CN112598238A - Intelligent identification method for urban special vehicle service vacuum area - Google Patents

Intelligent identification method for urban special vehicle service vacuum area Download PDF

Info

Publication number
CN112598238A
CN112598238A CN202011473997.4A CN202011473997A CN112598238A CN 112598238 A CN112598238 A CN 112598238A CN 202011473997 A CN202011473997 A CN 202011473997A CN 112598238 A CN112598238 A CN 112598238A
Authority
CN
China
Prior art keywords
grid
special vehicle
service
special
specific
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202011473997.4A
Other languages
Chinese (zh)
Other versions
CN112598238B (en
Inventor
杨海强
刘银华
葛树志
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Qingdao University
Original Assignee
Qingdao University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Qingdao University filed Critical Qingdao University
Priority to CN202011473997.4A priority Critical patent/CN112598238B/en
Publication of CN112598238A publication Critical patent/CN112598238A/en
Application granted granted Critical
Publication of CN112598238B publication Critical patent/CN112598238B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group
    • G06Q10/063114—Status monitoring or status determination for a person or group
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Tourism & Hospitality (AREA)
  • Theoretical Computer Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Physics & Mathematics (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • Educational Administration (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Game Theory and Decision Science (AREA)
  • Development Economics (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention provides an intelligent identification method for a special vehicle service vacuum area of an urban, belonging to the technical field of intelligent urban traffic. The invention provides an absolute vacuum area for the service of the urban special vehicle, which can provide a reference suggestion for the long-term service level improvement of the special vehicle; the invention also provides a relatively vacuum area for the service of the urban special vehicle, which can provide a basis for the real-time management and scheduling of the special vehicle and relieve the problem of the service level reduction of the special vehicle in a specific time.

