A bus operation optimization simulation method based on big data

Abstract

The present invention discloses a bus operation optimization simulation method based on big data, which relates to the technical field of intelligent transportation information technology applications and includes the following steps. First, comprehensively considering factors such as the city's traffic conditions and route lengths, by counting the departure numbers of each station on urban bus lines at different times, accurately grasp the capacity supply of each line and each station, and calculate the capacity using the capacity calculation formula. Then, based on the existing basic data such as IC card data and GPS data of bus enterprises, through the fusion and in-depth mining of these multi-source data, combined with big data and AI technologies, analyze and predict the passenger flow, aiming to master the passenger flow prediction information of each line and each station. The present invention uses the passenger volume calculation formula to calculate the passenger volume, and statistically analyzes the passenger flow and capacity of each line at each time period, aiming to determine whether the capacity and passenger flow of the line are matched, and screen out the problem lines that require optimization of the operating capacity.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent transportation information technology applications, and in particular, to a bus operation optimization simulation method based on big data. Background Art

[0002] Public transportation is an important means of transportation for urban residents to travel currently and is also an effective way to solve traffic congestion. A reasonable transportation capacity allocation is an important symbol of the transportation development of a city. However, currently, with the rapid economic development, the number of cars has been increasing year by year, but the construction of corresponding supporting facilities such as roads and stations is relatively backward. In addition, due to the lack of effective statistical methods, it is impossible to accurately grasp the passenger flow, resulting in varying degrees of problems in the bus transportation capacity allocation in each city, such as overly long waiting times, overly high or low load factors, bunching and large headways during vehicle operation, directly affecting the riding experience and interest of residents, and also having a significant impact on the costs of bus companies. Therefore, there is an urgent need for a bus operation optimization simulation method based on big data to change this situation. Summary of the Invention

[0003] The purpose of the present invention is to solve the deficiencies existing in the prior art and propose a bus operation optimization simulation method based on big data. Its advantages lie in conducting passenger flow analysis and prediction based on the existing multi-source big data of buses, making the results more accurate; automatically generating departure schedules and driving plans using algorithms, reducing labor costs; carrying out relevant work using the generated departure schedules and driving plans, which can meet the passenger flow demand and improve the operation efficiency; reasonable operation configuration can ensure the passenger service quality and at the same time can reduce the input costs of bus companies, reduce the waste of bus resources and environmental pollution.

[0004] To achieve the above purpose, the present invention adopts the following technical solutions:

[0005] A bus operation optimization simulation method based on big data, comprising the following steps:

[0006] Step 1: First, comprehensively considering the traffic conditions and line length factors of the city, by counting the number of departures at each stop of the urban bus lines at different times, accurately master the transportation capacity supply situation of each line and each stop, and calculate the transportation capacity using the transportation capacity calculation formula;

[0007] Step 2: Next, based on the existing IC card data and GPS data of bus enterprises as basic data, through the fusion and in-depth mining of these multi-source data, combined with big data and AI technologies, analyze and predict the passenger flow. The purpose is to master the passenger flow prediction information of each line and each station, calculate the traffic volume using the traffic volume calculation formula, and conduct statistical analysis on the passenger flow and transport capacity of each line at each time period. The purpose is to determine whether the transport capacity of the line matches the passenger flow, and screen out the problem lines that require optimization of the operating transport capacity;

[0008] Step 3: Obtain passenger flow data, and use the cross-sectional passenger flow calculation formula to calculate the bus cross-sectional passenger flow through the calculation of basic data. Generally, the passenger flow through each cross-section of the bus line is not equal within a unit time. When calculating the cross-sectional passenger flow in hours, the maximum hourly cross-sectional passenger flow is a basic data for generating the departure schedule;

[0009] Step 4: Calculate the departure frequency using the departure frequency calculation formula, calculate the departure frequency by combining the inter-station travel time data and the peak-time cross-sectional passenger flow data, generate an initial schedule based on the departure frequency data for each hour. Under the premise of the initial schedule, calculate the peak first-stop departure time and the tolerance waiting time in sequence, then screen the peak departure times that need to be inserted based on the tolerance waiting time, execute the insertion of peak departure times into the initial schedule, and then smoothly process the departure times before and after the peak departure times to form the final departure schedule;

