System and method of multi-generation positive train control system

Abstract

A method of scheduling the movement of trains as a function of the predicted crew behavior and predicted rail conditions.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a simplified pictorial representation of a prior art rail system.

FIG. 1B is a simplified pictorial representation of the rail system of FIG. 1A divided into dispatch territories.

FIG. 2 is a simplified illustration of a merged task list for the combined dispatch territories of FIG. 1B.

FIG. 3A is a simplified pictorial representation of two consists approaching a merged track.

FIGS. 3B and 3C are simplified graphical representations of the predicted behavior of the consists from FIG. 3A in accordance with one embodiment of the present disclosure.

FIG. 4 is a simplified flow diagram of one embodiment of the present disclosure utilizing a behavior prediction model.

DETAILED DESCRIPTION

As railroad systems continue to evolve, efficiency demands will require that current dispatch protocols and methods be upgraded and optimized. It is expected that there will be a metamorphosis from a collection of territories governed by manual dispatch procedures to larger territories, and ultimately to a single all-encompassing territory, governed by an automated dispatch system.

At present, dispatchers control within a local territory. This practice recognizes the need for a dispatcher to possess local knowledge in performing dispatcher duties. As a result of this present structure, train dispatch is at best locally optimized. It is a byword in optimization theory that local optimization is almost invariably globally suboptimal. To move to fewer but wider dispatch territories would require significantly more data exchange and concomitantly much greater computational power in order to optimize a more nearly global scenario.

In one aspect of the present disclosure, in order to move forward in broadening and consolidating dispatch territories, it is desirable to identify and resolve exceptions at a centralized location or under a centralized authority. As the automation of dispatch control and exception handling progresses, the dispatch routines will be increasingly better tuned and fewer exceptions will arise. In another aspect, all rail traffic information, rail track information including rail track conditions, weather data, crew scheduling and availability information, is collected and territory tasks and their priorities across the broadened territory are merged, interleaved, melded, to produce a globally optimized list of tasks and their priorities.

FIG. 1A illustrates a global rail system 100 having a network of tracks 105. FIG. 1B represents the global rail system partitioned into a plurality of dispatch territories 110₁, 110₂ . . . 110_N. FIG. 2 represents one embodiment of the present disclosure wherein a prioritized task list is generated for combined dispatch territories 110₁ and 110₂. Territory 110₁ has a lists of tasks in priority order 210. Territory 110₂ has a list of tasks for its associated dispatch territory in priority order 220. The two territory task lists are merged to serve as the prioritized task list 230 for the larger merged territory of 110₁ and 110₂. The merging and assignment of relative priorities can be accomplished by a method identical or similar to the method used to prioritize the task list for the individual territories that are merged. For example, the prioritized task list can be generated using well known algorithms that optimize some parameter of the planned movement such as lowest cost or maximum throughput or maximum delay of a particular consist.

In another aspect of the present disclosure, the past behavior of a train crew can be used to more accurately predict train performance against the movement plan, which becomes a more important factor as dispatch territories are merged. Because the actual control of the train is left to the engineer operating the train, there will be late arrivals and in general a non-uniformity of behavior across train movements and the variance exhibited across engineer timeliness and other operational signatures may not be completely controllable and therefore must be presumed to persist. The individual engineer performances can reduce the dispatch system's efficiency on most territorial scales and certainly the loss of efficiency becomes more pronounced as the territories grow larger.

In one embodiment, a behavioral model for each crew can be created using an associated transfer function that will predict the movements and positions of the trains controlled by that specific crew under the railroad conditions experienced at the time of prediction. The transfer function is crafted in order to reduce the variance of the effect of the different crews, thereby allowing better planning for anticipated delays and signature behaviors. The model data can be shared across territories and more efficient global planning will result. FIG. 3A is an example illustrating the use of behavioral models for crews operating consist #1310 and consist #2330. Consist #1310 is on track 320 and proceeding to a track merge point 350 designated by an ‘X’ Consist #2330 is on track 340 and is also proceeding towards the merge point 350. At the merge point 350 the two tracks 320 and 340 merge to the single track 360. The behavior of the two consists under control of their respective crews are modeled by their respective behavior models, which take into account the rail conditions at the time of the prediction. The rail conditions may be characterized by factors which may influence the movement of the trains including, other traffic, weather, time of day, seasonal variances, physical characteristics of the consists, repair, maintenance work, etc. Another factor which may be considered is the efficiency of the dispatcher based on the historical performance of the dispatcher in like conditions.

Using the behavior model for each consist, a graph of expected performance for each consist can be generated. FIG. 3B is a graph of the expected time of arrival of consist #1310 at the merge point 350. FIG. 3 is a graph of the expected time of arrival of consist #2330 at the merge point 350. Note that the expected arrival time for consist #1 is T₁ which is earlier than the expected arrival time at the merger point 350 for consist #2 which is T₂, that is T₁<T₂.

