WO2019205278A1

WO2019205278A1 - Bus running state data adjustment processing method, smart terminal and storage medium

Info

Publication number: WO2019205278A1
Application number: PCT/CN2018/093655
Authority: WO
Inventors: 邹亮; 杨乐南; 张洪斌
Original assignee: 深圳大学
Priority date: 2018-04-28
Filing date: 2018-06-29
Publication date: 2019-10-31
Also published as: CN108615353B; CN108615353A

Abstract

A bus running state data adjustment processing method, a smart terminal and a storage medium. The method comprises: comparing data concerning departure times of bus shifts corresponding to an expected bus departure schedule with data concerning dynamically predicted bus return times, so as to acquire a shift and bus schedule feasibility result; constructing a dynamic bus scheduling entropy model according to data concerning predicted shifts that cannot be departed according to the expected schedule, and adjusting the bus schedule according to the data concerning predicted bus return times; verifying the dynamic bus scheduling entropy model by means of an example analysis, and adjusting the bus full load rates between two adjacent shifts according to the detected validity of the dynamic bus scheduling entropy model.

Description

Bus operation status data adjustment processing method, intelligent terminal and storage medium

Technical field

The invention relates to the application field of the bus travel adjustment technology, in particular to a bus operation state data adjustment processing method, an intelligent terminal and a storage medium.

Background technique

By 2017, the number of urban public transport in China reached 651,200, and the number of open lines reached 56,786; in 2017, the city's passenger traffic was 127.215 billion, of which 72.287 billion were completed, accounting for 56.82%. Whether it is from the scale of the bus system or from the passenger volume of the bus, the bus travel has an irreplaceable position to meet the travel needs of urban residents. However, in most cities in China, there are widespread problems such as long waiting times for passengers and crowded passengers. One of the main reasons is the poor stability of public transportation. Taking the actual operation data of a bus line in Guangzhou as an example, the average on-time bus punctuality rate is about 70%, and the punctuality rate of bus departures during peak hours is more than 30%. The main reason for the low on-time rate of bus departures is that most cities in China use static bus dispatching methods. This type of dispatching method has high requirements for road smoothness and vehicle configuration, and it is difficult to face the increasingly complex and deteriorating current. traffic condition.

In the actual operation of the bus, due to the influence of passenger flow, traffic conditions and other factors, the bus return time often delays, making the prepared timetable difficult to implement according to the plan, frequent bus breaks, causing inconvenience to the user's travel. .

Therefore, the prior art has yet to be improved and developed.

Summary of the invention

The technical problem to be solved by the present invention is that, in the prior art, since the bus return time cannot meet the expected departure time, the actual departure interval is seriously deviated from the expected departure interval, causing the problem that the bus breaks and the bus full load rate is out of balance. The invention provides a bus operation state data adjustment processing method, an intelligent terminal and a storage medium, which aims to extend the interval of the previous departures in advance to eliminate the off-duty, and realize the fairness and rationality of the adjustment of the departure intervals in the early stage, and reduce the bus breaks. Balance the bus passenger load rate of each shift, improve the stability of bus operation, and facilitate passengers to travel.

The technical solution adopted by the present invention to solve the technical problem is as follows:

A bus operation state data adjustment processing method, wherein the bus operation state data adjustment processing method comprises:

Comparing the bus departure time data corresponding to the bus departure schedule with the dynamically predicted bus return time data, and obtaining the feasibility of the shift and the bus schedule;

According to the shift data that is expected to be unable to start according to the expected timetable, the bus dynamic scheduling entropy model is constructed, and the bus schedule is adjusted according to the predicted bus return time data;

Through the analysis of the example, the dynamic scheduling entropy model of the bus is verified, and the full load rate of the bus before and after the shift is adjusted according to the validity of the detected dynamic scheduling entropy model.

The bus operation state data adjustment processing method, wherein the bus departure time data corresponding to the bus departure schedule is compared with the dynamically predicted bus return time data, and the result of obtaining the feasibility of the shift includes: :

Comparing the bus departure time data corresponding to the bus departure schedule with the dynamically predicted bus return time data, and evaluating the feasibility of the corresponding bus departure according to the expected timetable;

The feasibility evaluation function of the shift and the feasibility function of the bus schedule are constructed to judge the feasibility of the shift and the bus schedule.