[0010] Step 5: Based on the vehicle allocation strategy using the deficit function method, this function adds 1 when a train departs and subtracts 1 when it arrives. Considering the minimum number of allocated vehicles, the strategy allows for empty runs. In the departure yard, among the vehicles participating in the operation, the vehicles with fewer current trips depart first. Combine the initial times of the up and down schedules. At this time, set t = the start time of operation, then determine whether there is a vehicle entering the depot. If so, update the vehicles in the depot. If not, determine whether there is a vehicle leaving the depot. If there is a vehicle leaving the depot, record the departure ID, departure time, and arrival time, and update the on-line vehicles. Determine whether t is greater than the operation time. If there is no vehicle leaving the depot either, also determine whether t is greater than the end time of operation. If not, increment t, then go back and continue to determine whether there is a vehicle entering the depot, and continue with the subsequent process. If t is determined to be greater than the end time of operation, summarize various departure times and arrival times, and determine whether there are the first and last shifts that connect two train chains after inserting empty runs. If so, return to the initial time. If not, output the final train operation plan;

[0011] Step 6: From the perspective of the input costs of bus enterprises, compare the aspects of departure by time period, cumulative departures, transport capacity input, cost savings, and passenger service quality, and then the simulation of bus operation optimization can be completed.

[0012] The present invention is further configured such that the transport capacity calculation formula is Among them, TCts represents the transport capacity of Station S within the time range t; Cts represents the number of vehicles passing through Station S within the time range t; CLi represents the rated passenger capacity of the i-th vehicle.

[0013] The present invention is further configured such that the calculation formula for the traffic volume is Among them, TVts represents the traffic volume of Station S within the time range t; Cts represents the number of vehicles passing through Station S within the time range t; PFi represents the sectional passenger flow from this station to the next station when the i-th vehicle passes.

[0014] The present invention is further configured such that the calculation formula for the sectional passenger flow is P i+1 = P i - P 下 + P 上 , where pi+1 is the passenger flow of the (i + 1)-th section, in persons; pi is the passenger flow of the i-th section, in persons; p on is the number of boarding passengers, in persons; p off is the number of alighting passengers, in persons.

[0015] The present invention is further configured such that the calculation formula for the departure frequency is Among them, F represents the number of vehicles required per hour; P represents the maximum sectional passenger flow per hour; g represents the maximum allowable full load rate of the vehicle; c represents the allowable passenger capacity of the vehicle.

[0016] The present invention is further configured such that the sectional passenger flow of the bus refers to the sectional passenger volume of a certain bus line within a certain period or the sectional passenger volume of all bus lines at this section within a certain period. The sectional passenger flow is divided into the upward sectional passenger flow and the downward sectional passenger flow. The sectional passenger flow of public transportation is essential basic data for optimizing the bus network.

[0017] The present invention is further configured such that the way to obtain the passenger flow data is obtained by jointly performing site association processing on operation and dispatch data and IC card data. For the investigation method of the sectional passenger flow of the bus, the traditional manual investigation method is abandoned, and the data acquisition efficiency is improved.

[0018] The present invention is further configured such that the following purposes can be conveniently obtained when comparing indicators: By comparing the departure situations in different time periods, understand whether the departure frequencies of different schemes are more matched with the passenger flow; by comparing the cumulative number of departure trips per hour, understand whether the transport capacity inputs of different schemes are more matched with the passenger flow; by comparing and analyzing the transport capacity input situations, analyze whether each scheme is matched with the selected reference passenger flow volume; by comparing and analyzing the transport capacity input cost situations, analyze whether each scheme is more cost-saving compared with the selected reference actual cost.

