CN108710996A - Gather region hotel addressing appraisal procedure in hotel based on tourism trip time and space usage - Google Patents
Gather region hotel addressing appraisal procedure in hotel based on tourism trip time and space usage Download PDFInfo
- Publication number
- CN108710996A CN108710996A CN201810403329.0A CN201810403329A CN108710996A CN 108710996 A CN108710996 A CN 108710996A CN 201810403329 A CN201810403329 A CN 201810403329A CN 108710996 A CN108710996 A CN 108710996A
- Authority
- CN
- China
- Prior art keywords
- hotel
- region
- day
- tourism
- degree
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000007667 floating Methods 0.000 claims abstract description 68
- 238000010168 coupling process Methods 0.000 claims abstract description 62
- 238000005859 coupling reaction Methods 0.000 claims abstract description 62
- 230000008878 coupling Effects 0.000 claims abstract description 49
- 238000004458 analytical method Methods 0.000 claims abstract description 29
- 230000002093 peripheral effect Effects 0.000 claims abstract description 18
- HUTDUHSNJYTCAR-UHFFFAOYSA-N ancymidol Chemical compound C1=CC(OC)=CC=C1C(O)(C=1C=NC=NC=1)C1CC1 HUTDUHSNJYTCAR-UHFFFAOYSA-N 0.000 claims description 10
- 230000002776 aggregation Effects 0.000 claims description 9
- 238000004220 aggregation Methods 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 6
- 230000002123 temporal effect Effects 0.000 claims description 6
- 238000011160 research Methods 0.000 claims description 4
- 238000007405 data analysis Methods 0.000 abstract description 4
- 238000005516 engineering process Methods 0.000 abstract description 2
- 238000010835 comparative analysis Methods 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 18
- 238000009826 distribution Methods 0.000 description 8
- 238000011156 evaluation Methods 0.000 description 5
- 230000003139 buffering effect Effects 0.000 description 4
- 238000013439 planning Methods 0.000 description 4
- 239000000523 sample Substances 0.000 description 3
- 238000010276 construction Methods 0.000 description 2
- 240000007594 Oryza sativa Species 0.000 description 1
- 235000007164 Oryza sativa Nutrition 0.000 description 1
- 238000009412 basement excavation Methods 0.000 description 1
- 230000003542 behavioural effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 235000009566 rice Nutrition 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 238000013316 zoning Methods 0.000 description 1
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
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
-
- 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
- G06Q50/10—Services
- G06Q50/12—Hotels or restaurants
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Tourism & Hospitality (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- Educational Administration (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Development Economics (AREA)
- Primary Health Care (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Traffic Control Systems (AREA)
Abstract
The present invention relates to a kind of hotels based on tourism trip space-time and Floating Car OD degree of coupling model analysis method, a kind of travel surge period hotel peripheral path traffic congestion judgment method based on Floating Car GPS data and a kind of hotel based on tourism trip time and space usage to gather region hotel addressing appraisal procedure.The present invention has found the relationship between section congestion and hotel's layout by comparative analysis tourism day and workaday hotel's peripheral path jam level situation, proposes that a kind of hotel based on trip time and space usage of travelling gathers region hotel addressing appraisal procedure.The present invention compared with the existing technology, fully excavates existing hotel's data and Floating Car OD data, with GIS Spatial Data Analysis, proposes more accurate, science hotel's Dynamic Location appraisal procedure, and better reference frame is provided for the rationality of location judgement in hotel.
Description
Technical field
The present invention is a kind of method for gathering region progress hotel's addressing assessment to hotel based on real-time traffic states variation,
It is related to the technical fields such as urban planning, tourism planning, geography information, big data analysis and excavation.
Background technology
Hotel is that city tourism develops essential material conditions, is Urban Tourism Image and service quality in tourism
Important factor in order.The construction in hotel is not only that the operating of city tourism provides good basis, is lived to urban society yet
Aspect has an immense impact on.Meanwhile probe into drinkery space Distribution Pattern, to the rationality of location in hotel judged also increasingly at
For a major issue of tourism planning and urban planning.
From the point of view of the siting analysis in previous hotel, people are primarily upon the position in hotel, the public service facility on periphery, people
The problems such as flow, traffic accessibility and construction cost, seldom inquires into influence of hotel's addressing to traffic behavior.Since city exists
Traffic behavior between date between travel date in different time has differences, therefore the present invention will travel day Floating Car OD
Data establish degree of coupling model with hotel's position data, while based on the POI data in hotel, being obtained by kernel density estimation method
To coverage of the hotel group on geographical space.On the basis of determining that hotel gathers region with high degree of coupling region, comparison
The jam situation of tourism day and working day hotel periphery road network travel surge period, analyses whether that the increase of tourist causes hotel
The raising of peripheral path jam level, with this establish it is a kind of based on tourism trip time and space usage hotel gather the addressing of region hotel
Appraisal procedure.
