Log Movement in the Superior Region – Rate and Capacity Based Analysis of Modal Shares – Alger County


PI:  Pasi Lautala

Project Period:  01.01.18-12.31.19

Task 1: Log and non-log product movement data collection

This study will use actual log movement data (both by truck and by train) by the participating forest products companies, as well as train routes and schedules from CN (and potentially E&LS) as a backbone for the spatial simulation model of the region. The main activities of the task 1 include log and non-log movement data collection. For the non-log movements, the main emphasis is in collecting data on origin-destination (O-D) pairs, both from the forest products companies and from the results of NRTC study.

Task 2: Development of GIS network and yard/siding/mill constraints

We are expecting the GIS (Geographical Information System) maps of truck/rail movements to be the first main outputs from our study. These electronic maps developed based on the log movement data will show not only the actual locations and capacities of yards and sidings, but the transportation infrastructure of study area.

Task 3: Log /non-log movement data reduction/cleaning (as necessary) and conversion to a common format

In this task, we will clean up and convert the log movements to a common format. We will identify the log volumes through each O-D pair, including volumes through rail sidings/yards. Data collected in the previous tasks will be collated and organized with the development of commodity flows. Commodity flows will provide the movements of goods (log) in the study area and will be the basic dataset for average daily/monthly O-D matrix of log movements.

Task 4: Development of operational constraints (rail) and operational model

The studies on the freight mode choice, especially the competition between truck and rail are becoming critical to improve the efficiency of freight transportation system. In this task, we will investigate observed/unobserved factors influencing freight mode choice, including truck and rail. The average daily/monthly O-D matrix of log movements in the previous task will utilize to develop log freight mode choice model.

Task 5: Development of “non-log” product movement graphics, data tables and maps

In the same way as in Task 2 and 3 for a log product, this task identifies the “non-log” volume through each O-D pair and develops movement tables based on data collected in previous Task 1. The task includes generating volume matrices and GIS maps on the movements to identify opportunities.

Task 6: 1st simulation runs and development of results

In this task, we will develop the first simulation model that seeks to optimize log transportation considering the operational constraints of rail. This model will analyze log movements (from several companies/mills) by rail and truck, and look at where and how opportunities may be created to improve the business case for CN or a short line operator to provide cost effective service. One potential example is identifying locations where larger shipment sizes can be concentrated at once. Figure 1 presents the framework of the spatial model, using all processes from Tasks 1 to 5 as its main components.

Task 7: Industry/sponsor review of results

Progress report will be provided to get industry/sponsor reviews of the optimization and non-log movement results. This report will include the results of five tasks in the phase 1 (from Task 1 to Task 5) as well as the results derived from the first operational simulation model of task 6.

Task 8: 2nd simulation runs and analysis of additional logistics considerations.

We will run the second simulation and analyze on the car demand, effects on log trucks, opportunities for reduced peaking and consolidated rail yards. The comments/opinions from the first review will be reflected in the second simulation runs.

Task 9: Industry sponsor/review of results and final report

Pasi Lautala
Pasi Lautala
Kuilin Zhang
Kuilin Zhang


Log Movement in the Superior Region – Rate and Capacity Based Analysis of Modal Shares – MDOT


PI:  Pasi Lautala

Project Period:  01.01.18-12.31.19

Dr. Pasi Lautala will provide coordination of the project and data collection from industry stakeholders.

Dr. Kuilin Zhang will lead the operational constraint and model development for log transportation model. He will also lead the analytical tasks related to the model, supported by the graduate research assistants.

Pasi Lautala
Pasi Lautala
Kuilin Zhang
Kuilin Zhang

MDOT Statewise Passenger and Freight Travel Demand Model


PI:  Kuilin Zhang

Project Period:  10.01.16-09.30.18

Dr. Zhang is an advisor for the project on freight modeling and OD elimination using traffic counts.  This project develops a statewide passenger and freight travel demand model for Michigan, including data assessment, model specification development, model development, model calibration and validation, forecasting future years, model documentation, and training.

Kuilin Zhang
Kuilin Zhang

MTTI Researchers and Students Attend 2018 Transportation Research Board (TRB) Annual Meeting

The 97th Transportation Research Board (TRB) annual meeting titled Transportation: Moving the Economy of the Future was held in Washington, DC, January 7-11.  The meeting program covers all transportation modes, with more than 5,000 presentations in nearly 800 sessions and workshops.  Attending this year’s meeting were 26 members and students of the Michigan Tech Transportation Institute (MTTI).