[0019] The beneficial effects of the present invention are as follows: The bus operation optimization simulation method based on big data conducts passenger flow analysis and prediction based on the existing multi-source big data of buses, making the results more accurate; uses algorithms to automatically generate departure schedules and driving plans, reducing labor costs; uses the generated departure schedules and driving plans to carry out relevant work, which can meet the passenger flow demand and improve the operation efficiency; reasonable operation configuration can ensure the passenger service quality and at the same time reduce the input cost of bus enterprises, reduce the waste of bus resources and environmental pollution. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic flow structure diagram of a bus operation optimization simulation method based on big data proposed by the present invention;

[0021] Figure 2 It is a schematic cross-sectional passenger flow algorithm flow structure diagram of a bus operation optimization simulation method based on big data proposed by the present invention;

[0022] Figure 3 It is a schematic flow structure diagram of generating a departure schedule of a bus operation optimization simulation method based on big data proposed by the present invention;

[0023] Figure 4 It is a schematic flow structure diagram of generating a driving plan of a bus operation optimization simulation method based on big data proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] The technical solutions of this patent will be further described in detail below in conjunction with the specific embodiments.

[0025] The embodiments of this patent are described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below by referring to the drawings are exemplary and are only used to explain this patent and should not be construed as a limitation of this patent.

[0026] In the description of this patent, it should be understood that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing this patent and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this patent.

[0027] In the description of this patent, it should be noted that unless otherwise clearly specified and defined, the terms "installation", "connection", "linkage", and "setting" should be understood in a broad sense. For example, it can be fixedly connected and set, or detachably connected and set, or integrally connected and set. For those of ordinary skill in the art, the specific meanings of the above terms in this patent can be understood according to specific circumstances.

[0028] Referring to Figures 1-4 , a bus operation optimization simulation method based on big data, comprising the following steps:

[0029] Step 1: First, comprehensively considering the traffic conditions and route length factors of the city, by counting the departure frequencies of each station on the urban bus routes at different times, accurately grasping the transport capacity supply of each route and each station, and calculating the transport capacity using the transport capacity calculation formula;

[0030] Step 2: Then, based on the existing basic data of IC card data and GPS data of the bus enterprise, by fusing and deeply mining these multi-source data, combining big data and AI technologies, analyzing and predicting the passenger flow, aiming to master the passenger flow prediction information of each route and each station, calculating the passenger volume using the passenger volume calculation formula, and statistically analyzing the passenger flow and transport capacity of each route at each time period, aiming to master whether the transport capacity and passenger flow of the route match, and screening out the problem routes that require optimization of the operating transport capacity;

[0031] Step 3: Obtain the passenger flow data, use the cross-sectional passenger flow calculation formula, and obtain the bus cross-sectional passenger flow by calculating the basic data. Generally, the passenger flow through each cross-section of the bus route within a unit time is not equal. When calculating the cross-sectional passenger flow in hours, the maximum hourly cross-sectional passenger flow is a basic data for generating the departure schedule;

[0032] Step 4: Calculate the departure frequency using the departure frequency calculation formula, calculate the departure frequency by combining the data of the time consumed between stations and the cross-sectional passenger flow data at peak times, generate an initial schedule based on the hourly departure frequency data. On the premise of the initial schedule, calculate the departure time at the peak first station and the tolerance waiting time in sequence, then screen the peak departure times that need to be inserted based on the tolerance waiting time, execute inserting the peak departure times into the initial schedule, and then smoothly process the departure times before and after the peak departure times to form the final departure schedule;

[0033] Step 5: Vehicle allocation strategy based on the deficit function method. This function increments by 1 when a vehicle departs and decrements by 1 when a vehicle arrives. Considering the minimum number of allocated vehicles, the strategy allows for deadheading. Among the vehicles participating in operations at the departure station, the vehicles with fewer current trips depart first. Initialize the time based on the up and down timetables. At this time, set t = start time of operation. Then, determine if there is a vehicle entering the depot. If so, update the vehicles in the depot. If not, determine if there is a vehicle departing the depot. If there is a vehicle departing the depot, record the departure ID, departure time, and arrival time, and update the online vehicles. Determine if t is greater than the operation time. If there is no vehicle departing the depot either, also determine if t is greater than the end time of operation. If not, increment t and then go back to determine if there is a vehicle entering the depot and continue with the subsequent process. If t is determined to be greater than the end time of operation, summarize various departure times and arrival times, and determine if there are the first and last trips of two vehicle chains that can be connected after inserting deadheading. If so, return to the initial initialization time. If not, output the final train operation plan;

[0034] Step 6: From the perspective of the input costs of the bus enterprise, compare aspects such as departure by time period, cumulative departures, capacity input, cost savings, and passenger service quality, and then the simulation of bus operation optimization can be completed.

