JPS61235264A - Railway schedule contradiction detection system - Google Patents

Abstract

(57) [Summary] This bulletin contains application data before electronic filing, so abstract data is not recorded.

Description

[Detailed Description of the Invention] [Field of Industrial Application] This invention is intended to solve the problem of inconsistencies in topological relationships such as train departure order, track usage order, vehicle operation, etc. among the information specified in the train schedule, and hourly Train schedule discrepancy detection method for detecting infeasible parts of a train schedule by detecting discrepancies with operating conditions such as train arrival/departure time information [Prior art] Conventional train schedule Inconsistency detection methods include a method that detects inconsistencies in schedules such as the departure order of one train in a train diagram and the order in which the first track is used for the purpose of deadlock detection; There are two main types of methods: methods that detect discrepancies between the arrival and departure times of the timetable and operating conditions.

The former method considers train travel as a series of equipment usage by moving objects, and there is a method that focuses on changes in train schedules and detects inconsistencies due to changes.

The method for the entire train schedule detects deadlocks using an algorithm that detects cycles in the equipment usage series, and the method for train schedule changes uses a graph to represent the schedule and detect cycles that include changed parts in the graph. , to detect deadlocks that occur due to timetable changes.

Furthermore, the latter method sequentially checks the arrival and departure times of the train schedule for each operating condition.

[Problem that the invention seeks to solve]

However, the conventional train schedule contradiction detection method detects causal contradictions and temporal contradictions in train diagrams separately, and cannot handle both in a unified manner, and the processing method is complicated and requires a large amount of effort. In order to be effective in real-time use for large-scale diamonds, there is a problem in that more systemization and efficiency are required.

[Means for solving problems]

Therefore, the train schedule contradiction detection method according to the present invention includes a first means for expressing all information regarding one train schedule using a graph, and a second means for detecting cycles by topological sorting on the graph created by this first means. The third method expresses the operating conditions as branches of the graph and calculates the longest route from one point to each point on the graph.
This means that the

[Effect]

In the train schedule contradiction detection method configured in this way, the first means expresses the train schedule as a graph. In the second means, contradictions in causal relationships on the train schedule are efficiently detected as cycles on the graph by topological sorting. Furthermore, in the third method, since the operating conditions are also expressed as branches of the graph, inconsistencies with the operating conditions of train arrival and departure times in the train schedule can be resolved more efficiently by searching for the longest route from one point on the graph to each point. will be required.

〔Example〕

The figure is a processing outline diagram showing an embodiment of the train schedule contradiction detection method according to the present invention, and shows a case where the method is applied to train schedule contradiction correction by a man-machine.

Steps 51 to S2 are for displaying the first means of graphically displaying train schedule information. First, in step Sl, data such as train schedules and operating conditions are input into the computer, and then these data are used to Step S2
Create a graph that represents train operation. With respect to this graph, in the cycle detection method using topological sorting of the second means consisting of steps 53 to S7, first, cycle detection is performed by topological sorting in step S3, and in step S4 it is determined that a cycle exists. If so, it is assumed that a deadlock has occurred, and in step s5, the branches constituting the cycle are examined to present a contradiction on the schedule that causes the deadlock, and in step S8, the schedule is revised to eliminate the contradiction. Then, in response to the diamond correction, the graph is corrected in step S7 and the process returns to step S3. If no cycle exists in step S4 and the tropological sort ends normally, the longest path search method of the third means consisting of steps 58 to Sll is used, and first, in step S8, the longest path search on the graph is performed. By comparing the longest route length at each point with the arrival/departure time of the train diagram in step S8, a contradiction with the operating conditions of the arrival/departure time is detected. If there is a contradiction in step 510, in step Sll The inconsistency is presented, and the process goes to step S12, where the timetable is revised to eliminate the inconsistency. Then, in step S13, the graph is corrected in response to the diamond correction, and the process returns to step S8. Further, if there is no contradiction in step SIG, it means that there is no contradiction in the train schedule and the possibility of execution is guaranteed, and the process ends.

