CN116386311A - Traffic accessibility assessment method considering extreme weather conditions - Google Patents
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Abstract
The invention discloses a traffic accessibility assessment method considering extreme weather conditions, which comprises the steps of carrying out urban road matching; screening urban road real-time data; decomposing the screened speed and travel time by using a local weighted regression seasonal trend decomposition method; performing generalized extremum student dispersion test on the decomposed residual data, identifying a vehicle speed and a travel time observation value with larger deviation from the travel time observation value under a normal traffic condition, and determining an extreme vehicle speed and the travel time observation value in a travel time data set; collecting an extreme weather data set, and performing global space-time superposition analysis on the extreme weather data set and the starting and ending time of the vehicle speed and travel time observation value with larger deviation to form a new data set; traffic reachability is calculated on the new dataset using an improved gravity model. The method can solve the problem that the reachability measurement and calculation is not accurate enough because factors such as area, grade and competition of residents in the demand area to terminal resources are not taken into consideration.
Description
Technical Field
The invention relates to the technical field of public transportation, in particular to a traffic accessibility assessment method considering extreme weather conditions.
Background
The common methods and models for calculating reachability mainly comprise a minimum distance method, a buffer area analysis method, a network analysis method, a two-step mobile search method and an gravitation model method. In recent years, scholars have used a buffer analysis method, a two-step mobile search method and an attraction model method to measure reachability.
The buffer area analysis method has the defects that the service radius of the area is defined manually, and factors such as the area and the grade of the area are not taken into consideration; the two-step mobile search method considers the service capability of the supply area and the population of residents in the demand area, but does not consider the actual situation of competition of residents in the demand area for terminal resources; the main disadvantage of the gravity model method is that the travel distance and time cost are greatly different from the actual situation. Today's accessibility assessment is mostly not considered in extreme weather condition, and the actual travel mode of resident under extreme weather, actual road conditions, road grade and travel peak section and flat peak section etc. factors all influence the measurement and calculation of accessibility.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description summary and in the title of the application, to avoid obscuring the purpose of this section, the description summary and the title of the invention, which should not be used to limit the scope of the invention.
The present invention has been made in view of the above-described problems occurring in the prior art.
Therefore, the invention provides a traffic accessibility assessment method considering extreme weather conditions, which can solve the problem that the measurement and calculation of accessibility is not accurate enough because factors such as area, grade and competition of residents in a demand area to terminal resources are not taken into consideration.
In order to solve the technical problems, the invention provides a traffic accessibility assessment method considering extreme weather conditions, comprising the following steps:
carrying out urban road matching;
screening urban road real-time data;
decomposing the screened speed and travel time by using a local weighted regression seasonal trend decomposition method;
performing generalized extremum student dispersion test on the decomposed residual data, identifying a vehicle speed and a travel time observation value with larger deviation from the travel time observation value under a normal traffic condition, and determining an extreme vehicle speed and the travel time observation value in a travel time data set;
collecting an extreme weather data set, and performing global space-time superposition analysis on the extreme weather data set and the starting and ending time of the vehicle speed and travel time observation value with larger deviation to form a new data set;
traffic reachability is calculated on the new dataset using an improved gravity model.
As a preferred embodiment of the traffic accessibility assessment method according to the present invention, which considers extreme weather conditions, wherein: the urban road matching comprises the steps of selecting a candidate area by combining GPS track point information, finding the longitude and latitude of a sequence point of the candidate road section, substituting the longitude and latitude of the sequence point, the longitude and latitude of the GPS track point, the speed and the value of a course angle into a formula to calculate three geometric characteristic values and dynamic parameters of a projection distance, a course angle and a track angle, and finally performing GPS track data geometric matching.
As a preferred embodiment of the traffic accessibility assessment method according to the present invention, which considers extreme weather conditions, wherein: the formula of the geometric match is expressed as:
wherein i represents the number of candidate segments, N represents the total number of candidate segments, S i Representing candidate segment score value, d, for sequence number i i Representing the projection distance of GPS track point on i candidate road segment, delta theta h ，Δθ t Respectively represents the course included angle and the track included angle of the GPS track point and the i candidate road section,
respectively represents the sum of the projection distance, the course included angle and the track included angle of the GPS point and all candidate road sections, W d ，W h ，W t Respectively represent the weight coefficients of the three.
