CN108806249B - Passenger trip optimization method based on bus APP software - Google Patents
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Abstract
The invention discloses a passenger travel optimization method based on bus APP software, which comprises the steps of determining selection acceptance levels of arrival time T, passenger carrying rate M in a bus, degree of congestion H outside the bus and travel time G at a destination for passengers of different sexes S, ages L and occupations W according to a structural equation model through data investigation, measuring and calculating selection behaviors of the passengers by adopting the structural equation model capable of analyzing and processing a plurality of dependent variables simultaneously, recording travel tracks of the passengers by APP, analyzing habit characteristics of the passengers selecting bus shifts, and giving the best selection for a certain trip according to the habit characteristics of the passengers. The invention provides more humanized travel experience, passengers can only receive real-time running information provided by software to independently select a route, the software can help to make a decision according to personal preference of the passengers, and meanwhile, the passengers can operate the system in a one-key mode, and the software has more behavior habits of the passengers to make the optimal selection.
Description
Technical Field
The invention relates to the technical field of public transportation, in particular to a passenger travel optimization method based on public transportation APP software.
Background
Public transit trip is the indispensable partly in city resident's life, and along with the emergence of cell-phone public transit APP, the informationization level of resident public transit trip has had great promotion. Although the waiting time of the information provided by the bus APP cannot be reduced, psychologically, the information provided by the bus APP is beneficial to reducing the psychological feeling of a traveler in waiting, and meanwhile, the traveler can change a travel scheme in time.
The existing mobile phone bus APP has multiple types, but has single function and less query information amount, and is difficult to meet the requirements of residents on diversified buses and the like. The current mobile phone public transport APP mainly has the following defects:
1. the existing APP can only provide time from a certain line to a certain bus stop, and real-time comparative analysis of time from arrival of a plurality of bus lines is lacked, for example, 4 bus lines are provided from a point A to a point B for residents, and if the residents only see the arrival time of 1 line, the line which arrives at the first and is not the first line may occur, and the significance of line monitoring is lost; the bus station has long stations at part of bus stations, a plurality of passenger getting-on points are arranged in the stations, some bus stations are even divided into A, B two stations, and when the lines of getting-on in advance are not monitored lines (the passenger getting-on points are different), passengers can fly to the bus, so that the waiting time of the bus is influenced, and potential safety hazards are caused;
2. due to the influence of traffic jam, intersection queuing and the like, a phenomenon of 'train crossing' can occur on part of bus lines, namely, a previous vehicle and a next vehicle on the same bus line are very close to each other, the existing APP can already find that the two vehicles are very close to each other, but cannot inquire the passenger carrying rates of the two vehicles, possibly causing the phenomenon that passengers in the previous vehicle explode and crowd and passengers in the next vehicle are very empty, and also possibly causing the phenomenon that the passengers wait for the next vehicle for avoiding crowding for a lot of time and find that the vehicles are still very crowded;
3. when a plurality of bus routes arrive from the point A to the point B, when the driving routes of the bus routes are different, road facilities (lane scales, whether buses exist or not) and road congestion conditions of roads are possibly different, the time spent on reaching the destination is different, and passengers cannot comprehensively consider the waiting time and the driving time to determine which bus can reach the destination most quickly;
4. at present, public transport companies mainly adjust the bus lines by counting the passenger flow density through IC card data, but with the popularization of smart phone application, the counting of the passenger flow data on the buses through mobile phone data is more accurate and convenient than that of the IC card. The existing bus APP does not have the function of counting passenger flow data, and a bus company cannot adjust bus lines, implement customized buses, reserve buses and the like according to mobile phone data, so that the bus lines are more reasonable, and passengers take the buses more conveniently.
5. The existing bus APP cannot record the travel characteristics of passengers (for example, whether a certain passenger has high requirement on travel time or high requirement on travel comfort level in different time periods), common bus stops of the passengers and bus routes frequently selected by the passengers, so that the passenger can select the bus routes in a humanized manner.
Therefore, there is a need for an improvement to existing bus APP software to overcome the shortcomings of the prior art.
Disclosure of Invention
In view of the defects in the prior art, the invention provides a passenger travel optimization method based on bus APP software, and one of the realized purposes is to provide more humanized travel experience, so that passengers can only receive real-time running information provided by the software to autonomously select a route, and can also make the software help to make a decision according to own personal preference, and meanwhile, the software can be operated in a one-key mode, and has own behavior habit to make a decision on the optimal selection.
