Estimation of run times in a freight rail transportation network

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 49-51).The objective of this thesis is to improve the accuracy of individual freight train run time predictions defined as the time between departure from an origin node to arrival at a destination node not including yard time. A correlation analysis is conducted to identify explanatory variables that capture predictable sources of delay and influence run times for use in a regression model. A regression model is proposed utilizing the following explanatory variables: rolling historical average, congestion window, meets, passes, overtakes, direction, arrival headway, and departure headway to predict train run times. The performance of the proposed regression model is compared against a baseline simple historical averaging technique for a two year period of actual train operational data. The proposed regression model, though subject to specific limitations, offers substantial improvements in accuracy over the baseline technique and is recommended as justifying further exploration by the railroad to ultimately enable more accurate train schedules with subsequent improvements in railroad capacity, customer service, and asset utilization.by Kunal Bonsra and Joseph Harbolovic.M.Eng.in Logistic

TIMETABLE MANAGEMENT TECHNIQUE IN RAILWAY CAPACITY ANALYSIS: DEVELOPMENT OF THE HYBRID OPTIMIZATION OF TRAIN SCHEDULES (HOTS) MODEL

There are two general approaches to improve the capacity in a rail corridor, either by applying new capital infrastructure investment or by improving the operation of the rail services. Techniques to evaluate the railway operation include modeling and optimization through the use of commercial timetable management and rail simulation tools. However, only a few of the existing tools include complete features of timetable management techniques (e.g. timetable compression) are equipped with an optimization model for rescheduling and timetable improvement and this is especially true when it comes to the U.S. rail environment that prevalently uses unstructured operation practices. This dissertation explores an application of timetable (TT) management techniques (e.g. rescheduling and timetable compression techniques) in the U.S. rail environment and their effect on capacity utilization and level of service (LOS) parameters. There are many tools and simulation packages used for capacity analysis, by both European and the U.S. rail industry, but due to the differences in the operating philosophy and network characteristics of these two rail systems, European studies tend to use timetable-based simulation tools (e.g. RailSys, OpenTrack) while the non-timetable based tools (e.g. RTC) are commonly used in the U.S. (Chapter 1). This research study investigated potential benefits of using a “Hybrid Simulation” approach that would combine the advantages of both the U.S. and European tools. Two case studies (a single track and a multiple-track case study) were developed to test the hybrid simulation approach, and it was concluded that applying timetable management techniques (e.g. timetable compression technique) is promising when implemented in a single track corridor (Chapter 2), but it is only applicable for the multiple track corridors under directional operation pattern (Chapter 3). To address this, a new heuristic rescheduling and rerouting technique was developed as part of the research to convert a multiple track case study from non-directional operation pattern to a fully directional operation pattern (Chapter 4). The knowledge and skills of existing software, obtained during the development and testing of “Hybrid Simulation”, was used to develop an analytical rescheduling/optimization model called “Hybrid Optimization of Train Schedules” (HOTS) (Chapter 5). While the results of the “Hybrid simulation approach” are promising, the method was also time consuming and challenging, as all respective details and database of the given corridors had to be replicated in both simulation tools. The “HOTS Model” could provide the same functions and features of train rescheduling, but with much less efforts and challenges as in the hybrid simulation. The HOTS model works in conjunction with any commercial rail simulation software and it can reschedule an initial timetable (with or without conflict) to provide a “Conflict-Free” timetable based on user-defined criteria. The model is applicable to various types of rail operations, including single, double and multiple-track corridors, under both directional and nondirectional operation patterns. The capabilities of the HOTS model were tested for the two case studies developed in the research, and its outcomes were compared to those obtained from the commercial software. It was concluded that the HOTS model performed satisfactorily in each of the test scenarios and the model results either improved or maintained the initial timetable characteristics. The results are promising for the future development of the model, but limitations in the current model structure, such as station capacity limits, should be addressed to improve the potential of applying the model for industrial applications

Development of a multimodal port freight transportation model for estimating container throughput

