Energy consumption,pollutant production,and dollar costs of diesel suburban commuter trains

The results presented in this report are based on data obtained from Chicago's three largest diesel commuter railroads. Those aspects of their operations that relate to energy and pollution are described. Service characteristics, such as average occupancy and average trip distance, are presented. Energy consumption results are presented and discussed. With energy efficiency measured in passenger-miles per Btu, it is found that trips by diesel commuter train are 3.5 times more energy efficient than Chicago Central Area auto trips. The total trip from home to suburban station, then by train to a downtown terminal, is found to be 2.2 times more energy efficient than Chicago Central Area auto trips. Pollutant production rates are presented for five pollutants. For every pollutant except sulfur oxides, trains are found to be less polluting per passenger-mile than autos. Per passenger-mile pollutant emissions from trains are, overall, less damaging by a factor of 5.5 than the per passenger-mile emissions from autos. Travel on these diesel commuter trains is less costly to society than auto travel (1972 suburban-based autos). This is the case whether one compares the train trip alone with an auto trip or the home-to-suburb an-station-tlien-to-a-downtown-terminal trip with a home-to-downtown auto trip.     

Effect of mass on multimodal fuel consumption in moving people and freight in the U.S.

The United States transportation sector consumes 5 billion barrels of petroleum annually to move people and freight around the country by car, truck, train, ship and aircraft, emitting significant greenhouse gases in the process. Making the transportation system more sustainable by reducing these emissions and increasing the efficiency of this multimodal system can be achieved through several vehicle-centric strategies. We focus here on one of these strategies – reducing vehicle mass – and on collecting and developing a set of physics-based expressions to describe the effect of vehicle mass reduction on fuel consumption across transportation modes in the U.S. These expressions allow analysts to estimate fuel savings resulting from vehicle mass reductions (termed fuel reduction value, FRV), across modes, without resorting to specialized software or extensive modeling efforts, and to evaluate greenhouse gas emission and cost implications of these fuel savings. We describe how FRV differs from fuel intensity (FI) and how to properly use both of these metrics, and we provide a method to adjust FI based on mass changes and FRV. Based on this work, we estimate that a 10% vehicle mass reduction (assuming constant payload mass) results in a 2% improvement in fuel consumption for trains and light, medium, and heavy trucks, 4% for buses, and 7% for aircraft. When a 10% vehicle mass reduction is offset by an increase in an equivalent mass of payload, fuel intensity (fuel used per unit mass of payload) increases from 6% to 23%, with the largest increase being for aircraft.     

Probabilistic analysis of the release of liquefied natural gas (LNG) tenders due to freight-train derailments

Liquefied natural gas (LNG) has emerged as a possible alternative fuel for freight railroads in the United States, due to the availability of cheap domestic natural gas and continued pursuit of environmental and energy sustainability. A safety concern regarding the deployment of LNG-powered trains is the risk of breaching the LNG tender car (a special type of hazardous materials car that stores fuel for adjacent locomotives) in a train accident. When a train is derailed, an LNG tender car might be derailed or damaged, causing a release and possible fire. This paper describes the first study that focuses on modeling the probability of an LNG tender car release incident due to a freight train derailment on a mainline. The model accounts for a number of factors such as FRA track class, method of operation, annual traffic density level, train length, the point of derailment, accident speed, the position(s) of the LNG tender(s) in a train, and LNG tender car design. The model can be applied to any specified route or network with LNG-fueled trains. The implementation of the model can be undertaken by the railroad industry to develop proactive risk management solutions when using LNG as an alternative railroad fuel.     

An analysis of the methods used to calculate the emissions of rolling stock in the UK

The aim of this paper is to analyse and compare the methods used for calculating emissions of UK rolling stock based on their type and mode of operation. The three modes under comparison were; diesel, electric and bi-mode. As well as comparing these three modes of operation, a comparison between Conventional, Freight and High Speed Rail was made. Alternate fuels were considered for diesel and bi-mode locomotives and compared based on their environmental impact. The emissions of trains were studied using three methods. Specifically, the three chosen methods were used to calculate the emissions of each train and a comparison of these methods was made. In the current UK energy climate, diesel trains emit less emissions than electric trains when factoring in mechanical and air resistances. Bi-mode trains have their place in the UK network but with electrification of the network currently in place, this mode of operation will become redundant in the near future. High Speed Rail, although time efficient, releases high emissions due to energy consumption increasing with the square of speed. Alternative fuels, such as biodiesel, should be a consideration for the future of rail, as emissions fall dramatically with content of biodiesel in fuel blends.     