COLIN BROOKS (MTRI) presided over the Applications of Unmanned Aerial Systems to Help with Infrastructure Inspection technical session and also presented his paper “Implementing UAV Applications at the Michigan Department of Transportation”, displayed his research at the
Data-Driven Decisions for Bridge Management: Advancements in Inspection and Modeling poster session, shared his “Academic Research Perspectives on Creating High-Quality Data for UAS Transportation Applications” at the Emerging Technologies: The Role of Lidar and Unmanned Aerial Systems in Supporting the Transportation Spatial Information Infrastructure technical session, chaired the Sensing Technologies Subcommittee, ABJ50(1) meeting, and had his poster “A Comprehensive Overview of Improving Traffic Flow Observability Using UAVs as Mobile Sensors” displayed at the Sensing and Information Technology Innovation to Enhance Traffic Operation Efficiency session.

BROOKS was also interviewed by Tony Dorsey, AASHTO Manager of Media Relations, for AASHTO TV on the Unmanned Aerial Vehicle Implementation Research he has been leading for Michigan DOT so that results of this project could be shared with the larger transportation community. He also gave a flash talk to the ABJ50 Information Systems and Technology Committee meeting on ‘Using a Data Framework to manage large data sources: An example from drone research.’

Center for Technology & Training Director TIM COLLING attended committee meetings as a member of the Highway Safety Performance Committee (ANB25) and also participated in meetings with both the Pavement Preservation Committee ADH18 and Asset Management Education Subcommittee, ABC40(5).

MTTI Director PASI LAUTALA (CEE/RTP) and PhD student SANGPIL KO, displayed their “Advanced Woody Biomass Logistics for Cofiring in Existing Coal Power Plant: A Case Study of the Great Lakes States” poster at the Current Research in Freight Planning and Logistics session.  LAUTALA presented research details on “Assessment of Driver Compliance at Highway–Rail Grade Crossings Based on Naturalistic Driving Study Data” at the Human Behavior at Highway-Rail Grade Crossings technical session and presided over the Freight Rail Transportation Committee meeting.  In addition, he helped to coordinate the Freight Rail Propulsion: Where Does the Energy Come From? session and participated in the Rail Executive Group and Freight Executive Group dinners as a Freight Rail Transportation Committee chair , leading the committee meeting.

In addition to his poster presentation, RTP graduate student SANGPIL KO attended the Freight Rail Transportation Committee (AR040) meeting and the Rail and Public Transportation Caucus, sponsored by BNSF Railway.

AMLAN MUKHERJEE (CEE) presided over the Pain Points in the Use of Big Data Analytics for Transportation Project Delivery workshop and presented “From Trade-Offs to Equivalent Solutions: A Life-Cycle Thinking Informed Approach to Design Decision Making” at the Pavement Materials and the Urban Climate technical session with graduate student CHAITANYA GANESH BHAT.

DAVE NELSON (RTP/CEE) co-presented a research overview on “Assessment of Driver Compliance at Highway–Rail Grade Crossings Based on Naturalistic Driving Study Data” at the Human Behavior at Highway-Rail Grade Crossings technical session and attended the Standing Committee on Highway/Rail Grade Crossings as a member.

THOMAS OOMMEN (GMES) was an invited Lectern Lecturer at the   Innovations and Advances in Transportation Geotechnics technical session where he presented his research “Remote Sensing Technologies for Highway Infrastructure Monitoring”.

 LARRY SUTTER (MSE) participated in the following committee meetings:  AFN10 – Standing Committee on Basic Research and Emerging Technologies Related to Concrete, AFN40 – Standing Committee on Concrete Materials and Placement Techniques, AFN30 – Standing Committee on Durability of Concrete and the AFN20 – Standing Committee on Properties of Concrete.

ZHANPING YOU (CEE) chaired the ASCE Bituminous Materials Committee (BMC) meeting and presented posters at two sessions; “Influence of Elastic Characteristic of Asphalt Pavement Surface on Tire–Pavement Friction Behavior” at the Current Research Related to Asphalt Surface Mixtures session and “Laboratory Evaluation of Bio–Asphalt Binders Modified by Waste Cooking Oil” at the Asphalt Binder Additives and Modifiers session.

KUILIN ZHANG (CEE) displayed posters of current research at three sessions with graduate student SHUAIDONG ZHAO; “A Data-Driven Dynamic Route Choice Model under Uncertainty Using Connected Vehicle Trajectory Data” at the Behavioral Route Choice session, “A Data-Driven Model Predictive Control Framework for Robust Cooperative Adaptive Cruise Control Using Mobile Sensing Data from Connected Vehicles” at the Traffic Flow Theory and Characteristics session, and “Comprehensive Overview of Improving Traffic Flow Observability Using UAVs as Mobile Sensors”.