Description

Intelligent identification method for urban special vehicle service vacuum area
Technical Field
The invention belongs to the technical field of intelligent urban traffic, and particularly relates to an intelligent identification method for a special urban vehicle service vacuum area.
Background
Special vehicles such as taxies, net appointment vehicles, emergency vehicles and fire trucks in modern cities can provide travel, rescue, disaster relief and other services required by urban residents in life, and the convenience and safety of urban life are greatly improved. Ideally, a city is ideally provided with equivalent special vehicle services for each resident in the city, such as taxi and network appointment service, ambulance service in time of rescue, fire service in time of dealing with disasters, and the like, and corresponding services can be provided quickly according to response of the resident depending on whether the special vehicles can be distributed in the urban road network in a balanced manner. However, in practice, the distribution of special vehicles in urban road networks is unbalanced, and two problems exist: (1) due to the influence of factors such as unreasonable city planning and unbalanced traffic network coverage, the city range has absolute service vacuum areas, namely residents in the areas cannot obtain timely special vehicle service in most of time; (2) due to unreasonable overall scheduling, the dynamic change of the special vehicle brings a relative service vacuum area, namely an area where the service of the special vehicle cannot be responded in time in a short time.
Disclosure of Invention
The invention provides an intelligent identification method for a service vacuum area of a special vehicle in a city, which solves the problem that the service vacuum area of the special vehicle in the city cannot be accurately identified at present.
The invention provides an intelligent identification method for a service vacuum area of a special vehicle in an city, which comprises the following steps: dividing the city evaluation area into n × m square grids, counting the special vehicle service level nos of a specific grid (ii, jj) in all time periods through a formula (1),
Figure BDA0002834488170000011
wherein ii is [1, m ]]，jj∈[1,n]，
Figure BDA0002834488170000012
Is the number of all time intervals which do not meet the service level of the special vehicles of the residents in D days,
Figure BDA0002834488170000013
refers to the service level, δ, of a particular vehicle of a particular grid (ii, jj) during a particular time period T of a particular natural day d1For determining the threshold, the value range is (0,1)]S is the average matching index of the special city vehicle,
Figure BDA0002834488170000014
the number of unit time contained in each natural day d is shown, T is unit time,
when nos (ii, jj) ≧ δ2Then, it is determined that the particular grid (ii, jj) is serving the absolute vacuum grid, δ, for the special vehicle2Determining a threshold value for the absolute vacuum grid, wherein the value range is (0, 1); circularly calculating the special vehicle service levels of all n × m grids in all time periods, and obtaining all special vehicle service absolute vacuum grids, namely an urban special vehicle service absolute vacuum area;
calculating the current time period T of a particular grid (ii, jj) by equation (2)iThe service level of the special vehicle(s),
Figure BDA0002834488170000021
wherein the content of the first and second substances,
Figure BDA0002834488170000022
for a particular grid (ii, jj) at a current time period TiThe number of locating points of all vehicles in the vehicle;
Figure BDA0002834488170000023
for a particular grid (ii, jj) at a current time period TiThe number of positioning points of the inner special vehicle;
the particular grid (ii, jj) is synchronized in history, i.e. at a similar time period Ti-1,Ti,Ti+1The average service level of (3) is as in formula (3):
Figure BDA0002834488170000024
wherein the content of the first and second substances,
Figure BDA0002834488170000025
is that
Figure BDA0002834488170000026
Refers to a specific grid (ii, jj) on a specific natural day djFor a specific period of time TiService level of the interior special vehicle, dj＝{d1,d2,...,dD}，
Figure BDA0002834488170000027
When it is satisfied with
Figure BDA0002834488170000028
Then it may be determined that this particular grid (ii, jj) is a relative vacuum grid for servicing the special vehicle, where δ3And (3) judging a threshold value for the relative vacuum grid, wherein the value range is (0,1), and circularly calculating the service levels of the special vehicles of all n × m grids, so that the relative vacuum grid for the service of all the special vehicles can be obtained, namely the relative vacuum area for the service of the urban special vehicles.
Preferably, the average matching index S of the special vehicles in the city is calculated as shown in formula (4),
Figure BDA0002834488170000029
wherein the content of the first and second substances,
Figure BDA00028344881700000210
for a particular grid (ii, jj) on a particular natural day djT th (a)iThe number of anchor points of all vehicles in a time period,
Figure BDA00028344881700000211
for a particular grid (ii, jj) on a particular natural day djT th (a)iThe number of positioning points of the special vehicle in each time period.
Preferably, the computational grid (ii, jj) is on a particular natural day djT th (a)iThe method for the quantity of the positioning points of all vehicles or special vehicles in each time period comprises the following steps: first, it is determined whether a specific anchor point p (t, lng, lat) belongs to a specific grid (ii, jj), and the determination conditions are inequalities (5) and (6), and are fullIf the determination condition is satisfied, the specific anchor point p (t, lng, lat) belongs to the specific grid (ii, jj),
Figure BDA0002834488170000031
