METHOD, SYSTEM AND COMPUTER SOFTWARE CODE FOR TRIP OPTIMIZATION WITH TRAIN/TRACK DATABASE AUGMENTATION
This application is a Continuation-In-Part of U.S. Application No. ll/385,3'54, filed March 20, 2006, the contents of which are incorporated herein by reference in its entirety, and is based on Provisional Application No. 60/869 196 filed December 8, 2006.
FIELD OF THE INVENTION
The field of invention relates to a system and method for optimizing train operations, and more particularly to a system and method for augmenting and updating a train/track database associated with the system, method and/or computer software code for optimizing train operations.
BACKGROUND OF THE INVENTION
A locomotive is a complex system with numerous subsystems, each subsystem interdependent on other subsystems. An operator aboard a locomotive applies tractive and braking effort to control the speed of the locomotive and its load of railcars to assure safe and timely arrival at the desired destination. To perform this function and comply with prescribed operating speeds that may vary with the train's location on the track, the operator generally must have extensive experience operating the locomotive over the specified terrain with various railcar consists, i.e., different types and number of railcars.
However, even with sufficient knowledge and experience to assure safe operation, the operator generally cannot operate the locomotive to minimize fuel consumption (or other operating characteristics, e,g., emissions) during a trip. Multiple operating factors affect fuel consumption, including, for example, emission limits, locomotive fuel/emissions characteristics, size and loading of railcars, weather, traffic conditions and locomotive operating parameters. An operator can more effectively and efficiently operate a train (through the application of tractive and braking efforts) if provided control information that optimizes performance during a trip while meeting

a required schedule (arrival time) and using a minimal amount of fuel (or optimizing another operating parameter), despite the many variables that affect performance. Thus it is desired for the operator to operate the train under the guidance (or control) of a system or process that advises the application of tractive and braking efforts to optimize one or more operating parameters,
BRIEF DESCRIPTION OF THE INVENTION
Exemplary embodiments of the invention disclose a system, method, and computer software code for augmenting and updating a train/track database associated with a system, method, and/or computer software code for optimizing train operations. Towards this end, a system for providing train information and/or track characterization information for use in train performance is disclosed. The system includes a first element to determine at least one of a location of a train on a track segment and a time from a beginning of the trip. A track characterization element to provide track segment information is further disclosed. A sensor for measuring an operating condition of at least one of the locomotives in the train, and a database for storing track segment information and/or the operating condition of at least one of the locomotives is further disclosed. A processor is disclosed to correlate information from the first element, the track characterization element, the sensor, and the database, so that the database may be used for creating a trip plan that optimizes train performance in accordance with one or more operational criteria for the train.
In another exemplary embodiment, a system for operating a train during a trip along a track segment, the train comprising one or more locomotive consists with each locomotive consist comprising one or more locomotives is disclosed. The system includes a first element to determine a location of the train on the track segment and/or a time from a beginning of the trip. A track characterization element to provide track segment information, and a sensor for measuring an operating condition of at least one of the locomotives is also disclosed. A database is disclosed for storing track segment information and/or the operating condition of at least one of the locomotives. A processor is also disclosed, which is operable to receive information from the first element, the sensor, the track characterization element, and/or the

Database for creating a trip plan that optimizes locomotive performance in accordance with one or more operational criteria for the train.
In yet another exemplary embodiment, a method for operating a train during a trip along a track segment, the train comprising one or more locomotive consists with each locomotive consist comprising one or more locomotives is disclosed. The method includes a step for determining a location of the train on a track or a time from a beginning of the trip, and a step for determining track segment information. Two other steps include storing the track segment information, and determining at least one operating condition of at least one of the locomotives. Another step provides for creating a trip plan responsive to at least one of the location of the train, the track segment information, and at least one operating condition to optimize locomotive performance in accordance with one or more operational criteria for the train.
Another exemplary embodiment discloses a computer software code for operating a train having a computer processor, the code for operating the train during a trip along a track segment, the train comprising one or more locomotive consists with each locomotive consist comprising one or more locomotives. The software code includes a software module for determining track segment information, and a software module for storing the track segment information. A software module is also provided for determining at least one operating condition of one of the locomotives. The software code also includes a software module for creating a trip plan responsive to at least one of the location of the train, the track segment information and at least one operating condition to optimize locomotive performance in accordance with one or more operational criteria for the train.
BRIEF DESCRIPTION OF THE DRAWINGS
A more particular description of the invention briefly described above will be rendered by reference to specific embodiments thereof that are illustrated in the appended drawings. Understanding that these drawings depict only typical embodiments of the invention and are not therefore to be considered to be limiting of

