.

Missouri traffic and personal and vehicle crash data from 2002 to 2012 were used to analyze situations that increase the probability of injuries and fatalities, given a large-truck crash occurs. Chi-squared automatic interaction dedication (CHAID) decision trees were developed to predict values of injury severity based on environmental factors, contributing circumstances, and gender, to better understand predictor importance and uncover interactions among factors.

Results suggest that the major contributory predictors for crash severity for Missouri female drivers include: driving too fast for conditions, driving on the wrong side of the road, improper backing, speeding, and improper turning. Major contributing predictors for Missouri male drivers include: driving too fast for conditions, improper backing, violation of stop sign or signal, improper turning, and failing to secure loads. Additional results suggest that, when speeding, the probability for a fatality is 14.29% for Missouri female commercial driver’s license (CDL) drivers and an injury is 28.57%, given a crash occurs. For Missouri male CDL drivers, when driving too fast for conditions on the wrong side of the road, an 8.70% and 48.91% probability for fatality and injury exists, respectively.

Therefore, the researchers recommend that truck driver training programs focus on gender-specific behaviors that impact crash injury severity in order to enhance road safety measures.

About the research

The objectives of this research were to conduct theoretical and experimental investigations to develop a new battery health management paradigm based on a novel, self-cognizant dynamic system (SCDS) approach to predict and prevent failures of safety-critical battery systems (e.g., lithium plating and thermal runaway) for electric vehicles (EVs) and hybrid electric vehicles (HEVs) and develop an onboard diagnostics tool and alarm system for early awareness of these potential impending failures.

This research developed a technique that can adaptively recognize the dynamic characteristics of an operating battery system over time without relying on expensive, time-consuming battery tests for the prediction and prevention of safety-critical battery system failures. Battery failure prognostics employing the proposed SCDS-based health management paradigm can not only account for normal battery capacity fading over time but also identify abnormal safety-critical failures that usually happen in a relatively shorter time period.

Funding Sources:
Midwest Transportation Center
USDOT/OST-R ($49,000.00)
Wichita State University ($49,000.00)
Total: $98,000.00

Contract Number: DTRT13-G-UTC37

About the research

The transportation systems sector, one of the most critical infrastructure sectors in the US, has a subsector of highway and motor carrier industries that supports daily activities and emergency actions by providing services to other critical infrastructure segments such as healthcare and public health, emergency services, manufacturing, food and agriculture, etc. However, transportation networks face risks from natural and human-made events such as hurricanes, tsunamis, earthquakes, bridge collapse, and terrorist attacks. Thus, to improve the reliability of the components in interconnecting networks, it is necessary to consider these unpredictable failures in the network design. Resilient network design ensures that the network functionality is at an acceptable level of service in the presence of all probabilistic failures.

In this study, the authors addressed uncertainty in a transportation network by proposing a trilevel optimization model, which improves the resiliency of the network against uncertain disruptions. The link capacities are uncertain parameters and the origin-destination demands are deterministic. The goal was to minimize the total travel time under uncertain disruptions by designing a resilient transportation network. The trilevel optimization model has three levels. The lower level determines the network flow, the middle level assesses the resiliency of the network by identifying the worst-case scenario disruptions that could lead to a maximal travel time, and the upper level uses the system perspective to expand the existing transportation network to enhance the network’s resiliency. In addition, the authors propose a new formulation for the network flow problem that will significantly reduce the number the number of variables and constraints.

The results of solving the trilevel optimization model can improve the resiliency of the network. However, this study was subject to some limitations, which suggested future research directions. In reality, transportation demands are not consistent, but the proposed model considers origin-destination demands as deterministic parameters. Relaxing this assumption requires a more complicated model to reflect uncertain demands. Other possible future work would be designing an exact algorithm to find the optimal solution.

Funding Sources:
Iowa State University ($99,999.00)
Midwest Transportation Center
USDOT/OST-R ($124,998.00)
Total: $224,997.00

Contract Number: DTRT13-G-UTC37

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

A-walls are retaining structures composed of regularly spaced deep foundation elements battered in opposing directions and connected through a grade beam to mitigate movements of a slope or embankment. Analysis of A-walls for slope stabilization applications is challenging because of complex interactions between deep foundation elements and moving soils. A previous method was successful in modeling A-walls with consideration of both lateral and axial load transfer, but interaction between upslope and downslope A-wall elements through the capping beam is neglected in the “uncoupled” analysis. To evaluate the effect of coupling, the research team analyzed slopes stabilized with A-walls using finite element models with upslope and downslope piles connected at the pile heads. Results of the analyses were compared to those of uncoupled lateral and axial analyses utilizing the p-y and t-z methods. Load transfer parameters for the analyses were calibrated to field measurements of load transfer in A-walls to demonstrate viability of the revised methodology. Results of the coupled analyses were then compared to results from uncoupled analyses to evaluate the effect of interaction between upslope and downslope piles.

Coupled analyses produced bending moment and axial force profiles in reasonable agreement with measured values. Calibration of p-y and t-z curves to achieve predictions consistent with field measurements required significant softening of ultimate lateral and axial resistance values, but the softening was less than that required for calibration of uncoupled analyses. Modeling of interaction through the capping beam resulted in more reasonable calibrated values of lateral and axial soil resistance, better agreement with measured axial force profiles, and better agreement with measured bending moments and axial forces at shallow depths. The results indicate that interaction facilitated by the capping beam has a significant effect on development of forces within the A-wall elements. For deep sliding, reasonable predictions of pile resistance can be achieved using uncoupled models. However, for shallower sliding, the capping beam is likely more consequential and a coupled analysis is prudent. Coupled analysis is also beneficial for structural design of the capping beam.

Both calibration case histories involved relatively deep sliding and relatively small values of total soil movement. Additional analyses with new datasets from A-wall applications for shallower slides and greater movement are recommended.

Funding Sources:
Deep Foundations Institute ($18,700.00)
Midwest Transportation Center
University of Missouri – Columbia ($6,100.00)
USDOT/OST-R ($20,000.00)
Total: $44,800.00

Contract Number: DTRT13-G-UTC37

About the research

With the advent of transportation network companies, or TNCs, as they are labeled by the state of California, there has been considerable discussion, legislative action, and lawsuits regarding their attempts to operate without being subject to local taxi, sedan, limousine, or private-for-hire regulations. Indeed, across almost every continent, Uber has attempted to simply disregard local city and airport rules and regulations established for all commercial ground passenger transportation carriers. Uber argues that it is not a transportation company but rather a technology company, and so by definition it is not subject to commercial vehicle regulations. As a result, fierce and expensive legal and legislative battles have been bitterly fought. However, these legal proceedings rarely address just why there are regulations for commercial vehicles and their drivers.

It is therefore incumbent upon public officials to learn from this phenomenon and design a taxi system that provides drivers a fair income opportunity and maximum utilization from vehicles, to offer and maintain a high level of service at reasonable rates to residents and visitors alike. A best guess is that the industry will experience a form of hybrid taxi/TNC type transportation firm that offers both services in competition with national TNC brands for a while, but that ultimately there will be re-regulation and TNCs will be included within the local regulatory framework. There may be an opportunity for statewide or even national taxi/TNC regulations, but as in the past, drivers will be vetted, entry will be restricted, and public safety in the form of commercial liability insurance for all drivers will be standard requirements.

Funding Sources:
Metropolitan St. Louis Taxicab Commission ($25,000.00)
Midwest Transportation Center
University of Missouri – Saint Louis ($20,000.00)
USDOT/OST-R ($30,000.00)
Total: $75,000.00

Contract Number: DTRT13-G-UTC37