Figure BDA0002834488170000032
wherein t is a timestamp, Lng is a longitude coordinate of the specific positioning point, and lat is a latitude coordinate, [ Lng [ [ Lng ] of the specific positioning point0,Lng1]And [ Lat ]0,Lat1]A latitude and longitude coordinate range of the evaluation area;
then dividing each natural day d into a total number
Figure BDA0002834488170000033
The number of the time periods is one,
Figure BDA0002834488170000034
t is unit time, then the statistics can be obtained to obtain the specific grid (ii, jj) in the specific natural day djT th (a)iNumber of positioning points of all vehicles in each time period
Figure BDA0002834488170000035
At the same time, it is possible to statistically obtain the specific grid (ii, jj) on a specific natural day djT th (a)iNumber of positioning points of special vehicle in each time period
Figure BDA0002834488170000036
Wherein d isj＝{d1,d2,...,dD}，
Figure BDA0002834488170000037
The method identifies the special vehicle service absolute vacuum grid, namely identifies the special vehicle service absolute vacuum area, and the service absolute vacuum area defined by the invention is an area in which the number of the special vehicle positioning points cannot be matched with the number of all the vehicle positioning points for a long time.
The method identifies the special vehicle service relative vacuum grid, namely identifies the special vehicle service relative vacuum area, and the service relative vacuum area defined by the invention is an area in which the number of the current special vehicle positioning points cannot be matched with the number of the historical same-period special vehicle positioning points according to real-time (current time period) statistical information.
The invention has the beneficial effects that: the invention provides an intelligent identification method for a service vacuum area of a special vehicle in a city. Firstly, carrying out gridding division on an urban area; secondly, analyzing historical special service requirements in each grid; secondly, comparing historical special service requirements to calculate to obtain an absolute service vacuum area; and finally, calculating to obtain a relative service vacuum area according to the real-time service level. The invention has the following two advantages: (1) the method obtains the absolute vacuum area of the special vehicle service in the city by calculating that the historical special service requirement is not met for a long time, and provides a suggestion for improving the service level of the long-term property; (2) the relative service vacuum area of the special vehicle in the city is obtained by calculating that the real-time requirement is not met, so that the method can provide suggestions for special vehicle management departments and schedule in time to relieve service vacuum.
Drawings
FIG. 1 is a flow chart of an embodiment of an intelligent identification method for a special vehicle service vacuum area in a city;
fig. 2 is a schematic diagram of grid processing of the intelligent identification method for the service vacuum area of the special vehicle in the city.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and should not be construed as limiting the scope of the invention.
Examples
Referring to fig. 1, the method for intelligently identifying the urban special vehicle service vacuum area sequentially comprises four steps of urban area gridding, historical data statistics, special vehicle service absolute vacuum area identification and special vehicle service relative vacuum area identification.
1. Urban area meshing process
The method comprises the steps of firstly, carrying out gridding division on an urban area to be subjected to special vehicle service level evaluation, namely, dividing the urban evaluation area into n × m square grids, as shown in figure 2. The south-north direction is m square grids altogether, and the east-west direction is n square grids altogether, and the serial number of whole grids is: { (1,1), (1,2), (1,3),. }, (m, n-1), (m, n) }. The number of n and m and the size range of the evaluation area determine the size of each grid, and the size of each grid is 100 meters, 200 meters, 300 meters, 400 meters, 500 meters, 600 meters, 700 meters, 800 meters, 900 meters and 1000 meters in the invention, and the smaller the grid size is, the smaller the finally identified vacuum service area is.
Respectively acquiring longitude and latitude coordinate ranges of the evaluation area as follows: [ Lng0,Lng1]And [ Lat ]0,Lat1]The latitude and longitude coverage of each square grid can be computed separately, for a particular grid (ii, jj) (where ii e [1, m ∈)]，jj∈[1,n]) Its latitude and longitude ([ Lng(ii,jj),Lat(ii,jj)]) The coverage range is as follows:
Figure BDA0002834488170000041
Figure BDA0002834488170000042
2. statistics of historical data
The historical data of the embodiment mainly comprises two types, wherein the first type is all traffic travel data, the second type is travel data of a special vehicle, and the first type comprises the second type. The following are described respectively:
the first type is all traffic travel data mainly from private car GPS and public carThe traffic GPS, taxi GPS, electronic police detector, field survey and statistics, etc. mainly obtain the longitude and latitude information of the vehicle every 1 minute or 5 minutes, such as 15:30:00 in 2.9.9.2019, and the positioning point of the private vehicle with the license plate number 'Jing A99999' is [39.8922,116.3854 ]]. Such data may form a data set of all traffic, being a set of all anchor points
Figure BDA0002834488170000051
The specific information of each anchor point includes pall(t, ng, lat) are respectively a timestamp, longitude coordinates, latitude coordinates.
