Study on Capacity of Railroad Network and Airport Terminals For

from Bureau of Transportation Statistics (BTS) website. Railroad crossing inventory database was obtained from the Federal Railroad Administration (FRA). The BTS and FRA data obtained were large and could not be used directly into the model algorithm. Thus, selection criterion was developed to limit the railroad network and airports in the study region. The selected class I railroads and selected airports data were arranged in appropriate format for proper application of the above- specified methodologies. Application of methodologies to datasets identified the areas with excess capacity and limited capacity (Potential bottlenecks) for railroads in study region. Track utilization factor (TUF) in terms of usage to practical capacity was developed for segments of railroad network. It was observed from the study that the average train speed is the critical factor affecting the railroad capacity. The study also estimates the airport capacity and showed the freight flow (tons per month), number of passenger and freight aircrafts per hour served within each major intercity airports in the study region. It was observed that most of the major airports in the upper Midwest freight corridor study region have higher than 50 percent-unused capacities. iii Acknowledgements I would like to take this opportunity to thank my advisor, Dr. Jiwan Gupta, for his patience and invaluable guidance throughout my research and my stay at The University of Toledo. I am grateful to my committee members Dr. Peter Lindquist and Dr. Naser Mostaghel for their advice and suggestions on this work. I would like to express my gratitude to Dr. Peter Lindquist for providing me data for the study. I am very grateful to my parents, my brothers, my sister, my brother in law and my other family members for believing in me and inspiring me to achieve this goal. Special thanks are due to my friends Raja Ravi Varma Andela, Padma Priya Srinivasan and Srikanth Palem whose motivation and support made this research possible. I would like to acknowledge Ashwini Tandale, Raga Smitha Kalapati, CharanyaVaradarajan, Sreelatha Gajula, Sirisha Emani, Ramesh Reddolu and Ravi kanth Boyapally for the encouragement during my stay at the University of Toledo. I am thankful to the Department of Civil Engineering for giving me the opportunity to pursue my Masters program at The University of Toledo. iv Table of Contents Page No. ABSTRACT ii ACKNOWLEDGEMENTS iv TABLE OF CONTENTS v LIST OF TABLES ix LIST OF FIGURES xi Chapter 1 Introduction 1 1.1 Overview 1 1.2 Objectives 3 Chapter 2 Literature Review 5 2.1 Railroads in the U.S. 5 2.1.1 Railroad companies 5 2.1.2 Freight on Railroads 7 2.1.3 Class I freight railroads 9 2.1.4 Highway –Rail Freight 9 2.1.5 Benefits of using railroads 11 2.2 Importance of Railroad Capacity 12 v 2.2.1 Definitions of Railroad Capacity 12 2.2.2 Studies to Determine Railroad Capacity 13 2.3 Measures of Railroad Capacity 17 Theoretical Capacity 17 Practical Capacity 17 Used Capacity 18 Available Capacity 18 2.4 Parameters affecting Railroad Capacity 18 2.4.1 Plant Parameters 18 2.4.2 Traffic Parameters 19 2.4.3 Operating Parameters 21 2.4.4 Sensitivity Analysis 22 2.5 Introduction to Airport Terminals in the nation 25 2.5.1 Air freight 26 2.5 .2 Benefits of Airport Terminals 26 2.6 Airport Capacity 28 2.6.1 Airside and Landside Capacity defined by FAA 28 2.6.2 Parameters Affecting Airport Capacity 28 vi 2.6.3 Studies on Airport Capacity 29 2.7 Conclusions from literature Review 33 Chapter 3 Study Area 34 3.1 Identification of Railroads and Airport terminals in the 34 Study region Chapter 4 Database Development 37 4.1 Railroads data 37 4.1.1 Railroad link by link Database 37 4.1.2 Railroad crossing node Data 37 4.1.3 Processing of Railroad Data 39 4.1.4 Integrated Class I Railroad Network 40 4.1.5 Process of Linking databases to Integrated class I 41 railroad network 4.1.6 Selected Railroad Network 41 4.2 Airport Terminals Data 49 4.2.1 Processing the Airport Terminal Data 52 4.2.2 Selection Criteria for Airports 54 Chapter 5 Methodology 59 5.1 Railroad capacity Algorithm 59 5.1.1 Railroad capacity Program 62 vii 5.2 Airport Terminals Ultimate Capacity 64 5.2.1 Maximum Number of Passenger and Freight flights 68 within the intercity corridor Chapter 6 Results and Discussions 70 6.1 Railroads 70 6.1.1 List of Some Significant Bottlenecks in the Study 73 Region 6.1.2 Limitation of Railroad capacity Study 73 6.1.3 Effect of Parameters on Capacity 74 6.2 Airports 75 82 Chapter 7 Summary and Conclusions Recommendations for Future Research 84 References 85 Appendix –A: Railroads 88 Appendix –B: Airports 97 viii List of Tables Page No. Table 2.1 The U.S. Freight Railroad Statistics 8 Table 2.2 