its scope, the invention will be described and explained with additional specificity and detail through the use of the accompanying drawings in which:
FIG. I depicts an exemplary illustration of a flow chart for trip optimization;
FIG. 2 depicts a simplified model of a train that may be employed;
FIG. 3 depicts an exemplary embodiment of elements of a trip optimization system;
FIG. 4 depicts an exemplary embodiment of a fuel-use/travel time curve;
FIG. 5 depicts an exemplary embodiment of segmentation decomposition for trip planning;
FIG. 6 depicts an exemplary embodiment of a segmentation example;
FIG. 7 depicts an exemplary flow chart for trip optimization;
FIG. 8 depicts an exemplary illustration of a dynamic display for use by the operator;
FIG. 9 depicts another exemplary illustration of a dynamic display for use by the operator;
FIG. 10 depicts another exemplary illustration of a dynamic display for use by the operator;
FIG 11 depicts track database characteristics; and
FIG. 12 illustrates a flow chart of exemplary steps for operating a train during a trip along a track segment.
DETAILED DESCRIPTION OF THE INVENTION
Reference will now be made in detail to the embodiments consistent with the invention, examples of which are illustrated in the accompanying drawings. Wherever possible, the same reference numerals used throughout the drawings refer to the same or like parts.

The exemplary embodiment disclosed herein of the present invention solves the problems in the art by providing a system, method, and computer implemented method for determining and implementing an operating strategy for a train having a locomotive consist (i.e., a plurality of directly connected locomotives or one or more locomotive consists distributed within the train) to monitor and control a train's operations to improve certain objective operating criteria parameter requirements while satisfying schedule and speed constraints. Examples of the invention are also applicable to a distributed power train, i.e., a train having one or more locomotive consists spaced apart from the lead locomotive and controllable by the lead locomotive operator.
Persons skilled in the art will recognize that an apparatus, such as a data processing system, including a CPU, memory, I/O, program storage, a connecting bus, and other appropriate components, could be programmed or otherwise designed to facilitate the practice of the method of the invention. Such a system would include appropriate program means for executing the method of the invention.
In another embodiment, an article of manufacture, such as a pre-recorded disk or other similar computer program product, for use with a data processing system, includes a storage medium and a program recorded thereon for directing the data processing system to facilitate the practice of the method of the invention. Such apparatus and articles of manufacture also fall within the spirit and scope of the invention.
Broadly speaking, the technical effect is determining and implementing a driving strategy of a train to improve certain objective operating parameters while satisfying schedule and speed constraints wherein a train/track database is augmented with information about the train (usually the locomotives) and the track. To facilitate an understanding of examples of the present invention, it is described hereinafter with reference to specific implementations thereof.
Exemplary embodiments of the invention are described in the general context of computer-executable instructions, such as program modules, executed by a computer.

Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. For example, the software programs that underlie exemplary examples of the invention can be coded in different languages, for use with different processing platforms. In the description that follows, examples of the invention are described in the context of a web portal that employs a web browser. It will be appreciated, however, that the principles that underlie exemplary embodiments of the invention can be implemented with other types of computer software technologies as well.
Moreover, those skilled in the art will appreciate that examples of the invention may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like. The exemplary embodiments of the invention may also be practiced in a distributed computing environment where tasks are performed by remote processing devices that are linked through a communications network. In the distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices. These local and remote computing environments may be contained entirely within the locomotive, or within adjacent locomotives in consist or off-board in wayside or central offices where wireless communications are provided between the computing environments.
The term locomotive consist means one or more locomotives in succession, connected together so as to provide motoring and/or braking capability with no railcars between the locomotives. A train may comprise one or more locomotive consists. Specifically, there may be a lead consist and one or more remote consists, such as a first remote consist midway along the line of railcars and another remote consist at an end of train position. Each locomotive consist may have a first or lead locomotive and one or more trailing locomotives. Though a first locomotive is usually viewed as the lead locomotive, those skilled in the art will readily recognize that the first locomotive in a multi locomotive consist may be physically located in a physically trailing position. Also, even though a consist is usually considered as connected successive locomotives, those skilled in the art will readily recognize that a group of