[0035] In this embodiment, the capacity calculation formula is where TCts represents the capacity of Station S within the time range t; Cts represents the number of vehicles passing through Station S within the time range t; CLi represents the rated load capacity of the i-th vehicle. The traffic volume calculation formula is where TVts represents the traffic volume of Station S within the time range t; Cts represents the number of vehicles passing through Station S within the time range t; PFi represents the sectional passenger flow from this station to the next station when the i-th vehicle passes. The sectional passenger flow calculation formula is P i+1 = P i - P 下 + P 上 , where pi+1 is the passenger flow of the (i + 1)-th section, in persons; pi is the passenger flow of the i-th section, in persons; p_up is the number of boarding passengers, in persons; p_down is the number of alighting passengers, in persons. The departure frequency calculation formula where F represents the number of vehicles required per hour; P represents the maximum sectional passenger flow per hour; g represents the maximum allowable full load rate of the vehicle; c represents the allowable passenger capacity of the vehicle.

[0036] Further, in this embodiment, the sectional passenger flow of buses refers to the sectional passenger volume of a certain bus line within a certain period or the sectional passenger volume of all bus lines at a certain section within a certain period. The sectional passenger flow is divided into the upward sectional passenger volume and the downward sectional passenger volume. The sectional passenger flow of public transportation is essential basic data for optimizing the bus network. The way to obtain the passenger flow data is to perform site association processing on the operation and dispatch data and the IC card data together. For the investigation method of the sectional passenger flow of buses, the traditional manual investigation method is abandoned, and the data acquisition efficiency is improved.

[0037] It is worth mentioning that in this embodiment, the following purposes can be conveniently achieved when comparing indicators: By comparing the departure situations in different time periods, understand whether the departure frequencies of different schemes are more matched with the passenger flow; by comparing the cumulative number of departure trips per hour, understand whether the transport capacities invested in different schemes are more matched with the passenger flow; by comparing and analyzing the transport capacity investment situations, analyze whether each scheme is matched with the selected reference passenger flow volume; by comparing and analyzing the transport capacity investment cost situations, analyze whether each scheme is more cost-saving compared with the selected reference actual cost.

[0038] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution of the present invention and its inventive concept, makes equivalent substitutions or changes, and all should be covered within the protection scope of the present invention.

Claims

1. A bus operation optimization simulation method based on big data, characterized in that, it includes the following steps: Step 1: First, comprehensively considering the traffic conditions and route length factors of the city, by counting the departure numbers of each station on the urban bus lines at different times, accurately grasp the transport capacity supply situation of each line and each station, and calculate the transport capacity using the transport capacity calculation formula; Step 2: Then, based on the existing basic data of IC card data and GPS data of the bus enterprise, through the fusion and in-depth mining of these multi-source data, combined with big data and AI technologies, analyze and predict the passenger flow, aiming to master the passenger flow prediction information of each line and each station, calculate the passenger volume using the passenger volume calculation formula, and conduct statistical analysis on the passenger flow and transport capacity of each line at each time period, aiming to master whether the transport capacity and passenger flow of the line match, and screen out the problem lines that need to optimize the transport capacity; Step 3: Obtain the passenger flow data, use the cross-sectional passenger flow calculation formula, and obtain the bus cross-sectional passenger flow through the calculation of the basic data. Generally, the passenger flow through each cross-section of the bus line within a unit time is not equal. When calculating the cross-sectional passenger flow in hours, the maximum hourly cross-sectional passenger flow is a basic data for generating the departure schedule; Step 4: Calculate the departure frequency using the departure frequency calculation formula, calculate the departure frequency by combining the inter-station travel time data and the peak-time cross-sectional passenger flow data, generate an initial schedule based on the hourly departure frequency data. On the premise of the initial schedule, calculate the peak first-stop departure time and the tolerance waiting time in turn, then screen the peak departure times that need to be inserted based on the tolerance waiting time, execute the insertion of the peak departure times into the initial schedule, and then smoothly process the departure times before and after the peak departure times to form the final departure schedule; Step 5: The vehicle allocation strategy based on the deficit function method. This function adds 1 when a vehicle departs and subtracts 1 when a vehicle arrives. Considering the minimum vehicle allocation number, the strategy allows the addition of deadheading. In the departure yard, among the vehicles participating in the operation, the vehicles with fewer current running trips depart first. Combine the initialization times of the up and down schedules. At this time, set t = the start time of operation, then judge whether there is a vehicle entering the depot. If so, update the vehicles in the depot. If not, judge whether there is a vehicle leaving the depot. If there is a vehicle leaving the depot, record the departure id, departure time and arrival time, and update the online vehicles. Judge whether t is greater than the operation time. If there is no vehicle leaving the depot either, also judge whether t is greater than the end time of operation. If not, let t increment, and then go back to judge whether there is a vehicle entering the depot and continue the subsequent process. If t is determined to be greater than the end time of operation, summarize various departure times and arrival times, and judge whether there are the first and last shifts that connect two vehicle chains after inserting deadheading. If so, return to the initial initialization time. If not, output the final driving schedule; Step 6: From the perspective of the input costs of the bus enterprise, make comparisons in terms of departure by time period, cumulative departures, transport capacity input, cost savings, and passenger service quality, and then the simulation of bus operation optimization can be completed.