Computer based simulation models have often been used to study the multimodal freight transportation system. But these studies have not been able to dynamically couple the various modes into one model; therefore, they are limited in their ability to inform on dynamic system level interactions. This research thesis is motivated by the need to dynamically couple the multimodal freight transportation system to operate at multiple spatial and temporal scales. It is part of a larger research program to develop a systems modeling framework applicable to freight transportation. This larger research program attempts to dynamically couple railroad, seaport, and highway freight transportation models. The focus of this thesis is the development of the coupled railroad and seaport models. A separate volume (Wall 2010) on the development of the highway model has been completed. The model railroad and seaport was developed using Arena® simulation software and it comprises of the Ports of Savannah, GA, Charleston, NC, Jacksonville, FL, their adjacent CSX rail terminal, and connecting CSX railroads in the southeastern U.S. However, only the simulation outputs for the Port of Savannah are discussed in this paper. It should be mentioned that the modeled port layout is only conceptual; therefore, any inferences drawn from the model's outputs do not represent actual port performance. The model was run for 26 continuous simulation days, generating 141 containership calls, 147 highway truck deliveries of containers, 900 trains, and a throughput of 28,738 containers at the Port of Savannah, GA. An analysis of each train's trajectory from origin to destination shows that trains spend between 24 - 67 percent of their travel time idle on the tracks waiting for permission to move. Train parking demand analysis on the adjacent shunting area at the multimodal terminal seems to indicate that there aren't enough containers coming from the port because the demand is due to only trains waiting to load. The simulation also shows that on average it takes containerships calling at the Port of Savannah about 3.2 days to find an available dock to berth and unload containers. The observed mean turnaround time for containerships was 4.5 days. This experiment also shows that container residence time within the port and adjacent multimodal rail terminal varies widely. Residence times within the port range from about 0.2 hours to 9 hours with a mean of 1 hour. The average residence time inside the rail terminal is about 20 minutes but observations varied from as little as 2 minutes to a high of 2.5 hours. In addition, about 85 percent of container residence time in the port is spent idle. This research thesis demonstrates that it is possible to dynamically couple the different sub-models of the multimodal freight transportation system. However, there are challenges that need to be addressed by future research. The principal challenge is the development of a more efficient train movement algorithm that can incorporate the actual Direct Traffic Control (DTC) and / or Automatic Block Signal (ABS) track segmentation. Such an algorithm would likely improve the capacity estimates of the railroad network. In addition, future research should seek to reduce the high computational cost imposed by a discrete process modeling methodology and the adoption of single container resolution level for terminal operations. A methodology combining both discrete and continuous process modeling as proposed in this study could lessen computational costs and lower computer system requirements at a cost of some of the feedback capabilities of the model This tradeoff must be carefully examined.M.S.Committee Chair: Rodgers, Michael; Committee Member: Guensler, Randall; Committee Member: Hunter, Michae

Positive Train Control (PTC): Calculating Benefits and Costs of a New Railroad Control Technology

The purpose of this analysis was to quantify the business benefits of Positive Train Control (PTC) for the Class I freight railroad industry. This report does not address the safety benefits of PTC. These were previously quantified by the Rail Safety Advisory Committee (RSAC), which identified nearly a thousand "PPAs" (PTC-preventable accidents) on U.S. railroads over a 12-year period, and determined the savings to be realized from each avoided accident. The RSAC finding was that avoidance of these PPAs was not, by itself, sufficient (from a strictly economic point of view) to justify an investment in PTC. Examples of potential business benefits include: * Line capacity enhancement * Improved service reliability * Faster over-the-road running times * More efficient use of cars and locomotives (made possible by real-time location information) * Reduction in locomotive failures (due to availability of real-time diagnostics) * Larger "windows" (periods during which no trains operate and maintenance workers can safely occupy the track) for track maintenance (made possible by real-time location information) * Fuel savings This paper presents the results of the analysis. It is important to recognize, however, that the state of the art in making these estimates is not sufficiently mature to make exact answers feasible. Presented here are the best estimates now possible, with observations as to how better information may be developed. Benefits were estimated in the above areas and the cost of deploying PTC on the Class I network (99,000 route miles and 20,000 locomotives) were calculated. The conclusions of the analysis were as follows: * Deployment of PTC on the Class I railroad network (99,000 route miles, 20,000 locomotives) would cost between $2.3 billion and$4.4 billion over five years * Annual benefits, once the system was fully implemented, were estimated at $2.2 billion to$3.8 billion * Internal rate of return was estimated (depending on timing and cost) to be between 44% and 160