In order to achieve the purpose, the invention discloses a passenger trip optimization method based on bus APP software; the system comprises an 'autonomous selection' mode, an 'assistant decision' mode and a 'private subscription' mode; the steps of executing the "private subscription" mode are as follows:
a. through questionnaire survey and APP record of different passenger travel characteristics, the acceptance degree of the sex S, the age L, the occupation W of each passenger on the arrival time T, the passenger carrying rate M in the vehicle, the degree of congestion H outside the vehicle and the travel time G of the destination can be obtained;
b. according to the record of the travel characteristics of each passenger, adopting a structural equation model to measure and calculate the selection behavior of the passenger, wherein the structural equation model is as follows:
ΔΓy+Ωx+σ (1)；
in the formula (1), y represents a vector formed by endogenous variables of different passengers and is represented as a matrix [ Si, Li, Wi ]; x represents a vector composed of exogenous variables of different passengers, represented as a matrix [ Ti, Mi, Hi, Gi ]; Γ represents a random connection matrix between endogenous variables; Ω represents a direct random effect matrix of exogenous variables to endogenous variables; σ represents a residual term of the structural equation;
c. generating a covariance matrix S according to the questionnaire survey data and the model measurement data;
the data measured and calculated by the model is data of y obtained by inputting x by the structural equation model, and the data of x and y in the questionnaire survey data are obtained by surveying;
d. finding out a theoretical covariance matrix sigma (theta) implied when the structural equation model is established by a mathematical method according to the covariance matrix S and the structural equation model, wherein the theoretical covariance matrix sigma (theta) conforms to the structural equation model and the covariance matrix S simultaneously; the difference between the theoretical covariance matrix sigma (theta) and the covariance matrix S recorded by all the different passenger trip characteristics is smaller, and the data can be more consistent by the representation model;
the fact that the sigma (theta) is simultaneously in line with the structural equation model and the covariance matrix S means that the covariance matrix S and the structural equation model can both participate in calculation;
the difference between the theoretical covariance matrix sigma (theta) and the covariance matrix S is determined by comparing numerical values in the two matrixes, wherein the difference is smaller when the numerical values are closer; meanwhile, the difference of the results calculated by the matrix is smaller;
e. covariance matrix sigma of vector x composed of exogenous variables of different passengersxx(θ) — Φ, and the covariance matrix of the residual vector is Ψ, then:
f. determining the selection acceptance levels of the passengers with different sexes S, ages L and occupations W for the arrival time T, the passenger carrying rate M in the vehicle, the congestion degree H outside the vehicle and the travel time G of the destination according to the structural equation model;
g. the method comprises the steps that APP software records travel tracks of passengers, habit characteristics of the passengers in selecting bus shifts are analyzed, the best selection of a certain travel is given according to the habit characteristics of the passengers, namely according to a structural equation model, a vector y formed by endogenous variables of different passengers is a determined value, in a history record of multiple travel, a random connection matrix gamma among the endogenous variables of the passengers, a direct random effect matrix omega of exogenous variables to the endogenous variables and a residual error term sigma of the structural equation model are analyzed, and finally, a value delta of the structural equation model and a vector x formed by the exogenous variables are obtained.
Preferably, in the step a, the gender S of each of the passengers is classified into male and female 2; the age L is distributed in 6-76 years, 5 years is taken as one stage, and the stage is divided into 14 stages; the occupation W is divided into 9 types including office group and public institution staff, enterprise administrative office business staff, individual operators, business service staff, agriculture, forestry, animal husbandry and fishery workers, production workers, students, no industry and the like.
More preferably, in the "autonomous selection" mode, the APP software only provides operation information of the bus with the arrival time within 20 minutes, and the operation information includes position information, passenger carrying information and/or congestion information.
More preferably, in the "aid decision" mode, the APP software selects, for the passenger, a bus route satisfying that the arrival time is within 20 minutes according to the passenger's needs, including three options of shortest arrival time, looser in-vehicle and smooth road operation.
More preferably, the shortest arrival time means that the APP software sorts all the buses of all the bus lines from short to long according to the arrival time, and the passenger carrying rate and the road running condition are used as reference factors, and the buses with high passenger carrying rate and high road congestion degree are excluded.
More preferably, the fact that the bus is relatively loose means that the APP software sorts all buses of all bus routes from loose to crowded according to the passenger carrying rate in the bus, the arrival time and the road running condition are used as reference factors, and the buses with long arrival time and high road crowding degree are excluded.
More preferably, the smooth road operation means that the APP software sorts all buses of all bus lines from smooth to congested according to the road operation state, the arrival time and the passenger carrying rate in the bus are used as reference factors, and the buses with long arrival time and high passenger carrying rate on the bus are excluded.
