CN113724494A - Customized bus demand area identification method - Google Patents

Customized bus demand area identification method Download PDF

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CN113724494A
CN113724494A CN202110869017.0A CN202110869017A CN113724494A CN 113724494 A CN113724494 A CN 113724494A CN 202110869017 A CN202110869017 A CN 202110869017A CN 113724494 A CN113724494 A CN 113724494A
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starting
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CN113724494B (en
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王炜
李欣然
金坤
秦韶阳
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Southeast University
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Abstract

The invention provides a method for identifying a customized bus demand area, which comprises the following steps: (1) acquiring a road network structure database and a vehicle running track database of a specified city space-time range; (2) screening a commuting track in a vehicle running track database to form a commuting track database; (3) selecting k key commuting starting terminal pairs; (4) performing common track pairing for the selected k key commuting starting terminal pairs; (5) and according to a space-time limitation principle, local sub-track clustering is carried out by depending on the common tracks of the key commuting starting point pairs, and the identification of the customized bus demand area is completed. According to the method, the data cycle mode and frequent mode information are mined and the high-demand area of the customized bus is identified based on the big track data in the intelligent networking environment. The method and the system can quantitatively analyze the frequency degree and the overlapping degree of the commuting travel track, accurately determine the required area of the customized bus, and provide important technical support for the operation and popularization of the customized bus service.

Description

Customized bus demand area identification method
Technical Field
The invention relates to the field of urban intelligent bus planning management, in particular to a customized bus demand area identification method.
Background
In recent years, with the continuous improvement of the holding capacity of motor vehicles, urban traffic problems such as urban congestion and air pollution become more serious, and the quality of life of people is greatly influenced. The bus priority development strategy has become a consensus as an effective means to alleviate urban traffic problems. With the rapid development of mobile application technologies, demand response type travel services such as customized public transportation and the like are concerned. The customized public transportation service is characterized in that personalized customized travel service is provided for passengers with similar travel starting and ending points, travel time and service requirements by integrating individual travel requirements in a centralized manner. Compared with the traditional public transportation service, the customized public transportation service route is flexible, the bus can arrive at the station on time, the bus can take comfortably, meanwhile, the price is lower compared with that of the taxi service, and the method has a good application prospect.
At present, the research of customizing the public transportation service mainly focuses on the field of establishing and solving a route planning model, namely, on the premise that the requirements of passengers are obtained through a mobile application technology platform, reasonable service vehicles and running routes are distributed for all the requirements, so that the aims of the maximum number of service people or the lowest operation cost are achieved.
However, the customized public transportation service mainly faces to commuting requirements, and the travel space-time requirements are concentrated, so that when the customized public transportation service falls to the ground, a service area needs to be determined in advance to avoid the problems that detours or partial requirements cannot provide services and the like due to excessively dispersed demand points. At present, quantitative research on the aspect of a customized bus demand area is rare, experience principles are adopted in practice, data support is lacked, the travel demand cannot be effectively met, a service area needs to be adjusted for multiple times in the operation process, the customized bus operation cost is increased, and the travel experience of passengers is influenced. Therefore, the invention provides a customized bus demand area identification method, which is used for mining track big data information in an intelligent networking environment and accurately determining a customized bus service demand area.
Disclosure of Invention
In order to solve the problems, the invention provides a method for identifying a customized bus demand area. The invention aims to mine information of a data cycle mode and a frequent mode by depending on track big data in an intelligent networking environment, thereby identifying a high-demand area for customizing a bus. The method provided by the invention has the advantages of concise steps and clear logic, can quantitatively analyze the frequency degree and the overlapping degree of the commuting travel track, accurately determines the demand area of the customized bus, and provides important technical support for the operation and popularization of the customized bus service.
The technical scheme is as follows: in order to achieve the purpose, the invention provides a method for identifying a customized bus demand area, which comprises the following steps:
(A) acquiring an urban road network structure database and a vehicle running track database of an urban designated space-time range;
(B) screening a commuting track in a vehicle running track database to form a commuting track database;
(C) selecting m key commuting starting point pairs in a commuting track database;
(D) performing common track pairing for the selected m key commuting starting terminal pairs;
(E) and according to a space-time limitation principle, local sub-track clustering is carried out by depending on the common tracks of the key commuting starting point pairs, and the identification of the customized bus demand area is completed.
