CN113393104A - Method for evaluating influence of rail transit running state on peripheral public bicycles - Google Patents
Method for evaluating influence of rail transit running state on peripheral public bicycles Download PDFInfo
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Abstract
The invention discloses a method for evaluating the influence of a rail transit running state on peripheral public bicycles, which comprises the following steps: determining rail transit and public bicycles with piles as research objects and acquiring data; respectively screening a plurality of covariates to generate a data set; constructing to obtain a random effect model; influence evaluation was performed using a random effect model: and selecting a public bicycle station data set with piles within a set range from the rail transit operation station, taking the selected data set as input according to whether the station is closed in the early peak or not, outputting an estimation result by using a random effect model, and analyzing to obtain the influence of the rail transit operation state on the use amount of the public bicycles with piles at the periphery. The method can accurately evaluate the influence of the rail transit operation state on the bicycles at the peripheral public bicycle rental stations, and provides a scientific and effective method for relevant city planning, matching of a slow-moving traffic system and original traffic facilities and laying of the public bicycle rental stations.
Description
Technical Field
The invention relates to a method for evaluating influence of a rail transit running state on peripheral public bicycles, and belongs to the technical field of public bicycles.
Background
In recent years, with the rapid development of social economy, the urbanization process is accelerated continuously, the speed of the motorized development of the traffic in China is very fast, and the traffic jam and the environmental pollution are increasingly serious. Meanwhile, along with diversified traveling demands of residents, the conventional public transport cannot meet the increasing traveling demands. At the moment, the rail transit effectively overcomes the defects caused by the travel of motor vehicles by using the advantages of land saving, large transportation capacity, less and stable operation time, safety and environmental protection, and promotes the healthy and stable development of urban traffic. Due to the limited accessibility of rail traffic, it is necessary to combine with other approaches to effectively solve the "last mile" problem. The public bicycle suitable for short-distance travel can be complementary with rail transit, and is one of main connection modes of a destination and a subway station.
Although there has been a lot of research in rail transit and public bike, the relationship between the rail transit and public bike in terms of substitutability and complementarity is not fully researched, so that the influence of the rail transit running state on the self-consumption of the peripheral public bike rental stations cannot be effectively evaluated.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides an evaluation method of the influence of the rail transit running state on peripheral public bicycles, researches the relationship between the rail transit running state and the post bicycle leasing, can accurately evaluate the influence of the rail transit running state on the self-service consumption of the peripheral post public bicycle leasing stations, and provides a scientific and effective method for the future planning of relevant cities, the matching of a slow-moving traffic system and original traffic facilities and the arrangement of the post public bicycle stations.
The invention specifically adopts the following technical scheme to solve the technical problems:
a method for evaluating the influence of a rail transit running state on peripheral public bicycles comprises the following steps:
step (1), determining a research object and acquiring data: extracting the position distribution, the operation time, the type of the rail transit running fault state, the starting/ending time of the running state fault and the reason of the running state fault for rail transit and a public bicycle with piles; and extracting data of distribution of the public bicycle stations with piles, bicycle leasing amount, bicycle leasing numbers, starting/ending stations of each trip, starting/ending time and average trip time;
step (2), extracting covariates to generate a data set: respectively screening a plurality of covariates from four aspects of facility environment factors, social economy, alternative traffic modes and weather factors; generating a covariate xitAnd CiAnd a processing index DitAverage out-going quantity and average out-going time yitThe data set of (a);
selecting to obtain a random effect model;
and (4) carrying out influence evaluation by using the selected random effect model: and (3) selecting a data set of public bicycle stations with piles within a set range from the data set generated in the step (2), taking the selected data set as the input of a random effect model according to whether the station is closed when the early peak occurs or not, outputting an estimation result by using the random effect model, and analyzing the output estimation result to obtain the influence of the running state of the rail transit on the use amount of the public bicycles with piles at the periphery.
Further, as a preferred technical solution of the present invention, the covariates screened in step 2 include residential building area ratio, distance from the city center, population density, number of peripheral rail transit stations, number of peripheral bus stations, and weather.