Preferably, when the passenger uses the APP software for the first time, a registration step is further performed, wherein the registration step includes collecting age information, gender information and occupation information of the passenger.
The principle of the invention is as follows:
1. objective condition analysis
1) Arrival time T: inquiring all bus routes from the point A to the point B, inquiring the arrival time of the latest bus of each route in a list, wherein the time range is within 20 minutes, the arrival times are respectively assumed to be T1 and T2 … Tn, the routes are sorted from small to large according to the arrival time, and the list is updated in real time according to GPS real-time positioning, so that the APP monitoring precision is improved, and the route with the shortest arrival time is always arranged in the forefront row;
2) the in-vehicle passenger carrying rate M: monitoring the passenger carrying rate of the bus in real time through the IC card data and the video data of the passengers getting on and off the bus, and assuming that the passenger carrying rate of the nth bus at a passenger waiting station is Mn;
3) congestion status H of travel route: calculating the length of a congested section of a road section passing through a point A and a point B of a bus taken by the bus in the past by combining real-time data of road traffic, and predicting the length of the congested section of the nth bus from the point A to the point B as Hn by taking the traffic data of the past month as evidence;
4) travel time to destination G: predicting the driving time Gn of the nth bus from the point A to the point B by taking the traffic data of the previous month as evidence in combination with real-time data of road traffic and road facility conditions (such as lane width and whether a bus lane exists) of a road section where the nth bus passes;
5) the software mainly helps passengers to select the bus at the stop, so the software does not consider the transfer phenomenon of the bus line, and if the bus has the transfer behavior, the passengers need to separately input lines before and after the transfer into the starting and ending points.
2. Subjective condition analysis
1) The age L of the passenger;
2) the gender S of the passenger;
3) occupation W of the passenger;
3. impact factor analysis of decisions
Taking a certain station A as an example, among all buses from the station A to the station B, n buses which are about to arrive at the station within 20 minutes are provided for passengers, the arrival time of the ith bus is Ti, the passenger carrying rate in the bus is Mi, the congestion condition of a driving path is Hi, and the driving time to the station B is Gi;
the arrival time is Ti, the passenger carrying rate in the vehicle is Mi, the congestion condition of the driving route is Hi, and the driving time to the point B is Gi, which are influence factors determined by the age Li, the sex Si, and the occupation Wi of the passenger i. Different passengers have different requirements and weight values, and generally, young white-collar workers pay more attention to time and have higher weight values of corresponding arrival events and driving time; the senior non-professional attaches more importance to the comfort level, and the corresponding passenger carrying rate in the vehicle and the corresponding road section crowding degree weight value are higher.
The invention has the beneficial effects that:
1. the method gives more comprehensive waiting information to passengers, when a plurality of bus lines are arranged from a stop to a destination, software automatically sequences arrival time of the plurality of lines, the passengers do not need to wait for the only line all the time, and particularly for office workers of 'cell phone parties', the background ring (vibration) reminding of the method can inform the passengers of the most convenient getting-on instruction;
2. the phenomenon of 'train crossing' is a difficult problem which is difficult to solve by a public transport company all the time, the invention can relieve the troubles of waiting for trains caused by 'train crossing' from the perspective of passenger flow, namely, the passenger flow of the front and rear buses is balanced, even if 'train crossing' is realized, the passenger carrying capacity of the buses can be balanced, and the service level of the buses can still be ensured to a certain extent.
3. APP increases service options of 'travel time' and 'road section congestion' after passengers get on the bus, and for the passengers, the APP not only aims at getting on the bus early, but also can reach the destination early. The 'driving time' and 'road section congestion' can enable passengers to comprehensively consider taking buses, particularly, the running time and riding comfort after getting on the bus can be taken as consideration factors, and the selection is more comprehensive.
4. For public transport companies and transportation departments, the traffic data provided by the software can comprehensively and visually display the operation condition of the public transport lines in the whole city, so that the operation scheduling of the public transport lines is more conveniently organized, the coverage rate and the bus line restoration rate of the existing buses in certain areas can be reflected, and the software has a certain guiding effect on the adjustment of the public transport lines and the addition of large and medium-volume buses.
5. For passengers, the software can provide more humanized travel experience, the passengers can only receive real-time running information provided by the software to autonomously select a route, the software can help make a decision according to personal preference of the passengers, and meanwhile, the software can be operated in a one-key mode, and the software has more behavior habits of the passengers to make the optimal selection.
The conception, the specific structure and the technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, the features and the effects of the present invention.
