Improving Safety on Rural Local and Tribal Roads Safety Toolkit

Sponsor: Federal Highway Administration

PI: Timothy Colling

Rural roadway safety is an important issue for communities throughout the country and presents a challenge for state, local, and Tribal agencies. The Improving Safety on Rural Local and Tribal Roads – Safety Toolkit was created to help rural local and Tribal roadway safety practitioners address these challenges. The Safety Toolkit provides a step-by-step process to assist local agency and Tribal practitioners in completing traffic safety analyses, identify safety issues, countermeasures to address them, and an implementation process. Each step in the Toolkit contains a set of tools, examples, and links to resources appropriate to the needs of safety practitioners. The report presents a seven-step safety analysis process based on a similar process developed in the Highway Safety Manual. The seven steps are: compile data; conduct network screening; select sites for investigation; diagnose site conditions and identify countermeasures; prioritize countermeasures for implementation; implement countermeasures; and evaluate effectiveness of implemented countermeasures. Accompanying the Safety Toolkit are two User Guides (FHWA-SA-14-073 and FHWA-SA-14-074) which present step-by-step processes of example scenarios.

A copy of this report can be found on the Federal Highway Administration website.

Tim Colling
Tim Colling

I-Corps: Decision Support Systems for Managers of Civil Infrastructure Systems

Sponsor: National Science Foundation

PI: Amlan Mukherjee

The proposed technology is a methodology to assess alternative infrastructure management strategies based on project cost, system performance and estimates of greenhouse emissions. The supporting methods include stochastic analysis, life cycle assessment and Monte Carlo simulation based approaches. The technology is designed to address the problems of reducing life cycle emissions of civil infrastructure systems, helping agencies provide a consistent level of service, while optimally using available resources for construction, maintenance and rehabilitation. This is particularly significant given the twin challenges of climate change, and ongoing shortfalls in state and federal budget appropriations for public works. Finally, the underlying theory and methods are mathematically sophisticated and data intensive, and not easy for decision-makers to implement without significant training. The proposed technology promises efficient implementation by providing an innovative product/service that is reliable and intuitive, and has a friendly and easy to use interface.

Civil infrastructure systems are critical to socioeconomic success. Services such as access to clean drinking water, efficient sewer and waste management, easy mobility and access to multiple modes of transportation provide the backbone for multiple supply chains, besides supporting a healthy standard of living. Challenges due to climate change, aging infrastructure, and the impact of the economic crisis on local and state budgets are hurting the efficient delivery of these services. By providing support to decision-makers the proposed technology is likely to have a significant impact on maintaining and managing infrastructure sustainably.

Amlan Mukherjee
Amlan Mukherjee

Study of Greenhouse Gas Savings Associated with Congestion Reduction Using Multi-Modal Optimization of Timber Shipments in the North Central United States

Sponsor: US Department of Transportation

PI: Pasi Lautala

This study examined industry led models for the optimization of timber shipments in the North Central United States (norther third of Wisconsin, Minnesota, and the Upper Peninsula of Michigan). The research team analyzed optimization models of the delivery of logs to the wood products industry in the region. The research team investigated a multi-modal (rail/truck), surface transportation solution set and performed a sensitivity analysis based on changing energy costs. The team evaluated the potential effects on air emissions before and after the virtual routing optimization. The team also explored options for establishing a rail served super yard in the region to reduce consolidate logs with the object of reducing empty truck miles and increasing rail ton-miles.

A copy of this report can be found on the University of Wisconsin – Superior website.

Pasi Lautala
Pasi Lautala

Cost Effectiveness of the MDOT Preventive Maintenance Program

Sponsor: Michigan Department of Transportation

PI: Timothy Colling

The Michigan Department of Transportation’s (MDOT) pavement preservation program dates back to 1992. MDOT’s pavement preservation strategy is primarily implemented through its capital preventive maintenance (CPM) program, in which preventive maintenance treatments are used to protect existing pavement surfaces, slow deterioration, and correct surface deficiencies. An overall objective of the CPM program is to postpone major rehabilitation and reconstruction activities by extending the service life of pavements.