The bus operation state data adjustment processing method, wherein comparing the bus departure time data corresponding to the desired bus departure timetable with the dynamically predicted bus return time data, and evaluating the corresponding shift bus to start according to the expected timetable Feasibility, build the feasibility evaluation function of the shift:

Among them, f(t _i , T _i ) is the feasibility evaluation function of the next bus in accordance with the expected bus schedule. When f(t _i , T _i )=1, it means feasible, when f(t _i , When T _i )=0, it means that it is not feasible;

t _i is the expected departure time of the next i-th bus after the current dispatch, and it is assumed that t _i is the expected bus schedule optimized according to the predicted passenger traffic volume distribution;

T _i is the predicted return time of the next i-th bus after the current dispatch, including the on-station vehicle and the return-to-station vehicle, and the predicted return time of the to-be-launched vehicle in the station is defined as 0;

i=1, 2,...,N,N is the total number of vehicles in the station and the number of vehicles returning.

The bus operation state data adjustment processing method, wherein the bus departure time data corresponding to the bus departure schedule is compared with the dynamically predicted bus return time data, and the feasibility result of obtaining the bus schedule includes :

Evaluate the feasibility of the next N corresponding shift buses according to the expected bus schedule. The evaluation function for constructing the bus schedule is as follows:

F(t ₁ ,...,t _N ;T ₁ ,...,T _N )=f(t ₁ ,T ₁ )·f(t ₂ ,T ₂ )...f(t _N ,T _N );

F(t ₁ ,...,t _N ;T ₁ ,...,T _N ) is the feasibility of the next N shifts according to the expected departure schedule, when F(t ₁ ,...,t _{When N} ; T ₁ , . . . , T _N )=1, it means feasible; when F(t ₁ , . . . , t _N ; T ₁ , . . . , T _N )=0, it means that it is not feasible.

The bus operation state data adjustment processing method, wherein the bus dynamic scheduling entropy model is constructed according to the expected shift data that cannot be scheduled according to the expected timetable, and the public transit timetable according to the predicted bus return time data specifically includes:

If it is expected that the bus schedule is not feasible, as the node for adjusting the bus schedule, it is necessary to determine the farthest shift that cannot be issued according to the expected schedule, and dynamically adjust the departure interval of the shift;

When the bus schedule is dynamically adjusted, the total difference between the bus departure intervals before and after the control adjustment is the smallest, and the rate of change of the bus departure interval is consistent.

The bus operation state data adjustment processing method, wherein if the bus schedule is not feasible, as the node for adjusting the bus schedule, it is necessary to determine the farthest shift M that cannot be issued according to the expected schedule.

And dynamically adjust the departure interval of the Mth and previous shifts;

The departure interval of the Mth and the previous shifts is dynamically adjusted, and the adjustment range is (T _M -t _M ); T _M is the predicted return time of the Mth bus, and t _M is the departure time of the Mth shift after the adjustment;

The bus operation state data adjustment processing method, wherein the public transit dynamic scheduling entropy model is as follows:

Objective function:

Where H is the specified maximum departure time interval;

Restrictions:

Indicates the bus departure time of the jth shift after adjustment

Later than the jth bus predicted return time T _j ;

Indicates the adjusted bus departure interval

Less than or equal to the maximum allowable departure interval H and greater than 0;

Indicates the departure time of the Mth shift after adjustment

It should be equal to the predicted transit time T _{M of} the Mth bus, and the subsequent shifts can be executed according to the expected timetable;

j=1,2,...,M;

among them,

k _j is a constant, k _j >0, and k _j represents the rate of change of the bus departure interval before and after adjustment.