Theoretical Railroad Capacities 17 Table 2.3 U.S. Domestic Air Express Traffic 27 Table 4.1 Example of Original Format of Rail (1:100,000 scale) 38 Network Data Table 4.2 Example of Original Format of Rail (1:200,0000) scale 38 Network Data Table 4.3 Example of Original Format of Railroad Crossing 38 Inventory Data Table 4.4 Format and attributes in BTS, FRA and Integrated railroad 40 Network databases Table 4.5 Main Variables used for Capacity Estimations 47 Table 4.6 Example of Format of Final Airport Terminals Database 54 Table 4.7 List of Selected Airports for the Study 57 Table 4.8 Hourly Arrivals and Departures and Freight Volumes of 58 Selected Airports Table 5.1 Aircraft classification by type 66 ix Table 6.1 Results of Railroad Capacity Model 72 Table 6.2 Railroad Segments Approaching Theoretical Capacity 74 Table 6.3 Airside Capacities for Selected Airports 78 Table 6.4 Number of Passenger aircrafts per hour 79 Table 6.5 Number of Freight aircrafts per hour. 80 Table 6.6 Freight volume (Tons/month) 81 x List of Figures Page No. Figure 2.1 Type of freight carried by class I railroads 10 Figure 2.2 The Intermodal growth of US freight Railroads 10 Figure 2.3 Freight Market Share Trend 11 Figure 2.4 The types of Capacities of the Subdivisions 16 Figure 2.5 Effect of signal spacing on capacity 23 Figure 2.6 Effect of Siding spacing on capacity 23 Figure 2.7 Effect of Length of Segment on capacity 24 Figure 2.8 Effect of Speed on capacity 24 Figure 2.9 Airport classifications 25 Figure 2.10 Graphical Runway Capacity 32 Figure 3.1 Railroads in the study region 35 Figure 3.2 Airport Terminals in the Study Area 36 Figure 4.1 Databases for developing Integrated Class I Railroad 39 Network Figure 4.2 Final railroad base map network 42 Figure 4.3 Conceptual Development of Class I Railroads 44 xi Page No. Figure 4.4 The Process of Segmentation 45 Figure 4.5 Selected Railroad Network for Capacity Analysis 46 Figure 4.6 Flow of Activities to generate Final Database for Class I 48 Railroad Network Figure 4.7 Monthly arrivals (aircrafts/hour) for Chicago O’Hare 50 International Airport (ORD). Figure 4.8 Monthly departures (aircrafts/hour) for Chicago O’Hare 50 International Airport (ORD). Figure 4.9 Maximum frequencies of Arrivals 51 Figure 4.10 Maximum frequencies of Departures 51 Figure 4.11 Flow diagram Showing the Joining of Airports Database 53 Figure 4.12 Selected Airport Terminals in the Upper Midwest Freight 56 Corridor Study Region. Figure 5.1 Flow of Activities in Railroad Capacity Algorithm 63 Figure 5.2 Flow diagram showing Railroad Capacity Model Steps 64 Figure 5.3 Flow chart showing the Methodologies used for Airport 67 Capacity Study Figure 6.1 Track Utilization Factor for Railroad Segments 71 Figure 6.2 Graph showing relationship between Delay slope and 75 Average Speed xii Chapter 1 Introduction 1.1 Overview One of the most important elements in the transportation and transportation systems is Freight. The freight is carried by truck, rail, water and air. The multimodal freight transportation reduces the costs of congestion and thrives to maintain transportation connections among ports, manufacturing/distribution centers, agricultural areas, and tourist locations, thus having a direct impact on the economy and quality of life. In this modern world the demand to move goods from place to place is increasing drastically, resulting in significant increase in freight movements in the future [1]. Hence, a sound multimodal transportation system is needed to vitalize the economy and facilitate the desired growth. The best way to express corridor multimodal transportation system is through the capacity of each segment traveled by each mode in the network. There might be some areas in the corridor having either excess (available) capacity or limited capacity (bottlenecks) i.e. the current capacity is not able to meet the existing travel demands. These potential bottlenecks work as major obstacles for freight movements. 1 Therefore there is a need to calculate multimodal capacity and identify the areas with and excess capacity. Thus an understanding of the freight travel demand and the facility capacity will provide proper planning for the corridor. At present, of all multimodal transports, highways carry most of the freight, due to which the highways are experiencing rigorous congestion problem. Thus the highways will not be able to meet the future freight demand. Therefore to reduce congestion and meet the future freight demands the two immediate solutions are: (1) Either construct or expand highways which involve lot of time, money and resources or (2) To make utilize of the existing multimodes such as railroads, airports and waterways for transporting freight.

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