Standardization of capacity unit for headway-based rail capacity analysis

Determining the required capacity upgrades to accommodate future demand is a critical process in assisting public and private financing of capacity investments. Conventional railway systems usually operate multiple types of trains on the same track. These different types of trains can exert substantially different capacity impact, and can cause serious operational conflicts. In the past, rail line capacity is commonly defined as the maximum number of trains that can be operated on a section of track within a given time period. However, a specific unit (trains/hr or trains/day) does not reflect the heterogeneity of train types. According to the concept of base train equivalents (BTE) and base train unit (BTU), this study developed headway-based models to determine BTE for transforming different train types into a standard unit (i.e., BTU). An approximate method for lines with three and more types of trains was also proposed to compute BTEs for non-base trains. Results from the case studies demonstrate that this method enables the standardization of rail capacity unit, facilitates assessment of the impact from heterogeneous trains, and allows comparison and evaluation of the capacity measurements from different lines and systems.     

A new approach for improving the performance of freight train timetabling of a single-track railway system

A new approach for improving the performance of freight train timetabling for single-track railways is proposed. Using the idea of a fixed-block signaling system, we develop a matrix representation to express the occupation of inter- and intra-station tracks by trains illustrating the train blocking time diagram in its entirety. Train departure times, dwell times, and unnecessary stopping are adjusted to reduce average train travel time and single train travel time. Conflicts between successive stations and within stations are identified and solved. A fuzzy logic system is further used to adjust the range of train departure times and checks are made to determine whether dwell times and time intervals can be adjusted for passenger and freight trains at congested stations to minimize train waiting times. By combining manual scheduling expertise with the fuzzy inference method, timetable efficiency is significantly improved and becomes more flexible.     

Service-cost tradeoffs for carload freight traffic in the U.S. rail industry

Railroad technology permits a single train to move a large number of individual freight cars. However, cars which are not in dedicated unit train or intermodal service experience considerable delay due to the consolidation and breakup of trains. Rail operations thus involve a tradeoff between the economies of shipment consolidation, and the resulting delays. More direct and/or more frequent train connections will increase costs, but reduce transit times. This article quantifies the cost of providing a range of transit times for general carload traffic for several representative U.S. rail systems. It shows that significant reductions in transit time will require a large increase in the number of train connections and operating cost. Changes in labor contracts to reduce train crew cost will provide some incentive for higher service levels, but reductions in crew cost alone cannot be expected to dramatically improve the performance of the carload segment of the industry.     

模拟计算法在重型车辆燃油经济性标准中的应用研究

随着节能减排压力的日益严重,世界各国均开始制定重型车辆的燃油经济性标准。在对重型车燃油经济性的测量方面,模拟计算法由于操作简单、可重复性好、成本低的优点越来越受到行业的重视,在日本、中国和美国的标准中都得到了应用。本文在分析模拟计算法测量车辆燃油经济性的原理基础上,对比研究了日本、中国和美国标准中模拟计算法的应用情况,分析了各国在模拟计算法关键环节上策略的异同。     

Making meaningful comparisons between road and rail – substituting average energy consumption data for rail with empirical analysis

Within the transport sector, modal shift towards more efficient and less polluting modes could be a key policy goal to help meet targets to reduce energy consumption and carbon emissions. However, making comparisons between modes is not necessarily straightforward. Average energy and emissions data are often relied upon, particularly for, rail, which may not be applicable to a given context. Some UK train operating companies have recently fitted electricity metres to their trains, from which energy consumption data have been obtained. This has enabled an understanding to be gained of how energy consumption and related emissions are affected by a number of factors, including train and service type. Comparisons are made with existing data for road and rail. It is noted that although more specific data can be useful in informing policy and making some decisions, average data continue to play an important role when considering the overall picture.     

A practical capacity model of a single track line

The paper presents a model for determining the practical capacity of a single track line, i.e. the maximum number of trains which can be run along it in a time unit under the condition that each train enters its bottleneck segment with a definite delay.

The input data used in the model are: geometrical characteristics of the bottleneck segment of the line under study, the intensity and structure of demand expressed by a number of trains which are run over the line in a given time unit, the scenario of traffic running over the line under study and the operational tactics of individual train categories processing on the bottleneck segment.

(Two tactics can be applied in the train processing on the line under study; first, the trains of individual categories are given different priorities in the processing, and second, all the trains have the same priority).

The output results of the model are average delays of trains of each category occurring within the train processing performed on the bottleneck segment of the line under study in a given time unit.     

A railway capacity determination model and rail access charging methodologies

This article discusses approaches to the determination of railway capacity and the significance of the following factors on capacity: mix of trains, length and weight of trains, direction of train travel, acceleration and deceleration, stopping protocols of trains, location and length of crossing loops, location of signals, length of sections, dwell times and sectional running times. A more accurate method to calculate railway capacity is developed using previously unaddressed aspects for capacity determination. Capacity and pricing are two key issues for organizations involved with open track access regimes. A train access charging methodology is therefore developed and incorporated into a railway capacity determination model.     