As a preferred embodiment of the traffic accessibility assessment method according to the present invention, which considers extreme weather conditions, wherein: the screening of the real-time data of the urban road includes,
the map matching result is represented by a GPS recording sequence, all GPS track points matched to each road are divided into different time intervals according to the time interval T=15 minutes in the calculation process of the road section average travel speed, and the road length weighted space average speed is utilized to respectively obtain the instantaneous speed;
counting all vehicle records passing through the road within 15 minutes, calculating the average value of the records to obtain the average travel speed of the road section, and obtaining the travel time according to the speed and the travel distance;
and comparing and analyzing the average vehicle speed obtained according to the data observed in real time on site with the average vehicle speed obtained by the GPS track data, and calculating to obtain a correction coefficient so as to realize correction of the road network vehicle speed and the travel time of the research area.
As a preferred embodiment of the traffic accessibility assessment method according to the present invention, which considers extreme weather conditions, wherein: the decomposing comprises decomposing long-term trends, seasonal changes and residual parts of the vehicle speed and the travel time by using a local weighted regression seasonal trend decomposing method, wherein the calculation formula is expressed as follows: y is w ＝W T +W S +W R Wherein W is T ，W S ，W R A trend component, a seasonal component, and a residual component, respectively.
As a preferred embodiment of the traffic accessibility assessment method according to the present invention, which considers extreme weather conditions, wherein: the generalized extremum student dispersion test includes checking the deviation value, namely deleting the observed value of the maximum test statistic from the sample, recalculating the test statistic of the rest observed values, and repeating the process until r potential abnormal values are deleted.
As a preferred embodiment of the traffic accessibility assessment method according to the present invention, which considers extreme weather conditions, wherein: the test statistic for each of the observations in a sample of size n is expressed as:
wherein R is i Is the observed value of the maximum test statistic, x i Is the i-th observation in the sample; x is the average value;
is the average value and s is the standard deviation of the sample.
As a preferred embodiment of the traffic accessibility assessment method according to the present invention, which considers extreme weather conditions, wherein: corresponding to the calculated r test statistic, the r test threshold is expressed as:
wherein t is p,n-i-1 Is the t distribution corresponding to the 100 th percentile, n-i-1 represents freedom.
As a preferred embodiment of the traffic accessibility assessment method according to the present invention, which considers extreme weather conditions, wherein: the p value in the r test threshold calculation formula is expressed as:
where α is the confidence level, the number of outliers in the sample is determined by the largest i value, thus R i >λ i 。
As a preferred embodiment of the traffic accessibility assessment method according to the present invention, which considers extreme weather conditions, wherein: the traffic reachability is expressed as:
wherein, PA i The terminal space accessibility level of the aggregated residential area i; PA (Polyamide) i The more the valueA high means that the better the workplace reachability at the origin i; s is S j Is the service capability of the working area j, and the working area is represented; a is that j The suction index of the working area j is expressed by a grade, and the larger the value thereof is, the larger the suction capacity of the ground is; t (T) ij (M 1 )、T ij (M 2 ) And T ij (M 3 ) The travel time required from the starting point i to the end point j under 3 travel modes of a taxi, a bus and a shared bicycle is respectively; c (C) j (M 1 )、C j (M 2 )、C j (M 3 ) The method respectively shows competition of aggregated regional residents meeting a time threshold to working area resources under extreme weather influence in 3 travel modes of taxis, public transportation and shared single vehicles; p (P) k Population of the populated areas after aggregation at k; m is the number of populated areas after aggregation that satisfies the condition.