Preferably: in the step (A), the urban road network structure database at least comprises the adjacency relation between intersections and road sections and the road network structure information of the lengths of all the road sections; the vehicle running track database at least comprises vehicle track information of more than 1 working day, and the database elements comprise unique identification information of the vehicle, time information of the vehicle passing through a monitoring point, position information of the monitoring point and vehicle type information.
Preferably: the step (B) comprises the following steps:
(B1) sequencing the traffic monitoring data by taking the vehicle uniqueness identification information as a main key and the monitored time of the vehicle as a secondary key to obtain the position-time distribution of the vehicle, namely the trajectory data of the vehicle;
(B2) screening vehicle track data with running time meeting the specified commuting time window to form a commuting track alternative database;
(B3) extracting a vehicle track data set T of a certain working day in a commuting track alternative database, and using vehicle unique identification information i and vehicle track starting point information oiVehicle track end point information diForming characteristic items of the vehicle track data, extracting vehicle track data sets T' of other appointed working days in the commuting track alternative database, and using vehicle unique identification information i and vehicle track starting point information oiVehicle track end point information diForming characteristic items of vehicle track data, matching the track characteristic items in the vehicle track data set T with the track characteristic items in the vehicle track data set T 'one by one, and if the vehicle track data set T' has the characteristic items which are the same as the tracks to be matched in the vehicle track data set T, putting the track data with the characteristic items in the vehicle track data set T into a commuting track database TcIn (1).
Preferably: in the step (C), the method for selecting the key commuting starting point comprises the following steps: database T of commuting trajectoriescThe starting and end point pairs in the sequence are sorted according to the descending order of the occurrence frequency, and m starting and end point pairs with the highest occurrence frequency are selected
Figure BDA0003188382620000021
As a key commuting origin terminal pair, k ∈ {1,2.., m }, m ∈ N ·, N · is a positive integer set.
Preferably: in the step (D), the key commute starting terminal pair is adopted
Figure BDA0003188382620000022
k is in the commute track database T by taking k as a characteristic item {1,2cSelecting the track data with the same characteristic items to form a track data set
Figure BDA0003188382620000023
Taking key commute as an end point pair
Figure BDA0003188382620000031
And trajectory data set
Figure BDA0003188382620000032
The most frequent track, i.e. the common track
Figure BDA0003188382620000033
And carrying out correspondence.
Preferably: the step (E) comprises the steps of:
(E1) will commonly use the track
Figure BDA0003188382620000034
Extracting a set of starting and ending point pairs of the sub-tracks as a standard long track
Figure BDA0003188382620000035
(E2) Extracting commute track database TcThe starting and ending point pairs (p, q) of the trace in (1) and the set of starting and ending point pairs of the sub-traces of the standard long trace
Figure BDA0003188382620000036
k is 1, matching is performed, if (p, q) ∈ AkPut the track into the local sub-track set G of the k standard long trackkAnd from the commuting track database TcRemoving; if the matching is unsuccessful, continuing the matching process by taking the next value in the k value ordered set {1,2.., m }; if the matching is unsuccessful, discarding the track;
(E3) given standard long track corresponding key commuting starting terminal pair
Figure BDA0003188382620000037
At the start of operation
Figure BDA0003188382620000038
And the moment of termination of operation
Figure BDA0003188382620000039
Computing the kth set of local sub-trajectories GkThe starting point of the neutron track and the starting point of the kth standard long track
Figure BDA00031883826200000310
Minimum path value of time between
Figure BDA00031883826200000311
Computing the kth set of local sub-trajectories GkEnd point of neutron track and end point of kth standard long track
Figure BDA00031883826200000312
Minimum path value of time between
Figure BDA00031883826200000313
Obtaining the initial running time of the starting and ending point pairs of the sub-tracks
Figure BDA00031883826200000314
And the moment of termination of operation
Figure BDA00031883826200000315
According to the starting point and the ending point of the sub-track, the time of actually passing through the monitoring point
Figure BDA00031883826200000316
Calculating the acceptable time window of the sub-track according to the time interval h of the acceptable passing monitoring point
Figure BDA00031883826200000317
k∈{1,2...,m}；
If it is
Figure BDA00031883826200000318
And is
Figure BDA00031883826200000319
Placing the sub-track into the kth local sub-track set G 'with time window constraint'kPerforming the following steps;
(E4) according to the kth local sub-track set G 'containing time window constraint'kMultiplying the track number i with a given travel mode transfer coefficient alpha to obtain the potential passenger number alpha i; if the number of potential passengers alpha i is more than or equal to the minimum service number j of the customized bus, the stationPart sub-track set G'kAnd the area within the range of r meters around the corresponding kth standard long track is the area required by the customized bus.
