A method for rational analysis and optimization of public transportation resource allocation based on linear programming

Abstract

The present invention relates to the field of public transport resource allocation, and specifically to a method for analyzing and optimizing the rationality of public transport resource allocation based on linear programming, and the steps are as follows: Step one, statistically analyzing public transport characteristics; Step two, optimizing and solving; Step three, obtaining the configuration result: on the basis of Step two, the characteristics mentioned in Step one and Step two are calculated by big data technology, and the algorithm script is written in Python language, so as to finally obtain the optimized departure interval, optimized cost, optimized capacity and vehicle scheduling schedule that is more in line with the actual situation during the morning peak, flat peak and evening peak of the bus network. The present invention takes the capacity supply capability and capacity demand as the entry point to diagnose whether the line network resource allocation is reasonable and solve the problem of imbalance between capacity supply and demand. According to the overall situation of the capacity supply and demand ratio of the entire line network, the reasonable vehicle configuration information and operation strategy related to the line are obtained by using linear programming and other methods, and optimization suggestions are given.

Description

Technical Field

[0001] The invention relates to the field of public transport resource allocation, and in particular to a public transport resource allocation rationality analysis and optimization method based on linear programming. Background Art

[0002] Linear programming is one of the most important branches of operations research, with the most complete theory and the most extensive practical application. It is mainly used to study the optimal allocation of limited resources, that is, how to allocate and use limited resources in the best way, so as to give full play to the efficiency of resources and obtain the best economic benefits. At present, there are not many methods related to linear programming on the market to optimize and implement the allocation of public transportation resources. We use big data technology and real public transportation related data to obtain more accurate optimization features, thereby constructing an optimization algorithm and obtaining a more scientific resource allocation plan. Summary of the invention

[0003] The purpose of the present invention is to provide a method for analyzing and optimizing the rationality of public transportation resource allocation based on linear programming to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A method for rationality analysis and optimization of public transportation resource allocation based on linear programming, the steps are as follows:

[0006] Step 1: Count the characteristics of public transportation: First, count the full load rates of each type of vehicle during the morning peak, off-peak, and evening peak periods; then, for a route, find the specific types of vehicles operating on the route, and calculate the capacity supply-demand ratio of the route through the full load rates of these types of vehicles; second, obtain more accurate bus characteristics of route operating costs, departure intervals, and capacity-related conditions through real bus operation-related data;

[0007] Step 2: Optimization solution: Based on step 1, use linear programming related methods to make the operating cost as low as possible for a single line while ensuring the transport capacity, obtain reasonable vehicle configuration information and operation strategies related to the line, and give optimization suggestions for public transportation resource allocation;

[0008] Step three, obtain the configuration results: Based on step two, use big data technology to calculate the features mentioned in steps one and two, use Python to write algorithm scripts, and finally obtain the optimized departure intervals, optimized costs, optimized capacity and vehicle scheduling schedules that are more in line with actual conditions during morning rush hour, off-peak hour and evening rush hour.

[0009] As a further solution of the present invention: the full load rate is also called the "full load coefficient", which reflects the relative value of the full load degree of the vehicles running on the line within a certain period of time. It is an indicator to measure the utilization degree of the vehicles and is an important indicator to reflect the quality and level of urban public transportation services. The full load rate formula is as follows:

[0010]

[0011] As a further solution of the present invention: the ratio of transport capacity supply and demand is calculated using the following formula:

[0012]

[0013] Use the above formula to calculate the line capacity supply and demand ratio within one day, then calculate the average value of the line capacity supply and demand ratio within 30 days, and calculate the capacity supply and demand ratio of other lines in the entire network in the same way. Through the capacity supply and demand ratio of the entire network, use data analysis related methods to analyze the capacity of the entire network and obtain an overall network capacity portrait.