The invention has the beneficial effects that: the method of the invention is based on the calculation method of regional traffic accessibility under extreme weather of GPS big data, can give consideration to the resource competition condition of people on the working area under extreme weather condition, can help to calculate and compare regional traffic accessibility of different areas efficiently, comprehensively and consistently, helps to make better public decisions, for example, helps to comprehensively consider the connection of urban traffic and regional traffic and find a feasible way for improving regional traffic accessibility, enhances crowd evacuation capability under extreme weather, and the like. The method has wide application prospect in planning and management of regional traffic, homeland space, disaster weather crowd evacuation, urban traffic and the like.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a schematic flow chart of a traffic accessibility assessment method considering extreme weather conditions according to an embodiment of the present invention;
FIG. 2 is a schematic illustration of a bus commuter circle of a typical occupied cell under normal weather conditions for a lotus pool of a traffic reachability evaluation method that takes extreme weather conditions into account, in accordance with one embodiment of the present invention;
FIG. 3 is a diagram of a bus commuter circle of a typical occupancy cell in a normal weather condition of a thin western lake, in consideration of a traffic accessibility assessment method for extreme weather conditions, according to one embodiment of the present invention;
FIG. 4 is a diagram of a bus commuter circle of a typical occupancy cell under normal weather conditions in the Beijing city, in which extreme weather conditions are considered, according to one embodiment of the present invention;
FIG. 5 is a diagram of a bus commuter circle of a typical occupied cell under normal weather conditions of a Wanday square, in which extreme weather conditions are considered, according to one embodiment of the present invention;
FIG. 6 is a graph of the difference between the traffic commuter circle of a typical occupancy cell under normal weather and heavy rain weather conditions for a lotus pool of a traffic reachability assessment method that takes extreme weather conditions into account, according to one embodiment of the present invention;
FIG. 7 is a graph of the difference between the traffic commuter circle of a typical occupancy cell in a normal weather of a thin western lake and a heavy rain weather of a traffic reachability assessment method considering extreme weather conditions according to an embodiment of the present invention;
FIG. 8 is a graph of the difference between the bus commuter circle of a typical occupancy cell in the normal weather of Beijing city and the stormy weather of the traffic accessibility assessment method taking into account extreme weather conditions according to one embodiment of the present invention;
fig. 9 is a difference chart of bus commuter circles of a typical occupancy district under normal weather and heavy rain weather conditions of a wan da square, which is a traffic accessibility assessment method considering extreme weather conditions according to an embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
While the embodiments of the present invention have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the invention. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Example 1
Referring to fig. 1, a first embodiment of the present invention provides a traffic accessibility assessment method considering extreme weather conditions, comprising:
s1: carrying out urban road matching;
(1) Determining a dynamic candidate area with the GPS track point to be matched as the center,
(2) Finding the longitude and latitude of the sequence point of the candidate road section in the candidate region,
(3) Substituting longitude and latitude of road section sequence points, longitude and latitude, speed and course angle values of GPS points into a formula to calculate three geometrical characteristic values and dynamic parameters of projection distance, course included angle and track included angle
(4) Geometric matching of GPS track data is carried out, and the formula is as follows
i represents the number of candidate segments, N represents the total number of candidate segments, S i Representing candidate segment score value, d, for sequence number i i Representing the projection distance of GPS track point on i candidate road segment, delta theta h ，Δθ t Respectively represents the course included angle and the track included angle of the GPS track point and the i candidate road section,
respectively represents the sum of the projection distance, the course included angle and the track included angle of the GPS point and all candidate road sections, W d 、W h 、W t Respectively represent the weight coefficients of the three. And the candidate road section with the smallest total score is the matching road section of the GPS track point.
It should be noted that, combining with the GPS track point information, selecting a candidate area, wherein the road section in the candidate area is the candidate road section, searching the longitude and latitude of the sequence point of the candidate road section, substituting the longitude and latitude of the sequence point, the longitude and latitude of the GPS track point, the speed and the value of the course angle into a formula to calculate three geometric characteristic values and dynamic parameters of the projection distance, the course angle and the track angle, and finally performing geometric matching of the GPS track data.
S2: screening urban road real-time data;
furthermore, the map matching result is represented by the GPS record sequence, all GPS points matched to each road are divided into different time intervals according to the time interval T=15 minutes in the calculation process of the road section average travel speed, and the road length weighted space average speed is used for respectively obtaining the instantaneous speed (T=15 minutes)
All the vehicle records passing through the road within 15 minutes are counted, the average value is calculated, the average travel speed (T=1 hour) of the road section is obtained, and the travel time is obtained according to the speed and the travel distance.
