Bus driving timetable optimization method under delay condition and program product

Abstract

The invention discloses a bus driving timetable optimization method under a delay condition and a program product, and relates to the technical field of public transportation and data processing, and the method comprises the steps: predicting the inter-station running time of buses of each bus number through the analysis of the historical driving data of the buses; obtaining the predicted running time of the bus of each bus number between the stations; judging the predicted running time of the bus of each bus number between the stations, and if the predicted running time of the bus of a certain bus number between the stations is greater than the maximum allowable travel time, recording the bus number as a delayed bus number; and integrating the running time prediction result and the delayed bus number prediction result, optimizing the running time table, and outputting the optimized running time table, thereby obtaining the optimized departure time and arrival time of the buses of each bus number at each station. According to the method, by predicting the time when the bus arrives at each station, the deviation between the planned driving time table and the optimized driving time table considering midway delay is minimized.

Description

Technical Field

[0001] The invention relates to the technical field of public transportation and data processing, in particular to a method and a program product for optimizing a public transportation schedule under delay conditions. Background Art

[0002] In recent years, with the diversification of public transportation modes (such as subways and shared bicycles) and the accuracy of passenger travel needs, bus companies are required to formulate more detailed and scientific driving plans based on passenger flow and road conditions when arranging vehicles. Unlike the closed operation scene of subways, buses are easily affected by external factors such as weather, time, and road conditions, making it difficult to be punctual (buses arrive at each station on the line at a fixed time), which makes it impossible for passengers to accurately arrange their personal travel time and unable to "pick the right time" to take the bus.

[0003] In the current bus operation planning system, usually only the full-trip punctuality time is considered, that is, the total running time from the first stop to the end of the operation at the last stop, to calculate the vehicle turnover time. However, there is no good way to accurately predict the arrival time of each intermediate stop. Summary of the invention

[0004] In order to overcome the defects in the above-mentioned prior art, the present invention provides a method for optimizing a bus schedule under delay conditions, which minimizes the deviation between the planned schedule and the optimized schedule taking into account the mid-distance delay by predicting the time when the bus arrives at each station.

[0005] To achieve the above object, the present invention adopts the following technical solutions, including:

[0006] A method for optimizing a public transportation schedule under a delay condition comprises the following steps:

[0007] S1, running time prediction: by analyzing the historical driving data of buses, the running time of each bus between stations is predicted, and the predicted running time of each bus between stations is obtained;

[0008] S2, delayed train prediction: the predicted running time of buses of each train between stations is judged. If the predicted running time of buses of a certain train between stations is greater than the maximum allowed travel time, the train is recorded as a delayed train, and the delayed start station and delayed end station of the delayed train, as well as the delayed start time and delayed end time are obtained;

[0009] S3, comprehensively analyzing the running time prediction results and the delayed train number prediction results, optimizing the driving schedule, and outputting the optimized driving schedule, that is, obtaining the optimized departure time and arrival time of each bus number at each station.

[0010] Preferably, in step S3,

[0011] The relevant parameters for optimization are as follows:

[0012] s represents the station number, s = 1, 2, ..., n;

[0013] tr represents the bus number, tr = 1, 2, ..., m; when tr is marked as delay, it means that the bus number is delayed;

[0014] H min Indicates the minimum departure time interval;

[0015] H max Indicates the maximum departure time interval;

[0016] Dwell s Indicates the length of time the bus stops at each station;

[0017] Ot s Indicates the running time of the bus between each stop;

[0018] Delay_starttime indicates the delay start time;

[0019] Delay_endtime indicates the delay end time;

[0020] Delay_startstation indicates the delay start station;

[0021] Delay_endstation indicates the delay end station;

[0022] YD time Indicates the increased congestion duration of delayed trains in the congestion zone;

[0023] pd s,tr It indicates the scheduled departure time of train number tr at station s;

[0024] pa s,tr It indicates the planned arrival time of train number tr at station s;

[0025] The decision variables for optimization are as follows:

[0026] d s,tr Indicates the departure time of train number tr at station s;

[0027] a s,tr Indicates the arrival time of train number tr at station s;

[0028] The objective function of optimization is:

[0029]

[0030] The optimization constraints are:

[0031] d s,tr -a s,tr =Dwell s

[0032] H min ≤d 1,tr -d 1,tr-1 ≤H max

[0033] d s,tr -d s,tr-1 ≥10

[0034] min(Ot Delay_endstation )+YD time ≤a Delay_endstation,tr＝delay -d Delay_startstation,tr＝delay

[0035] a Delay_endstation,tr＝delay -d Delay_startstation,tr＝delay ≤max(Ot Delay_endstation )+YD time

[0036] min(Ot s )≤a s,tr＝delay -d s-1,tr＝delay ≤max(Ot s )

[0037] min(Ot s )≤a s,tr -d s-1,tr ≤max(Ot s ).

