Log truck value analysis from increased rail usage

Over the past several decades, the transportation of raw materials (logs) has increasingly shifted from the railway to trucks. However, the long-term sustainability of this shift is being questioned due to the shortage of truck drivers, fluctuation of fuel prices, and changes in hours of service laws. The industry is interested in the possibility to shift more logs back to the railway but the impact of such a shift on truckers has not been investigated. This study attempted to quantify the impact of such a change on the operations of log truckers by calculating time efficiency (percentage of daily hours of service for revenue activities) and value efficiency (average loaded versus total ton-kilometers per day) between a truck only and multimodal (truck/rail) alternatives. We used actual data from the forest products industry companies and truck performance data from an earlier study to investigate the impact through case studies in four different locations of the upper Midwest, US. The results of our analysis revealed that in three out of our four case studies, re-routing log movements through rail yard/siding improved the time efficiency and value efficiency. Finally, our sensitivity analysis found that increases in average truck speed and maximum hours or service had higher impact on multimodal transportation than in truck-only system

Leveraging Connected Highway Vehicle Platooning Technology to Improve the Efficiency and Effectiveness of Train Fleeting Under Moving Blocks

Future advanced Positive Train Control systems may allow North American railroads to introduce moving blocks with shorter train headways. This research examines how closely following trains respond to different throttle and brake inputs. Using insights from connected automobile and truck platooning technology, six different following train control algorithms were developed, analyzed for stability, and evaluated with simulated fleets of freight trains. While moving blocks require additional train spacing beyond minimum safe braking distance to account for train control actions, certain following train algorithms can help minimize this distance and balance fuel efficiency and train headway by changing control parameters

Coordinated Transit Response Planning and Operations Support Tools for Mitigating Impacts of All-Hazard Emergency Events

This report summarizes current computer simulation capabilities and the availability of near-real-time data sources allowing for a novel approach of analyzing and determining optimized responses during disruptions of complex multi-agency transit system. The authors integrated a number of technologies and data sources to detect disruptive transit system performance issues, analyze the impact on overall system-wide performance, and statistically apply the likely traveler choices and responses. The analysis of unaffected transit resources and the provision of temporary resources are then analyzed and optimized to minimize overall impact of the initiating event

Implementation of Unmanned aerial vehicles (UAVs) for assessment of transportation infrastructure - Phase II

Technological advances in unmanned aerial vehicle (UAV) technologies continue to enable these tools to become easier to use, more economical, and applicable for transportation-related operations, maintenance, and asset management while also increasing safety and decreasing cost. This Phase 2 project continued to test and evaluate five main UAV platforms with a combination of optical, thermal, and lidar sensors to determine how to implement them into MDOT workflows. Field demonstrations were completed at bridges, a construction site, road corridors, and along highways with data being processed and analyzed using customized algorithms and tools. Additionally, a cost-benefit analysis was conducted, comparing manual and UAV-based inspection methods. The project team also gave a series of technical demonstrations and conference presentations, enabling outreach to interested audiences who gained understanding of the potential implementation of this technology and the advanced research that MDOT is moving to implementation. The outreach efforts and research activities performed under this contract demonstrated how implementing UAV technologies into MDOT workflows can provide many benefits to MDOT and the motoring public; such as advantages in improved cost-effectiveness, operational management, and timely maintenance of Michigan’s transportation infrastructure

Developing Safe and Efficient Driving and Routing Strategies at Railroad Grade Crossings based on Highway-Railway Connectivity

693JJ619C000022This report documents the research involved in developing safe and efficient driving and routing strategies at highway-rail grade crossings (i.e., highway-rail intersections) based on highway-rail connectivity for economic evaluation, driver behavior analysis, and Eco-Driving and Eco-Routing strategies. From September 2019 to December 2022, a research team from Michigan Technological University led this project with a team from the University of Kentucky as an academic partner and Escanaba & Lake Superior (E&LS) Railroad as an industry partner. This project was sponsored by the Federal Railroad Administration through the 2018 Broad Agency Announcement on Intelligent Railroad System Research

A data-driven dynamic route choice model under uncertainty using connected vehicle trajectory data

This paper proposes a data-driven dynamic route choice model to understand traveler’s routing behavior in a time-dependent network under uncertainty using connected vehicle trajectory data over many days. Different from existing efforts on stochastic route choice models using a random term with a given distribution, this paper directly uses connected vehicle trajectory data over many days without knowing the underlying distribution in a data-driven stochastic optimization model. Specifically, the authors apply a Bayesian risk formulation for parametric underlying distributions that optimizes a risk measure taken with respect to the posterior distribution estimated from the connected vehicle trajectory data. Two risk measures (i.e. Value-at-Risk and Conditional Value-at-Risk) of the travel time uncertainty are considered in the proposed data-driven dynamic route choice model. Based on the risk measures, the proposed model allows a flexible choice on the risk preferences of individual users (i.e. from risk-neutral to risk-averse). To test the data-driven dynamic route choice model in a large network, the authors implement the model in Southeast Michigan using a high-resolution (i.e. 0.1 seconds) trajectory dataset of connected vehicles from the Safety Pilot Model Deployment (SPMD) project over many days