locomotives may also be recognized as a consist even with at least one railcar separating the locomotives, such as when the consist is configured for distributed power operation, wherein throttle and braking commands are relayed from the lead locomotive to the remote trails by a radio link or physical cable. Towards this end, the term locomotive consist should be not be considered a limiting factor when discussing multiple locomotives within the same train.
Referring now to the drawings, embodiments of the present invention will be described. Exemplary embodiment of the invention can be implemented in numerous ways, including as a system (including a computer processing system), a method (including a computerized method), an apparatus, a computer readable medium, a computer program product, a graphical user interface, including a web portal, or a data structure tangibly fixed in a computer readable memory. Several embodiments of the exemplary examples of the invention are discussed below.
FIG. 1 depicts an illustration of an exemplary flow chart for trip optimization. As illustrated, instructions are input specific to planning a trip either on board or from a remote location, such as a dispatch center 10. Such input information includes, but is not limited to, train position, consist composition (such as locomotive models), locomotive tractive power performance of locomotive traction transmission, consumption of engine fuel as a function of output power, cooling characteristics-intended trip route (effective track grade and curvature as function of milepost or an "effective grade" component to reflect curvature, following standard railroad practices), car makeup and loading (including effective drag coefficients), desired trip parameters including, but not limited to, start time and location, end location, travel time, crew (user and/or operator) identification, crew shift expiration time and trip route.
This data may be provided to the locomotive 42 according to various techniques and processes, such as, but not limited to, manual operator entry into the locomotive 42 via an onboard display, linking to a data storage device such as a hard card, hard drive and/or USB drive or transmitting the information via a wireless communications channel from a central or wayside location 41, such as a track signaling device and/or

a wayside device, to the locomotive 42. Locomotive 42 and train 31 load characteristics (e.g., drag) may also change over the route (e.g., with altitude, ambient temperature and condition of the rails and rail-cars), causing a plan update to reflect such changes according to any of the methods discussed above. The updated data that affects the trip optimization process can be supplied by any of the methods and techniques described above and/or by real-time autonomous collection of locomotive/train conditions. Such updates include, for example, changes in locomotive or train characteristics detected by monitoring equipment on or off board the locomotive(s) 42.
A track signal system indicates certain track conditions and provides instructions to the operator of a train approaching the signal. The signaling system, which is described in greater detail below, indicates, for example, an allowable train speed over a segment of track and provides stop and run instructions to the train operator. Details of the signal system, including the location of the signals and the rules associated with different signals are stored in the onboard database 63.
Based on the specification data input into the present the exemplary embodiment of the invention, an optimal trip plan that minimizes fuel use and/or generated emissions subject to speed limit constraints and a desired start and end time is computed to produce a trip profile 12. The profile contains the optimal speed and power (notch) settings for the train to follow, expressed as a function of distance and/or lime from the beginning of the trip, train operating limits, including but not limited to, the maximum notch power and brake settings, speed limits as a function of location and the expected fuel used and emissions generated. In an exemplary embodiment, the value for the notch setting is selected to obtain throttle change decisions about once every 10 to 30 seconds*
Those skilled in the art will readily recognize that the throttle change decisions may occur at longer or shorter intervals, if needed and/or desired to follow an optimal speed profile. In a broader sense, it should be evident to ones skilled in the art that the profiles provide power settings for the train, either at the train level, consist level and/or individual locomotive level. As used herein, power comprises braking power.

motoring power and airbrake power. In another preferred embodiment, instead of operating at the traditional discrete notch power settings, the example of the present invention determines a desired power setting, from a continuous range of power settings, to optimize the speed profile. Thus, for example, if an optimal profile specifies a notch setting of 6.8, instead of a notch setting of 7, the locomotive 42 operates at 6.8; Allowing such intermediate power settings nay provide additional efficiency benefits as described below.
The procedure for computing the optimal profile can include any number of methods for computing a power sequence that drives the train 31 to minimize fuel and/or emissions subject to locomotive operating and schedule constraints, as summarized below. In some situations the optimal profile may be sufficiently similar to a previously determined profile due to the similarity of train configurations, route and environmental conditions. In these cases it may be sufficient to retrieve the previously-determined driving trajectory from the database 63 and operate the train accordingly.
When a previous plan is not available, methods to compute a new plan include, but are not limited to, direct calculation of the optimal profile using differential equation models that approximate train physics of motion. According to this process, a quantitative objective function is determined; commonly the function comprises a weighted sum (integral) of model variables that correspond to a fuel consumption rate and emissions generated plus a term to penalize excessive throttle variations.
An optimal control formulation is established to minimize the quantitative objective function subject to constraints including but not limited to, speed limits, minimum and maximum power (throttle) settings, and maximum cumulative and instantaneous emissions. Depending on planning objectives at any time, the problem may be setup to minimize fuel subject to constraints on emissions and speed limits or to minimize emissions subject to constraints on fuel use and arrival time. It is also possible to setup, for example, a goal to minimize the total travel time without constraints on total emissions or fuel use where such relaxation of constraints is permitted or required for the mission.