[0014] As a further solution of the present invention: data analysis related method:

[0015] min(x1·COST K1 +x2 COST K2 +x3 COST K3 )

[0016]

[0017] The departure time interval: The unit is min, left_rate and right_rate are the scaling ratios of the independent variables, which are two hyperparameters, K1, K2, K3 are vehicle models, x1, x2, x3 are the number of trips of each model of vehicle, COSTK1, COSTK2, COSTK3 are the operating costs of each model of vehicle.

[0018] As a further solution of the present invention: actual constraints:

[0019] min(x1·COST K1 +x2 COST K2 +x3 COST K3 )

[0020]

[0021] i: number of days;

[0022] K1 乘客人数 : The average passenger flow of a K1 vehicle per trip;

[0023] K1 实际趟数 : The actual number of trips made by the K1 model vehicle;

[0024] K2 乘客人数 : The average passenger flow of a K2 model vehicle per trip;

[0025] K2 实际趟数 : The actual number of trips made by the K2 model vehicle;

[0026] K3 乘客人数 : The average passenger flow of a K3 vehicle per trip;

[0027] K3 实际趟数 : The actual number of trips of the K3 model;

[0028] Load K1 : The average passenger flow of a K1 vehicle per trip;

[0029] Load K2 : The average passenger flow of a K2 vehicle per trip;

[0030] Load K3 : The average passenger flow of a K3 vehicle per trip;

[0031]

[0032] T=N×t_block

[0033] t_block: the total time for a vehicle to complete a trip, min;

[0034] t upriver : Upward one-way travel time min during the morning peak period;

[0035] t downriver : Downward one-way travel time min during the morning peak period;

[0036] The dwell time min of the upstream station during the morning peak of a certain period;

[0037] The dwell time min at the downlink station during the morning peak of a certain period;

[0038] t rest : The maximum rest time min of the secondary station during the morning peak period of a certain period;

[0039] N: The time period analyzed is a multiple of t_block.

[0040] Compared with the prior art, the beneficial effects of the present invention are: through optimization solution, the present invention can obtain the optimized departure intervals, optimized costs, optimized transportation capacity and more practical vehicle scheduling timetables for the bus network during morning peak, off-peak and evening peak hours. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is an overall flow chart of a method for rational analysis and optimization of public transportation resource allocation based on linear programming. DETAILED DESCRIPTION

[0042] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0043] The disclosure below provides many different embodiments or examples to implement different structures of the present invention. In order to simplify the disclosure of the present invention, the parts and settings of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the present invention. In addition, the present invention can repeat reference numbers and / or letters in different examples. This repetition is for the purpose of simplification and clarity, and does not itself indicate the relationship between the various embodiments and / or settings discussed.

[0044] The capacity allocation of the public transportation system is related to the efficiency of urban transportation operation. Irrational capacity allocation will cause a mismatch between transportation supply capacity and supply resources, resulting in a waste of resources. Excessive capacity deployment will not only lead to a waste of social resources, but will also generate vicious competition and trigger a series of social problems; insufficient capacity deployment will not only fail to meet the travel needs of the general public, but will also be detrimental to operators improving the quality of transportation services.

[0045] Based on the capacity supply and demand, we can diagnose whether the network resource allocation is reasonable and solve the problem of imbalance between capacity supply and demand. Based on the overall situation of the capacity supply and demand ratio of the entire network, we can use linear programming and other methods to obtain reasonable vehicle configuration information and operation strategies related to the line, and give optimization suggestions.

[0046] See also Figure 1 In an embodiment of the present invention, a method for analyzing and optimizing the rationality of public transportation resource allocation based on linear programming comprises the following steps:

[0047] Step 1: Count the characteristics of public transportation: First, count the full load rates of each type of vehicle during the morning peak, off-peak, and evening peak periods; then, for a route, find the specific types of vehicles operating on the route, and calculate the capacity supply-demand ratio of the route through the full load rates of these types of vehicles; second, obtain more accurate bus characteristics of route operating costs, departure intervals, and capacity-related conditions through real bus operation-related data;

[0048] Step 2: Optimization solution: Based on step 1, use linear programming related methods to make the operating cost as low as possible for a single line while ensuring the transport capacity, obtain reasonable vehicle configuration information and operation strategies related to the line, and give optimization suggestions for public transportation resource allocation;

[0049] Step three, obtain the configuration results: Based on step two, use big data technology to calculate the features mentioned in steps one and two, use Python to write algorithm scripts, and finally obtain the optimized departure intervals, optimized costs, optimized capacity and vehicle scheduling schedules that are more in line with actual conditions during morning rush hour, off-peak hour and evening rush hour.