The annual meeting was also well represented by graduate research students:


  • High Temperature Performance Evaluation of Bio-asphalt Based on Multiple Stress Creep Recovery Test, presenter: JUNFENG GAO
  • Lab Testing and Modeling of Asphalt Mixtures Compaction with Selected Coarse Aggregates Shapes, Presenter: FANGYUAN GONG
  • The influence of repeatedly heating on the performance of asphalt binders in laboratory, Presenter: DONGDONG GE
  • Investigating the effect of pore pressure on the permeability and porosity of dense-graded HMA, Presenter: SIYU CHEN
  • The thermal storage stability of asphalt binder modified by bio-oil generated from waste wood resources, Presenter: RAN ZHANG


  • Polyvinyl Alcohol (PVA) Fiber-Reinforced Rubber Concrete, Presenter: JIAQING WANG
  • Influence of Calcium Hydroxide in Metakaolin-Based alkali-activated cement, Presenter: RUIZHE SI

Graduate research students also presented their work at the The International Association for Chinese Infrastructure Professionals (IACIP)’s 8th Annual IACIP Workshop:

  • Dynamic Behavior of Asphalt Pavement Thermal-Hydraulic-Mechanical Multi-Physical Coupled System, Presenter: CHUNDI SI
  • Effect of Silane Coupling Agent on Improving Adhesive Properties between Asphalt Binder and Aggregate, Presenter: CHAO PENG
  • Fatigue Damage Properties of Asphalt Mixture and New Method for Axle Load Conversion, Presenter: SONGTAO LV
  • Damage Characterization and Thermodynamic Mechanism Simulation of Alkali-Silica Reaction in Recycled Glass Mortar Samples, Presenter: SHUICHENG GUO
  • Properties of Fractured Brittle Material after Grouting Reinforcement, Presenter: ZHI WANG
  • Study on Transportation System in Deep Sea and Analysis of the Driving Performance of the Mining Collector, Presenter: WENBO MA

In addition, SHUAICHENG GUO and SIYU CHEN each received the IACIP Outstanding Graduate Student Award certificate and a cash award.

Image Data-Driven-Deep Learning in Geosystems: Exploratory Investigation into the Stability of Retaining Walls


PI:  Zhen Liu


Project Period:  09.01.17 – 08.31.19

The project is proposed to introduce the most recent breakthroughs in computer vision attributed to deep learning to address a rarely discussed yet urgent issue in most engineering disciplines: how to analyze the explosively increasing image data including images and videos, which can hardly be analyzed with traditional methods? Therefore, the proposed work is very time-sensitive. The core concepts enabling the breakthroughs in image recognition and adopted in AlphaGo, i.e., deep convolutional neural nets, will be used to explore the possibility to accurately assessing the safety of retaining walls with image data. The analysis will be validated against traditional methods including limit analysis and numerical simulation. The research is hypothesis-driven and rationally built on our preliminary study, which clearly supports the hypothesis and shows the high potential of the proposed study. In addition to further proving the power of the proposed concept, this collaborative effort between geotechnical engineering and computer science will understand the data and deep learning in geotech analysis, relate image patterns to physical mechanisms, and investigate two key potential issues associated with the methodology.

The proposed work will be organized into three tasks. Task 1 is to understand how the geotechnical data and analysis results can be related to the input and output of deep learning using convolutional nets. This is a key to the successful application of deep learning in geotech. In Task 2, eight factors in three categories, i.e., geology (soil property), topography (geometry) and boundary conditions, will be evaluated regarding their influence on the safety factors. The relationships between these factors, their roles and significance in deep learning, and correlation between the patterns identified by the CNN and the physical mechanisms will be compared and revealed to connect the deep learning to the existing geotech knowledge pool. In Task 3, two additional key aspects representing the unknowns and possible weaknesses of the machine learning method will be analyzed: model robustness and extrapolation. Testing data prepared in ways different from the input data, i.e., appearance and ranges, will be used to assess and improve these two aspects.

Zhen Liu
Zhen Liu
Shiyan Hu
Shiyan Hu


Develop and Implement a Freeze Thaw Model Based Seasonal Load Restriction Decision Support Tool


PI:  Zhen Liu

Spring (or Seasonal) Load Restriction (SLR) policies that limit the axle loads of trucks have been implemented in many states of the United States and other countries to minimize costly roadway damage that occurs in seasonally frozen areas during the annual spring thaw and strength recovery period (Zarrillo et al., 2012). This is because concrete and asphalt, though look indestructible, can actually be quite fragile in late winter as frost comes out of the ground (County Road Association of Michigan).

The overall objective of the project is to establish a thawing model and a process for setting and removing seasonal load restrictions in a manner that will give industry the most amount of time to prepare for the restrictions and minimize the time to lift the restrictions based on the MDOT Project RC 1619. The overall objective will be accomplished through a series of objectives and tasks leveraging existing research, technology, and resources that MDOT already has in place.