Capacity evaluation and infrastructure planning techniques for heterogeneous railway traffic under structured, mixed, and flexible operation

North American railroads have a strong business incentive to match rail line capacity to traffic demand. Since insufficient capacity reduces level of service and excess capacity represents inefficient use of capital, either one of these situations is undesirable. Various processes, models, and tools have been developed to assist the railroads in determining appropriate infrastructure projects and operational plans to balance network capacity. In North America, these approaches have typically been tailored to operating conditions on rail corridors that are dominated by freight trains that do not run according to a precise schedule. Changes in the composition of rail traffic have resulted in new operating conditions that require new approaches to rail capacity evaluation. The long-term growth of freight rail traffic (with particular increases in premium intermodal traffic) and recent interest in the expansion of passenger service on freight corridors have increased rail traffic volume and heterogeneity, while altering the level of randomness involved in train departure and trip times. The single-track lines that comprise the majority of the North American rail network have limited capacity and can frequently become congested under these new rail traffic demands. The combined impact of traffic volume, heterogeneity, and level of randomness in train plans has not always been fully considered by previous approaches to the study of rail line capacity. This dissertation develops new capacity evaluation and infrastructure planning techniques for single-track lines that consider the impact of relationships between infrastructure layout, train operating plans including train-specific levels of service, and train characteristics on line capacity. In this study, the randomness involved in a train operating plan is described by “schedule flexibility” and “operating style”. In chapter 1, the concepts of operating style and schedule flexibility are proposed and defined. In chapters 2 and 3, a capacity evaluation and alternative comparison process are proposed to assist the capacity evaluation and planning of single-track lines under mixed or flexible operation. In chapter 4, an optimization model is developed to determine the optimal number and locations of passing sidings for single-track lines under structured operation. In chapter 5, the concept of traffic conflict analysis is introduced as a research direction to address rail infrastructure and operational planning problems. The methods developed in this dissertation can help to better assess mainline capacity under current operating conditions and determine more effective infrastructure expansion projects or changes in operational strategy for railroads and passenger rail agencies in North America. Use of these methods can help railroads improve their service quality and maximize returns to their stakeholders

Prediction of arrival times of freight traffic on us railroads using support vector regression

Variability of the travel times on the United States freight rail network is high due to large network demand relative to infrastructure capacity especially when traffic is heterogeneous. Variable runtimes pose significant operational challenges if the nature of runtime variability is not predictable. To address this issue, this article proposes a data-driven approach to predict estimated times of arrival (ETAs) of individual freight trains, based on the properties of the train, the properties of the network, and the properties of potentially conflicting traffic on the network. The ETA problem is posed as a machine learning regression problem and solved using a support vector regression machine trained and cross validated on over two years of historical data for a 140 mile stretch of track located primarily in Tennessee, USA. The article presents the data used in this problem and details on feature engineering and construction for predictions made across the full route. It also highlights findings on the dominant sources of runtime variability and the most predictive factors for ETA, identified by applying the data framework. ETA improvement results exceeded 20% over baseline methods for predictions made at some locations and averaged over 15% across the study area. Ideas for further ETA improvement using the prediction algorithms are also discussed

Assessing temporary speed restrictions and associated unavailability costs in railway infrastructure

This paper analyses the occurrence of temporary speed restrictions in railway infrastructure associated with railway track geometry degradation. A negative binomial regression model is put forward to estimate the expected number of temporary speed restrictions, controlling for the main quality indicators of railway track geometry degradation and for the maintenance and renewal actions/decisions. The prediction of temporary speed restrictions provides a quantitative way to support the assessment of unavailability costs to railway users. A case study on the Lisbon–Oporto Portuguese line is explored, comparing three statistical models: the Poisson, the ‘over-dispersed’ Poisson and the proposed negative binomial regression. Main findings suggest that the main quality indicators for railway track geometry degradation are statistically significant variables, apart from the maintenance and renewal actions. Finally, a discussion on the impacts of the unavailability costs associated with temporary speed restrictions is also provided in a regulated railway context.info:eu-repo/semantics/acceptedVersio