Invention content
It is an object of the invention to overcome the deficiencies of the prior art and provide a kind of hotel based on tourism trip space-time and float
Motor-car OD degree of coupling model analysis methods；A kind of judgement side changing verification hotel's rationality of location based on real-time traffic states
Method；It is a kind of based on tourism trip time and space usage hotel gather region hotel addressing appraisal procedure.
Technical scheme is as follows：
A kind of hotel and Floating Car travelling OD degree of coupling model analysis method based on tourism trip space-time, using road network as
Analysis condition, the room number R of hotel HHThe gathereding degree for representing the hotel, with H to its nearest crossing OintersectionEuclidean away from
Go on a journey by bus as this hotel tourist tolerable maximum walking distance from r, obtain centered on H, r for radius hotel's shadow
Ring range SH, count and fall into SHIn Floating Car OD quantity NHODInfluence degree I as HH, to which the coupling of hotel H be calculated
Right CH；It is as follows：
Step 101) matches the latitude and longitude information in hotel, Floating Car OD data with region contour cartographic information：
The geographical location in each hotel, the position of each passenger getting on/off point are shown on region contour map；
Step 102) enables angle { θ |0<θ≤pi/2 } road be two of hotel's H arest neighbors and have intersection crossing Ointersection
Road, H to its nearest crossing OintersectionEuclidean distance r go on a journey by bus as tourist tolerable maximum walking range, build
It is vertical centered on H, r for radius hotel coverage SH；
Step 103) chosen distance H vertical lines L is most short and in SHInterior section d is fallen into as research object, statistics on d
Floating Car travelling OD quantity NHODInfluence degree I as hotel HH；
Step 104) sets shared M hotel, repeats step 101) to step 103) and calculates the room number that each hotel possesses
RmWith influence degree Im, wherein m ∈ { 1,2 ..., M }；
Step 105) counts the number R that room number is most in M hotelmax=max { Rm, and find out ImMiddle quantity maximum value
Imax=max { Im}；
Step 106) calculates each hotel's degree of coupling C according to the following formulam, as follows：
Step 107) obtains the degree of coupling result { C in M hotel according to step 106)1、C2…CM}；
Step 108) is by the degree of coupling result { C in M hotel1、C2…CMCarry out Density Estimator；
The cuclear density value expression of M hotel's degree of coupling on region contour map and is overlapped by step 109), then into
Row divides, if being divided into KoClass, KoIf the Ganlei more than class center density value is high degree of coupling region Scoupling。
A kind of travel surge period hotel peripheral path traffic congestion judgment method based on Floating Car GPS data, is based on
Described hotel and Floating Car travelling OD degree of coupling model analysis method based on tourism trip space-time, by the high coupling in whole hotels
Right region Scoupling, whole hotel group gather region J and tourism hot spot areas ZtourIt is overlapped, is met simultaneously floating
Motor-car is more, hotel is more, W survey region more than tourist, and compares tourism day in W region using the GPS information of Floating Car
With the workaday road degree of crowding, to determine whether the traffic pressure of travel surge period hotel peripheral path increases；Specifically
Steps are as follows：
Step 201) matches the latitude and longitude information in hotel with region contour cartographic information：On region contour map
Show the geographical location in each hotel；
Step 202) gathers principle by opposite, and all hotels are divided into QSUMA hotel group；
Step 203) sets Q as QSUMOne in a hotel group, include N number of hotel, a for the n-th ∈ { 1,2 ..., N }
The maximum search radius in hotel, Density Estimator is R, indicates the biggest impact range in a hotel, is equipped with search radius r ∈
[0,R], S is shared in rrOther a hotels,For SrThe latitude and longitude coordinates in a hotel,For sr∈{1,
2 ..., SrA hotel is to the shortest distance in this hotel, K () is kernel function, and the kernel function f (r) of the hotels Ze Ci gathereding degree is：
Step 204) is performed both by step 203) to the N number of hotel for belonging to Q, and N number of hotel is found out according to the kernel function f (r)
Cuclear density value；
The cuclear density value in N number of hotel is indicated on region contour map and is overlapped by step 205), obtains gathering for Q
Region SQ；
Step 206) is to whole QSUMA hotel group repeats step 203) to step 205), and to the collection of each hotel group
Poly- region is overlapped to obtain whole hotel group aggregation zonesIt is rightNature step-wise process division is carried out, if being divided into
KclassificationClass, then KclassificationIf the Ganlei more than the hotels Lei Zhong cuclear density value is hotel aggregation zone J；
Step 207) is by the high degree of coupling region Scoupling, hotel aggregation zone J and known tourist travel popular area
Domain ZtourIt is overlapped, obtains the main region that W overlapping region between three is traffic state analysis；
Step 208) analyzes the average travel speed of floating car data for day of travelling, obtain Floating Car one day it
Interior road network average speed temporal behavior finds out the trip peak period t of tourism day；
Step 209) chooses a region w ∈ { 1,2 ..., W }, by the latitude and longitude information in hotel, region in w-th of region
Profile cartographic information is matched with the floating car data of the region peak period：The ground in hotel is shown on region contour map
Manage position, the road network in region and Floating Car specific location；
Step 210) calculates tourism day floating car data and obtains each section jam level { LD on w-th of regionw1、
LDw2..., evaluation work day floating car data obtain each section jam level { FD in w-th of regionw1、FDw2..., compare tourism
The mileage and proportion of day different from working day section jam levels, the increase for analyzing tourism day tourist are all for hotel
The influence degree on by-pass road.