Fueling for contingencies: The hidden cost of unpredictability in the air transportation system

The aviation community is increasing its attention on the concept of predictability when conducting aviation service quality assessments. Reduced fuel consumption and the related cost is one of the various benefits that could be achieved through improved flight predictability. A lack of predictability may cause airline dispatchers to load more fuel onto aircraft before they depart; the flights would then in turn consume extra fuel just to carry excess fuel loaded. In this study, we employ a large dataset with flight-level fuel loading and consumption information from a major US airline. With these data, we estimate the relationship between the amount of loaded fuel and flight predictability performance using a statistical model. The impact of loaded fuel is translated into fuel consumption and, ultimately, fuel cost and environmental impact for US domestic operations. We find that a one-minute increase in the standard deviation of airborne time leads to a 0.88 min increase in loaded contingency fuel and 1.66 min in loaded contingency and alternate fuel. If there were no unpredictability in the aviation system, captured in our model by eliminating standard deviation in flight time, the reduction in the loaded fuel would between 6.12 and 11.28 min per flight. Given a range of fuel prices, this ultimately would translate into cost savings for US domestic airlines on the order of $120–$452 million per year.     

Energy factors for flexible fuel engines and vehicles operating with gasoline-ethanol blends

This work investigates the energy factors for fuel conversion from the analysis of brake specific fuel consumption (BSFC) maps of a sample of 15 engines, representative of 75% of current models available in the Brazilian market. The method also employs the engine driving patterns of power output versus crankshaft speed obtained from bench dynamometer tests. The energy factors obtained from the engine analysis was validated against experiments carried out with two production vehicles in laboratory tests following the 1975 US Federal Test Procedure (FTP-75) procedure and road tests following 16 different urban and highway routes. The fuels used in the tests were hydrous ethanol (E100, 6 v/v % water) and a blend of 22 v/v % anhydrous ethanol and 78 v/v % gasoline (E22). The energy factors found from the 3D engine BSFC map analysis were higher than those obtained from the Willans line, currently adopted as a standard, by 52% for E22 and 57% for E100. The results from the 3D engine BFSC maps and the first vehicle following the FTP-75 cycle and 15 road routes were similar, also close to the results from the second vehicle, qualifying them to be representative of modern flexible fuel spark ignition engines and vehicles.     

A fuzzy approach to addressing uncertainty in Airport Ground Movement optimisation

Allocating efficient routes to taxiing aircraft, known as the Ground Movement problem, is increasingly important as air traffic levels continue to increase. If taxiways cannot be reliably traversed quickly, aircraft can miss valuable assigned slots at the runway or can waste fuel waiting for other aircraft to clear. Efficient algorithms for this problem have been proposed, but little work has considered the uncertainties inherent in the domain. This paper proposes an adaptive Mamdani fuzzy rule based system to estimate taxi times and their uncertainties. Furthermore, the existing Quickest Path Problem with Time Windows (QPPTW) algorithm is adapted to use fuzzy taxi time estimates. Experiments with simulated taxi movements at Manchester Airport, the third-busiest in the UK, show the new approach produces routes that are more robust, reducing delays due to uncertain taxi times by 10–20% over the original QPPTW.     

The effect of uncertainty on US transport-related GHG emissions and fuel consumption out to 2050

The future of US transport energy requirements and emissions is uncertain. Transport policy research has explored a number of scenarios to better understand the future characteristics of US light-duty vehicles. Deterministic scenario analysis is, however, unable to identify the impact of uncertainty on the future US vehicle fleet emissions and energy use. Variables determining the future fleet emissions and fuel use are inherently uncertain and thus the shortfall in understanding the impact of uncertainty on the future of US transport needs to be addressed. This paper uses a stochastic technology and fleet assessment model to quantify the uncertainties in US vehicle fleet emissions and fuel use for a realistic yet ambitious pathway which results in about a 50% reduction in fleet GHG emissions in 2050. The results show the probability distribution of fleet emissions, fuel use, and energy consumption over time out to 2050. The expected value for the fleet fuel consumption is about 450 and 350 billion litres of gasoline equivalent with standard deviations of 40 and 80 in 2030 and 2050, respectively. The expected value for the fleet GHG emissions is about 1360 and 850 Mt CO₂ equivalent with standard deviation of 130 and 230 in 2030 and 2050 respectively. The parameters that are major contributors to variations in emissions and fuel consumption are also identified and ranked through the uncertainty analysis. It is further shown that these major contributors change over time, and include parameters such as: vehicle scrappage rate, annual growth of vehicle kilometres travelled in the near term, total vehicle sales, fuel economy of the dominant naturally-aspirated spark ignition vehicles, and percentage of gasoline displaced by cellulosic ethanol. The findings in this paper demonstrate the importance of taking uncertainties into consideration when choosing amongst alternative fuel and emissions reduction pathways, in the light of their possible consequences.     