A Quantitative Framework for Assessing Vulnerability and Redundancy of Freight Transportation Networks

Freight transportation networks are an important component of everyday life in modern society. Disruption to these networks can make peoples’ daily lives extremely difficult as well as seriously cripple economic productivity. This dissertation develops a quantitative framework for assessing vulnerability and redundancy of freight transportation networks. The framework consists of three major contributions: (1) a two- stage approach for estimating a statewide truck origin-destination (O-D) trip table, (2) a decision support tool for assessing vulnerability of freight transportation networks, and (3) a quantitative approach for measuring redundancy of freight transportation networks.The dissertation first proposes a two-stage approach to estimate a statewide truck O-D trip table. The proposed approach is supported by two sequential stages: the first stage estimates a commodity-based truck O-D trip table using the commodity flows derived from the Freight Analysis Framework (FAF) database, and the second stage uses the path flow estimator (PFE) concept to refine the truck trip table obtained from the first stage using the truck counts from the statewide truck count program. The model allows great flexibility of incorporating data at different spatial levels for estimating the truck O- D trip table. The results from the second stage provide us a better understanding of truck flows on the statewide truck routes and corridors, and allow us to better manage the anticipated impacts caused by network disruptions.A decision support tool is developed to facilitate the decision making system through the application of its database management capabilities, graphical user interface, GIS-based visualization, and transportation network vulnerability analysis. The vulnerability assessment focuses on evaluating the statewide truck-freight bottlenecks/chokepoints. This dissertation proposes two quantitative measures: O-D connectivity (or detour route) in terms of distance and freight flow pattern change in terms of vehicle miles traveled (VMT). The case study adopts a “what-if” analysis approach by generating the disruption scenarios of the structurally deficient bridges in Utah due to earthquakes. In addition, the potential impacts of disruptions to multiple bridges in both rural and urban areas are evaluated and compared to the single bridge failure scenarios.This dissertation also proposes an approach to measure the redundancy of freight transportation networks based on two main dimensions: route diversity and network spare capacity. The route diversity dimension is used to evaluate the existence of multiple efficient routes available for users or the degree of connections between a specific O-D pair. The network spare capacity dimension is used to quantify the network- wide spare capacity with an explicit consideration of congestion effect. These two dimensions can complement each other by providing a two-dimensional characterization of freight transportation network redundancy. Case studies of the Utah statewide transportation network and coal multimodal network are conducted to demonstrate the features of the vulnerability and redundancy measures and the applicability of the quantitative assessment methodology

An application of a simulation technique on rail container transport between Laem Chabang Port and Inland Container Depot Ladkrabang, Thailand

Since the increasing demand of container transport through Laem Chabang Port, Thailand, the port faces landside congestion due to the large share of road transport. The Inland Container Depot Ladkrabang is the dry port facility to support and increase the efficiency, and ease the congestion at the port by linking the dry port and the seaport by rail; however, the share of transport between the two facilities is largely by road. There are three modes of transport at Laem Chabang Port i.e. road, rail, and barge. The large share of a modal split by road creates severe landside congestion. Moreover, the Inland Container Depot Ladkrabang, which serves the direct rail link to Laem Chabang Port has a larger share of road transport. This causes traffic congestion from the increasing container throughput and other problems, i.e. air emissions, pollution in port and local community, excess cost of transport, and road accidents. This dissertation aims to study the reason why the modal share by rail has not increased, which hinders the growth of the rail transport between the two facilities by rail container transport data analysis. Furthermore, the research studies the effectiveness of government policies and investment into the rail transport system between Laem Chabang Port and Inland Container Depot Ladkrabang. Finally, the simulation technique is utilized using Rockwell Software Arena 14 to generate the virtual model, which is based on the real world system parameters and characteristics. The virtual simulation model allows free configurations and adjustments based on the real world applicability. Three scenarios are assumed, constructed, and quantified by the software. The results show that there are possibilities to create more efficient systems in the virtual models. The presentation of the real world application shows how the virtual model could be implemented and which measures are required to maintain the system efficiency. This also illustrates the impact of the system application to the current situation