Further, as a preferred technical solution of the present invention, the random effect model constructed in step 3 is represented as:
yit＝β0+Ditβ1+xitβ2+β3Ci+δi+εit
wherein, yitIs the number of trips of the public bike with the pile or the average trip time of the station i in the time period t,
xitand CiIs a covariate, where xitIs a variable which varies with time, CiIs a variable that does not change over time; beta is a0、β1、β2、β3Are respectively the coefficient, δiIs an individual error term, DitIs a processing index, εitIs the inter-group error term.
Further, as a preferred technical solution of the present invention, the analyzing of the random effect model in step 4 to obtain the influence of the rail transit running state on the usage amount of the public bicycles with piles at the periphery includes: the influence on the average running amount and the average single-time running time of the public bicycle rental spots within the influence range when the running state of the rail transit is changed.
By adopting the technical scheme, the invention can produce the following technical effects:
the method for evaluating the influence of the rail transit running state on the peripheral public bikes comprises the steps of grabbing a subway line service state, the metro peripheral bicycle rental data and related covariates, matching the rail transit running state with the bicycle rental data by using time items, analyzing the data by using a random effect model in a parallel data model, outputting an estimation result by using the random effect model, analyzing the influence of the rail transit running state on the station trip characteristics of the public bikes with piles from a microscopic angle according to the output estimation result, and effectively analyzing the influence of the rail transit running state on the average trip amount and the average single trip time of the public bicycle rental points in an influence range when the rail transit running state changes. A scientific and effective method is provided for the matching of a related city planning and slow traffic system with the original traffic facilities and the arrangement of public bicycle stations with piles in the future.
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FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
The following describes embodiments of the present invention with reference to the drawings.
As shown in fig. 1, the invention relates to a method for evaluating the influence of a rail transit running state on peripheral public bicycles, which specifically comprises the following steps:
step (1), determining a research object and acquiring data: extracting position distribution of rail transit lines, operation time, types of rail transit operation fault states, start time Ts and end time Te of operation state faults and reasons aiming at rail transit and public bicycles with piles; and extracting data of distribution of the piled public bicycle stations, bicycle rental amount, bicycle rental numbers, start station BS and end station ES of each trip, start time TS and end time TE, and average trip time. The types of the rail transit operation fault states comprise outage and delay, and the operation fault state variables are non-occurrence and non-occurrence.
Step (2), extracting covariates: considering four aspects of facility environment factors, social economy, alternative traffic modes and weather factors, respectively screening a plurality of covariates, specifically:
1) residential building area ratio rhoGround: the building environmental characteristics can influence the use of public bicycle with stake, and the area of peripheral resident's residential building closely correlates with the demand of commuting trip, uses formula (1) to calculate:
where ρ isGroundThe area of the residential building is the percentage, A is the residential building area, and M is the total area of the affiliated area.
2) Distance L from city centerab: the bicycle renting amount is related to the crowding degree, the demand is smaller as the bicycle is farther away from the city center, and therefore the linear distance of the rail transit station from the city center also needs to be taken into consideration, and the bicycle renting amount is calculated by the following formula (2):
Lab＝R*arccos[cosβ1cosβ2cos(α1-α2)+sinβ1sinβ2] (2)
wherein L isabThe distance between two straight lines (alpha 1, beta 1) and (alpha 2, beta 2) are longitude and latitude coordinates of two points a and b respectively, and R is the radius of the earth.
3) Population density ρHuman being: calculating population density rho by considering two factors of general population and employment populationHuman beingThe calculation formula is as follows:
where ρ isHuman beingThe population density is N is the total population, and M is the area of the region.
4) The number of peripheral rail transit stations T;
5) number of peripheral bus stations B: since there are a plurality of alternative transportation modes near one station, the influence of rail transit operation faults can be reduced, and therefore the rail transit operation faults need to be brought into a model. Acquiring data within a range of 400 meters from a rail transit station according to previous research experience;
6) weather W: weather conditions have a significant impact on the use of a public bicycle with piles, and therefore whether it rains, i.e. if it is 1, then it is 0, is also considered in the model.