  1. Evaluate existing thaw/freeze depth prediction models, practice for SLR in state DOTs and MDOT’s needs and available resources, and based on that, determine if existing thaw depth models suffice for application as a decision support tool for Michigan or if a refined model would be prudent.
  2. Identify the type, sources, and format of the soil and weather information used for analysis by the decision support tool.
  3. Building on this project and the research of RC 1619, develop a thaw depth model th!it utilizes the existing data sources in Objective 2.
  4. Identify locations for potential virtual Road Weather Information System (RWIS) sites and collect necessary data to implement those locations.
  5. Develop a user friendly decision support tool that could be easily utilized by public and private sector in estimating potential thaw conditions and setting of SLRs for any location on the MDOT road network.
  6. Recommend processes for predicting the time to post and remove SLR signs to protect the pavement structures from excessive damage during the spring thaw season.
  7. Identify opportunities to collect, present, and apply data and develop models to refine pavement designs.
  8. Develop professional training materials and course for training MDOT staff in the use of the decision support tool.
Zhen Liu
Zhen Liu
Stan Vitton
Min Wang
Min Wang

Sproule Named Airport Cooperative Research Program Ambassador

By Sue Hill (College of Engineering)

Bill Sproule
Bill Sproule

Bill Sproule (CEE) has been appointed by the Transportation Research Board (TRB) to be an Airport Cooperative Research Program (ACRP) Ambassador for a two-year term.

ACRP is an industry-driven, applied research program that develops practical solutions to problems faced by airport operators. It is managed by TRB and sponsored by the Federal Aviation Administration (FAA).

ACRP Ambassadors are volunteers who serve as liaisons between the TRB and ACRP, the research community, and airports operators at conferences and industry events and will make presentations on the ACRP research process and products, and other airport topics, and promote opportunities for others to be involved in ACRP research panels and projects.

Implementation of Roadsoft for MDOT Safety Services Unit


PI:  Tim Colling

The Center for Technology & Training (CTT) developed and has supported the Roadsoft asset management system in 1991 for the Michigan Department of Transportation, when such a system was envisioned as a “best practice” for supporting Michigan’s local agencies in their effort to manage their road and bridge assets efficiently.

Over the years, Roadsoft became a “one-stop shop” for asset and safety management data for local agencies due to the continued support of MDOT as well as to specific developments supported by the Safety Programs Unit. In the fall of 2016, the CTT completed a two-year project to develop the needed functionality in Roadsoft that allows MDOT to take advantage of Roadsoft as a tool for the Department’s use. These functional changes were necessary because Roadsoft has historically been geared toward local agency needs. With the completion of this development, the safety functions of Roadsoft have evolved to the point where MDOT now uses Roadsoft as a tool for accomplishing its misslon to improve safety on Michigan’s roads.

While this development task has been completed, needs like ongoing maintenance, training technical support, and data handling will have to be addressed in order for MDOT to take full advantage of the Roadsoft tool. This proposal outlines the tasks and level of effort necessary to support MDOT’ s use of the safety tools in Road soft.

  1. Technical Support
  2. Annual Statewide Database Migration
  3. Automated Crash Data Retrieval
Tim Colling
Tim Colling
Gary Schlaff
Gary Schlaff
Nick Koszykowski
Nick Koszykowski

Transportation Asset Management Council Technical Assistance Activities Program


PI:  Tim Colling

The Michigan Transportation Asset Management Council (TAMC) began delivering its education program and providing technical services in 2004. Since that time, the Center for Technology  & Training (CTT) has assisted the TAMC with its education programs and technical assistance services. The CTT is a logical choice for this program because, in addition to the TAMC
Education Program, the CTT houses other programs funded by the Michigan Department of Transportation (MOOT) including the Michigan Local Technical Assistance Program (LTAP), Roadsoft, Michigan Engineer’s Resource Library (MERL), and the Bridge Load Rating Program. This array of programs economizes upon professional, development, and support staff to make project delivery cost effective. The CTT focuses its efforts specifically on projects related to local government agencies and transportation.

The tasks for this proposal were identified from priorities outlined by TAMC in the TAMC 2017-2019 Work Program.

Task 1: Attend and Participate in TAMC Council Meetings
Task 2: Attend and Participate in TAMC Committee Meetings
Task 3: Review of the Data and QC Collection Results
Task 4: Maintain Roadsoft -IRT Data Submission Protocols
Task 5: Maintenance of TAMC PASER Training Certification Testing Instruments and Records
Task 6: Investment Reporting Project Cost and Treatment Life Study
Task 7: Undefined Staff Support
Task 8: Project Management and Monthly Reporting

Tim Colling
Tim Colling
Mary Crane
Mary Crane