Throughout the document exemplary equations and objective functions are presented for minimizing locomotive fuel consumption. These equations and functions are for illustration only as other equations and objective functions can be employed to optimize fuel consumption or to optimize other locomotive/train operating parameters.
Mathematically, the problem to be solved may be stated more precisely. The basic physics are expressed by:

where x is the position of the train, v is train velocity, t is time (in miles, miles per hour and minutes or hours as appropriate) and u is the notch (throttle) command input. Further, D denotes the distance to be traveled, Tf the desired arrival time at distance D along the track, T’ is the tractive effort produced by the locomotive consist, Ga is the gravitational drag (which depends on train length, train makeup and travel terrain) and R is the net speed dependent drag of the locomotive consist and train combination. The initial and final speeds can also be specified, but without loss of generality are taken to be zero here (train stopped at beginning and end of the trip).
The model is readily modified to include other dynamics factors such the lag between a change in throttle u and a resulting tractive or braking effort.
All these performance measures can be expressed as a linear combination of any of the following:



V
In this equation E is the quantity of emissions in grams per horse power-hour (gm/phr) for each of the notches (or power settings). In addition a minimization could be done based on a weighted total of fuel and emissions.
A commonly used and representative objective function is thus

The coefficients of the linear combination depend on the importance (weight) given to each of the terms. Note that in equation (OP), u(t) is the optimizing variable that is the continuous notch position. If discrete notch is required, e.g. for older locomotives, the solution to equation (OP) is discretized, which may result in lower fuel savings. Finding a minimum time solution (ai set to zero and a2 set to zero or a relatively small value) is used to find a lower bound for the achievable travel time (Tf ‘ Taming). In this case, both u(t) and Tf are optimizing variables. The preferred embodiment solves the equation (OP) for various values of Tf with Tf > Tamer. with ay set to zero. In this latter case, Tf is treated as a constraint.
For those familiar with solutions to such optimal problems, it may be necessary to adjoin constraints, e.g. the speed limits along the path;
0 < V < Six)

or when using minimum time as the objective, the adjoin constraint may be that an end point constraint must hold, e.g. total fuel consumed must be less than what is in the tank, e.g. via:

where Wl is the fuel remaining in the tank at Tf. Those skilled in the art will readily recognize that equation (OP) can presented in other forms and that the version above is an exemplary equation for use in the example of the present invention.
Reference to emissions in the context of the present invention is generally directed to cumulative emissions produced in the form of oxides of nitrogen (NOx), carbon oxide (COx), hydrocarbons (HC)and particulate matter (PM). Other emissions may include, but not be limited to a maximum value of electromagnetic emission, such as a limit on radio frequency (RF) power output, measured in watts, for respective frequencies emitted by the locomotive. Yet another form of emission is the noise produced by the locomotive, typically measured in decibels (dB), An emission requirement may be variable based on a time of day, a time of year, and/or atmospheric conditions such as weather or pollutant level -in the atmosphere. Emission regulations may vary geographically across a railroad system. For example, an operating area such as a city or state may have specified emission objectives, and an adjacent area ma} have different emission objectives, for example a lower amount of allowed emissions or a higher fee charged for a given level of emissions.
Accordingly, an emission profile for a certain geographic area may be tailored to include maximum emission values for each of the regulated emissions including in the profile to meet a predetermined emission objective required for that area. Typically, for a locomotive, these emission parameters are determined by, but not limited to, the power (Notch) setting, ambient conditions, engine control method, etc. By design, every locomotive must be compliant with EPA emission standards, and thus in an embodiment of the present invention that optimizes emissions this may refer to mission-total emissions, for which there is no current EPA specification. Operation of

the locomotive according to the optimized trip plan is at all times compliant with EPA emission standards.
If a key objective during a trip is to reduce emissions, the optimal control formulation, equation (OP), is amended to consider this trip objective. A key flexibility in the optimization process is that any or all of the trip objectives can vary by geographic region or mission. For example, for a high priority train, minimum time may be the only objective on one route because of the train's priority. In another example emission output could vary from state to state along the planned train route.
To solve the resulting optimization problem, in an exemplary embodiment the present invention transcribes a dynamic optimal control problem in the time domain to an equivalent static mathematical programming problem with N decision variables, where the number 'N' depends on the frequency at which throttle and braking adjustments are made and the duration of the trip. For typical problems, this N can be in the thousands* In an exemplary embodiment a train is traveling a 172-mile stretch of track in the southwest United States. Utilizing an example of the present invention, a 7.6% fuel consumption may be realized when comparing a trip determined and followed using an exemplary example of the present invention versus a iris where the throttle/speed is determined by the operator according to standard practices. The improved savings is realized because the optimization provided by an example of the present invention produces a driving strategy with both less drag loss and Hotel or no braking loss compared to the operator controlled trip.
To make the optimization described above computationally tractable, a simplified model of the train may be employed, such as illustrated in FIG, 2 and set forth in the equations discussed above. A key refinement to the optimal profile is produced by deriving a more detailed model with the optimal power sequence generated, to test if any thermal, electrical and mechanical constraints are violated, leading to a modified profile with speed versus distance that is closest to a run that can be achieved damaging the locomotive or train equipment, i.e. satisfying additional implied constraints such thermal and electrical limits on the locomotive and in-train forces.