[0038] Preferably, the departure time and arrival time of each bus at each station are optimized and solved using a Gurobi solver to obtain an optimized driving schedule.

[0039] Preferably, in step S2, if the predicted running time of a bus from station i to station j is greater than the maximum allowed travel time max(Ot j ), then the train is recorded as a delayed train, station i is recorded as the delay start station, the departure time of the train at station i is recorded as the delay start time, station j is recorded as the delay end station, and the arrival time of the train at station j is recorded as the delay end time.

[0040] Preferably, in step S1, the actual running time of the current train from station i to station j on the operation day is t ij , according to the historical driving data analysis, obtain the historical average running time of the train from station i to station j under the same conditions And obtain the historical average running time of subsequent trains from station i to station j under the same conditions Calculate the predicted running time y of the subsequent train from station i to station j ij ,

[0041] Preferably, by analyzing the historical driving data of buses, the historical average running speed of different buses under different conditions (such as from station i to station j) is obtained. Then through the stand distance s ij Calculate the historical average running time from site i to site j

[0042] Preferably, the predicted inter-station running time of subsequent trains is updated in real time according to the actual running time of the current train on the operation day, and the predicted results of delayed trains are updated in real time, so as to perform vehicle operation simulation in real time and output the optimized driving schedule in real time.

[0043] The present invention also provides a computer program product, which includes a computer program / instruction. When the computer program / instruction is executed by a processor, the method for optimizing a public transport schedule under a delay condition is implemented.

[0044] The present invention also provides a readable storage medium on which a computer program is stored. When the computer program is executed, the method for optimizing a public transport schedule under a delay condition is implemented.

[0045] The advantages of the present invention are:

[0046] (1) The present invention provides a method for optimizing a bus schedule under delay conditions, which minimizes the deviation between a planned bus schedule and an optimized bus schedule that takes into account intermediate delays by predicting the time it takes for a bus to arrive at each station.

[0047] (2) Compared with the existing timetable optimization algorithm, the present invention realizes the on-time prediction of each bus stop during operation, optimizes its departure interval, allows passengers to take the bus "on time", and improves the service quality of the bus.

[0048] (3) The optimized timetable is sent to passengers and drivers respectively, and drivers can obtain certain psychological expectations and driving plans based on the optimized timetable. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 The present invention is a flow chart of a method for optimizing a public transportation schedule under delay conditions.

[0050] Figure 2 This is a schematic diagram of a real driving timetable.

[0051] Figure 3 This is a schematic diagram of the optimized driving timetable.

[0052] Figure 4 A schematic diagram showing the comparison between the actual driving timetable and the optimized driving timetable. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Example 1

[0055] Depend on Figure 1 As shown in FIG. 1 , a method for optimizing a bus schedule under delay conditions includes running time prediction, delayed train number prediction, and simulation solution. The specific steps are as follows:

[0056] S1, running time prediction.

[0057] By analyzing the historical driving data of buses, we can obtain the historical average running speed of different buses (equivalent to different times) from station i to station j under different conditions (such as different dates and different weather conditions). Then through the stand distance s ij Calculate the historical average running time from site i to site j

[0058] The actual running time of the current train from station i to station j on the operation day is t ij , according to the historical driving data, obtain the historical average running time of the train from station i to station j under the same conditions And obtain the historical average running time of subsequent trains from station i to station j under the same conditions Calculate the predicted running time y of the subsequent train from station i to station j ij ,

[0059] S2, delayed train prediction.

[0060] The predicted running time y of the subsequent train from station i to station j is ij , and the maximum allowed travel time between the stations (i.e., from station i to station j) max(Ot j ) to compare, if the predicted running time y ij Greater than the maximum permissible travel time max(Otj ), then the train number is recorded as a delayed train number, station i is recorded as the delay start station, the departure time of the train number at station i is recorded as the delay start time, station j is recorded as the delay end station, and the arrival time of the train number at station j is recorded as the delay end time;

[0061] S3, simulation solution.

[0062] The vehicle operation simulation is carried out based on the operation time prediction results and the delayed train number prediction results, and the optimized driving timetable is output, that is, the optimized departure time and arrival time of each bus number at each station are obtained.