Then generating a covariate xitAnd a processing index DitAverage out-going quantity and average out-going time yitThe data set of (2). Wherein the average travel time y in the data setitThe method is divided into four types: starting point trip amount, end point arrival amount, starting point trip time and end point arrival time; the four data sets correspond to the final analysis, respectively: the result output by the starting point trip amount data set obtains the influence on the starting point trip amount, the result output by the end point trip amount data set obtains the influence on the end point trip amount, the result output by the starting point trip time data set obtains the influence on the starting point trip time, and the result output by the end point trip time data set obtains the influence on the end point trip time.
And (3) selecting to obtain a random effect model, which specifically comprises the following steps:
analyzing the data set by using a parallel data model, and selecting the model: parallel data refers to sample data formed by taking a plurality of sections on a time series and simultaneously selecting sample observation values on the sections. The parallel data model includes two dimensions of time and section, i (i ═ 1, …, N) represents the section (individual), T (T ═ 1, …, T) represents time, and the following linear model is set:
yit＝αi+xitβ+εit (4)
wherein y isitIs a dependent variable, xitBeing vectors of independent variables, epsilonitFor the model error term, β is the parameter to be estimated, representing yitOf a marginal influence ofiRepresenting individual effects. The parallel data model is divided into a static parallel data model and a dynamic parallel data model. The daily trip volume of the public bicycle with piles researched by the invention is independent and is not influenced by the trip of the previous day, so that only a static parallel data model is considered.
Models built using static parallel data are generally of three types: a mixed regression model, a fixed effect model, and a random effect model. The method selects a relevant random effect model because the model can estimate the effect of time-varying and time-invariant variables on the dependent variable. The invention selects a linear expression form of a relevant random effect model as follows:
yit＝β0+Ditβ1+xitβ2+β3Ci+δi+εit (5)
wherein, yitIs the number of trips of the public bicycle with piles or the average trip time of the station i in the time period t, xitAnd CiIs a covariate, where xitIs a time-varying variable including site closure and weather;iis a time invariant variable including distance to downtown, residence area ratio, population density and site count; beta is a0、β1、β2、β3Is a coefficient, δiIs an individual error term, DitIs a processing index, εitIs the inter-group error term. When the traveling quantity of the public bicycle with the piles is within the influence range of rail traffic, Dit1 is ═ 1; otherwise Dit＝0。
Step (4) utilizing the selected random effectInfluence evaluation should be performed on the model: selecting a data set of public bicycle stations with piles within a set range from the data set generated in the step (2), taking the selected data set as the input of a random effect model according to whether the station is closed when the early peak occurs or not, and utilizing an estimation result output by the random effect model, namely beta1And analyzing the value to obtain the influence of the running state of the rail transit on the usage amount of the public bicycles with piles at the periphery.
According to the method, 400 meters is used as a key walking distance in public transport network and service planning, therefore, a public bicycle station with piles within a range of 400 meters from a rail transit operation station is selected, a data set of the selected public bicycle station with piles within the range of 400 meters is used as an input of a random effect model according to whether early peaks occur or not, and how the variables along with time and the variables not along with time influence the traveling of the public bicycle with piles is analyzed, wherein the variables along with time comprise experimental variables (the occurrence of faults is 1 and the occurrence of normal is 0) and weather change factors (the raining is 1 and the raining is not 0), and the variables not along with time comprise the residential building area, the distance from the city center, the population density and the number of peripheral public transport stations. In order to estimate the inter-individual and internal effects, the steps of applying the stochastic effect model can be divided into three steps: first step to generate xiSecond step of creating a deviation score, third step of applying Stata to the output beta1The values are evaluated for effectiveness.
The method is used for analyzing the travel characteristics of the public bicycles with piles around the rail transit and the influence of the public bicycles on the public bicycle leasing points in the influence range when the rail transit state changes based on the statistical analysis and the random effect model of the public bicycles, and the estimation result beta output by the random effect model1The positive and negative values of the values determine the change, and the effects that can be analyzed can include: the influence on the average running amount and the average single-time running time of the public bicycle rental spots within the influence range when the running state of the rail transit is changed.
Therefore, the method can analyze the influence of the rail transit running state on the traveling characteristics of the public bicycle stations with piles from a microscopic angle, and provides a scientific and effective method for relevant urban planning, matching of a slow-moving traffic system and original transportation facilities and laying of public bicycle stations with piles in the future.