The bus operation state data adjustment processing method, wherein when λ _j tends to be concentrated, the more consistent the distribution of the bus departure interval before and after the adjustment, the more satisfying the passenger demand distribution, the more fair the adjustment method; otherwise, the more unfair; the fairest; As a result, the adjusted bus departure interval is an equal extension of the bus departure interval before the adjustment, ie:

At this time, k ₁ =k ₂ =...=k _M =k, the distribution of the bus departure interval before and after the adjustment is consistent with the traffic distribution of the passenger traffic;

According to the nature of entropy, λ _{j is} used as the index to calculate the entropy value. When the λ _j distribution tends to be concentrated, it indicates that the distribution of the bus departure interval is more consistent, and the distribution of the passenger traffic volume is more consistent, the entropy value is larger; The fairness evaluation function can be expressed as:

An intelligent terminal, comprising: a memory, a processor, and a bus operation state data adjustment processing program stored on the memory and operable on the processor, the bus operation state data adjustment processing The steps of the bus operation state data adjustment processing method as described above are implemented when the program is executed by the processor.

A storage medium, wherein the storage medium stores a bus operation state data adjustment processing program, and the bus operation state data adjustment processing program is executed by a processor to implement the steps of the bus operation state data adjustment processing method as described above.

The invention discloses a bus operation state data adjustment processing method, an intelligent terminal and a storage medium, the method comprising: comparing the bus departure time data corresponding to the desired bus departure timetable with the dynamically predicted bus return time data. Yes, the results of the feasibility of obtaining the shift and the bus schedule; constructing the bus dynamic scheduling entropy model according to the expected shift data that cannot be scheduled according to the expected timetable, adjusting the bus schedule according to the predicted bus return time data; verifying the bus by example analysis The dynamic scheduling entropy model adjusts the full load rate of the bus before and after the shift according to the validity of the detected dynamic scheduling entropy model. The invention is based on a dynamic bus scheduling entropy model for predicting the return time, and pre-expanding the interval of each departure in advance to eliminate the off-duty, and realizes the fairness and rationality of the adjustment of the departure intervals in the early stage, reduces the bus breaks, and balances the shifts. Bus passenger load rate, improve the stability of bus operation, and facilitate passengers to travel.

DRAWINGS

1 is a flow chart of a preferred embodiment of a method for adjusting bus operation status data according to the present invention;

2 is a flowchart of step S10 in a preferred embodiment of the bus operation state data adjustment processing method of the present invention;

3 is a flow chart of step S20 in the preferred embodiment of the bus operation state data adjustment processing method of the present invention;

4 is a schematic diagram of a departure interval distribution of a timetable with an early peak as an example in a preferred embodiment of the bus operation state data adjustment processing method of the present invention;

5 is a schematic diagram of a departure interval distribution of a timetable with a night peak as an example in a preferred embodiment of the bus operation state data adjustment processing method of the present invention;

FIG. 6 is a schematic diagram of an operating environment of a preferred embodiment of the smart terminal of the present invention.

detailed description

The present invention will be further described in detail below with reference to the accompanying drawings. It is understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

The bus operation state data adjustment processing method according to the preferred embodiment of the present invention, as shown in FIG. 1 , a bus operation state data adjustment processing method is applied to an intelligent terminal, wherein the bus operation state data adjustment processing method includes The following steps:

Step S10: Comparing the bus departure time data corresponding to the desired bus departure timetable with the dynamically predicted bus return time data, and obtaining the feasibility of the shift and the bus schedule.

For the specific process, please refer to FIG. 2 , which is a flowchart of step S10 in the bus operation state data adjustment processing method provided by the present invention.

As shown in FIG. 2, the step S10 includes:

S11. Comparing the bus departure time data corresponding to the desired bus departure timetable with the dynamically predicted bus return time data, and evaluating the feasibility of the corresponding shift bus according to the expected timetable;

S12, constructing the feasibility evaluation function of the shift and the feasibility evaluation function of the bus schedule, respectively determining the feasibility of the shift and the bus schedule.