Referring back to FIG. 1, once the trip is started 12, power commands are generated 14 to put the start the plan. Depending on the operational set-up of the example of the present invention, one command causes the locomotive to follow the optimized power command 16 so as to achieve optimal speed. An example of the present invention obtains actual speed and power information from the locomotive consist of the train 18. Due to the common approximations in the models used for the optimization’ a closed-loop calculation of corrections to the optimized power is obtained to track the desired optimal speed. Such corrections of train operating limits can be made automatically or by the operator, who always has ultimate control of the train.
In some cases, the model used in the optimization may differ significantly from the actual train. This can occur for many reasons, including but not limited to, extra cargo pickups or setouts, locomotives that fail in-route, errors in the initial database 63 and data entry errors by the operator. For these reasons a monitoring system uses realties train data to estimate locomotive and/or train parameters in real time 20. The estimated parameters are then compared to the assumed parameters when the trip was initially created 22. Based on any differences in the assumed and estimated values, the trip may be re-planned 24. Typically the trip is re-planned if significant savings can be realized from a new plan.
Other reasons a trip may be re-planned include directives from a remote location, such as dispatch, and/or an operator request of a change in objectives to be consistent with global movement planning objectives. Such global movement planning objectives may include, but are not limited to, other train schedules, time required to dissipate exhaust from a tunnel, maintenance operations, etc. Another reason may be due to an onboard failure of a component. Strategies for re-planning may be grouped into incremental and major adjustments depending on the severity, of the disruption, as discussed in more detail below. In general, a "new" plan must be derived from a solution to the optimization problem equation (OP) described above, but frequently faster approximate solutions can be found, as described herein.
In operation, the locomotive 42 will continuously monitor system efficiency and continuously update the trip plan based on the actual measured efficiency whenever

such an update may improve trip performance. Re-planning computations may be carried out entirely within the locomotive(s) or fully or partially performed at a remote location, such as dispatch or wayside processing facilities where wireless technology can communicate the new plan to the locomotive 42. An example of the present invention may also generate efficiency trends for developing locomotive fleet data regarding-efficiency transfer functions. The fleet-wide data may be used when determining the initial trip plan, and may be used for network-wide optimization tradeoff when considering locations of a plurality of trains. For example, the travel-time fuel-use tradeoff curve as illustrated in FIG. 4 reflects a capability of a train on a particular route at a current time, updated from ensemble averages collected for many similar trains on the same route. Thus, a central dispatch facility collecting curves like FIG. 4 from many locomotives could use that information to better coordinate overall train movements to achieve a system-wide advantage in fuel use or throughput.
Many events during daily operations may motivate the generation of a view or modified plan, including a new or modified trip plan that retains the same trip objectives, for example, when a train is not on schedule for a planned meet or pass with another train and therefore must make up the lost time. Using the actual speed, power and location of the locomotive, a planned arrival time is compared with a currently estimated (predicted) arrival time 25. Based on a difference in the times, as well as the difference in parameters (detected or changed by dispatch or the operator) the plan is adjusted 26. This adjustment may be made automatically responsive to a railroad company's policy for handling departures from plan or manually as the onboard operator and dispatcher jointly decide the best approach for returning the plan. Whenever a plan is updated but where the original objectives, such as but not limited to arrival time remain the same, additional changes may be factored in concurrently, e,g, new future speed limit changes, which could affect the feasibility of recovering the original plan. In such instances if the original trip plan cannot be maintained, or in other words the train is unable to meet the original trip plan objectives, as discussed herein other trip plan(s) may be presented to the operator, remote facility and/or dispatch.