[0063] 1. Define the optimization parameters as follows:

[0064] s represents the station number, s = 1, 2, ..., n;

[0065] tr represents the bus number, tr = 1, 2, ..., m; when tr is marked as delay, it means that the bus number is delayed;

[0066] H min Indicates the minimum departure time interval;

[0067] H max Indicates the maximum departure time interval;

[0068] Dwell s Indicates the length of time the bus stops at each station;

[0069] Ot s Indicates the running time of the bus between each stop;

[0070] Delay_starttime indicates the delay start time;

[0071] Delay_endtime indicates the delay end time;

[0072] Delay_startstation indicates the delay start station;

[0073] Delay_endstation indicates the delay end station;

[0074] YD time Indicates the increased congestion duration of delayed trains in the congestion zone;

[0075] pd s,tr It indicates the scheduled departure time of train number tr at station s;

[0076] pa s,tr Indicates the planned arrival time of train number tr at station s.

[0077] 2. Define the decision variables for optimization as follows:

[0078] d s,tr Indicates the departure time of train number tr at station s;

[0079] a s,tr Indicates the arrival time of train number tr at station s.

[0080] 3. Define the optimization objective function as:

[0081]

[0082] 4. Define the optimization constraints as:

[0083] (1) The departure time minus the arrival time of each bus at each stop equals the stay time:

[0084] d s,tr -a s,tr =Dwell s

[0085] Among them, the difference between the arrival time of the bus at the last stop and the departure time at the first stop is the return time.

[0086] (2) The time interval between buses of each bus number at the first stop should be greater than or equal to the minimum time interval between buses and less than or equal to the maximum time interval between buses:

[0087] H min ≤d 1,tr -d 1,tr-1 ≤H max

[0088] (3) The departure time of each bus at each stop should ensure that the previous bus leaves at least 10 seconds later than the next bus:

[0089] d s,tr -d s,tr-1 ≥10

[0090] (4) The arrival time of the delayed train at the delayed end station minus the departure time of the delayed train at the delayed start station must be greater than or equal to the minimum allowed travel time between stations plus the excess congestion duration, and less than or equal to the maximum allowed travel time between stations plus the excess congestion duration:

[0091] min(Ot Delay_endstation )+YD time ≤a Delay_endstation,tr＝delay -d Delay_startstation,tr＝delay

[0092] ≤max(Ot Delay_endstation )+YDtime

[0093] (5) The travel time of the delayed train at other non-congested stations must be greater than or equal to the minimum allowed travel time between the stations and less than or equal to the maximum allowed travel time between the stations:

[0094] min(Ot s )≤a s,tr＝delay -d s-1,tr＝delay ≤max(Ot s )

[0095] Alternatively, the inter-station travel time before the delayed station is directly equal to the planned inter-station running time, and the inter-station travel time after the delayed station satisfies the above constraints.

[0096] (5) The travel time between all stations of a non-delayed train must be greater than or equal to the minimum allowed travel time between the stations and less than or equal to the maximum allowed travel time between the stations:

[0097] min(Ot s )≤a s,tr -d s-1,tr ≤max(Ot s )

[0098] Or, it is directly equal to the planned station-to-station running time, i.e., a s,tr -d s-1,tr =pa s,tr -pd s-1,tr .

[0099] In this embodiment, the Gurobi solver is used to optimize the timetable and obtain an optimized driving timetable. Figure 2 This is a schematic diagram of the actual driving schedule (the first 5 cars on the up route); Figure 3 This is a schematic diagram of the optimized driving timetable; Figure 4 The figure is a comparison diagram of the real driving schedule and the optimized driving schedule. The optimized driving schedule can be sent to passengers and drivers respectively, and the driver can obtain certain psychological expectations and driving plans based on the optimized driving schedule.

[0100] The present invention updates the predicted inter-station running time of subsequent trains and the predicted results of delayed trains in real time according to the actual running time of the current train on the operation day, thereby performing vehicle operation simulation in real time and outputting the optimized driving timetable in real time.

[0101] The present invention proposes a method for optimizing a bus schedule under delay conditions, which minimizes the deviation between the planned schedule and the optimized schedule that takes into account the delays in the middle by predicting the time when the bus arrives at each station. Compared with the existing schedule optimization algorithm, the present invention realizes the prediction of the punctuality of each station during the operation of the bus, optimizes its departure interval, allows passengers to take the bus "on time", and improves the service quality of the bus.

[0102] Example 2

[0103] In addition to the above method, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the decision-making behavior decision method according to various embodiments of the present application described in the above embodiment 1 of this specification.