Hereinafter, the operation of the train dynamic contradiction detection method according to the present invention will be explained in detail. In this train schedule contradiction detection method,
Train operation is represented by a graph, and the simulation is performed as a search for the longest route with the graph. First, we will explain how to represent train operation in a graph, and then we will detect contradictions in the causal relationships between timetables and H by topological sorting with the graph. A method for detecting discrepancies in train arrival and departure times by finding the longest route from one point to each point on a graph will be explained.

■Graphic representation of 0 train operation The constraint conditions for column * entrainment are as follows.

Train schedule> (1) Arrival/departure time at each station, track number used (2) Order of departure from each station, order number track used (3) * Operating conditions for two-way operation> (1) Minimum running time between stations (2) Minimum stopping time at each station ( 3
) Minimum approach/exit time interval for each station (4) Allowable train distance between stations (5) Minimum turnaround time Here.

a (i, j): Queue time d (i
, j): Departure time of the 3 trains at station i A(i, j): Arrival time of igj train on the train schedule D(i, j): Departure time of train j at i station on the train diagram Rri, I ): i w
Minimum running time S (i, l) for exchange trains between i+1 stations
:i Minimum running time for Class 1 trains in IR (j): Class 3 trains ha C11,! ;L2): Time interval pattern when a type 2 train arrives following a #11 train bundle hd (interval 1.Q2): Time interval pattern when a type 122 train departs following a class 1?111 train hb (also l9 sentence 2) 2nd type fL1 train followed by type Q
Time interval pattern H(i
, h): Minimum time interval ja at time interval pattern h at station i
(i, j) = train jd(
i, j): Train jb(i
, j): Train K(i) that used the i station arrival number of 3 trains immediately before the 3 trains; Allowable number of trains between i~1◆l stations jk(i, j,
k): Train U departed from i station in front of Kimae in 3 trains (traffic 1.Q2.n1.n2): n Turnaround pattern when a 1-car 11-class train connects to an n2-car fL2-class train n ( j) Number of cars in the formation of 2 j trains 1u (j): 3 trains in advance of operation U (i, u): Minimum turnaround time for pattern U at station i.

One train operation is expressed as a directed graph by representing each event such as a train's arrival and departure from a station as a point, and by representing the relationship between events defined by constraint conditions as an edge.

Let the graph change be the event that a (i, jf): j train arrives at i station, and the event that ma (i, jf): 3 train departs from i station.7.The constraint conditions are as follows. can be expressed as a directed branch.

Conditions regarding running time and stopping time are expressed by the following running route branch representing the train running route defined by the train diagram.

Edge (vd(i,j), va(i+1.j)), weight R(i,1(j)) edge(va(i,j), vd(i,
j)), kmiS (i, fLU)) Also, by setting a dummy reference point Ma0, the constraints are set according to the arrival and departure times on the timetable, so that the next reference time branch (Ma0.Mad(i, D
), weight D(i, j,) branch (ma0.maa(i,j))
, weight A (i, j,). Roughly speaking, the constraints due to time intervals are: departure (arrival: i) order branch (ma d (i, j d (i, j)), ma d (i, j))
Weight H(i, hd(intersection(jd(i,j)), sentence(jl)
) branch (ma(i, ja(i, j)), ma(i, j)
) weight H(i, ha(1(ja(i,j)), intersection(j)
)) branch(ma d(i, jb(i, jD , va(i, j)
) H(i, hb(1(jb(i,j)), u U)
)). In addition, the constraint based on the number of permissible rows between stations is the following number of trains between stations (va (i + 1.
(i, j)), weight O, and the constraint on the return time in equation (4) is expressed by the first-order vehicle operation branch (maari, ju(D), mad(i, j)). U
(i, u(31(ju(D), intersection rj), n(ju(
j)), n(jN).