Specifically, firstly, the feasibility evaluation of the shift is performed, and the bus departure time data corresponding to the desired bus departure schedule is compared with the dynamically predicted bus return time data, and the feasibility of the corresponding shift bus according to the expected schedule is evaluated. , construct a shift feasibility evaluation function:

Then, the feasibility evaluation of the bus schedule is carried out to evaluate the feasibility of the next N corresponding shift buses according to the expected bus schedule. The evaluation function of the feasibility of constructing the bus schedule is as follows:

Step S20: construct a bus dynamic scheduling entropy model according to the expected shift data that cannot be scheduled according to the expected timetable, and adjust the bus schedule according to the predicted bus return time data.

For a specific process, please refer to FIG. 3, which is a flowchart of step S20 in the bus operation state data adjustment processing method provided by the present invention.

As shown in FIG. 3, the step S20 includes:

S21. If the bus schedule is not feasible, as the node for adjusting the bus schedule, it is necessary to determine the farthest shift that cannot be issued according to the expected schedule, and dynamically adjust the departure interval of the shift;

S22. When the bus schedule is dynamically adjusted, the total difference of the bus departure intervals before and after the control adjustment is minimum, and the rate of change of the bus departure interval before and after the adjustment is the same.

Specifically, if the advancement adjustment shift is determined, if the bus schedule is not feasible, as the node for adjusting the bus schedule, it is necessary to determine the farthest shift M that cannot be issued according to the expected schedule, as shown in the following formula.

The departure interval between the Mth and the previous shifts is dynamically adjusted to avoid bus breaks.

Then, the adjustment range is determined. In order to avoid the problem of the shift, the departure interval of the Mth and the previous shifts needs to be dynamically adjusted. The adjustment range is (T _M -t _M ), where T _M is the Mth bus. Predict the return time, t _M is the departure time of the Mth shift after adjustment.

In the dynamic adjustment of the bus schedule, the following two objectives should be considered: (1) deviation, that is, the total difference between the bus departure intervals before and after the adjustment is minimum; (2) fairness, that is, the rate of change of the bus departure interval before and after the adjustment. Consistency, assuming that the expected departure interval distribution matches the passenger demand distribution, the more consistent the change rate of the bus departure interval before and after the adjustment, the more satisfying the passenger demand distribution, the more fair (reasonable) the adjustment.

Considering the particularity of the method of the present invention, it can be assumed that the interval between the departures may only increase after the adjustment, so the sum of the difference intervals before and after the adjustment can be directly used to represent the total difference, taking into account that the adjustment range (T _M -t _M ) is Fixed, that is, the total difference is fixed, so this method does not need to consider the deviation, only need to consider the goal of fairness.

The fairness of this method is mainly reflected in whether the rate of change of bus departure interval before and after adjustment is consistent; entropy is a mathematical abstract concept, which is widely used in various fields and can be understood as the probability of occurrence of certain information, variable The greater the uncertainty, the greater the entropy. The present invention regards the rate of change of each interval as a fair uncertainty, and the smaller the entropy, the better the fairness. Therefore, the present invention will use the "entropy" theory to design a fairness evaluation function of the adjustment method, and based on this, construct a prediction based on Dynamic bus scheduling entropy model for station time.

The concept of entropy is originally derived from thermodynamics and is a measure of system state uncertainty. In information theory, as a measure of system disorder, the function is introduced:

Where p _i represents the degree of uncertainty or probability of occurrence of certain information, which satisfies:

In the formula, the base of the logarithm does not have a specified value, and generally takes 2, e, and 10, and the unit of measurement of the difference is different. The smaller the information entropy value, the larger the amount of information, the higher the order of information, and the probability distribution tends to concentrate.

When entropy is the smallest, the larger the information entropy value, the smaller the amount of information, which means that the disorder of information is higher, and the probability distribution tends to be scattered, if and only if

When the entropy value is the largest.

The entropy theory is widely used in the research of difference and fair distribution. In order to measure the fairness of the adjustment of bus departure interval, the fairness evaluation function of the adjustment method is designed by using the theory of entropy.