A re-plan may also be made when it is desired to change the original objectives. Such re-planning can be done at either fixed preplanned times, manually at the discretion of the operator or dispatcher or autonomously when predefined limits, such a train operating limits, are exceeded. For example, if the current plan execution is running late by more than a specified threshold, such as thirty minutes, an example of the present invention can re-plan the trip to accommodate the delay at the expense of increased fuel consumption as described above or to alert the operator and dispatcher as to IH extent to which lost time can be regained, if at all, (i.e. what is the minimum time remaining or the maximum fuel that can be saved within a time constraint). Other triggers for re-plan can also be envisioned based on fuel consumed or the health of the power consist, including but not limited time of arrival, loss of horsepower due to equipment failure and/or equipment temporary malfunction (such as operating too hot or too cold), and/or detection of gross setup errors, such in the assumed train load. That is, if the change reflects impairment in the locomotive performance for the current trip, these may be factored into the models and/or equations used In the optimization process.
Changes in plan objectives can also arise from a need to coordinate events where the plan for one train compromises the ability of another train to meet objectives and arbitration at a different level, e.g. the dispatch office, is required. For example, the coordination of meets and passes may be further optimized through train-to-train communications. Thus, as an example, if an operator knows he is he hind schedule in reaching a location for a meet and/or pass, communications from the other train can advise the operator of the late train (and/or dispatch). The operator can enter information pertaining to the expected late arrival into an example of the present invention for recalculating the train's trip plan. An example of the present invention can also be used at a high level or network-level, to allow a dispatch to detennined which train should slow down or speed up should it appear that a scheduled meet and/or pass time constraint may not be met. As discussed herein, this is accomplished by trains transmitting data to dispatch to prioritize how each train should change its planning objective. A choice can be made either based on schedule or fuel saving benefits, depending on the situation.

For any of the manually or automatically initiated re-plans, an example of the present invention may present more than one trip plan to the operator. In an exemplary embodiment the present invention presents different profiles to the operator, allowing the operator to select the arrival time and also understand the corresponding fuel and/or emission impact. Such information can also be provided to the dispatch for similar considerations, either as a simple list of alternatives or as a plurality of tradeoff curves such as illustrated in FIG, 4,
In one embodiment the present invention includes the ability to learn and adapt to key changes in the train and power consist that can be incorporated either in the current plan and/or for future plans. For example, one of the triggers discussed above is loss of horsepower. When building up horsepower over time, either after a loss of horsepower or when beginning a trip, transition logic is utilized to determine when a desired horsepower is achieved. This information can be saved in the locomotive database 61 for use in optimizing either future trips or the current trip should loss of horsepower occur again later.
FIG. 3 depicts an exemplary embodiment of elements of the trip optimizer. A locator element 30 determines a location of the train 31. The locator element 30 comprises a GPS sensor or a system of sensors that determine a location of the train 31. Examples of such other systems may include, but are not limited to, wayside devices, such as radio frequency automatic equipment identification (RF AEI) tags, dispatch, and/or video-based determinations. Another system may use tachometer(s) aboard a locomotive and distance calculations from a reference point. As discussed previously, a wireless communication system 47 may also be provided to allow communications between trains and/or with a remote location, such as dispatch. Information about travel locations may also be transferred from other trains over the communications system.
A track characterization element 33 provides information about a track, principally grade, elevation and curvature information. The track characterization element 33 may include an on-board track integrity database 36. Sensors 38 measure a tractive effort 40 applied by the locomotive consist 42, throttle setting of the locomotive

consist 42, locomotive consist 42 configuration information, speed of the locomotive consist 42, individual locomotive configuration information, individual locomotive capability, etc. In an exemplary embodiment the locomotive consist 42 configuration information may be loaded without the use of a sensor 38, but is input by other approaches as discussed above. Furthermore, the health of the locomotives in the consist may also be considered. For example, if one locomotive in the consist is unable to operate above power notch level 5 this information is used when optimizing the trip plan.
Information from the locator element may also be used to determine an appropriate arrival time of the train 31. For example, if there is a train 31 moving along a track 34 toward a destination and no train is following behind it, and the train has no fixed arrival deadline to satisfy, the locator element, including but not limited to radio frequency automatic equipment identification (RF AEI) tags, dispatch, and/or video-based determinations, may be used to determine the exact location of the train 31. Furthermore, inputs from these signaling systems may be used to adjust the train speed. Using the on-board track database, discussed below, and the locator element, such as GPS, an example of the present invention can adjust the operator interface to reflect the signaling system state at the given locomotive location. In a situation where signal states indicate restrictive speeds ahead, the planner may elect to slow the train to conserve fuel consumption.
Information from the locator element 30 may also be used to change planning objectives as a function of distance to a destination. For example, owing to inevitable uncertainties about congestion along the route, ''faster" time objectives on the early part of a route may be employed as hedge against delays that statistically occur later If on a particular trip such delays do not occur, the objectives on a latter part of the journey can be modified to exploit the built-in slack time that was banked earlier and thereby recover some fuel efficiency. A similar strategy can be invoked with respect to emission-restrictive objectives, e.g. emissions constraints that apply when approaching an urban area.