Drawings
FIG. 1 shows a flow chart of the implementation of an embodiment of the present invention.
Detailed Description
Example 1
According to the execution steps shown in fig. 1, the passenger trip optimization method based on the bus APP software provides services according to the requirements of users.
A passenger in a city needs to go from a station a to a station B at 8 am (no transfer phenomenon), and there are 4 bus routes selected, namely, the route 69, the route 573, the route 669 and the route 732.
The first mode is as follows:
the passenger uses the APP software to input the station A and the station B, selects the 'autonomous selection' mode, and can see four bus lines including a line 69, a line 573, a line 669 and a line 732;
click on line 69:
| license plate number | Arrival time (min) | Load factor (%) | Congestion information (%) |
| Shanghai B11111 | 6 | 80 | 56 |
| Shanghai B11112 | 9 | 35 | 57 |
| Shanghai B11113 | 15 | 86 | 59 |
Click line 573:
| license plate number | Arrival time (min) | Load factor (%) | Congestion information (%) |
| Shanghai B22221 | 3 | 96 | 72 |
| Shanghai B22222 | 13 | 58 | 76 |
| Shanghai B22223 | 18 | 72 | 59 |
Click on line 669:
| license plate number | Arrival time (min) | Load factor (%) | Congestion information (%) |
| Shanghai B33331 | 12 | 52 | 75 |
| Shanghai B33332 | 16 | 33 | 76 |
Click on line 732:
| license plate number | Arrival time (min) | Load factor (%) | Congestion information (%) |
| Shanghai B44441 | 5 | 58 | 58 |
| Shanghai B44442 | 11 | 76 | 63 |
The passenger can select the corresponding bus route according to the self requirement.
And a second mode:
the passenger uses this APP software, inputs website A and website B, selects "aid decision" mode, can look over "arrival time is the shortest", "comparatively loose in the car", "the road operation is unobstructed" three options, clicks "arrival time is the shortest" option, and the APP is according to the arrival time from short to long sequencing to along with the timely adjustment of time lapse information, the passenger can select the bus according to the shortest arrival time. As in the following table:
clicking the option of 'loose in-vehicle', sequencing the APP from low to high according to the passenger carrying rate, and timely adjusting the information along with the time lapse, so that the passenger can select the bus according to the minimum passenger carrying rate. As in the following table:
and clicking the option of smooth road operation, sequencing the APP from low to high according to congestion information, and timely adjusting the information along with the time lapse, so that the passengers can select the bus according to the minimum congestion proportion. As in the following table:
and a third mode:
suppose the passenger is a male, 45 years old, professional, individual operator. Through all going out in this city survey and APP of the record of different passenger's trip characteristics carry out the analysis to the record of different passenger's trip characteristics, the public transit trip weighted value of this class of passenger in this city is as follows: the weight of the arrival time is 51%, the weight of the passenger carrying rate in the vehicle is 27%, the weight of the congestion degree outside the vehicle is 5%, and the travel time of the destination is 17%. The passenger selects a private customization mode, and the APP performs comprehensive sequencing according to arrival time, the passenger carrying rate in the vehicle, the degree of congestion outside the vehicle and the traveling time of the destination, and the information is adjusted in time along with the time. As in the following table:
example 2
Passengers waiting at station A, inputting bus route names (such as 573), APP displaying arrival time of a certain bus (such as Shanghai B23147) in minutes; APP displays the passenger carrying capacity of the vehicle, and the proportion of the passenger carrying capacity of the vehicle to the passenger deciding capacity of the vehicle is represented; and APP displaying the congestion of the bus running route, wherein the congestion length is represented by the proportion of the total route length. The passenger makes the selection of taking a car by himself according to the information that APP provided.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (8)
1. The passenger trip optimization method based on the bus APP software is characterized by comprising an 'autonomous selection' mode, an 'auxiliary decision' mode and a 'private customization' mode; the steps of executing the "private subscription" mode are as follows:
a. through questionnaire survey and APP record of different passenger travel characteristics, the acceptance degree of the sex S, the age L, the occupation W of each passenger on the arrival time T, the passenger carrying rate M in the vehicle, the degree of congestion H outside the vehicle and the travel time G of the destination can be obtained;
b. according to the record of the travel characteristics of each passenger, adopting a structural equation model to measure and calculate the selection behavior of the passenger, wherein the structural equation model is as follows:
in equation (1), y represents a vector composed of endogenous variables of different passengers, and is expressed as a matrix [ Si, Li, Wi [ ]](ii) a x represents a vector of exogenous variables of different passengers, represented as a matrix [ Ti, Mi, Hi, Gi](ii) a Γ represents a random connection matrix between endogenous variables; Ω represents a direct random effect matrix of exogenous variables to endogenous variables;
a residual term representing a structural equation;
c. generating a covariance matrix S according to the questionnaire survey data and the model measurement data;