The second type is travel data of special vehicles, mainly from positioning point data of taxies, net appointment cars, emergency vehicles and fire trucks, similar to the first type, and is to acquire longitude and latitude information of the vehicles every 1 minute or 5 minutes, such as 15:30:00 in 2.9.2019.a positioning point of a taxi with a license plate of 'Jing A77777' [39.889,116.3712 ]]. The data can form a data set of the special vehicle, and is a set of positioning points of the special vehicle
Figure BDA0002834488170000052
The specific information of each anchor point includes pspe(t, ng, lat) are respectively a timestamp, longitude coordinates, latitude coordinates.
For a specific positioning point p (t, lng, lat), determining the dependency relationship with the grid (ii, jj), namely when the longitude and latitude coordinates of the positioning point satisfy the following conditions:
Figure BDA0002834488170000053
Figure BDA0002834488170000054
it may be determined that the particular anchor point p (t, lng, lat) belongs to mesh (ii, jj).
When the evaluated area (n x m grid range) was obtained for a certain period (D days, { D }1,d2,...,dD}) of the anchor point data, the calculation of historical statistical information can be carried out, and the calculation comprises the following steps:
the unit time T is the minimum time step for performing statistical calculation, and is usually 10 minutes, 15 minutes, 30 minutes, 60 minutes, and the like;
for each natural day, the total includes
Figure BDA0002834488170000055
Per unit time, i.e.
Figure BDA0002834488170000056
Then the statistically available grid (ii, jj) is given a certain natural day djT th (a)iNumber of positioning points of all vehicles in each time period
Figure BDA0002834488170000057
At the same time, the statistically available grid (ii, jj) is given a certain natural day djT th (a)iNumber of positioning points of special vehicle in each time period
Figure BDA0002834488170000058
Then, it can be known that within D days of the evaluation area (n × m grid ranges), there are n × m × D × nt total vehicle positioning points
Figure BDA0002834488170000059
And the number of n, m, D, nt special vehicle positioning points
Figure BDA00028344881700000510
3. Special vehicle service absolute vacuum area identification
And (3) based on the statistical information obtained in the step (2), identifying the absolute vacuum area of the special vehicle service. Firstly, calculating an average matching index S of special vehicles in the whole city to be used for evaluating the average service level of the special vehicles in the city, wherein the calculation mode is as follows:
Figure BDA0002834488170000061
then, the service level of the special vehicle in the specific time period T of the specific natural day d of the specific grid (ii, jj) is calculated as follows:
Figure BDA0002834488170000062
when in use
Figure BDA0002834488170000063
It can be determined that the special vehicle service level of the grid during this time period fails to meet the resident's requirements. Delta1A determination threshold value with a value range of (0,1)]The larger the threshold value is, the greater the model sensitivity is, namely, more service levels are judged to be not meeting the requirements of residents.
The service level nos of a particular grid (ii, jj) over all time periods is counted, calculated as follows:
Figure BDA0002834488170000064
wherein the content of the first and second substances,
Figure BDA0002834488170000065
the number of all the time intervals which do not meet the service level of the special vehicles of the residents in D days is referred to.
When nos (ii, jj) ≧ δ2Then, it can be determined that the grid (ii, jj) is the absolute vacuum grid, δ, serving the special vehicle2Determining a threshold value for the absolute vacuum grid, wherein the value range is (0,1), and when delta is2The larger the model sensitivity, the more difficult this grid is to identify as a special vehicle service absolute vacuum grid.
And circularly calculating the service levels of all the n-by-m grids to obtain all the special vehicle service absolute vacuum grids, namely the urban special vehicle service absolute vacuum area.
4. Special vehicle service relative vacuum area identification
For grid (ii, jj) at the current time period TiThe special vehicle service level of (2) is as follows:
Figure BDA0002834488170000066
the grid is synchronized in history (close time period, i.e. T)i-1,Ti,Ti+1) The average service level of (a) is as follows:
Figure BDA0002834488170000067
when it is satisfied with
Figure BDA0002834488170000071
Then it can be determined that the grid is a relatively vacuum grid, δ, for servicing the special vehicle3Determining a threshold value for the relative vacuum grid, wherein the value range is (0,1) when delta is3The larger the model sensitivity, the more difficult this grid is to identify as a special vehicle service relative vacuum grid.
And circularly calculating the service levels of all the n-x-m grids, so as to obtain all the special vehicle service relative vacuum grids, namely the city special vehicle service relative vacuum area.
According to the method, on the basis of urban grid division, the matching degree of the number of historical special vehicle positioning points and the number of all vehicle positioning points is utilized, the special vehicle service level of all grids is calculated and obtained, and accordingly, the method for identifying the absolute vacuum area of the special vehicle service is provided, and the area where the long-term special vehicle service cannot meet the requirements of residents can be accurately and quantitatively identified.
By utilizing the matching degree of the number of the real-time special vehicle positioning points and the number of the historical special vehicle positioning points, the method for identifying the relative vacuum area of the special vehicle service is provided, and the area which cannot meet the requirements of residents and is short-term and real-time special vehicle service can be accurately identified in a quantitative mode.