Has the advantages that: compared with the prior art, the technical scheme of the invention has the following beneficial technical effects:
(1) the method provided by the invention is based on massive vehicle track data, a periodic mode and a frequent mode contained in the data are mined, and the tracks are clustered by considering space-time limitation conditions, so that the required area for customizing the public transportation service can be quantitatively determined, and the method belongs to mining application of big data information under the background of intelligent networking.
(2) The method aims at the problem of identification of the customized bus demand area, takes engineering practice conditions into consideration, selects discrete vehicle track data (such as vehicle AVI data) as a data source, and has the advantages of wide data coverage, real-time monitoring, no need of complex road network matching and the like.
(3) The method has simple steps and clear logic, can quantitatively analyze the frequency degree and the overlapping degree of the commuting travel track, accurately determines the demand area of the customized bus, can provide important technical support for the operation and the popularization of the customized bus service, and further promotes the development of a novel traffic mode under the wave of the mobile interconnection technology.
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FIG. 1 is a general flow diagram of the process of the present invention;
FIG. 2 is a schematic diagram of an exemplary road network structure according to the present invention.
Detailed Description
The invention is described in further detail below with reference to the following detailed description and accompanying drawings:
the invention provides a method for identifying a customized bus demand area, which aims to mine information of a data cycle mode and a frequent mode by depending on track big data in an intelligent networking environment so as to identify a high demand area of a customized bus. The method provided by the invention has the advantages of concise steps and clear logic, can quantitatively analyze the frequency degree and the overlapping degree of the commuting travel track, accurately determines the demand area of the customized bus, and provides important technical support for the operation and popularization of the customized bus service.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
As an embodiment, it is known that a specified Sioux Falls road network is shown in fig. 2, a road network structure database and a vehicle running track database of the city are obtained, and the method provided by the present invention is used to determine the required area of the customized bus.
As shown in fig. 1, the method for identifying the customized bus demand area provided by the invention comprises the following steps:
(A) acquiring a road network structure database and a vehicle running track database of a specified city space-time range;
the urban road network structure database at least comprises road network structure information such as the adjacency relation between intersections and road sections, the length of each road section and the like; the vehicle running track database at least comprises vehicle track information of more than 1 working day, and the database elements comprise unique identification information of the vehicle, the time when the vehicle passes through a monitoring point, the position information of the monitoring point, the vehicle type information and the like. In this example, a simplified Sioux Falls road network is designated as the area of study, and the vehicle operations database assumes vehicle AVI monitoring data at 7/6/2021 (Monday), and 8/6/2021 (Tuesday).
(B) Screening a commuting track in a vehicle running track database to form a commuting track database;
(B1) sequencing the traffic monitoring data by taking the vehicle uniqueness identification information as a main key and the monitored time of the vehicle as a secondary key to obtain the position-time distribution of the vehicle, namely the trajectory data of the vehicle;
in this example, the VEHICLE number information VEHICLE _ ID is used as a primary key, the monitored TIME of the VEHICLE is used as a secondary key to sequence the traffic monitoring data, and the obtained VEHICLE discrete trajectory data table is shown in table 1. The READER _ IP represents monitoring point position information, the TYPE represents vehicle TYPE information, and the commuting travel vehicle TYPE mainly comprises a small bus and a taxi.