the data measured and calculated by the model is data of y obtained by inputting x by the structural equation model;
d. finding out a theoretical covariance matrix sigma (theta) implied when the structural equation model is established by a mathematical method according to the covariance matrix S and the structural equation model, wherein the theoretical covariance matrix sigma (theta) conforms to the structural equation model and the covariance matrix S simultaneously; the difference between the theoretical covariance matrix sigma (theta) and the covariance matrix S recorded by all the different passenger trip characteristics is smaller, and the data can be more consistent by the representation model;
the fact that the sigma (theta) is simultaneously in line with the structural equation model and the covariance matrix S means that the covariance matrix S and the structural equation model can both participate in calculation;
the difference between the theoretical covariance matrix sigma (theta) and the covariance matrix S is determined by comparing numerical values in the two matrixes, wherein the difference is smaller when the numerical values are closer; meanwhile, the difference of the results calculated by the matrix is smaller;
e. covariance matrix sigma of vector x composed according to exogenous variables of different passengersxx(θ), then derive:
f. determining the selection acceptance levels of the passengers with different sexes S, ages L and occupations W for the arrival time T, the passenger carrying rate M in the vehicle, the congestion degree H outside the vehicle and the travel time G of the destination according to the structural equation model;
g. the method comprises the steps that APP software records travel tracks of passengers, habit characteristics of the passengers in selecting bus shifts are analyzed, the best selection of a certain travel is given according to the habit characteristics of the passengers, namely according to a structural equation model, a vector y formed by endogenous variables of different passengers is a determined value, and in a history record of multiple trips, a random connection matrix gamma among the endogenous variables of the passengers, a direct random effect matrix omega of the exogenous variables to the endogenous variables and a residual error item of the structural equation model are analyzed
And finally, obtaining a vector x consisting of the value delta of the structural equation model and the exogenous variable.
2. The passenger travel optimization method based on bus APP software according to claim 1, wherein in the step a, the gender S of each passenger is classified into male and female type 2; the age L is distributed in 6-76 years, 5 years is taken as one stage, and the stage is divided into 14 stages; the occupation W is divided into 9 types including office group and public institution staff, enterprise administrative office business staff, individual operators, business service staff, agriculture, forestry, animal husbandry and fishery workers, production workers, students, no industry and the like.
3. The bus APP software-based passenger travel optimization method according to claim 1 or 2, characterized in that in the autonomous selection mode, the APP software only provides operation information of buses with arrival time within 20 minutes, and the operation information includes position information, passenger carrying information and/or congestion information.
4. The bus APP software-based passenger travel optimization method according to claim 1 or 2, characterized in that in the ' decision-making assistance ' mode, the APP software selects a bus route satisfying the arrival time within 20 minutes for a passenger according to the passenger's demand, and the three options include the shortest arrival time, looser inside the bus and smooth road operation.
5. The bus APP software-based passenger travel optimization method according to claim 4, wherein the shortest arrival time means that the APP software sorts all buses of all bus routes from short to long according to arrival time, and the passenger carrying rate and the road running condition are used as reference factors, and the buses with high passenger carrying rate and high road congestion degree are excluded.
6. The bus APP software-based passenger travel optimization method according to claim 4, wherein the relatively loose inside bus means that the APP software sorts all buses of all bus routes from loose to crowded according to passenger carrying rates inside buses, arrival time and road running conditions are used as reference factors, and the buses with long arrival time and high road congestion degree are excluded.
7. The bus APP software-based passenger travel optimization method according to claim 4, wherein the smooth road operation means that the APP software sorts all buses of all bus lines from smooth to congested according to road operation states, arrival time and in-vehicle passenger carrying rate are used as reference factors, and buses with overlong arrival time and overhigh passenger carrying rate on the bus are excluded.
8. The bus APP software-based passenger travel optimization method according to claim 1, wherein a passenger needs to perform a registration step when using the APP software for the first time, and the registration step includes collecting age information, gender information and occupation information of the passenger.
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| CN113870559B (en) * | 2021-09-27 | 2022-08-16 | 北京理工新源信息科技有限公司 | Traffic flow calculation method based on big data Internet of vehicles |
| CN114973727B (en) * | 2022-08-02 | 2022-09-30 | 成都工业职业技术学院 | Intelligent driving method based on passenger characteristics |
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