A data-driven model predictive control framework for robust cooperative adaptive cruise control using mobile sensing data from connected vehicles

This paper proposes a data-driven Model Predictive Control (MPC) framework to improve a robust Cooperative Adaptive Cruise Control (CACC) system by optimally controlling a Connected and Automated Vehicle (CAV) platoon under uncertainty. Speed variation, spacing variability, and driving comfort are considered in the objective function of the proposed MPC model. Although vehicle connectivity technologies are available in traffic networks, a CACC system should be represented as a stochastic system due to uncertainties in traffic flow dynamics. These uncertainties will affect vehicle automation of the CACC platoon. To deal with these uncertainties, the authors formulate a data-driven MPC model by solving a likelihood robust optimization (LRO) problem in a rolling horizon framework to consider the worst-case scenario for robust connected and automated driving using mobile sensing observations of the leading CAV in the CACC platoon. The authors also explicitly address the constrained driving situation (where a preceding vehicle is present in front of the CACC platoon) in the proposed MPC model formulation through predicting the driving speed and trajectory of the preceding vehicle using real-time mobile sensing data from other connected vehicles in front of the CACC platoon by forecasting the forecasts of others. This data-driven MPC model can be applied to both constrained and unconstrained driving conditions for robust connected and automated driving. The authors test the data-driven MPC model on a congested freeway segment on I-94 in Ann Arbor, Michigan using the microscopic simulator VISSIM

A distributionally robust stochastic optimization-based model predictive control with distributionally robust chance constraints for cooperative adaptive cruise control under uncertain traffic conditions

Motivated by connected and automated vehicle (CAV) technologies, this paper proposes a data-driven optimization-based Model Predictive Control (MPC) modeling framework for the Cooperative Adaptive Cruise Control (CACC) of a string of CAVs under uncertain traffic conditions. The proposed data-driven optimization-based MPC modeling framework aims to improve the stability, robustness, and safety of longitudinal cooperative automated driving involving a string of CAVs under uncertain traffic conditions using Vehicle-to-Vehicle (V2V) data. Based on an online learning-based driving dynamics prediction model, we predict the uncertain driving states of the vehicles preceding the controlled CAVs. With the predicted driving states of the preceding vehicles, we solve a constrained Finite-Horizon Optimal Control problem to predict the uncertain driving states of the controlled CAVs. To obtain the optimal acceleration or deceleration commands for the CAVs under uncertainties, we formulate a Distributionally Robust Stochastic Optimization (DRSO) model (i.e. a special case of data-driven optimization models under moment bounds) with a Distributionally Robust Chance Constraint (DRCC). The predicted uncertain driving states of the immediately preceding vehicles and the controlled CAVs will be utilized in the safety constraint and the reference driving states of the DRSO-DRCC model. To solve the minimax program of the DRSO-DRCC model, we reformulate the relaxed dual problem as a Semidefinite Program (SDP) of the original DRSO-DRCC model based on the strong duality theory and the Semidefinite Relaxation technique. In addition, we propose two methods for solving the relaxed SDP problem. We use Next Generation Simulation (NGSIM) data to demonstrate the proposed model in numerical experiments. The experimental results and analyses demonstrate that the proposed model can obtain string-stable, robust, and safe longitudinal cooperative automated driving control of CAVs by proper settings, including the driving-dynamics prediction model, prediction horizon lengths, and time headways. Computational analyses are conducted to validate the efficiency of the proposed methods for solving the DRSO-DRCC model for real-time automated driving applications within proper settings

Online predictive connected and automated eco-driving on signalized arterials considering traffic control devices and road geometry constraints under uncertain traffic conditions

For energy-efficient Connected and Automated Vehicle (CAV) Eco-driving control on signalized arterials under uncertain traffic conditions, this paper explicitly considers traffic control devices (e.g., road markings, traffic signs, and traffic signals) and road geometry (e.g., road shapes, road boundaries, and road grades) constraints in a data-driven optimization-based Model Predictive Control (MPC) modeling framework. This modeling framework uses real-time vehicle driving and traffic signal data via Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communications. In the MPC-based control model, this paper mathematically formulates location-based traffic control devices and road geometry constraints using the geographic information from High-Definition (HD) maps. The location-based traffic control devices and road geometry constraints have the potential to improve the safety, energy, efficiency, driving comfort, and robustness of connected and automated driving on real roads by considering interrupted flow facility locations and road geometry in the formulation. We predict a set of uncertain driving states for the preceding vehicles through an online learning-based driving dynamics prediction model. We then solve a constrained finite-horizon optimal control problem with the predicted driving states to obtain a set of Eco-driving references for the controlled vehicle. To obtain the optimal acceleration or deceleration commands for the controlled vehicle with the set of Eco-driving references, we formulate a Distributionally Robust Stochastic Optimization (DRSO) model (i.e., a special case of data-driven optimization models under moment bounds) with Distributionally Robust Chance Constraints (DRCC) with location-based traffic control devices and road geometry constraints. We design experiments to demonstrate the proposed model under different traffic conditions using real-world connected vehicle trajectory data and Signal Phasing and Timing (SPaT) data on a coordinated arterial with six actuated intersections on Fuller Road in Ann Arbor, Michigan from the Safety Pilot Model Deployment (SPMD) project