Claims (3)

1. The intelligent identification method for the service vacuum area of the special urban vehicle is characterized by comprising the following steps: dividing the city evaluation area into n × m square grids, counting the special vehicle service level nos of a specific grid (ii, jj) in all time periods through a formula (1),
Figure FDA0002834488160000011
wherein ii is [1, m ]]，jj∈[1,n]，
Figure FDA0002834488160000012
Is the number of all time intervals which do not meet the service level of the special vehicles of the residents in D days,
Figure FDA0002834488160000013
refers to the service level, δ, of a particular vehicle of a particular grid (ii, jj) during a particular time period T of a particular natural day d1For determining the threshold, the value range is (0,1)]S is the average matching index of the special city vehicle,
Figure FDA0002834488160000014
the number of unit time contained in each natural day d is shown, T is unit time,
when nos (ii, jj) ≧ δ2Then, it is determined that the particular grid (ii, jj) is serving the absolute vacuum grid, δ, for the special vehicle2Determining a threshold value for the absolute vacuum grid, wherein the value range is (0, 1); circularly calculating the special vehicle service levels of all n × m grids in all time periods, and obtaining all special vehicle service absolute vacuum grids, namely an urban special vehicle service absolute vacuum area;
calculating the current time period T of a particular grid (ii, jj) by equation (2)iThe service level of the special vehicle(s),
Figure FDA0002834488160000015
wherein the content of the first and second substances,
Figure FDA0002834488160000016
for a particular grid (ii, jj) at a current time period TiThe number of locating points of all vehicles in the vehicle;
Figure FDA0002834488160000017
for a particular grid (ii, jj) at a current time period TiThe number of positioning points of the inner special vehicle;
the particular grid (ii, jj) is synchronized in history, i.e. at a similar time period Ti-1,Ti,Ti+1The average service level of (3) is as in formula (3):
Figure FDA0002834488160000018
wherein the content of the first and second substances,
Figure FDA0002834488160000019
is that
Figure FDA00028344881600000110
Refers to a specific grid (ii, jj) on a specific natural day djFor a specific period of time TiService level of the interior special vehicle, dj＝{d1,d2,...,dD}，
Figure FDA00028344881600000111
When it is satisfied with
Figure FDA00028344881600000112
Then it may be determined that this particular grid (ii, jj) is a relative vacuum grid for servicing the special vehicle, where δ3The threshold is determined relative to the vacuum grid, with a range of values (0,1) and circularly calculating the service levels of the special vehicles of all the n-x-m grids, so as to obtain all the special vehicle service relative vacuum grids, namely the city special vehicle service relative vacuum area.
2. The intelligent urban special vehicle service vacuum area identification method according to claim 1, wherein the method comprises the following steps: the average matching index S of the special vehicles in the city is calculated as shown in formula (4),
Figure FDA0002834488160000021
wherein the content of the first and second substances,
Figure FDA0002834488160000022
for a particular grid (ii, jj) on a particular natural day djT th (a)iThe number of anchor points of all vehicles in a time period,
Figure FDA0002834488160000023
for a particular grid (ii, jj) on a particular natural day djT th (a)iThe number of positioning points of the special vehicle in each time period.
3. The intelligent city special vehicle service vacuum area identification method as claimed in claim 2, wherein the computational grid (ii, jj) is used for d specific natural dayjT th (a)iThe method for the quantity of the positioning points of all vehicles or special vehicles in each time period comprises the following steps: first, it is determined whether a specific anchor point p (t, lng, lat) belongs to a specific grid (ii, jj), if the determination conditions are satisfied as inequalities (5), (6), then the specific anchor point p (t, lng, lat) belongs to the specific grid (ii, jj),
Figure FDA0002834488160000024
Figure FDA0002834488160000025
wherein t is a timestamp, Lng is a longitude coordinate of the specific positioning point, and lat is a latitude coordinate, [ Lng [ [ Lng ] of the specific positioning point0,Lng1]And [ Lat ]0,Lat1]A latitude and longitude coordinate range of the evaluation area;
then dividing each natural day d into a total number
Figure FDA0002834488160000026
The number of the time periods is one,
Figure FDA0002834488160000027
t is unit time, then the statistics can be obtained to obtain the specific grid (ii, jj) in the specific natural day djT th (a)iNumber of positioning points of all vehicles in each time period
Figure FDA0002834488160000028
At the same time, the statistically available grid (ii, jj) is given a certain natural day djT th (a)iNumber of positioning points of special vehicle in each time period
Figure FDA0002834488160000029
Wherein d isj＝{d1,d2,...,dD}，
Figure FDA00028344881600000210
CN202011473997.4A 2020-12-14 2020-12-14 Intelligent identification method for urban special vehicle service vacuum area Active CN112598238B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011473997.4A CN112598238B (en) 2020-12-14 2020-12-14 Intelligent identification method for urban special vehicle service vacuum area

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011473997.4A CN112598238B (en) 2020-12-14 2020-12-14 Intelligent identification method for urban special vehicle service vacuum area

Publications (2)

Publication Number Publication Date
CN112598238A true CN112598238A (en) 2021-04-02
CN112598238B CN112598238B (en) 2022-07-01

Family

ID=75195438

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011473997.4A Active CN112598238B (en) 2020-12-14 2020-12-14 Intelligent identification method for urban special vehicle service vacuum area

Country Status (1)

Country Link
CN (1) CN112598238B (en)