2. Conflict detection method If the arrival and departure times of each train in the train schedule satisfy the operating conditions, then when finding the longest route from the reference point to each city on the graph in h, the longest route length at each point is one train. Matches the arrival and departure times in the timetable. Therefore, by finding the longest route length from & to each point and comparing it with the arrival and departure times of trains DA and C, it is possible to detect inconsistencies with the operating conditions.

When finding the longest path, a tobological sort of the graph is first performed. If there are cycles in the graph,
This means that there is a contradiction in the cause-and-effect relationships in the train schedule, resulting in a deadlock in train operation. This is detected during topological sorting, and the branches that make up the cycle detect contradictory points on the diagram that are the cause of dead ends and curves.

At this time, once the process is turned down, first correct the inconsistencies,
The result is an acyclic graph, and once the topological sorting is completed, the longest path length of each point is then determined. Note that if the purpose is only to detect contradictions in causal relationships on the diagram, the process is finished, and edge weights are not necessary for the graph, and only connection relationships are sufficient.

The longest path length of each point can be easily determined by using topological order. The amount of calculation to find the longest route is
Including topological sort, 0 (IVl+ IE
l) (V: set of points, E: set of edges).

In addition, although the above embodiment describes the case where the interactive format is used in a man-machine, it may also be used automatically as part of another computer program. Although we have described a case in which both causal contradictions and temporal contradictions in a train schedule are detected by continuously searching for the longest route, it is also possible to apply this method to only one of them.

〔Effect of the invention〕

As described above, according to the present invention, by representing train operations in a graph and performing topological sorting and longest route search on the graph, causal contradictions and temporal contradictions in train schedules can be systematically and efficiently resolved. This has the effect of being able to be detected.

[Brief explanation of the drawing]

The figure is a processing outline diagram of a train timetable contradiction detection method according to an embodiment of the present invention. A is the lsl means, B is the second means, and C is the third means. A: 11th Mekisen B; Sho 2's 8 Throw C: Sho 3's ◆Death Procedures Amendment. CI spontaneously? ' 13 Showa Year, Month, Day 2, Invention Name Train Schedule Inconsistency Detection Method 3, Relationship with the Amendment Person Case Patent Applicant Address 2-2-3 Marunouchi, Chiyoda-ku, Tokyo Name (601) Mitsubishi Electric Stock Company representative Moriya Shiki 4, agent address 2-2-3-5 Marunouchi, Chiyoda-ku, Tokyo, detailed description of the invention and brief description of the drawings in the specification to be amended. 6. Contents of the amendment (1) The statement ``We are catching up and the drop-down car schedule has been changed'' on page 2, line 13 of the specification will be amended as follows. [A system that detects contradictions on the entire train schedule and changes to train schedules] (2) The statement ``complex'' on page 3, line 10 of the specification is corrected to ``complicated.'' (3) The statement "real time" on page 3, line 11 of the specification is corrected to "real time." (0) Correct the description “Train Daikeya” on page 6, line w49 of the specification to “train schedule.” (6) The statement "departs from i station" on page 9, line 2 of the specification is amended to "departs from i station". (7) "Departs from i station" on page 9, line 1 O of the specification. ” is corrected to “depending on the time.” (8) The statement “(of formula 0”) on page 10, line 6 of the specification is deleted. (8) The statement “(of formula 0)” on page 12, line 1 of the specification is amended. (10) The statement "Train schedule" on page 12, line 18 of the specification is amended to "Train schedule."

Claims (1)

[Claims] A first means for expressing information regarding the train schedule by a digraph, and a second means for detecting cycles on the graph by performing topological sorting on the digraph created in the first means are provided to eliminate inconsistencies in the train schedule. In addition, by providing a third means to express the operating conditions as branches of the graph and find the longest route length from one point to each point on the graph, it is possible to detect train schedule times that are inconsistent with the operating conditions. A method for detecting train schedule discrepancies.