[0050] The full load rate is also called the "full load coefficient". It reflects the relative value of the degree of passenger load of vehicles running on the line within a certain period of time. It is an indicator to measure the degree of vehicle utilization and an important indicator to reflect the quality and level of urban public transportation services. The full load rate formula is as follows:

[0051]

[0052] The formula for calculating the capacity supply-demand ratio is as follows:

[0053]

[0054] Use the above formula to calculate the line capacity supply and demand ratio within one day, then calculate the average value of the line capacity supply and demand ratio within 30 days, and calculate the capacity supply and demand ratio of other lines in the entire network in the same way. Through the capacity supply and demand ratio of the entire network, use data analysis related methods to analyze the capacity of the entire network and obtain an overall network capacity portrait.

[0055] Below we use the data during the morning rush hour as an example to analyze whether the supply and demand of transportation capacity is balanced. The statistical data is shown in the following table.

[0056] Data table for the K1 vehicle that made 3 trips during the morning rush hour

[0057]

[0058]

[0059] Note:

[0060] The "number of passengers" in the table refers to the number of people on the bus after the bus enters the station, passengers get off and get on, and the bus starts to leave the station.

[0061] A table of bus-related data during the morning rush hour on a certain day

[0062]

[0063]

[0064] The table calculates the capacity supply-demand ratio of Line 1 during the morning peak of one day, and then calculates the average capacity supply-demand ratio of Line 1 during the morning peak of 30 days. The other lines are calculated in the same way. After calculating the entire line network, the data in the following table is obtained.

[0065] The ratio of line capacity supply to demand in the network

[0066]

[0067] Through the data in the above table and using relevant data analysis methods, we can analyze the capacity of the entire network during the morning peak period and obtain an overall capacity portrait of the network.

[0068] Important Notes:

[0069] Some cities provide data on the dimension of "full load rate" in their public transportation data, while some cities may not. If this indicator is not available, some existing algorithms (such as algorithms based on station attractiveness) can be used to estimate the number of people getting off at a certain station in a certain period of time. In general, buses have data on the number of people swiping their cards at stations, and the "full load rate" of the station can be calculated based on the number of people getting off. For details, please refer to Section 4.2.2 of the paper: "A method for estimating passenger flow at bus stations based on IC card data".

[0070] It is worth pointing out that by estimating the number of alightings through an algorithm based on station attractiveness, the characteristics related to bus service capacity used in calculating the demand index of bus stations can be added to the alighting attraction intensity. These characteristics can well characterize the attraction intensity of the station. For details, please refer to the document: "Bus Line Travel Demand Index Algorithm-Scheme-Final Draft-20200408-V1.0.0".

[0071] Data analysis related methods:

[0072] min(x1·COST K1 +x2 COST K2 +x3 COST K3 )

[0073]

[0074] The departure time interval: After investigation, the relevant public transportation departments expect the departure interval to be no more than 8 minutes. This threshold may be different in different places and can be adjusted according to the actual operation situation. left_rate and right_rate are the scaling ratios of the independent variables, which are two hyperparameters. By setting such a scaling ratio, more realistic optimization results can be obtained. They are two hyperparameters. K1, K2, and K3 are vehicle models, x1, x2, and x3 are the number of trips of each model of vehicle, and COST K1 、COST K2 、COST K3 The operating costs for each vehicle model.