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103077605A (en) * 2012-12-14 2013-05-01 中国航天系统工程有限公司 Scheduling method and device for vehicle
CN104574967A (en) * 2015-01-14 2015-04-29 合肥革绿信息科技有限公司 City large-area road network traffic sensing method based on plough satellite
CN105575110A (en) * 2016-03-08 2016-05-11 江苏物联网研究发展中心 GIS-based smart city management special vehicle monitoring system
CN110111575A (en) * 2019-05-16 2019-08-09 北京航空航天大学 A kind of Forecast of Urban Traffic Flow network analysis method based on Complex Networks Theory
JP2019219845A (en) * 2018-06-19 2019-12-26 日産自動車株式会社 Vehicle management system and vehicle management method
CN111861217A (en) * 2020-07-22 2020-10-30 上海汽车集团股份有限公司 Vehicle allocation method and device and computer readable storage medium
CN111882877A (en) * 2020-08-31 2020-11-03 北京航空航天大学 Traffic area analysis method and device

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103077605A (en) * 2012-12-14 2013-05-01 中国航天系统工程有限公司 Scheduling method and device for vehicle
CN104574967A (en) * 2015-01-14 2015-04-29 合肥革绿信息科技有限公司 City large-area road network traffic sensing method based on plough satellite
CN105575110A (en) * 2016-03-08 2016-05-11 江苏物联网研究发展中心 GIS-based smart city management special vehicle monitoring system
JP2019219845A (en) * 2018-06-19 2019-12-26 日産自動車株式会社 Vehicle management system and vehicle management method
CN110111575A (en) * 2019-05-16 2019-08-09 北京航空航天大学 A kind of Forecast of Urban Traffic Flow network analysis method based on Complex Networks Theory
CN111861217A (en) * 2020-07-22 2020-10-30 上海汽车集团股份有限公司 Vehicle allocation method and device and computer readable storage medium
CN111882877A (en) * 2020-08-31 2020-11-03 北京航空航天大学 Traffic area analysis method and device

Also Published As

Publication number Publication date
CN112598238B (en) 2022-07-01

Similar Documents

Publication Publication Date Title
CN107610469B (en) Day-dimension area traffic index prediction method considering multi-factor influence
Calabrese et al. Understanding individual mobility patterns from urban sensing data: A mobile phone trace example
Levine et al. Spatial analysis of Honolulu motor vehicle crashes: II. Zonal generators
CN102460534B (en) Computer implementation method of predicting expected road traffic conditions based on historical and current data and computing system
CN107918826B (en) Driver evaluation and scheduling method for driving environment perception
Pan et al. Exploring spatial variation of the bus stop influence zone with multi-source data: A case study in Zhenjiang, China
CN105512741A (en) Bus passenger traffic combined prediction method
CN110751311A (en) Data extraction and real-time prediction method for sporadic traffic jam duration
He et al. Using spatial regression methods to evaluate rural emergency medical services (EMS)
Li et al. Simulation model for estimating bus dwell time by simultaneously considering numbers of disembarking and boarding passengers
Tavafoghi et al. A queuing approach to parking: Modeling, verification, and prediction
CN115345373A (en) Accessibility prediction method for old people in public service facilities
Fu et al. Spatial analysis of taxi speeding event using GPS trajectory data
CN114549075A (en) Regional parking evaluation method based on parking and video big data
Sun et al. Analyzing spatiotemporal daily travel source carbon emissions based on taxi trajectory data
CN112598238B (en) Intelligent identification method for urban special vehicle service vacuum area
Raiyn Speed adaptation in urban road network management
CN113095387A (en) Road risk identification method based on networking vehicle-mounted ADAS
Sun et al. A data-driven method for measuring accessibility to healthcare using the spatial interpolation model
Cho et al. GIS-based analysis on vulnerability of ambulance response coverage to traffic condition: a case study of Seoul
Thomas et al. Variations in urban traffic volumes
CN108920655B (en) Method and device for quantifying space-time coverage range of road weather information system
CN114202923B (en) Urban road traffic jam space-time distribution comprehensive index evaluation method
Bao et al. Spatial analysis for the usage of ride-sourcing services, an application of geographically weighted regression
CN116703011B (en) Aviation passenger flow distribution prediction method and device, electronic equipment and storage medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
CB02 Change of applicant information

Address after: Laoshan District of Shandong city of Qingdao province Hongkong 266000 Road No. 7

Applicant after: QINGDAO University

Address before: 266000 No. 308, Ningxia Road, Qingdao, Shandong

Applicant before: QINGDAO University

CB02 Change of applicant information
GR01 Patent grant
GR01 Patent grant