TABLE 1 vehicle discrete trajectory data sheet (part)
VEHICLE_ID READER_IP TIME TYPE …
101 1 2021-06-07 07:03:00 Small-sized passenger car …
101 3 2021-06-07 07:05:00 Small-sized passenger car …
101 4 2021-06-07 07:11:00 Small-sized passenger car …
101 5 2021-06-07 07:20:00 Small-sized passenger car …
101 6 2021-06-07 07:32:00 Small-sized passenger car …
101 8 2021-06-07 07:48:00 Small-sized passenger car
… … … … …
168 1 2021-06-08 07:02:00 Taxi …
168 3 2021-06-08 07:04:00 Taxi …
168 12 2021-06-08 07:11:00 Taxi …
168 13 2021-06-08 07:48:00 Taxi …
… … … … …
(B2) Screening vehicle track data with running time meeting the specified commuting time window to form a commuting track alternative database;
in this example, the designated commute time window is 07:00:00-08:00: 00. And putting tracks which pass through the monitoring points and meet the commuting time window into a commuting track alternative database.
(B3) Extracting a vehicle track data set T of a certain working day in a commuting track alternative database, and using vehicle unique identification information i and vehicle track starting point information oiVehicle track end point information diForming characteristic items of the vehicle track data, extracting vehicle track data sets T' of other appointed working days in the commuting track alternative database, and using vehicle unique identification information i and vehicle track starting point information oiVehicle track end point information diForming characteristic items of vehicle track data, and characterizing the track in the vehicle track data set TMatching the items with the track characteristic items in the vehicle track data set T 'one by one, and if the vehicle track data set T' has the characteristic items which are the same as the tracks to be matched in the vehicle track data set T, putting the track data with the characteristic items in the vehicle track data set T into the commuting track database TcIn (1).
In this example, the commute trajectory matching process is shown in table 2.
TABLE 2 commuting track match (part)
Date of monitoring Characteristic item Date of monitoring Whether it is matched
2021-06-07 (101,1,8) 2021-06-08 Is that
2021-06-07 (168,1,13) 2021-06-08 Whether or not
2021-06-07 … 2021-06-08 …
(C) Selecting m key commuting starting point pairs in a commuting track database;
the method for selecting the key commute starting and ending point comprises the following steps: database T of commuting trajectoriescThe starting and end point pairs in the sequence are sorted according to the descending order of the occurrence frequency, and m starting and end point pairs with the highest occurrence frequency are selected
Figure BDA0003188382620000061
As a key commuting origin terminal pair, k ∈ {1,2.., m }, m ∈ N ·, N · is a positive integer set.
In this example, m is 1 and the key commute start endpoint pair is (1, 8).
(D) Performing common track pairing for the selected m key commuting starting terminal pairs;
starting up terminal pair with key commute
Figure BDA0003188382620000062
k is in the commute track database T by taking k as a characteristic item {1,2cSelecting the track data with the same characteristic items to form a track data set Tc kThe key commute is taken as the terminal pair
Figure BDA0003188382620000063
And trajectory data set
Figure BDA0003188382620000064
The most frequent track, i.e. the common track
Figure BDA0003188382620000065
And carrying out correspondence.
In this example, the common trajectory of the key commuting origin pair (1,8) is (1,3,4,5,6, 8).
(E) And according to a space-time limitation principle, local sub-track clustering is carried out by depending on the common tracks of the key commuting starting point pairs, and the identification of the customized bus demand area is completed.
(E1) Will commonly use the track
Figure BDA0003188382620000066
Extracting a set of starting and ending point pairs of the sub-tracks as a standard long track
Figure BDA0003188382620000067
The starting and end point pairs of the sub-tracks of the standard long track (1,3,4,5,6,8) are A1＝{(1,3),(1,4),(1,5),(1,6),(1,8),(3,4),(3,5),(3,6),(3,8),(4,5),(4,6),(4,8),(5,6),(5,8),(6,8)}。
(E2) Extracting commute track database TcThe starting and ending point pairs (p, q) of the trace in (1) and the set of starting and ending point pairs of the sub-traces of the standard long trace
Figure BDA0003188382620000068
k is 1, matching is performed, if (p, q) ∈ AkPut the track into the local sub-track set G of the k standard long trackkAnd from the commuting track database TcRemoving; if the matching is unsuccessful, k takes the next value in the ordered set {1,2.., m }; if the matching is unsuccessful, discarding the track;
in this example, the local sub-track clustering process is as follows.