[0104] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0105] Example 3

[0106] An embodiment of the present application may also be a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor executes the steps of the decision-making behavior decision method according to various embodiments of the present application described in the above-mentioned embodiment 1.

[0107] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0108] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing a public transport schedule under delay conditions, characterized in that: The following steps are involved: S1, running time prediction: by analyzing the historical driving data of buses, the running time of each bus between stations is predicted, and the predicted running time of each bus between stations is obtained; S2, delayed train prediction: the predicted running time of buses of each train between stations is judged. If the predicted running time of buses of a certain train between stations is greater than the maximum allowed travel time, the train is recorded as a delayed train, and the delayed start station and delayed end station of the delayed train, as well as the delayed start time and delayed end time are obtained; S3, comprehensively analyzing the running time prediction results and the delayed train number prediction results, optimizing the driving schedule, and outputting the optimized driving schedule, that is, obtaining the optimized departure time and arrival time of each bus number at each station.

2. The method for optimizing a public transportation schedule under delay conditions according to claim 1, characterized in that: In step S3, The relevant parameters for optimization are as follows: s represents the station number, s = 1, 2, ..., n; tr represents the bus number, tr = 1, 2, ..., m; when tr is marked as delay, it means that the bus number is delayed; H min Indicates the minimum departure time interval; H max Indicates the maximum departure time interval; Dwell s Indicates the length of time the bus stops at each station; Ot s Indicates the running time of the bus between each stop; Delay_starttime indicates the delay start time; Delay_endtime indicates the delay end time; Delay_startstation indicates the delay start station; Delay_endstation indicates the delay end station; YD time Indicates the increased congestion duration of delayed trains in the congestion zone; pd s,tr It indicates the scheduled departure time of train number tr at station s; pa s,tr It indicates the planned arrival time of train number tr at station s; The decision variables for optimization are as follows: d s,tr Indicates the departure time of train number tr at station s; a s,tr Indicates the arrival time of train number tr at station s; The objective function of optimization is: The optimization constraints are: d s,tr -a s,tr =Dwell s H min ≤d 1,tr -d 1,tr-1 ≤H max d s,tr -d s,tr-1 ≥10 min(From Delay_endstation )+YD time ≤a Delay_endstation,tr＝delay -d Delay_startstation,tr＝delay a Delay_endstation,tr＝delay -d Delay_startstation,tr＝delay ≤max(Ot Delay_endstation )+YD time min(From s )≤a s,tr＝delay -d s-1,tr＝delay ≤max(Ot s ) min(From s )≤a s,tr -d s-1,tr ≤max(Ot s )。 3. The method for optimizing a public transportation schedule under delay conditions according to claim 2, characterized in that: The Gurobi solver is used to optimize the departure and arrival times of each bus at each station to obtain the optimized bus schedule.

4. The method for optimizing a public transportation schedule under delay conditions according to claim 1, characterized in that: In step S2, if the predicted travel time of a bus from station i to station j is greater than the maximum allowed travel time max(Ot j ), then the train is recorded as a delayed train, station i is recorded as the delay start station, the departure time of the train at station i is recorded as the delay start time, station j is recorded as the delay end station, and the arrival time of the train at station j is recorded as the delay end time.

5. The method for optimizing a public transportation schedule under delay conditions according to claim 1, characterized in that: In step S1, the actual running time of the current train from station i to station j on the operating day is t ij , according to the historical driving data analysis, obtain the historical average running time of the train from station i to station j under the same conditions And obtain the historical average running time of subsequent trains from station i to station j under the same conditions Calculate the predicted running time y of the subsequent train from station i to station j ij ,y ij = 6. The method for optimizing a public transportation schedule under delay conditions according to claim 5, characterized in that: By analyzing the historical driving data of buses, we can obtain the historical average running speed of different buses under different conditions (such as from station i to station j). Then through the stand distance s ij Calculate the historical average running time from site i to site j 7. The method for optimizing a public transportation schedule under delay conditions according to claim 5, characterized in that: According to the actual running time of the current train on the day of operation, the predicted running time between stations of subsequent trains is updated in real time, and the predicted results of delayed trains are updated in real time, so as to simulate the vehicle operation in real time and output the optimized driving timetable in real time.

8. A computer program product, characterized in that It includes a computer program / instruction, which, when executed by a processor, implements a method for optimizing a public transportation schedule under delay conditions as described in any one of claims 1-7.

9. A readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, a method for optimizing a public transportation schedule under delay conditions as described in any one of claims 1 to 7 is implemented.

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