2. The bus operation optimization simulation method based on big data according to claim 1, characterized in that, The calculation formula for the transport capacity is as follows where TC ts represents the transport capacity of Station S within the time range of t; C ts represents the number of vehicles passing through Station S within the time range of t; CL i represents the rated load capacity of the i-th vehicle.

3. The bus operation optimization simulation method based on big data according to claim 2, characterized in that, The calculation formula for the traffic volume is as follows wherein, TV ts represents the traffic volume of Station S within the time range of t; Cts represents the number of vehicles passing through Station S within the time range of t; PFi represents the cross-sectional passenger flow from this station to the next station when the i-th vehicle passes by.

4. The bus operation optimization simulation method based on big data according to claim 3, characterized in that, The cross-section passenger flow calculation formula is P i+1 = P i - P 下 + P 上 , where P i+1 : The passenger flow of the (i + 1)-th cross-section, in units of people; P i : The passenger flow of the i-th cross-section, in units of people; P 上 : The number of people getting on the vehicle, in units of people; P 下 : The number of people getting off the vehicle, in units of people.

5. The bus operation optimization simulation method based on big data according to claim 4, characterized in that, The formula for calculating the departure frequency Where, F represents the number of vehicles required per hour; P represents the maximum sectional passenger flow per hour; g represents the maximum allowable load factor of the vehicle; c represents the allowable passenger capacity of the vehicle.

6. The bus operation optimization simulation method based on big data according to claim 5, characterized in that, The cross-sectional passenger flow of the bus refers to the cross-sectional passenger volume of a certain bus line within a certain period or the cross-sectional passenger volume of all bus lines at this cross-section within a certain period. The cross-sectional passenger flow is divided into the up-bound cross-sectional passenger volume and the down-bound cross-sectional passenger volume. The cross-sectional passenger flow of public transportation is the essential basic data for optimizing the bus network.

7. The bus operation optimization simulation method based on big data according to claim 6, characterized in that, The way to obtain the passenger flow data is obtained after performing site association processing on the operation and dispatch data and the IC card data together.

8. The bus operation optimization simulation method based on big data according to claim 7, characterized in that, When comparing indicators, it is convenient to achieve the following purposes: By comparing the departure situations in different time periods, understand the departure frequencies of different schemes and whether they match the passenger flow better; by comparing the cumulative departure trips per hour, understand the transportation capacity inputs of different schemes and whether they match the passenger flow better; by analyzing the transportation capacity input situations, analyze whether each scheme matches the selected reference passenger flow volume; by analyzing the transportation capacity input cost situations, analyze whether each scheme is more cost-saving compared to the selected reference actual cost.

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