Suppose there are:

Where: Δt _j is the bus departure interval between the jth shift and the j-1 shift before the adjustment, that is, the expected departure interval, Δt _j =t _j -t _j-1 , and t ₀ is the departure time of the current dispatched bus;

For the adjusted bus departure interval between the jth shift and the j-1 shift,

For the dynamically adjusted after the current dispatch, the next i-shift bus departure time; definition

k _j is a constant, k _j >0, which indicates the rate of change of the bus departure interval before and after the adjustment; normalization of k _{j results} in:

Then, when λ _j tends to concentrate, the more consistent the distribution of bus departures before and after the adjustment, the more satisfying the distribution of passenger demand, the more fair the adjustment method; on the contrary, the more unfair; the fairest result is that the adjusted bus departure interval is The proportional extension of the bus departure interval before the adjustment, namely:

At this time, k ₁ =k ₂ =...=k _M =k, the distribution of the bus departure interval before and after the adjustment is consistent with the passenger OD flow distribution.

Therefore, according to the nature of entropy, λ _j can be used as an index to calculate the entropy value. When the λ _j distribution tends to be concentrated, it means that the distribution before and after the bus departure interval adjustment is more consistent, and the distribution with the passenger traffic volume is more consistent, and the entropy value is more Big. Therefore, the fairness evaluation function of the adjustment method can be expressed as:

In order to predict the return time of the bus according to the forecast, the departure interval of the previous period is extended in advance to eliminate the problem of the off-duty. The "entropy" theory is used to realize the fairness of the adjustment of the departure interval in the previous period, and the dynamic entropy model of the dynamic bus scheduling based on the predicted return time is constructed. as follows:

Objective function:

Where H is the specified maximum departure time interval;

Restrictions:

Indicates the bus departure time of the jth shift after adjustment

Later than the jth bus predicted return time T _j ;

Indicates the adjusted bus departure interval

Indicates the departure time of the Mth shift after adjustment

j=1, 2,..., M.

The model is solved as follows:

(1) T _{j ≤} t ₀ + H*j

One of the properties of the entropy function is that H(X) is a strictly convex function of the probability distribution p _i . Obviously, the set of solutions consisting of constraint equations is a convex set. Therefore, the optimization problem is a convex programming problem, and its local optimal solution is its global optimal solution. Lingo (Operational Optimization Analysis Software) is a very classic tool for solving linear programming, quadratic programming, integer programming and nonlinear programming optimization problems. It has a series of fully built solvers and can be read by The equation automatically selects a suitable solver, which is suitable for solving the model of the present invention. The present invention can solve the model by using Lingo software.

In the present invention, subject to the maximum departure time interval H,

If

then

The constraint does not satisfy the constraint and the objective function has no feasible solution. At this time, the strategy of adjusting the bus schedule is to find the departure Q that can not meet the maximum departure interval constraint, and adjust the bus departure interval before the N shift to start at the maximum departure interval H, or when the departure is at the maximum departure interval. When there is no car to be sent, the car will start at the predicted return time T _i , namely:

among them,

For the departure interval of the shift after the Qth shift, start from the bus of the Qth shift and adjust according to the method of (1).

Step S30, verifying the dynamic dispatching entropy model of the bus by analyzing the example, and adjusting the full load rate of the bus before and after the shift according to the validity of the detected dynamic scheduling entropy model.

In order to further explain the model and verify the validity of the model, the following examples are taken from 08:00-09:00 morning peak, 18:00-19:00 night peak, and two time periods where bus breaks are more likely to occur. .

Example 1

Taking the bus dispatch during the morning peak period of 08:00-09:00 as an example, the time of the current bus departure is t ₀ = 08:00, and the number of buses in the station and back to the station is N=8, assuming the maximum departure interval constraint H= 15min, the expected bus departure time t _i for the next 8 shifts and the corresponding scheduled shift back time T _i , adjusted departure schedule

Unadjusted actual departure time

as follows:

Table 1 Bus departure time for the next 8 shifts before and after adjustment (Case 1)

Observed and found that in the first example: 1) If the bus schedule is not adjusted, there may be

This leads to the occurrence of bus breaks and reduces the stability of bus operation. The dynamically adjusted departure interval can meet the constraint of maximum departure time interval H=15min; 2) as shown in Figure 4 (t _i ,

The departure interval of the timetable is shown as the expected departure interval t _i and the adjusted departure interval.