In order to verify that the method of the invention can effectively analyze the influence of the rail transit running state on the travel characteristics of the public bicycle station with piles, a verification example is listed for description.
To achieve the above object, the present embodiment provides a method for evaluating the influence of a rail transit operating state on a public bicycle with piles around, including the following steps:
step (1), determining a research object and acquiring data: operating data of one month of rail transit is extracted by using Python in half an hour, and comprises position distribution, operating time, a category Ab of rail transit fault, a starting time Ts and an ending time Te and reasons. The data of the distribution of the piled bicycle stations, the bicycle renting amount, the serial number, the starting station BS, the ending station ES, the starting time TS and the ending time TE of each trip are acquired by an official website. The data obtained are shown in tables 1 and 2 below:
table 1: public bicycle rental data
| Transaction number | Length of trip | Bicycle number | End time | Terminating site | Starting time | Start ofSite |
| No.1 | T1 | B1 | TE1 | ES1 | TS1 | BS1 |
| No.2 | T2 | B2 | TE2 | ES2 | TS2 | BS2 |
| No.3 | T3 | B3 | TE3 | ES3 | TS3 | BS3 |
| No.4 | T4 | B4 | TE4 | ES4 | TS4 | BS4 |
| No.5 | T5 | B5 | TE5 | ES5 | TS5 | BS5 |
| No.6 | T6 | B6 | TE6 | ES6 | TS6 | BS6 |
| No.7 | T7 | B7 | TE7 | ES7 | TS7 | BS7 |
| … | … | … | … | … | … | … |
Table 2: rail transit operation state data
| Rail transit route | Operational status | State description | Failed site and cause | Starting time | End time |
| R1 | O1 | S1 | Ab1 | Ts1 | Te1 |
| R2 | O2 | S2 | Ab2 | Ts2 | Te2 |
| R3 | O3 | S3 | Ab3 | Ts3 | Te3 |
| R4 | O4 | S4 | Ab4 | Ts4 | Te4 |
| R5 | O5 | S5 | Ab5 | Ts5 | Te5 |
| … | … | … | … | … | … |
Step (2), extracting related covariates: considering four aspects of facility environment factors, social economy, alternative transportation modes and weather factors, respectively screening related covariates:
1) residential building area ratio sigmaGround: the environmental characteristics of the building can influence the use of the public bicycle with the piles, and the area of the residential building of the surrounding residents is closely related to the commuting and traveling demands.
Wherein σGroundThe area of the residential building is the percentage, A is the residential building area, and M is the total area of the affiliated area.
2) Distance L from city centerab: the bicycle renting amount is related to the crowding degree, the demand is smaller as the bicycle is farther away from the city center, and therefore the linear distance of the rail transit station from the city center also needs to be taken into consideration, and the bicycle renting amount is calculated by using the following formula:
Lab＝R*arccos[cosβ1cosβ2cos(α1-α2)+sinβ1sinβ2] (2)
wherein L isabThe distance between two straight lines (alpha 1, beta 1) and (alpha 2, beta 2) are longitude and latitude coordinates of two points a and b respectively, and R is the radius of the earth.
3) Population density ρHuman being: calculating population density rho by considering two factors of general population and employment populationHuman beingThe calculation formula is as follows:
where ρ isHuman beingThe population density is N is the total population, and M is the area of the region.
4) The number of peripheral rail transit stations T;
5) number of peripheral bus stations B: since there are a plurality of alternative transportation modes near one station, the influence of rail transit operation faults can be reduced, and therefore the rail transit operation faults need to be brought into a model. Selecting a 400-meter distance according to previous research experience, and acquiring data within a range of 400 meters from a rail transit station by using Mapinfo software;
6) weather W: weather conditions have a significant impact on the use of a public bicycle with piles, and therefore whether it rains (raining is noted as 1, and not raining as 0) is also considered in the model.
Thus, a specific summary of the screened covariates is given in table 3:
table 3 covariate summary data
| Covariates | Mean value of | Standard deviation of | Minimum value | Maximum value |
| Distance from city center | A1 | σ1 | Min1 | Max1 |
| Weather (weather) | A2 | σ2 | Min2 | Max2 |
| Residential area ratio | A3 | σ3 | Min3 | Max3 |
| Population density | A4 | σ4 | Min4 | Max4 |
| Number of subway stations | A5 | σ5 | Min5 | Max5 |
| Number of bus stops | A6 | σ6 | Min6 | Max6 |
Then, an inclusion covariate x is generateditAnd CiAnd a processing index DitAverage out-going quantity and average out-going time yitThe data set of (2).