Real-time energy consumption minimization in railway networks

A new timetable must be calculated in real-time when train operations are perturbed. Although energy consumption is becoming a central issue both from the environmental and economic perspective, it is usually neglected in the timetable recalculation. In this paper, we formalize the real-time Energy Consumption Minimization Problem (rtECMP). It finds in real-time the driving regime combination for each train that minimizes energy consumption, respecting given routing and precedences between trains. In the possible driving regime combinations, train routes are split in subsections for which one of the regimes resulting from the Pontryagin’s Maximum Principle is to be chosen. We model the trade-off between minimizing energy consumption and total delay by considering as objective function their weighted sum. We propose an algorithm to solve the rtECMP, based on the solution of a mixed-integer linear programming model. We test this algorithm on the Pierrefitte-Gonesse control area, which is a critical area in France with dense mixed traffic. The results show that the problem is tractable and an optimal solution of the model tackled can often be found in real-time for most instances.     

Integrated modeling of high performance passenger and freight train planning on shared-use corridors in the US

This paper studies strategic level train planning for high performance passenger and freight train operations on shared-use corridors in the US. We develop a hypergraph-based, two-level approach to sequentially minimize passenger and freight costs while scheduling train services. Passenger schedule delay and freight lost demand are explicitly modeled. We explore different solution strategies and conclude that a problem-tailored linearized reformulation yields superior computational performance. Using realistic parameter values, our numerical experiments show that passenger cost due to schedule delay is comparable to in-vehicle travel time cost and rail fare. In most cases, marginal freight cost increase from scheduling more passenger trains is higher than marginal reduction in passenger schedule delay cost. The heterogeneity of train speed reduces the number of freight trains that can run on a corridor. Greater tolerance for delays could reduce lost demand and overall cost on the freight side. The approach developed in the paper could be applied to other scenarios with different parameter values.     

西部原油管道输量与能耗分析

为降低西部原油管道管输能耗,通过计算管道在不同季节、不同流量下的摩阻损耗,结合管道参数和输油泵特性曲线,分析得出影响输油生产单位能耗的因素包括管输流量和输油温度。进一步计算得出了不同流量和不同季节对应的输油生产单耗。结果表明,流量对输油生产单耗影响较大,西部原油管道管输流量在1 000~1 400 m~3/h区间时生产单耗相对较低;流量大于1 600 m3/h时,生产单耗随流量上升接近线性增长。而输油温度对生产单耗的影响较小,同一流量下冬季生产单耗略高于夏季生产单耗。     

Comparison of external costs of rail and truck freight transportation

In this article we estimate external costs for four representative types of freight trains. For each type of freight train, we estimate three general types of external costs and compare them with the private costs experienced by railroad companies. The general types of external costs include: accidents (fatalities, injuries, and property damage); emissions (air pollution and greenhouse gases); and noise. Resulting private and external costs are compared with those of freight trucking, estimated in an earlier article. Rail external costs are 0.24 cent to 0.25 cent (US) per ton-mile, well less than the 1.11 cent for freight trucking, but external costs for rail generally constitute a larger amount relative to private costs, 9.3–22.6%, than is the case for trucking, 13.2%.     

Energy-efficient operation of rail vehicles

This paper describes an analytical process that computes the optimal operating successions of a rail vehicle to minimize energy consumption. Rising energy prices and environmental concerns have made energy conservation a high priority for transportation operations. The cost of energy consumption makes up a large portion of the Operation and Maintenance (O&M) costs of transit especially rail transit systems. Energy conservation or reduction in energy cost may be one of the effective ways to reduce transit operating cost, therefore improve the efficiency of transit operations.From a theoretical point of view, the problem of energy efficient train control can be formulated as one of the functions of Optimal Control Theory. However, the classic numerical optimization methods such as discrete method of optimum programming are too slow to be used in an on-board computer even with the much improved computation power, today. The contribution of this particular research is the analytical solution that gives the sequence of optimal controls and equations to find the control change points. As a result, a calculation algorithm and a computer program for energy efficient train control has been developed. This program is also capable of developing energy efficient operating schedules by optimizing distributions of running time for an entire route or any part of rail systems.We see the major application of the proposed algorithms in fully or partially automated Train Control Systems. The modern train control systems, often referred as “positive” train control (PTC), have collected a large amount of information to ensure safety of train operations. The same data can be utilized to compute the optimum controls on-board to minimize energy consumption based on the algorithms proposed in this paper. Most of the input data, such as track plan, track profile, traction and braking characteristics, speed limits and required trip time are located in an on-board database and/or they can be transmitted via radio link to be processed by the proposed algorithm and program.