And comparing and analyzing the average speed obtained by the data of the on-site real-time observation with the average speed obtained by the GPS track data, and calculating to obtain a correction coefficient, thereby realizing the correction of the road network speed and the travel time of the research area.
S3: decomposing the screened speed and travel time by using a local weighted regression seasonal trend decomposition method;
furthermore, a local weighted regression seasonal trend decomposition method is selected to decompose the vehicle speed and the travel time, so as to decompose long-term trend, seasonal change and residual part, and the formula is as follows:
y w ＝W T +W S +W R
wherein W is T 、W S 、W R A trend component, a seasonal component, and a residual component, respectively.
S4: performing generalized extremum student dispersion test on the decomposed residual data, identifying a vehicle speed and a travel time observation value with larger deviation from the travel time observation value under a normal traffic condition, and determining an extreme vehicle speed and the travel time observation value in a travel time data set;
test statistics for each observation in a sample of size n
Wherein R is i Is the observed value of the maximum test statistic, x i Is the i-th observation in the sample; x is the average value;
is the average value and s is the standard deviation of the sample.
It should be noted that the maximum test statistic R is deleted from the sample i And recalculate the test statistics for the remaining observations, repeating the process until r potential outliers are deleted.
Corresponding to the calculated r test statistic, the r test threshold is calculated by:
wherein t is p,n-i-1 Is the t distribution corresponding to the 100 th percentile, n-i-1 represents the degree of freedom.
The p value is expressed as:
where α is the confidence level, the number of outliers in the sample is determined by the largest i value, thus R i >λ i The value of r is set to 20% of the total travel time observation and α is set to 0.05. Selecting a relatively high value for r prevents any outliers from being identified at the selected confidence level.
S5: and collecting an extreme weather data set, and performing global space-time superposition analysis on the extreme weather data set and the starting and ending time of the vehicle speed and the travel time observation value with larger deviation to form a new data set.
Further, the meteorological station data are observed, DEM data and GSMaP radar remote sensing data are collected, and waterlogging points are identified by using the DEM data: carrying out dynamic simulation of waterlogging spatial diffusion on 30mDEM data by using an ArcGIS model generator, and identifying possible waterlogging points;
preparing radar satellite remote sensing precipitation data: collecting a global precipitation data product (time resolution 0.5 hours, spatial resolution 0.07 ° ×0.07 °) comprising CMORPH published by the united states national marine and atmospheric administration (NOAA); and GSMaP precipitation data products (spatial coverage of 60 ° N to 60 ° S, temporal resolution of 1 hour, spatial resolution of 0.1 ° ×0.1 °) issued by japan aerospace research and development agency (JAXA).
It should be noted that, collecting the observation data of hydrologic stations and the like, mining text information in extreme weather event reports, and verifying the affected road sections and time of extreme weather and the like again
S6: calculating traffic reachability using the improved gravity model on the new dataset;
traffic reachability is expressed as:
wherein, PA i The terminal space accessibility level of the aggregated residential area i; PA (Polyamide) i Higher values mean better availability of the work area at the starting point i; s is S j Is the service capability of the working area j, and the working area is represented; a is that j The suction index of the working area j is expressed by a grade, and the larger the value thereof is, the larger the suction capacity of the ground is; t (T) ij (M 1 )、T ij (M 2 ) And T ij (M 3 ) The travel time required from the starting point i to the end point j under 3 travel modes of a taxi, a bus and a shared bicycle is respectively; c (C) j (M 1 )、C j (M 2 )、C j (M 3 ) The method respectively shows competition of aggregated regional residents meeting a time threshold to working area resources under extreme weather influence in 3 travel modes of taxis, public transportation and shared single vehicles; p (P) k At kPopulation of populated areas after aggregation; m is the number of populated areas after aggregation that satisfies the condition.
Example 2
The embodiment further describes a traffic accessibility assessment method considering extreme weather conditions based on actual application scenes.
G represents a candidate region, namely a circular region range where a road section to be matched is located, and all road sections in the polygonal region G range are regarded as candidate road sections; s is S r Representing a set of candidate road segments; l (L) r Representing a road segment; n (N) r Representing a road network set; p (P) i The representative sequence point is always located in the candidate region G.