Step (3), analyzing the data set by using a parallel data model, and selecting a model: parallel data refers to sample data formed by taking a plurality of sections on a time series and simultaneously selecting sample observation values on the sections. The parallel data model includes both time and cross-section dimensions, i (i ═ 1, …, N) represents a cross-section (individual), T ═ 1, …, T represents time, and the following linear model is set:
yit＝αi+xitβ+εit (4)
wherein y isitIs a dependent variable, xitBeing vectors of independent variables, epsilonitFor the model error term, β is the parameter to be estimated, representing yitOf a marginal influence ofiRepresenting individual effects. The parallel data model is divided into a static parallel data model and a dynamic parallel data model. The daily trip volume of the public bicycle with piles researched by the discovery is that the public bicycle with piles is independent and is not influenced by the trip of the previous day, so that only a static parallel data model is considered.
Models built using static parallel data are generally of three types: a mixed regression model, a fixed effect model, and a random effect model. The method selects a relevant random effect model because the model can estimate the effect of time-varying and time-invariant variables on the dependent variable. The linear representation form of the relevant random effect model selected by the invention is as follows:
yit＝β0+Ditβ1+xitβ2+β3Ci+δi+εit(5)
wherein, yitThe traveling times or stations of the public bicycle with the piles areAverage travel time, x, over time period titAnd CiIs a covariate, where xitVariables that are time-varying include site closure and weather,ivariables that are not time-varying include distance to downtown, residence area ratio, population density and number of sites, β0、β1、β2、β3Is a coefficient, δiIs an individual error term, DitIs a processing index, εitIs the inter-group error term. When the traveling quantity of the public bicycle with the piles is within the influence range of rail traffic, Dit1 is ═ 1; otherwise Dit＝0。
Step (4), realization of a random effect model and influence evaluation: 400 meters is generally used as a key walking distance in public transportation networks and service planning, so that a data set of a public bicycle station with piles within a range of 400 meters from the rail transit operation station is selected from the data set in the step (2), the selected data set is used as an input of a random effect model according to whether early peaks occur or not, and how the variable changing along with time and the variable not changing along with time influence the travelling of the public bicycles with piles is analyzed, as shown in table 4. The variable changing along with time comprises an experimental variable (the fault is 1, the normal is 0) and a weather change factor (the rain is 1, the non-rain is 0), and the variable not changing along with time comprises the residential building area, the distance from the city center, the population density and the number of peripheral bus stations. In order to estimate the inter-individual and internal effects, the steps of applying the stochastic effect model can be divided into three steps: first step to generate xitThe second step of creating deviation scores and the third step of using Stata to estimate the effect. Thus, the estimation result beta outputted from the stochastic effect model is analyzed by inputting the stochastic effect model1The results of the influence of the rail transit fault on the public bikes with piles around the periphery are shown in table 5.