As an example of the hedging strategy, if a trip is planned from New York to Chicago, the system may provide an option to operate the train slower at either the beginning of the trip, at the middle of the trip or at the end of the trip. An example of the present invention optimizes the trip plan to allow for slower operation at the end of the trip since unknown constraints, such as but not limited to weather conditions, track maintenance, etc., may develop and become known during the trip. As another consideration, if traditionally congested areas are known, the plan is developed with an option to increase the driving flexibility around such regions. Therefore, an example of the present invention may also consider weighting/penalizing as a function of time/distance into the future and/or based on known/past experiences. Those skilled in the art will readily recognize that such planning and re-planning to take into consideration weather conditions, track conditions, other trains on the track, etc., may be considered at any time during the trip wherein the trip plan is adjusted accordingly.
FIG. 3 further discloses other elements that may be part of an example of the present invention. A processor 44 operates to receive information from the locator element 30, track characterizing element 33 and sensors 38. An algorithm 46 operates within the processor 44. The algorithm 46 computes an optimized trip plan based on parameters involving the locomotive 42, train 31, track 34, and objectives of the mission as described herein. In an exemplary embodiment the trip plan is established based on models for train behavior as the train 31 moves along the track 34 as a solution of non-linear differential equations derived from applicable physics with simplifying assumptions that are provided in the algorithm. The algorithm 46 has access to the information from the locator element 30, track characterizing element 33 and/or sensors 38 to create a trip plan minimizing fuel consumption of a consist 42, minimizing emissions of a locomotive consist 42, establishing a desired trip time, and/or ensuring proper crew operating time aboard the locomotive consist 42, In an exemplary embodiment, a driver or controller element, 51 is also provided. As discussed herein the controller element 51 may control the train as it follows the trip plan. In an exemplary embodiment discussed further herein, the controller element 51 makes train operating decisions autonomously. In another exemplary

embodiment the operator may be involved with directing the train to follow or deviate from the trip plan in his discretion.
In one embodiment of the present invention the trip plan is modifiable in real time as the plan is being executed. This includes creating the initial plan for a long distance trip, owing to the complexity of the plan optimization algorithm. When a total length of a trip profile exceeds a given distance, an algorithm 46 may be used to segment the mission by dividing the mission into waypoints. Though only a single algorithm 46 is discussed, those skilled in the art will readily recognize that more than one algorithm may be used and that such multiple algorithms are linked to create the trip plan.
The trip waypoints may include natural locations where the train 31 stops, such as, but not limited tOj single mainline sidings for a meet with opposing traffic or for a pass with a train behind the current train, a yard siding, an industrial spur where cars are picked up and set out and locations of planned maintenance work. At " the train 31 may be required to be at the location at a scheduled time, slopped or moving with speed in a specified range. The time duration from arrival to departure at waypoints is called dwell time.
In an exemplary embodiment,*the present invention is able to break down a longer trip into smaller segments according to a systematic process. Each segment can be somewhat arbitrary in length, but is typically picked at a natural location such as a stop or significant speed restriction, or at key waypoints or mileposts that define junctions with other routes. Given a partition or segment selected in this way, a driving profile is created for each segment of track as a function of travel time taken as an independent variable, such as shown in FIG, 4, discussed in more detail below. The fuel used/travel-time tradeoff associated with each segment can be computed prior to the train 31 reaching that segment of track. A total trip plan can therefore be created from the driving profiles created for each segment. An example of the invention optimally distributes travel time among all segments of the trip so that the total trip time required is satisfied and total fuel consumed over all the segments is minimized. An exemplary three segment trip is disclosed in FIG. 6 and discussed

below. Those skilled in the art will recognize however, though segments are discussed, the trip plan may comprise a single segment representing the complete trip.
FIG. 4 depicts an exemplary embodiment of a fuel-use/travel time curve. As mentioned previously, such a curve 50 is created when calculating an optimal trip profile for various travel times for each segment. That is, for a given travel time 51, fuel used 52 is the result of a detailed driving profile computed as described above. Once travel times for each segment are allocated, a power/speed plan is determined for each segment from the previously computed solutions. If there are any waypoint speed constraints between the segments, such as, but not limited to, a change in a speed limit, they are matched during creation of the optimal trip profile. If speed restrictions change only within a single segment, the fuel use/travel-time curve 50 has to be re-computed for only the segment changed. This process reduces the time required for re-calculating more parts, or segments, of the trip. If the locomotive consist or train changes significantly along the route, e.g. loss of a locomotive or pickup or set-out of railcars, then driving profiles for all subsequent segments must be recomputed creating new instances of the curve 50. These new curves 50 are then used along with new schedule objectives to plan the remaining trip.
Once a trip plan is created as discussed above, a trajectory of speed and power versus distance allows the train to reach a destination with minimum fuel and/or emissions at the required trip time. There are several techniques for executing the trip plan. As provided below in more detail, in one exemplary embodiment of a coaching mode, an example of the present invention displays control information to the operator. The operator follows the information to achieve the required power and speed as determined according to the optimal trip plan. Thus in this mode the operator is provided with operating suggestions for use in driving the train. In another exemplary embodiment, control actions to accelerate the train or maintain a constant speed are performed by examples of the present invention. However, when the train 31 must be slowed, the operator is responsible for applying brakes by controlling a braking system 52. In another exemplary embodiment, the present invention commands power and braking actions as required to follow the desired speed-distance path.