Expressed as:
after the candidate road segment set is determined, a geometric characteristic value is obtained, wherein the geometric characteristic value comprises the projection distance, the course included angle and the track included angle of the GPS track point to be matched and the candidate road segment.
Projection distance calculation:
the Euclidean distance between the GPS point and the candidate road segment is calculated and defined as the projection distance d proj ，(x p ,y p ) Is the longitude and latitude coordinates of the GPS point, (x) α ,y α )(x β ,y β ) Is the longitude and latitude coordinates of the candidate road segment sequence points.
Judging whether the slope of the road section exists or not, judging whether the track point is on the extension line of the projection road section according to the existence condition of the slope, and when the GPS track point is projected on the road section, setting the projection distance as the vertical distance of the track point road section; when the GPS track point is projected on the extension line of the road section, the projection distance is the minimum value of Euclidean distance from the track point to two sequence points.
The heading angle is defined as: an included angle is formed between the running direction of the vehicle and the north direction clockwise; the road segment direction is defined as: the connecting line formed by the road section and two adjacent points forms a clockwise included angle with the north direction; defining the heading angle as: the difference between the heading angle and the road segment direction. In general, the smaller the value of the heading angle, the higher the similarity between the candidate road section and the actual driving road section.
The course angle is directly obtained through the sampling equipment, the road section direction is determined by the starting point and the end point of the sequence point, and the road section direction is obtained through geometric angle calculation. In the calculation, the angle must be the clockwise angle of the road segment with respect to the north direction (y-axis), passing through the road segment origin P A (x 1 ,y 1 ) Endpoint P B (x 2 ,y 2 ) The track direction is defined as the difference between the longitude and the latitude: the clockwise angle formed by the connecting line of the GPS track point at the last moment and the current matching GPS track point and the north direction defines the track included angle as the difference between the track direction and the road section direction, and the smaller the value is, the higher the matching degree is.
In the above evaluation method, the closer the parallel degree between the connecting line of the historical track point and the current point and the road section is, the smaller the track included angle is, which means that the GPS track point is matched with the road section. θ vehicle Is the track direction, and the starting point (x) 1 ,y 1 ) The later time point is (x 2 ,y 2 )，Δx＝x 2 -x 1 ，Δy＝y 2 -y 1 。
Calculating a distance coefficient:
the closer the GPS point and the candidate road segment are, the higher the probability that the candidate road segment is a vehicle driving road segment, the value of the distance coefficient is related to the projection distance, and the distance score is changed by dynamically setting the distance coefficient, and the value is between 0 and 1. The calculation formula is as follows:
W d is a distance coefficient, d is the projection distance from the GPS point to the candidate road section, d 1 、d 2 Is the projection distance threshold.
And (3) calculating a heading coefficient:
the heading coefficient is closely related to the speedSmaller, less accurate, whereas higher speed, more reliable, heading coefficient W d Positively correlated with speed, a value between 0 and 1
v is the vehicle speed, obtained by direct acquisition, v 1 、v 2 For the speed threshold, 10.8km/h and 80km/h, respectively.
Track coefficient calculation:
the value of the track coefficient is between 0 and 1, and the magnitude of the track coefficient is in positive correlation with the running distance S of the vehicle
S ′ The threshold value for the vehicle travel distance is set to 600m.
Substituting the calculated value into the following formula to carry out weighted summation to obtain a total score:
wherein i represents the number of candidate segments, N represents the total number of candidate segments, S i Representing candidate segment score value, d, for sequence number i i Representing the projection distance of GPS track point on i candidate road segment, delta theta h 、Δθ t Respectively represents the course included angle and the track included angle of the GPS track point and the i candidate road section,
respectively represents the sum of the projection distance, the course included angle and the track included angle of the GPS point and all candidate road sections, W d 、W h 、W t Respectively represent the weight coefficients of the three. And the candidate road section with the smallest total score is the matching road section of the GPS track point.
And (3) representing map matching results by using a GPS recording sequence, dividing all GPS points matched to each road into different time intervals according to the time interval T=15 minutes in the calculation process of the road section average journey vehicle speed, and respectively obtaining the instantaneous vehicle speed (T=15 minutes) by using the road length weighted space average vehicle speed.