Table 4: data set substitution model schematic
| ID | D | xit | Ci | y |
| 1 | 0 | x11 | C1 | y11 |
| 1 | 1 | x12 | C1 | y12 |
| 1 | 1 | x13 | C1 | y13 |
| ... | ... | ... | C1 | ... |
| 2 | 0 | x21 | C2 | y21 |
| 2 | 1 | x22 | C2 | y22 |
| ... | ... | ... | C2 | … |
| ... | ... | ... | ... | … |
Table 5: influence on piled bicycle when rail traffic fault occurs
| Fault of | Early peak | Late peak | Flat peak | Weekend |
| Site shutdown | β11 | β21 | β31 | β41 |
| Severe delay | β12 | β22 | β32 | β42 |
| Delay | β13 | β23 | β33 | β43 |
The different effects of the rail traffic state in the morning and evening and the peak/off-peak period on different departure and arrival times and the average traffic of the public bicycles with piles at the periphery are analyzed through the results of the above example. (1) Inputting a random effect model from a starting point trip quantity data set: when the rail transit fault occurs, and the estimation result beta output by the random effect model1When the numerical value of (A) is positive, the running quantity of the public bike with the pile at the starting point is increased by beta1(ii) a On the contrary, the running quantity of the public bicycle with the pile at the starting point is reduced by beta1. (2) Random effects model was input from endpoint arrival volume data set: estimation result beta output by random effect model when rail transit fault occurs1When the numerical value of (1) is positive, the terminal arrival quantity of the public bicycle with the pile is increased by beta1(ii) a Otherwise, it indicates that there is a stake public selfReduction of driving end arrival amount beta1. (3) Inputting a random effect model from a starting point travel time data set: estimation result beta output by random effect model when rail transit fault occurs1When the numerical value of (A) is positive, the time of the public bicycle with the pile is increased by beta1(ii) a On the contrary, the starting point travel time of the public bicycle with the piles is reduced by beta1. (4) Random effects model was input from the endpoint arrival time dataset: when the rail transit fault occurs, the random effect model outputs an estimation result beta1When the numerical value of (A) is positive, the terminal arrival time of the public bicycle with the pile is increased by beta1(ii) a On the contrary, the arrival time of the public bicycle terminal with the pile is reduced by beta1。
In conclusion, the method provided by the invention captures the service state of the subway line, the rental data of the piled bicycles around the subway and the related covariates, matches the rail transit operation state with the rental data of the piled bicycles by using the time items, analyzes the data by using the random effect model, analyzes the influence of the rail transit operation state on the trip characteristics of the piled public bicycle stations from a microscopic angle, and provides a scientific and effective method for the future related city planning, the matching of a slow-moving traffic system and the original transportation facilities and the arrangement of the piled public bicycle stations.
The embodiments of the present invention have been described in detail with reference to the drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.
Claims (4)
1. A method for evaluating the influence of a rail transit running state on peripheral public bicycles is characterized by comprising the following steps:
step (1), determining a research object and acquiring data: extracting the position distribution, the operation time, the type of the rail transit running fault state, the starting/ending time of the running state fault and the reason of the running state fault for rail transit and a public bicycle with piles; and extracting data of distribution of the public bicycle stations with piles, bicycle leasing amount, bicycle leasing numbers, starting/ending stations of each trip, starting/ending time and average trip time;
step (2), extracting covariates to generate a data set: respectively screening a plurality of covariates from four aspects of facility environment factors, social economy, alternative traffic modes and weather factors; generating a covariate xitAnd CiAnd a processing index DitAverage out-going quantity and average out-going time yitThe data set of (a);
selecting to obtain a random effect model;
and (4) carrying out influence evaluation by using the selected random effect model: and (3) selecting a public bicycle station data set with piles within a set range from the data set generated in the step (2), taking the selected data set as the input of a random effect model according to whether the station is closed when the early peak occurs, outputting an estimation result by using the random effect model, and analyzing the output estimation result to obtain the influence of the rail transit running state on the use amount of the public bicycles with piles at the periphery.
2. The method for evaluating the influence of the rail transit running state on the peripheral public bikes according to claim 1, wherein the covariates screened in the step 2 comprise residential building area ratio, distance from a city center, population density, peripheral rail transit station number, peripheral bus station number and weather.
3. The method for evaluating the influence of the rail transit running state on the peripheral public bikes according to claim 1, wherein the random effect model constructed in the step 3 is represented as follows:
yit＝β0+Ditβ1+xitβ2+β3Ci+δi+εit
wherein, yitIs the number of trips of the public bicycle with piles or the average trip time of the station i in the time period t, xitAnd CiIs a covariate, whereinxitIs a variable which varies with time, CiIs a variable that does not change over time; beta is a0、β1、β2、β3Are respectively the coefficient, δiIs an individual error term, DitIs a processing index, εitIs the inter-group error term.
4. The method for evaluating the influence of the rail transit running state on the peripheral public bikes according to claim 1, wherein the step 4 of analyzing the influence of the rail transit running state on the usage amount of the peripheral public bikes with piles by using a stochastic effect model comprises the following steps: the influence on the average running amount and the average single-time running time of the public bicycle rental spots within the influence range when the running state of the rail transit is changed.
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