Actual departure interval

Distribution, obviously

It is more similar to the distribution interval of t _i , and it is more in line with the distribution of passengers' needs. It can avoid bus breaks as much as possible, improve the stability of bus operation, and balance the bus passenger load rate before and after.

Study 2

Taking the bus dispatch during the peak period of 18:00-19:00 as an example, the current dispatch time of the bus is t ₀ = 18:00, and the number of buses in the station and the returning station is N=11, assuming the maximum departure interval constraint H= 15min, the expected bus departure time t _i for the next 11 shifts and the corresponding shift time T _i , the adjusted departure schedule

Unadjusted actual departure time

as follows:

Table 2 Bus departure time of the next 11 shifts before and after adjustment (Case 2)

Observed that in the second example, although there is no adjustment in the case, there will be no departure interval.

The situation, but there is still a situation in which the bus stop time is not satisfied with the expected departure time, so that there is a significant difference between the actual departure interval and the expected departure interval, which leads to a decrease in the stability of the bus operation and an imbalance in the bus load rate before and after the trip. Δt ₇ = Δt ₈ = 6min, actual

Dynamically adjusted

Figure 5 (t _i ,

Actual departure interval

Distribution.

With the continuous development of intelligent public transport, the method of predicting passenger flow based on bus IC card technology and video counting technology provides more accurate passenger flow information support for static bus dispatch optimization, and the promotion and application of bus travel forecast technology enables the forecasting of public transport. It is possible to compare the time of returning to the station and compare it with the expected departure timetable, and then predict the potential bus stop problem in advance. The invention applies the entropy theory to the dynamic scheduling of public transportation, dynamically adjusts the bus schedule based on the optimization of static bus scheduling, and constructs a dynamic bus scheduling “entropy” model based on the predicted return time. Finally, the model is given. The method of solving and the example analysis demonstrate the effectiveness of the model. Therefore, it is feasible to dynamically adjust the bus schedule by predicting the bus return time to avoid the bus break and balance the bus full load rate before and after the shift.

The invention proposes a dynamic bus scheduling entropy model based on predicting the return time, by extending the interval of the previous departures in advance to eliminate the problem of off-duty, and using the entropy theory to realize the fairness of the adjustment of the departure intervals in the early stage; finally, through the morning and evening The two models of the peak verified the model. The results show that the model can maximize the distribution of the departure interval before and after the adjustment, reduce the bus breaks, balance the bus load rate of each shift, and improve the stability of the bus operation.

Further, as shown in FIG. 6, the present invention further provides an intelligent terminal, which includes a processor 10, a memory 20, and a display 30, based on the above-described bus operation state data adjustment processing method. Figure 6 shows only some of the components of the smart terminal, but it should be understood that not all illustrated components may be implemented, and more or fewer components may be implemented instead.

The memory 20 may be an internal storage unit of the smart terminal, such as a hard disk or memory of the smart terminal, in some embodiments. The memory 20 may also be an external storage device of the smart terminal in other embodiments, such as a plug-in hard disk equipped on the smart terminal, a smart memory card (SMC), and a secure digital (Secure) Digital, SD) cards, flash cards, etc. Further, the memory 20 may also include both an internal storage unit of the smart terminal and an external storage device. The memory 20 is configured to store application software and various types of data installed on the smart terminal, such as the program code for installing the smart terminal. The memory 20 can also be used to temporarily store data that has been output or is about to be output. In an embodiment, the bus operation state data adjustment processing program 40 is stored on the memory 20, and the bus operation state data adjustment processing program 40 can be executed by the processor 10, thereby implementing the bus operation state data adjustment processing method in the present application.

The processor 10, in some embodiments, may be a Central Processing Unit (CPU), a microprocessor or other data processing chip for running program code or processing data stored in the memory 20, such as The bus operation state data adjustment processing method and the like are executed.

The display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch sensor, or the like in some embodiments. The display 30 is used to display information on the smart terminal and a user interface for displaying visualizations. The components 10-30 of the intelligent terminal communicate with one another via a system bus.