[0075] As a further solution of the present invention: actual constraints:

[0076]

[0077] i: number of days;

[0078] K1 乘客人数 : The average passenger flow of a K1 vehicle per trip;

[0079] K1 实际趟数 : The actual number of trips made by the K1 model vehicle;

[0080] K2 乘客人数 : The average passenger flow of a K2 model vehicle per trip;

[0081] K2 实际趟数 : The actual number of trips made by the K2 model vehicle;

[0082] K3 乘客人数 : The average passenger flow of a K3 vehicle per trip;

[0083] K3 实际趟数 : The actual number of trips of the K3 model;

[0084] Load K1 : The average passenger flow of a K1 vehicle per trip;

[0085] Load K2 : The average passenger flow of a K2 vehicle per trip;

[0086] Load K3 : The average passenger flow of a K3 vehicle per trip;

[0087]

[0088] T=N×t_block

[0089] t_block: the total time for a vehicle to complete a trip, min;

[0090] t upriver : Upward one-way travel time min during the morning peak period;

[0091] t downriver : Downward one-way travel time min during the morning peak period;

[0092] The dwell time min of the upstream station during the morning peak of a certain period;

[0093] The dwell time min at the downlink station during the morning peak of a certain period;

[0094] t rest : The maximum rest time min of the secondary station during the morning peak period of a certain period;

[0095] N: The time period analyzed is a multiple of t_block.

[0096] In actual operation, it can be simpler. There is no need for N or t_block. It only needs to give the total time of the time period to be analyzed. For example, the total time during the morning rush hour, from 7:30 to 9:30 in the morning, is 2 hours, that is, T = 120 minutes.

[0097] By using linear programming related methods, we can make the operating cost as low as possible for a single line while ensuring the transport capacity, obtain reasonable vehicle configuration information and operation strategies related to the line, and give optimization suggestions for public transportation resource allocation.

[0098] Assume that Line 1 is equipped with three types of vehicles, denoted as K1, K2, and K3; the number of trips of each type of vehicle during the morning rush hour is x1, x2, and x3 respectively; the departure time interval is t; the cost of the three types of vehicles during the morning rush hour (the cost can be calculated by hour or by day) is COST K1 , COST K2 , COST K3 .

[0099] It should be noted here that in order to make the final results calculated through linear programming more accurate and scientific, the cost calculation for each model of vehicle must be consistent with the actual situation.

[0100] The following tables list the characteristics related to vehicle costs (if the bus-related departments have ready-made cost data, there is no need to calculate it again; otherwise, the cost of the vehicle is calculated according to the characteristics listed in the table and the actual bus operation rules) and some important attributes related to the vehicle, such as the number of people the vehicle can carry.

[0101] Vehicle cost accounting dimension table

[0102] Cost characteristics

[0103] Purchase price Driver salary Ticket seller salary Fuel cost (fuel consumption per 100 kilometers) Fuel consumption unit Fuel consumption unit price Company management fees insurance Vehicle inspection fee Repair costs Depreciation Passenger agency fee Taxes Office Expenses Other expenses

[0104] Cost table of different vehicle models

[0105] Bus Model cost K1 <![CDATA[COST K1 ]]> K2 <![CDATA[COST K2 ]]> K3 <![CDATA[COST K3 ]]>

[0106] Dimension table for exploratory data analysis of bus data

[0107]

[0108]

[0109] Line 1's morning peak period data for one month

[0110]

[0111] Here we only consider the capacity allocation problem during the morning peak, assuming that the morning peak period is from 7 am to 9 am, a total of two hours. After counting the relevant bus data, we get t block =60(min),

[0112] All N here = 2, left rate =0.5 and right rate =1.5.

[0113]

[0114]

[0115] Take line 28 to illustrate the final result after solving by linear programming, as shown in the following table.