And counting all vehicle records passing through the road in the period, calculating the average value of the records, obtaining the average travel speed (T=1 hour) of the road section, and obtaining the travel time according to the speed and the travel distance.
And comparing and analyzing the average speed obtained by the data of the on-site real-time observation with the average speed obtained by the GPS track data, and calculating to obtain a correction coefficient, thereby realizing the correction of the road network speed and the travel time of the research area.
Decomposing long-term trends, seasonal changes, and residual fractions; the time series decomposition formula is as follows:
y t ＝T t +S t +R t
wherein T is t 、S t 、R t A trend component, a seasonal component, and a residual component, respectively.
The generalized extremum biochemical deviation testing method is used for checking deviation values, and the following formula is the testing statistic of each observed value in a sample with the size of n:
wherein R is i Is the observed value of the maximum test statistic, x i Is the i-th observation in the sample; x is the average value; x is x i Is the average value and s is the standard deviation of the sample. Deleting maximum test statistic R from sample i And recalculate the test statistics for the remaining observations. This process is repeated until r potential outliers are deleted.
Corresponding to the calculated r test statistic, the r test threshold is calculated by:
wherein t is p,n-i-1 Is the t distribution corresponding to the 100 th percentile, n-i-1 represents the degree of freedom.
The p value is calculated by the following formula:
alpha is the confidence level and the number of outliers in the sample is determined by the largest i value, thus R i >λ i . The r value is set to 20% of the total travel time observation and α is set to 0.05. Note that selecting a relatively high value for r prevents any outliers from being identified at the selected confidence level.
Because the reachability values of different modes are different, in order to facilitate the integration of the reachability of multiple traffic modes, a Min-max standardization method is adopted to standardize the reachability.
The Min-max normalization method of (2) is as follows:
the public transport travel mode, the reachable time threshold and the place attraction index are modified according to the following formula:
PA i a workspace space reachability level for the aggregated residential area i; PA (Polyamide) i Higher means better workplace accessibility at populated area i, S j Is the service capability of the workplace j, and is represented by the workplace area; a is that j The suction index of the work place j is expressed by a grade, and the larger the value thereof is, the larger the suction capacity of the place is; t (T) ij (M 1 )、T ij (M 2 ) And T ij (M 3 ) The travel time required from the living area i to the working place j under 3 travel modes of taxis, buses and shared single buses is respectively; c (C) j (M 1 )、C j (M 2 )、C j (M 3 ) Respectively representing competition of 3 travel modes of taxis, buses and shared single buses for working area resources among residents in the aggregated living areas meeting the time threshold under the influence of extreme weather; p (P) k And (3) the population number of the populated areas after aggregation at k, and m is the number of the populated areas after aggregation which meets the condition.
Example 3
Referring to fig. 2-9, the present embodiment provides a traffic accessibility assessment method considering extreme weather conditions, and in order to verify the beneficial effects of the present invention, scientific demonstration is performed through experiments of specific application scenarios.
According to GPS data of buses provided by Yangzhou public transportation company, GPS point position data of 2021, 7 months, 15 days to 30 days and 12 months to 18 days are extracted, wherein 24 days to 30 days of 7 months are extreme weather (typhoon-storm), and 12 days to 18 days are sunny weather. 51 bus lines in the main urban area of Yangzhou city are extracted from the data, and the total data is 89077360, and the total size is 41.2G. Screening data of bus commuter trips in the main urban area of Yangzhou city at the early and late peak time periods (07:00-09:00, 17:00-19:00) between 24 days of 7 months and 30 days of 7 months (typhoon-stormy weather) and the early and late peak time periods (07:00-09:00, 17:00-19:00) between 12 months and 18 days of 2021 (normal weather conditions) through Python programming, and directly eliminating invalid or abnormal data. After pretreatment, about thirty-thousand pieces of effective line data are obtained every day, and each piece of track data represents the running state of the bus and comprises the position information such as longitude and latitude, speed and time information of each data point of the bus at the current time.