In an embodiment, when the processor 10 executes the bus operation state data adjustment processing program 40 in the memory 20, the following steps are implemented:

The dynamic dispatching entropy model of the bus is verified by a case study. According to the validity of the detected bus dynamics scheduling entropy model, the full load rate of the bus before and after the shift is adjusted.

The present invention also provides a storage medium, wherein the storage medium stores a bus operation state data adjustment processing program, and the bus operation state data adjustment processing program is implemented by the processor to implement the bus operation state data adjustment processing method. Step; specifically as described above.

In summary, the present invention provides a bus operation state data adjustment processing method, an intelligent terminal, and a storage medium, the method comprising: setting a bus departure time data corresponding to a desired bus departure schedule and a dynamically predicted bus return station The time data is compared to obtain the feasibility of the shift and the bus schedule; according to the shift data that is expected to be unable to start according to the expected schedule, the bus dynamic scheduling entropy model is constructed, and the bus schedule is adjusted according to the predicted bus return time data; The example analysis verifies the dynamic scheduling entropy model of the bus, and adjusts the full load rate of the bus before and after the bus according to the validity of the detected dynamic scheduling entropy model. The invention is based on a dynamic bus scheduling entropy model for predicting the return time, and pre-expanding the interval of each departure in advance to eliminate the off-duty, and realizes the fairness and rationality of the adjustment of the departure intervals in the early stage, reduces the bus breaks, and balances the shifts. Bus passenger load rate, improve the stability of bus operation, and facilitate passengers to travel.

Certainly, those skilled in the art can understand that all or part of the processes in the foregoing embodiments can be implemented by a computer program to instruct related hardware (such as a processor, a controller, etc.), and the program can be stored in one. In a computer readable storage medium, the program, when executed, may include the processes of the various method embodiments as described above. The storage medium described therein may be a memory, a magnetic disk, an optical disk, or the like.

It is to be understood that the application of the present invention is not limited to the above-described examples, and those skilled in the art can make modifications and changes in accordance with the above description, all of which are within the scope of the appended claims.

Claims

A method for adjusting a bus operation state data, wherein the bus operation state data adjustment processing method comprises:

Comparing the bus departure time data corresponding to the bus departure schedule with the dynamically predicted bus return time data, and obtaining the feasibility of the shift and the bus schedule;

According to the shift data that is expected to be unable to start according to the expected timetable, the bus dynamic scheduling entropy model is constructed, and the bus schedule is adjusted according to the predicted bus return time data;

Through the analysis of the example, the dynamic scheduling entropy model of the bus is verified, and the full load rate of the bus before and after the shift is adjusted according to the validity of the detected dynamic scheduling entropy model.
The bus operation state data adjustment processing method according to claim 1, wherein the bus departure time data corresponding to the desired bus departure time table is compared with the dynamically predicted bus return time data to obtain the shift. The results of the feasibility include:

Comparing the bus departure time data corresponding to the bus departure schedule with the dynamically predicted bus return time data, and evaluating the feasibility of the corresponding bus departure according to the expected timetable;

The feasibility evaluation function of the shift and the feasibility function of the bus schedule are constructed to judge the feasibility of the shift and the bus schedule.
The bus operation state data adjustment processing method according to claim 2, wherein the bus departure time data corresponding to the desired bus departure timetable is compared with the dynamically predicted bus return time data, and the corresponding bus is evaluated. According to the feasibility of starting the schedule, construct a shift feasibility evaluation function:

Among them, f(t i , T i ) is the feasibility evaluation function of the next bus in accordance with the expected bus schedule. When f(t i , T i )=1, it means feasible, when f(t i , When T i )=0, it means that it is not feasible;

t i is the expected departure time of the next i-th bus after the current dispatch, and it is assumed that t i is the expected bus schedule optimized according to the predicted passenger traffic volume distribution;

T i is the predicted return time of the next i-th bus after the current dispatch, including the on-station vehicle and the return-to-station vehicle, and the predicted return time of the to-be-launched vehicle in the station is defined as 0;

t=1,2,...,N,N is the total number of vehicles in the station and the number of vehicles returning.
The bus operation state data adjustment processing method according to claim 2, wherein the bus departure time data corresponding to the desired bus departure timetable is compared with the dynamically predicted bus return time data, and the bus schedule is obtained. The results of the feasibility include:

Evaluate the feasibility of the next N corresponding shift buses according to the expected bus schedule. The evaluation function for constructing the bus schedule is as follows:

F(t 1 , . . . , t N ; T 1 , . . . , T N )=f(t 1 , T 1 )·f(t 2 , T 2 )...f(t N , T N );

F(t 1 ,...,t N ;T 1 ,...,T N ) is the feasibility of the next N shifts according to the expected departure schedule, when F(t 1 ,...,t When N ; T 1 , . . . , T N )=1, it means feasible; when F(t 1 , . . . , t N ; T 1 , . . . , T N )=0, it means that it is not feasible.
The bus operation state data adjustment processing method according to claim 1, wherein the bus dynamic scheduling entropy model is constructed according to the shift data that is expected to be unable to start according to the expected timetable, and the bus time is adjusted according to the predicted bus return time data. The table specifically includes:

If it is expected that the bus schedule is not feasible, as the node for adjusting the bus schedule, it is necessary to determine the farthest shift that cannot be issued according to the expected schedule, and dynamically adjust the departure interval of the shift;

When the bus schedule is dynamically adjusted, the total difference between the bus departure intervals before and after the control adjustment is the smallest, and the rate of change of the bus departure interval is consistent.
The bus operation state data adjustment processing method according to claim 5, wherein if the bus schedule is not feasible, as the node for adjusting the bus schedule, it is necessary to determine the farthest shift M that cannot be issued according to the expected schedule. for
And dynamically adjust the departure interval of the Mth and previous shifts;

The departure interval of the Mth and the previous shifts is dynamically adjusted, and the adjustment range is (T M -t M ); T M is the predicted return time of the Mth bus, and t M is the departure time of the Mth shift after the adjustment;

When the bus schedule is dynamically adjusted, the total difference between the bus departure intervals before and after the control adjustment is the smallest, and the rate of change of the bus departure interval is consistent.
The bus operation state data adjustment processing method according to claim 6, wherein the dynamic transit scheduling entropy model is constructed as follows:

Objective function:

Where H is the specified maximum departure time interval;

Restrictions:

Indicates the bus departure time of the jth shift after adjustment
Later than the jth bus predicted return time T j ;

Indicates the adjusted bus departure interval
Less than or equal to the maximum allowable departure interval H and greater than 0;

Indicates the departure time of the Mth shift after adjustment
It should be equal to the predicted transit time T M of the Mth bus, and the subsequent shifts can be executed according to the expected timetable;

j=1,2,...,M;

among them,
k j is a constant, k j >0, and k j represents the rate of change of the bus departure interval before and after adjustment.
The bus operation state data adjustment processing method according to claim 7, wherein when λ j tends to be concentrated, the more consistent the distribution of the bus departure interval before and after the adjustment, the more satisfying the passenger demand distribution, the more fair the adjustment method; The more unfair; the fairest result is that the adjusted bus departure interval is an equal extension of the bus departure interval before the adjustment, ie:

At this time, k 1 =k 2 =...=k M =k, the distribution of the bus departure interval before and after the adjustment is consistent with the traffic distribution of the passenger traffic;

According to the nature of entropy, λ j is used as the index to calculate the entropy value. When the λ j distribution tends to be concentrated, it indicates that the distribution of the bus departure interval is more consistent, and the distribution of the passenger traffic volume is more consistent, the entropy value is larger; The fairness evaluation function can be expressed as:
An intelligent terminal, comprising: a memory, a processor, and a bus operation state data adjustment processing program stored on the memory and operable on the processor, the bus operation state data The step of implementing the bus operation state data adjustment processing method according to any one of claims 1-8 when the adjustment processing program is executed by the processor.
A storage medium, characterized in that the storage medium stores a bus operation state data adjustment processing program, and the bus operation state data adjustment processing program is executed by a processor to implement the bus operation according to any one of claims 1-8 The steps of the state data adjustment processing method.