Feedback control strategies are used to correct the power control sequence in the profile to account for such events as, but not limited to, train load variations caused by fluctuating head winds and/or tail winds. Another such error may be caused by an error in train parameters, such as, but not limited to, train mass and/or drag, as compared with assumptions in the optimized trip plan, A third type of error may occur due to incorrect information in the track database 36. Another possible error may involve un-modeled performance differences due to the locomotive engine, traction motor thermal duration and/or other factors. Feedback control strategies compare the actual speed as a function of position with the speed in the desired optimal profile. Based on this difference, a correction to the optima) power profile is added to drive the actual velocity toward the optimal profile. To assure stable regulation, a compensation algorithm may be provided that filters the feedback speeds into power corrections to assure closed-loop performance stability. Compensation may include standard dynamic compensation as used by those skilled in the art of control system design to meet performance objectives.
Examples of the present invention allow the simplest and therefore fastest means to accommodate changes in trip objectives, which is the rule rather than the exception in railroad operations. In an exemplary embodiment, to determine the fuel-optimal trip from point A to point B where there are stops along the way, and for updating the trip for the remainder of the trip once the trip has begun, a sub-optimal decomposition method can be used for finding an optimal trip profile. Using modeling methods, the computation method can find the trip plan with specified travel time and initial and final speeds to satisfy all the speed limits and locomotive capability constraints when there are stops. Though the following discussion is directed to optimizing fuel use, it can also be applied to optimize other factors, such as, but not limited to, emissions, schedule, crew comfort and load impact. The method may be used at the outset in developing a trip plan, and more importantly to adapting to changes in objectives after initiating a trip.
As discussed herein, examples of the present invention may employ a setup as illustrated in the exemplary flow chart depicted in FIG. 5 and as an exemplary three segment example depicted in detail in FIGS. 6. As illustrated, the trip may be broken

into two or more segments, Tl, T2, and T3, though as discussed herein, it is possible to consider the trip as a single segment As discussed herein, the segment boundaries may not result in equal-length segments. Instead the segments use natural or mission specific boundaries. Optimal trip plans are pre-computed for each segment. If fuel use versus trip time is the trip object to be met, fuel versus trip time curves are generated for each segment. As discussed herein, the curves may be based oi\ other factors wherein the factors are objectives to be met with a trip plan. When trip time is the parameter being determined, trip time for each segment is computed while satisfying the overall trip time constraints.
FIG. 6 illustrates speed limits for an exemplary three segment 200 mile trip 97, Further illustrated are grade changes over the 200 mile trip 98. A combined chart 99 illustrating curves of fuel used for each segment of the trip over the travel time is also shown.
Using the optimal control setup described previously, the present computation method can find the trip plan with specified travel time and initial and final speeds, to satisfy all the speed limits and locomotive capability constraints when there are stops. Though the following detailed discussion is directed to optimizing fuel use, it can also be applied to optimize other factors as discussed herein, such as, but not limited to, emissions. The method can accommodate desired dwell times at stops and considers constraints on earliest arrival and departure at a location as may be required, for example, in single-track operations where the time to enter or pass a siding is critical.
Examples of the present invention find a fuel-optimal trip from distance Do to DM, traveled in time T, with M-i intermediate stops at D],.,.,DM-I, and with the arrival and departure times at these stops constrained by
C, (A) +A/, other trains traversing the track segment.

28. The computer software code according to claim 25, wherein the track segment information comprises allowed speed, speed restrictions, train inertia, barometric pressure, images, track grade, track age, track condition, weather conditions, track information affecting the ability to propel the train, track information affecting the ability to stop the train, track friction coefficient, applied tractive effort, applied braking effort, location and track altitude, signals for forward track blocks.
29. The computer software code according to claim 25, further comprising a soft, are code module for controlling the train according to the trip plan.

30. The computer software code according to claim 25, further comprising a software code module for informing a train operator of the trip plan, wherein the operator can control the train according to the trip plan.