Taking four typical cells in Yangzhou city as an example, in order to analyze the influence of extreme weather conditions on the commute accessibility of typical living cells, the accessibility is calculated by using the evaluation method, the area of a bus commute travel circle under different weather conditions is calculated by using a space statistical analysis method, and the change quantity of the commute accessibility under different weather influences is compared. The areas of the bus commuter travel circles under different weather conditions constructed by using a lotus pool, a thin western lake, a Beijing, a Wanda square as the commuter travel points are analyzed.
Analysis shows that the areas of the bus commuter travel circles of the four typical occupied communities are reduced under the condition of heavy rain weather: the area of the travel circle of which the bus commute at the lotus pool is less than or equal to 20min is reduced most obviously, and the area is reduced by 10% as a whole; the whole area of the bus commuter travel circle of the thin western lake is reduced by not more than 6.8%; the area of a bus commuter circle in Beijing city is obviously reduced within the commuter time of 20-50 min, and the average area is reduced by 8%; the whole area of the bus commuter travel circle of the Wandadi square is reduced by not more than 5.5%, wherein the area of the bus commuter travel circle for 10min is reduced by only 0.46%.
Under the condition of heavy rain, the area change of the public traffic commuter travel circle in the lotus pool area is most remarkable, and the area change of the public traffic commuter travel circle in the Wanday square is the smallest. The lotus pool is located in the central zone of the main urban area of Yangzhou city, the traffic flow is the largest area in the four typical areas, and under the condition of heavy rain weather, the travel of the local buses is influenced by the influence of heavy rain, such as high traffic flow and road traffic capacity; and the intersection in front of the Wandao square is provided with a special bus turn signal lamp, and a city and south expressway is nearby, and the bus is only affected by rain fall in the trip relative to other typical cells. According to the commute accessibility analysis result of the typical community under the extreme weather condition, the stormwater weather influences the bus travel speed, so that the area of the bus commute travel circle of resident commute travel in the same time is reduced, the accessibility of resident commute travel is influenced, the comfort level of resident travel is reduced, the resident travel time is increased, and the elastic travel (such as recreation, culture, sports and the like) requirements of the resident are reduced.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The solutions in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java, and an transliterated scripting language JavaScript, etc.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (10)
1. A traffic accessibility assessment method considering extreme weather conditions, characterized by: comprising the steps of (a) a step of,
carrying out urban road matching;
screening urban road real-time data;
decomposing the screened speed and travel time by using a local weighted regression seasonal trend decomposition method;
performing generalized extremum student dispersion test on the decomposed residual data, identifying a vehicle speed and a travel time observation value with larger deviation from the travel time observation value under a normal traffic condition, and determining an extreme vehicle speed and the travel time observation value in a travel time data set;
collecting an extreme weather data set, and performing global space-time superposition analysis on the extreme weather data set and the starting and ending time of the vehicle speed and travel time observation value with larger deviation to form a new data set;
traffic reachability is calculated on the new dataset using an improved gravity model.
2. The traffic accessibility assessment method considering extreme weather conditions according to claim 1, wherein: the urban road matching includes that,
and combining GPS track point information, selecting a candidate area, wherein a road section in the candidate area is a candidate road section, searching the longitude and latitude of a sequence point of the candidate road section, substituting the longitude and latitude of the sequence point, the longitude and latitude of a GPS track point, the speed and the value of a course angle into a formula to calculate three geometric characteristic values and dynamic parameters of a projection distance, a course angle and a track angle, and finally performing geometric matching of GPS track data.
3. The traffic accessibility assessment method considering extreme weather conditions according to claim 2, wherein: the formula of the geometric match is expressed as:
wherein i represents the number of candidate segments, N represents the total number of candidate segments, S i Representing candidate segment score value, d, for sequence number i i Representing the projection distance of GPS track point on i candidate road segment, delta theta h 、Δθ t Respectively represents the course included angle and the track included angle of the GPS track point and the i candidate road section,
respectively represents the sum of the projection distance, the course included angle and the track included angle of the GPS point and all candidate road sections, W d 、W h 、W t Respectively represent the weight coefficients of the three.