This study evaluated the benefits and costs of various preventive maintenance treatments used in MDOT’s CPM program. Defining the benefit as the percent increase in performance over a “do nothing” or untreated pavement performance curve, where data were available benefits were calculated for preventive maintenance treatments. Using unit costs, benefit-cost ratios were calculated, permitting the comparison of the cost-effectiveness of similar treatments. The overall performance of MDOT’s CPM program was also examined by comparing the life-cycle costs (LCC) of a rehabilitation strategy to a preservation strategy using a simplified approach. The outcome showed that the preservation strategy results in agency cost savings of approximately 25 percent per lane-mile over the rehabilitation strategy.

Findings from this study can be used to help MDOT improve its CPM project selection, treatment selection, and performance monitoring and modeling practices.

Tim Colling
Tim Colling

National University Rail (NURail) Center

Sponsor: University of Illinois Urbana Champaign

PI: Pasi Lautala

In 2012, the seven university consortium, including Michigan Tech, was awarded the first National University Rail Transportation Center (NURail) by the USDOT Research and Innovative Technology Administration (RITA). The University of lllinois at Urbana-Champaign (UIUC) leads the consortium.

The primary objective of the NURail Center is to improve and expand rail education, research, workforce development, and technology transfer in the U.S.

Pasi Lautala
Pasi Lautala
Timothy Havens
Timothy Havens
Paul G. Sanders
Paul G. Sanders
Myounghoon Jeon
Myounghoon Jeon

Evaluating the Use of Unmanned Aerial Vehicles for Transportation Purposes

Sponsor: Michigan Department of Transportation

PI: Colin Brooks

Advances in unmanned aerial vehicle (UAV) technology have enabled these tools to become easier to use and afford. In a budget-limited environment, these flexible remote sensing technologies can help address transportation agency needs in operations, maintenance, and asset management while increasing safety and decreasing cost.

This project tested and evaluated five main UAV platforms with a combination of optical, thermal, and LiDAR sensors to assess critical transportation infrastructure and issues such as bridges, confined spaces, traffic flow, and roadway assets.

A State of the Practice report was completed, and a series of lab testing were accomplished to ensure practicality and safe operations. Field demonstrations were completed at bridges, pump stations, and conferences. The project team gave a series of technical demonstrations at the Intelligent Transportation Systems World Congress in Detroit in September, 2014, enabling outreach to a wide domestic and international audience who gained understanding of the advanced research that MDOT is funding. These demonstrations showed that UAV technologies provide many advantages to helping MDOT cost-effectively assess, manage, and maintain its resources, providing benefit to staff and the traveling public.

A copy of this report can be found on the Michigan Department of Transportation website.

Colin N. Brooks
Colin N. Brooks
Tess Ahlborn
Tess Ahlborn
Timothy Havens
Timothy Havens
Thomas Oommen
Thomas Oommen

Driver Behavior at Highway-Rail Grade Crossing Using NDS and Driving Simulation

SPONSOR:  FEDERAL RAILROAD ADMINISTRATION (FRA)

PI:  Pasi Lautala 

Project Period:  09.09.16-09.18.17

According to the USDOT Federal Railroad Administration, highway-rail grade crossing and trespasser fatalities still account for 96 percent of all rail-related deaths.  A review of accident causes reveals that one of the main accident factors is human driving behavior.

One of the potential approaches to improve drivers’ behavior at crossings is to systematically examine their actions and use the outcomes to develop alternative methods or approaches to decrease the probability of missing or ignoring warnings.  The Naturalistic Driving Study (NDS) approach, using in-vehicle video and other sensors to directly observe drivers during normal driving activities, is a technique that shows promise toward improved understanding of driver actions in the crossings.

This two-year project is divided into two phases with an overall objective to investigate driver behavior at highway-rail grade crossings using two distinct, but complimentary techniques.  Phase I will use data collected under the Strategic Highway Research Program (SHRP) Naturalistic Driving Study to look at how normal drivers react at crossings in every day driving situations.  Phase II will use the understanding developed in the first phase to create scenarios that resemble environments similar to those found in the NDS for use in our driver simulator environment.  The research will look for two basic results.  First, we will develop and use the organized NDS crossing database to examine behavioral trends at the crossings. Second, we will compare driver behavior in the simulator with that found in the NDS data to determine the level of correlation between the two environments.   Our hypothesis is that a strong correlation would allow us to outline how to use the simulator environment in the future to predict driver response to a variety of crossing parameters.