TABLE 3 local subtrajectory clustering (parts)
Figure BDA0003188382620000071
(E3) Given standard long track corresponding key commuting starting terminal pair
Figure BDA0003188382620000072
At the start of operation
Figure BDA0003188382620000073
And the moment of termination of operation
Figure BDA0003188382620000074
Computing the kth set of local sub-trajectories GkThe starting point of the neutron track and the starting point of the kth standard long track
Figure BDA0003188382620000075
Minimum path value of time between
Figure BDA0003188382620000076
Computing the kth set of local sub-trajectories GkEnd point of neutron track and end point of kth standard long track
Figure BDA0003188382620000077
Minimum path value of time between
Figure BDA0003188382620000078
Obtaining the initial running time of the starting and ending point pairs of the sub-tracks
Figure BDA0003188382620000079
And the moment of termination of operation
Figure BDA00031883826200000710
According to the starting point and the ending point of the sub-track, the time of actually passing through the monitoring point
Figure BDA00031883826200000711
Calculating the acceptable time window of the sub-track according to the time interval h of the acceptable passing monitoring point
Figure BDA00031883826200000712
k∈{1,2...,m}；
If it is
Figure BDA00031883826200000713
And is
Figure BDA00031883826200000714
Placing the sub-track into the kth local sub-track set G 'with time window constraint'kPerforming the following steps;
in this example, given the start running time 7:00 and the end running time 7:45 of each standard long track start and end point pair (1,8), the time interval h acceptable to pass the monitoring point is 10 minutes. Local set of sub-trajectories GkThe time between the starting point of each sub-track and the starting point 1 of the standard long track is the mostShort circuit
Figure BDA00031883826200000715
Local set of sub-trajectories GkMinimum path in time between the end point of the neutron trajectory and the end point 8 of the standard long trajectory
Figure BDA00031883826200000716
The impedance of the required time can be calculated by Dijkstra shortest-circuit algorithm, and is marked in FIG. 2.
TABLE 3 local sub-track time window constraint matching (partial)
Figure BDA00031883826200000717
Figure BDA0003188382620000081
(E4) According to the kth local sub-track set G 'containing time window constraint'kMultiplying the track number i with a given travel mode transfer coefficient alpha to obtain the potential passenger number alpha i; if the potential passenger number alpha i is larger than or equal to the minimum service number j of the customized buses, the local sub-track set G'kAnd the area within the range of r meters around the corresponding kth standard long track is the area required by the customized bus.
In this example, the number i of tracks in the local sub-track set containing the time window constraint is 30, the travel transfer coefficient α is 0.7, the number α i of potential passengers is 21, and the minimum service number j of the customized bus is 15, so that the area within 300 meters around the corresponding standard long track (1,3,4,5,6,8) is the area required by the customized bus.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, but any modifications or equivalent variations made according to the technical spirit of the present invention are within the scope of the present invention as claimed.

Claims (6)

1. A method for identifying a customized bus demand area is characterized by comprising the following steps:
(A) acquiring an urban road network structure database and a vehicle running track database of an urban designated space-time range;
(B) screening a commuting track in a vehicle running track database to form a commuting track database;
(C) selecting m key commuting starting point pairs in a commuting track database;
(D) performing common track pairing for the selected m key commuting starting terminal pairs;
(E) and according to a space-time limitation principle, local sub-track clustering is carried out by depending on the common tracks of the key commuting starting point pairs, and the identification of the customized bus demand area is completed.
2. The method for identifying the customized bus demand area according to claim 1, characterized in that: in the step (A), the urban road network structure database at least comprises the adjacency relation between intersections and road sections and the road network structure information of the lengths of all the road sections; the vehicle running track database at least comprises vehicle track information of more than 1 working day, and the database elements comprise unique identification information of the vehicle, time information of the vehicle passing through a monitoring point, position information of the monitoring point and vehicle type information.
