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

SPONSOR:  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

SPONSOR:  MICHIGAN DEPARTMENT OF TRANSPORTATION

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

SPONSOR:  RESOURCE SYSTEMS GROUP, INC.

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

Impact of High-Speed Passenger Trains on Freight Train Efficiency in Shared Railway Corridors

SPONSOR: UNIVERSITY OF ILLINOIS URBANA CHAMPAIGN

PI:  Kuilin Zhang

Michigan Tech will lead the efforts in Tasks 1, 2, 4, 5, 6, and will participate in all the remaining tasks.

Task 1: Literature review.
A comprehensive literature review will be conducted on existing research in railway capacity and train delay to assess the state of knowledge and to ensure that all relevant previous work is incorporated into the work to be conducted in this proposed project.

Task 2: Analytical corridor capacity model.
The core task of this research project is to develop an analytical f framework to estimate rail corridor capacity under mixed high-speed passenger traffic and regular freight traffic. The outcome will help quantify the following: how high-speed passenger trains affect the capacity of a shared railway corridor, what are the relationships among various operational and design factors (e.g. speed, headway, and siding spacing), and bow do these design factors affect the railroad capacity.

Task 3: Simulation validation.
A commercial software called Rail Traffic Controller (RTC) will be used to evaluate the effects of homogeneous and heterogeneous train operations. We will analyze train delays caused by introducing passenger trains on a single track freight network (the most common track configuration in North America). We will then validate the analytical model proposed in Task 2.

Task 4: Optimization model and design guidelines.
Based on the validated capacity model, optimization models and design guidelines (e.g. speed, headway, and infrastructure design) will be developed to maximize corridor capacity for both freight and passenger traffic. The optimization model will cope with train delays due to the knock-on effects, i.e. meet, pass, overtake, and possible delay propagation in a mixed traffic system.

Task 5: Policy development and analysis.
The proposed modeling framework will be used as the basis for policy analysis (regarding planning, management, and operations of the shared rail corridor). Several key issues such as infrastructure investment, service charge/pricing, and public subsidies (for accommodating the high-speed passenger trains) will be addressed to support decision making for both public agencies and the private sector.

Task 6: Final report.
Each individual task will be documented in a progress report. The final report will include the literature review, model development, validation, and key technical and policy findings. We will publish journal papers and make conference presentations to disseminate findings from this project.

Kuilin Zhang
Kuilin Zhang

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

Impact of High Speed Passenger Trains on Freight Train Efficiency in Shared Railway Corridors

Sponsor:  National University Rail Center

PI:  Kuilin Zhang

Task 1: Literature review. A comprehensive literature review will be conducted on existing research in railway capacity and train delay to assess the state of knowledge and to ensure that all relevant previous work is incorporated into the work to be conducted in this proposed project.

Task 2: Analytical corridor capacity model. The core task of this research project is to develop an analytical framework to estimate rail corridor capacity under mixed high-speed passenger traffic and regular freight traffic. The outcome will help quantify the following: how high-speed passenger trains affect the capacity of a shared railway corridor, what are the relationships among various operational and design factors (e.g. speed, headway, and siding spacing), and how do these design factors affect the railroad capacity.

Task 3: Simulation validation. A commercial software called Rail Traffic Controller (RTC) will be used to evaluate the effects of homogeneous and heterogeneous train operations. We will analyze train delays caused by introducing passenger trains on a single track freight network (the most common track configuration in North America). We will then validate the analytical model proposed in Task 2.

Task 4: Optimization model and design guidelines. Based on the validated capacity model, optimization models and design guidelines (e.g. speed, headway, and infrastructure design) will be developed to maximize corridor capacity for both freight and passenger traffic. The optimization model will cope with train delays due to the knock-on effects, i.e. meet, pass, overtake, and possible delay propagation in a mixed traffic system.

Task 5: Policy development and analysis. The proposed modeling framework will be used as the basis for policy analysis (regarding planning, management, and operations of the shared rail corridor). Several key issues such as infrastructure investment, service charge/pricing, and public subsidies (for accommodating the high-speed passenger trains) will be addressed to support decision making for both public agencies and the private sector.

Task 6: Final report. Each individual task will be documented in a progress report. The final report will include the literature review, model development, validation, and key technical and policy findings. We will publish journal papers and make conference presentations to disseminate findings from this project. Michigan Tech will lead the efforts in Tasks 1, 2, 4, 5, 6, and will participate in all the remaining tasks.

Kuilin Zhang
Kuilin Zhang

Implementation of Unmanned Aerial Vehicles (UAVs) for Assessment of Transportation Infrastructure – Phase II

Sponsor:  Michigan Department of Transportation

PI:  Colin Brooks

Through Phase I of MDOT’s “Evaluating the Use of Unmanned Aerial Vehicles for Transportation Purposes” project, the Michigan Tech combined project team was successfully able to plan, demonstrate, and document UAV capabilities in the assessment of transportation assets.

With the rapid development of UAV’s, MDOT has requested additional research concerning their use for transportation asset management.  The work plan of the MTU combined team includes:

TASK 1:

  • Collect data from the UAV platform using sensing technology in near-time (as real-time as can be achieved) demonstrating, developing, and implementing storage capabilities of large amounts of data, usage of data, and application development that complements current data usage and application at MDOT.
  • Provide data collection from UAVs to the MDOT Data, Use, Analysis, and Process (DUAP) project that meets the quality, low latency delivery and data format requirements.
  • Provide a report that describes and recommends optional methods to store and distribute potentially large imaging, point cloud, and 3D surface datasets created through UAV-based data collection.

TASK 2:

  • Demonstrate, develop, and implement high-accuracy simultaneous thermal/photo/video/Light Detection and Ranging (LIDAR) measurement using a high-fidelity sensor-fused UAV positioning approach.

TASK 3:

  • Demonstrate the capabilities to complete aerial remote sensing data collections to meet MDOT mapping and construction monitoring needs.  Coordinate with MDOT Survey Support to identify pilot projects and meet data delivery needs satisfying MDOT requirements for spatial data collection as it pertains to data density, absolute and relative 3D positional accuracy.

TASK 4:

  • Demonstrate, develop and implement uses of data collection from UAV(s) and sensors for operations, maintenance, design, and asset management.

TASK 5:

  • Demonstrate, develop and implement enhanced testing of UAV-based thermal imaging for bridge deck structural integrity.
  • Compare data collected from UAV sensors to current data collected and systems used at MDOT for highway assets/operations.

TASK 6:

  • Demonstrate, develop, and implement systems management and operations uses.

TASK 7:

  • Provide a benefit/cost analysis and performance measures that define the return on investment as a result of deploying UAVs and related sensory technologies for transportation purposes.

TASK 8:

  • Secure a Federal Aviation Administration (FAA) Certificate of Authorization (COA) to complete the tasks and deliverables.
Colin N. Brooks
Colin N. Brooks
Tess Ahlborn
Tess Ahlborn
Timothy Havens
Timothy Havens
Thomas Oommen
Thomas Oommen
Kuilin Zhang
Kuilin Zhang