Pasi Lautala
Pasi Lautala
Myounghoon Jeon
Myounghoon Jeon
Dave Nelson
Dave Nelson

 

Coordinated Transit Response Planning & Operation Support Tools for Mitigating Impacts of All-Hazard Emergency Events

SPONSOR: UNIVERSITY OF CHICAGO

PI:  Kuilin Zhang

The nation’s critical infrastructure is aging and vulnerable to natural and man-made disasters. According to a national report in 2008, one of the 14 grand challenges for engineering in the 21st century is to restore and improve urban infrastructure including public transportation systems. To ensure that the public transit systems meet life-safety standards and other operational objectives, robust and resilient transit systems are needed.

In order to increase the resiliency of public transportation systems, we aim to identify and develop methods in terms of system robustness to provide a resilient transit system under the impact from different levels of natural hazards or other emergency situations.

To this end, we propose a performance-based engineering based approach to address the robustness in resilient transit systems by conducting vulnerability (static vulnerability is an antonym of robustness) analysis of transit systems under different hazard scenarios and determining optimal investment strategies and policies using a chance-constrained optimization formulation in response to different performance measure constraints.

Kuilin Zhang
Kuilin Zhang

 

Improving Spatial Observability of Dynamic Traffic Systems through Active Mobile Sensor Networks and Crowdsourced Data

SPONSOR:  NATIONAL SCIENCE FOUNDATION (NSF)

PI:  Kuilin Zhang

To provide effective traffic congestion mitigation strategies, transportation agencies need to effectively design sensor networks to reliably estimate and predict traffic conditions across large transportation networks. The next generation traffic sensor network will offer large, diverse data streams not only from fixed traffic detectors, but also from many emerging active mobile traffic sensors such as Unmanned Aerial Vehicles, self-driving cars, and crowdsourced data sources from social sensors and transportation network companies. This new generation of agile sensors can provide a much richer but also increasingly complex traffic data environment. Moreover, crowdsourced data is generally uncontrolled, inaccurate and unreliable. This research focuses on new sensor design/control applications to transform the interconnection between travelers, sensors, data and transportation management systems.

The objective of this research is to develop rigorous mathematical foundations and innovative algorithms to accurately quantify spatial observability of dynamic traffic states, optimize active mobile sensor locations, and mine information from crowdsourced data sources. The research team will first characterize analytical space-time distributions of different traffic states at both macroscopic and microscopic scales, and further develop time-geography-oriented optimization for quantifying spatial observability for dynamic networks. A new class of ubiquitous sensor network design problems is studied for the traffic state estimation stage, and the integration of the well-fused crowdsourced data with optimized fixed and active mobile sensor data is investigated under different levels of activity/penetration rates. Utilizing the structure of underlying dynamic transportation networks, this research aims to develop computationally efficient optimization algorithms to create a distributed and scalable computing framework, which can solve joint scheduling and routing problems of active mobile sensors to increase coverage and accuracy. The research team will develop generic measures of spatial network observability that can provide additional theoretical findings for general civil engineering systems such as earthquake impact detection, ground water pollution source identification, and critical infrastructure monitoring.

Kuilin Zhang
Kuilin Zhang
Colin N. Brooks
Colin N. Brooks

Remote Sensing Based Assessment System for Evaluating Risk to Transportation Infrastructure Following Wildfires

Sponsor:  University of Arkansas

PI:  Thomas Oommen

Photo courtesy of FEMA

Michigan Tech will aid in developing an empirical model to predict the probability of mudflow/rockslide and a Remote Sensing Based Decision Support System to evaluate the risk to transportation infrastructure following wildfires.

The empirical modeling will include developing a probabilistic model using logistic regression that relates the remote sensing inputs to the occurrence and non-occurrence of mudflows/rockslides.  The remote sensing derived parameters for logistic regression will include soil suction, soil volumetric water content, soil temperature, soil density, predominant mineral type, clay content, etc.

The RSBDSS will be built upon the existing decision support system that is being developed for Phase 5 Geotechnical Asset Management project (PI: Thomas Oommen).  Specifically, following the development of the RSBDSS, input parameters obtained from existing data or newly collected remote sensing data will be entered into the logistic regression model to determine the amount of risk associated with mudflows or rockslides following wildfire events.  This RBDSS is anticipated to aid highway managers in determining the risk to transportation infrastructure, which would help in developing plans for required road closures and ensuring safety of the users of transportation infrastructure.

Thomas Oommen
Thomas Oommen