3. The method for identifying the customized bus demand area according to claim 1 or 2, characterized in that: the step (B) comprises the following steps:
(B1) sequencing the traffic monitoring data by taking the vehicle uniqueness identification information as a main key and the monitored time of the vehicle as a secondary key to obtain the position-time distribution of the vehicle, namely the trajectory data of the vehicle;
(B2) screening vehicle track data with running time meeting the specified commuting time window to form a commuting track alternative database;
(B3) extracting a vehicle track data set T of a certain working day in a commuting track alternative database, and using vehicle unique identification information i and vehicle track starting point information oiVehicle track end point information diMake up vehicle railExtracting other vehicle track data sets T' of appointed working days in the commuting track alternative database by using the characteristic items of the track data, and using the vehicle unique identification information i and the vehicle track starting point information oiVehicle track end point information diForming characteristic items of vehicle track data, matching the track characteristic items in the vehicle track data set T with the track characteristic items in the vehicle track data set T 'one by one, and if the vehicle track data set T' has the characteristic items which are the same as the tracks to be matched in the vehicle track data set T, putting the track data with the characteristic items in the vehicle track data set T into a commuting track database TcIn (1).
4. The method for identifying the customized bus demand area according to claim 3, wherein the method comprises the following steps: in the step (C), the method for selecting the key commuting starting point comprises the following steps: database T of commuting trajectoriescThe starting and end point pairs in the sequence are sorted according to the descending order of the occurrence frequency, and m starting and end point pairs with the highest occurrence frequency are selected
Figure FDA0003188382610000011
As a key commuting origin terminal pair, k ∈ {1,2.., m }, m ∈ N ·, N · is a positive integer set.
5. The method for identifying the customized bus demand area according to claim 4, wherein the method comprises the following steps: in the step (D), the key commute starting terminal pair is adopted
Figure FDA0003188382610000012
As characteristic item, in the commuting track database TcSelecting the track data with the same characteristic items to form a track data set
Figure FDA0003188382610000021
Taking key commute as an end point pair
Figure FDA0003188382610000022
And trajectory data set
Figure FDA0003188382610000023
The most frequent track, i.e. the common track
Figure FDA0003188382610000024
And carrying out correspondence.
6. The method for identifying the customized bus demand area according to claim 5, wherein the method comprises the following steps: the step (E) comprises the steps of:
(E1) will commonly use the track
Figure FDA0003188382610000025
Extracting a set of starting and ending point pairs of the sub-tracks as a standard long track
Figure FDA0003188382610000026
(E2) Extracting commute track database TcThe starting and ending point pairs (p, q) of the trace in (1) and the set of starting and ending point pairs of the sub-traces of the standard long trace
Figure FDA0003188382610000027
Matching is carried out, if (p, q) ∈ AkPut the track into the local sub-track set G of the k standard long trackkAnd from the commuting track database TcRemoving; if the matching is unsuccessful, continuing the matching process by taking the next value in the k value ordered set {1,2.., m }; if the matching is unsuccessful, discarding the track;
(E3) given standard long track corresponding key commuting starting terminal pair
Figure FDA0003188382610000028
At the start of operation
Figure FDA0003188382610000029
And the moment of termination of operation
Figure FDA00031883826100000210
Computing the kth set of local sub-trajectories GkThe starting point of the neutron track and the starting point of the kth standard long track
Figure FDA00031883826100000211
Minimum path value of time between
Figure FDA00031883826100000212
Computing the kth set of local sub-trajectories GkEnd point of neutron track and end point of kth standard long track
Figure FDA00031883826100000213
Minimum path value of time between
Figure FDA00031883826100000214
Obtaining the initial running time of the starting and ending point pairs of the sub-tracks
Figure FDA00031883826100000215
And the moment of termination of operation
Figure FDA00031883826100000216
According to the starting point and the ending point of the sub-track, the time of actually passing through the monitoring point
Figure FDA00031883826100000217
Calculating the acceptable time window of the sub-track according to the time interval h of the acceptable passing monitoring point
Figure FDA00031883826100000218
If it is
Figure FDA00031883826100000219
And is
Figure FDA00031883826100000220
Will sub-railPutting the trace into the k local sub-trace set G 'containing time window constraint'kPerforming the following steps;
(E4) according to the kth local sub-track set G 'containing time window constraint'kMultiplying the track number i with a given travel mode transfer coefficient alpha to obtain the potential passenger number alpha i; if the potential passenger number alpha i is larger than or equal to the minimum service number j of the customized buses, the local sub-track set G'kAnd the area within the range of r meters around the corresponding kth standard long track is the area required by the customized bus.
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