4. A traffic accessibility assessment method taking into account extreme weather conditions according to any one of claims 1 to 3, characterised in that: the screening of the real-time data of the urban road includes,
the map matching result is represented by a GPS recording sequence, all GPS track points matched to each road are divided into different time intervals according to the time interval T=15 minutes in the calculation process of the road section average travel speed, and the road length weighted space average speed is utilized to respectively obtain the instantaneous speed;
counting all vehicle records passing through the road within 15 minutes, calculating the average value of the records to obtain the average travel speed of the road section, and obtaining the travel time according to the speed and the travel distance;
and comparing and analyzing the average vehicle speed obtained according to the data observed in real time on site with the average vehicle speed obtained by the GPS track data, and calculating to obtain a correction coefficient so as to realize correction of the road network vehicle speed and the travel time of the research area.
5. The traffic accessibility assessment method considering extreme weather conditions according to claim 4, wherein: the decomposing comprises decomposing long-term trends, seasonal changes and residual parts of the vehicle speed and the travel time by using a local weighted regression seasonal trend decomposing method, wherein the calculation formula is expressed as follows: y is w ＝W T +W S +W R Wherein W is T 、W S 、W R A trend component, a seasonal component, and a residual component, respectively.
6. A traffic accessibility assessment method taking into account extreme weather conditions according to any one of claims 1 to 3 or 5, characterised in that: the generalized extremum student dispersion test includes checking the deviation value, namely deleting the observed value of the maximum test statistic from the sample, recalculating the test statistic of the rest observed values, and repeating the process until r potential abnormal values are deleted.
7. The traffic reachability evaluation method considering extreme weather conditions according to claim 6, wherein: the test statistic for each of the observations in a sample of size n is expressed as:
wherein R is i Is the observed value of the maximum test statistic, x i Is the i-th observation in the sample; x is the average value;
is the average value and s is the standard deviation of the sample.
8. The traffic accessibility assessment method considering extreme weather conditions according to any one of claims 1 to 3, 5 or 7, wherein: corresponding to the calculated r test statistic, the r test threshold is expressed as:
wherein t is p,n-i-1 Is the t distribution corresponding to the 100 th percentile, n-i-1 represents the degree of freedom.
9. The traffic reachability evaluation method considering extreme weather conditions according to claim 8, wherein: the p value in the r test threshold calculation formula is expressed as:
where α is the confidence level, the number of outliers in the sample is determined by the largest i value, thus R i >λ i 。
10. The traffic accessibility assessment method considering extreme weather conditions according to any one of claims 1 to 3, 5, 7 or 8, wherein: the traffic reachability is expressed as:
wherein, PA i The terminal space accessibility level of the aggregated residential area i; PA (Polyamide) i Higher values mean better availability of the work area at the starting point i; s is S j Is the service capability of the working area j, and the working area is represented; a is that j The suction index of the working area j is expressed by a grade, and the larger the value thereof is, the larger the suction capacity of the ground is; t (T) ij (M 1 )、T ij (M 2 ) And T ij (M 3 ) The travel time required from the starting point i to the end point j under 3 travel modes of a taxi, a bus and a shared bicycle is respectively; c (C) j (M 1 )、C j (M 2 )、C j (M 3 ) The method respectively shows competition of aggregated regional residents meeting a time threshold to working area resources under extreme weather influence in 3 travel modes of taxis, public transportation and shared single vehicles; p (P) k Population of the populated areas after aggregation at k; m is the number of populated areas after aggregation that satisfies the condition.
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| CN117807450A (en) * | 2024-01-02 | 2024-04-02 | 浙江恒隆智慧科技集团有限公司 | Urban intelligent public transportation system and method |
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| CN117807450A (en) * | 2024-01-02 | 2024-04-02 | 浙江恒隆智慧科技集团有限公司 | Urban intelligent public transportation system and method |
| CN117807450B (en) * | 2024-01-02 | 2024-06-11 | 浙江恒隆智慧科技集团有限公司 | Urban intelligent public transportation system and method |
| CN117522174A (en) * | 2024-01-05 | 2024-02-06 | 贵州省第一测绘院(贵州省北斗导航位置服务中心) | Territorial space planning space data mutation checking method, application system and cloud platform |
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