[0116] Table of relevant characteristics of a single line after optimization

[0117]

[0118] Table of relevant characteristics of a single line after optimization

[0119]

[0120] Based on the data in the above table, we can do a lot of things. For example, we can give an approximate value of the total cost of bus operation; we can give a scheduling schedule, etc. It should be pointed out here that, for example, for the vehicle model: ZK6125BEVG60E, there may be "vehicle number 21" and "vehicle number 34" under the model. In order to make the scheduling more in line with the actual operation strategy, we can add another column in the above table, "operating vehicles under the model" and count the number of trips they actually scheduled during peak hours, etc., to achieve a more realistic vehicle scheduling schedule.

[0121] The present invention can obtain optimized departure intervals, optimized costs, optimized transport capacity and a more practical vehicle scheduling timetable for the bus network during morning peak, off-peak and evening peak times through optimization solution.

[0122] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0123] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A method for rationality analysis and optimization of public transportation resource allocation based on linear programming, characterized in that: Here are the steps: Step 1: Count the characteristics of public transportation: First, count the full load rates of each type of vehicle during the morning peak, off-peak, and evening peak periods; then, for a route, find the specific types of vehicles operating on the route, and calculate the capacity supply-demand ratio of the route through the full load rates of these types of vehicles; second, obtain the bus characteristics of the route operation cost, departure interval, and capacity-related conditions through real bus operation-related data; Step 2: Optimization solution: Based on step 1, use linear programming related methods to reduce the operating cost of a single line while ensuring the transport capacity, obtain reasonable vehicle configuration information and operation strategies related to the line, and give optimization suggestions for public transportation resource allocation; Step 3, obtain the configuration results: Based on step 2, the features mentioned in step 1 and step 2 are calculated by big data technology, and the algorithm script is written in Python language to finally obtain the optimized departure interval, optimized cost, optimized transportation capacity and vehicle scheduling schedule of the bus network during the morning peak, off-peak and evening peak; The full load rate reflects the relative value of the passenger load of vehicles on the line within a certain period of time. It is an indicator to measure the utilization of vehicles. The full load rate formula is as follows: The formula for calculating the capacity supply-demand ratio is as follows: Use the above formula to calculate the line capacity supply-demand ratio within one day, then calculate the average of the line capacity supply-demand ratio within 30 days, and calculate the capacity supply-demand ratio of other lines in the entire network. Through the capacity supply-demand ratio of the entire network, use data analysis related methods to analyze the capacity of the entire network and obtain an overall line network capacity portrait; Data analysis related methods: min(x1·COST K1 +x2·COST K2 +x3·COST K3 ) The departure time interval: The unit is min, left_rate and right_rate are the ratios of independent variable scaling, which are two hyperparameters, K1, K2, K3 are vehicle models, x1, x2, x3 are the number of trips of each model of vehicle, COST K1 、COST K2 、COST K3 The operating costs for each vehicle model; The actual constraints are: min(x1·COST K1 +x2·COST K2 +x3·COST K3 ) i: number of days; K1 乘客人数 : The average passenger flow of a K1 model vehicle per trip; K1 实际趟数 : The actual number of trips made by the K1 model vehicle; K2 乘客人数 : The average passenger flow of a K2 model vehicle per trip; K2 实际趟数 : The actual number of trips made by the K2 model vehicle; K3 乘客人数 : The average passenger flow of a K3 vehicle per trip; K3 实际趟数 : The actual number of trips of the K3 model; Load capacity K1: the average passenger flow of a K1 vehicle per trip; Load capacity K2: the average passenger flow of a K2 vehicle per trip; Load capacity K3: The average passenger flow of a K3 vehicle per trip; T=N×t_block t_block: the total time for a vehicle to complete a trip, min; t upriver : Upward one-way travel time min during the morning peak period; t downriver : Downward one-way travel time min during the morning peak period; The dwell time min of the upstream station during the morning peak of a certain period; The dwell time min at the downlink station during the morning peak of a certain period; t rest : The maximum rest time min of the secondary station during the morning peak period of a certain period; N: The time period analyzed is a multiple of t_block.

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