The variance of expected arrival time 370 for consist #1310 is however much larger than the variance of expected arrival time 380 for consist #2330 and therefore the railroad traffic optimizer may elect to delay consist #1310 and allow consist #2330 to precede it onto the merged track 360. Such a decision would be expected to delay operations for consist #1310, but the delay may have nominal implications compared to the possibility of a significantly longer delay for both consists #1310 and #2330 should the decision be made to schedule consist #1310 onto the merged track 360 ahead of consist #2330. In prior art scheduling systems, the behavior of the crew was not taken into account, and in the present example, consist #1310 would always be scheduled to precede consist #2330 onto the merged track 360. Thus, by modeling each specific crew's behavior, important information can be collected and utilized to more precisely plan the movement of trains.

The behavior of a specific crew can be modeled as a function of the past performance of the crew. For example, a data base may be maintained that collects train performance information mapped to each individual member of a train crew. This performance data may also be mapped to the rail conditions that existed at the time of the train movement. This collected data can be analyzed to evaluate the past performance of a specific crew in the specified rail conditions and can be used to predict the future performance of the crew as a function of the predicted rail conditions. For example, it may be able to predict that crew A typically operates consist Y ahead of schedule for the predicted rail conditions, or more specifically when engineer X is operating consist Y, consist Y runs on average twelve minutes ahead of schedule for the predicted rail conditions.

FIG. 4 illustrates one embodiment of the present disclosure for planning the movement of trains as a function of the behavior of the specific train crew. First the crew identity managing a particular consist is identified 410. This identity is input to the crew history database 420 or other storage medium or facility. The crew history database may contain information related to the past performance of individual crew members, as well as performance data for the combined individuals operating as a specific crew. The stored information may be repeatedly adjusted with each crew assignment to build a statistical database of crew performance. The crew history database 420 inputs the model coefficients for the particular crew model into the consist behavior prediction model 430. The model coefficients may be determined by historical parameters such as means and standard deviations of times required by a particular crew to travel standard distances at specific grades and measures of crew sensitivities to different and specific weather conditions. In one embodiment of the present disclosure, the model coefficients may be determined by statistical analysis using multivariant regression methods. Track condition information 440, track traffic conditions 450, weather conditions 460, and consist information 465, are also input to the behavior prediction model 430. The behavior prediction model 430 is run and its output is used to calculate a transfer function 470 that will supply the optimizer 480 with statistics respecting the expected behavior of the train such as its expected time to reach a rail point, the variance of the prediction, and other predicted data of interest. The optimizer 480 will be used to optimize the movement of the trains as a function of some objective function such as lowest cost, fewest exceptions, maximum throughput, minimum delay.

The embodiments disclosed herein for planning the movement of the trains can be implemented using computer usable medium having a computer readable code executed by special purpose or general purpose computers.

While embodiments of the present disclosure have been described, it is understood that the embodiments described are illustrative only and the scope of the disclosure is to be defined solely by the appended claims when accorded a full range of equivalence, many variations and modifications naturally occurring to those of skill in the art from a perusal hereof.

Claims

1. A method of predicting the performance of the movement of trains over a rail network comprising the steps of: (a) generating a movement plan for a first train;(b) assigning a crew to operate the first train;(c) monitoring the performance of the movement of the first train against the movement plan as a function of the assigned crew;(d) monitoring conditions of the railway;(e) storing information related to the monitored performance and the monitored conditions of the railway;(f) predicting the performance of the movement of a second train as a function of the stored information.

2. The method of claim 1 further comprising the step of scheduling the movement of the second train as a function of the predicted performance of the second train.

3. The method of claim 1 wherein the step of monitoring the performance of the first train includes comparing the actual movement of the first train with the movement plan of the first train.

4. The method of claim 1 wherein the rail network is divided into a plurality of track elements and wherein the step of monitoring the performance of the first train includes comparing the actual movement of the first train with the movement plan of the first train over each track element that the first train traverses.

5. The method of claim 1 wherein the railway conditions include at least one of traffic conditions, weather conditions, time of day, seasonal variances, physical characteristics of the train and dispatcher efficiency.

6. The method of claim 1 wherein the stored information is repeatedly adjusted with each crew assignment to build a statistical database of crew performance.

7. A method of scheduling the movement of plural trains over a rail network, each train having an assigned crew to operate the train comprising the steps of: (a) maintaining a database of information related to the past performance of the movement of a first train as a function of the crew assigned to operate the first train; and(b) scheduling the movement of a second train as a function of the information in the database.

8. The method of claim 7, wherein the step of maintaining a database of information related to the past performance of the movement of a first train includes comparing the actual movement of the first train with the movement plan of the first train.

9. The method of claim 7 wherein the step of scheduling the movement includes: (i) assigning a second crew to operate the second train;(ii) predicting a behavior of the second crew as a function of the information maintained in the database;(iii) predicting the performance of the movement of the second train as a function of the predicted behavior of the crew.(iv) scheduling the second train as a function of the predicted performance.