It is a kind of based on tourism trip time and space usage hotel gather region hotel addressing appraisal procedure, based on it is described based on
The travel surge period hotel peripheral path traffic congestion judgment method of Floating Car GPS data, is as follows：
Step 301) calculates the traffic behavior of respective distances nearest road in hotel's in w-th of region, compares tourism day and work
The variation for making day traffic jam level, by day section YD more increased than working day traffic jam level of travellingw={ YDw1,YDw2,…
YDwroad... carry out hotel's siting analysis；
Step 302) is for certain a road section YDwroadOn a certain hotel JSIt is analyzed, traffic impact sectionFor on its nearest road, from the midpoint for the road for reaching its left side arest neighbors hotel to reaching its right
The midpoint of the road in arest neighbors hotel；
Step 303) seeks YDwThe average value N of upper tourism day OD quantity more increased than trip's working dayaveYDw；
Step 304) compares tourism day than working day in section YDwroadJsUpper increased Floating Car OD quantity
WhenMore than NaveYDw, this hotel JSTo influence the hotel of traffic behavior；
Step 305) is to YDwroadOn whole hotels execute step 304) analysis determine whether it is influence traffic behavior
Hotel；
Step 306) is for day whole section YD more increased than working day traffic jam level of travelling in w-th of regionwWeight
Multiple step 302) is to step 305), and so far whole hotels in region have carried out rationality of location judgement；
The hotel for influencing traffic behavior is identified and is shown on map by step 307).
Beneficial effects of the present invention are as follows：
(1) coupling model in the present invention considers the relationship of hotel and peripheral path, with true road network distance
To define the distance arrived involved in coupling model, while starting with from the behavioural habits of tourist's go off daily, using crossing as tourist
The tolerable walking maximum magnitude of Floating Car is taken, establishes the hotel influence area as radius, it is contemplated that tourist generally selects
The road trip nearest apart from oneself is selected, therefore selects the nearest road in coverage, statistics falls into Floating Car OD therein
Influence power of the number as hotel；And coupling model is finally substituted into, calculate its coupling in space；To really reflect hotel
With the coupled relation between Floating Car OD data, and OD data show as the travel behaviour feature of passenger getting on/off just, will be big
Data are preferably associated with visitor behavior, to reflect the service condition in hotel by the travel behaviour of tourist so that point
Analysis more gears to actual circumstances, accurately；
(2) present invention considers essential attribute of the hotel as tourism infrastructure, by hotel and tourist's trip (Floating Car
OD data), tourism hot spot areas closely combined, gather in the high degree of coupling region in whole hotels, whole hotel group
Analyze the traffic congestion situation of hotel's peripheral path on the basis of region and tourism hot spot areas, while from tourism day and working day
Two time points set out, interactively of the hotel for peripheral path congestion during more prominent tourism so that analysis more added with
Specific aim；
(3) the traffic behavior evaluation after the present invention is towards addressing, can more accurately analyze hotel after building up to week
The actual influence degree of by-pass road operation, really reflects whether hotel's addressing is reasonable from traffic behavior angle；Simultaneously in view of floating
Traffic big data of the motor-car data as comparative maturity at this stage has wide coverage, and data reliability is high, and operability is strong
The characteristics of, it can really reflect road in not same date, the true passage situation of time.Future can be added as net about vehicle,
The data such as windward driving carry out the differentiation of traffic behavior to the addressing instance in more thoroughly evaluating hotel, have stronger generalization
With popularization.
Invention is further described in detail with reference to the accompanying drawings and embodiments, but one kind of the present invention is based on travelling out
Gather region hotel addressing appraisal procedure and be not limited to embodiment in the hotel of row time and space usage.
Description of the drawings
The hotels Tu1Shi coverage schematic diagram；
The hotels Tu2Shi cuclear density area schematic；
Fig. 3 is kernel function curve synoptic diagram；
The hotels Tu4Shi traffic impact section schematic diagram；
Fig. 5 is hotel room distributed number schematic diagram；
The hotels Tu6Shi influence degree distribution schematic diagram；
Fig. 7 is m=1 hotel's degree of coupling kernel function schematic diagram；
Fig. 8 is to be shown in schematic diagram on region contour map to 2766 hotel's degree of coupling kernel function image superpositions；
Fig. 9 is the n-th=1 hotel's kernel function schematic diagram；
Figure 10 is that 299 hotel's kernel function image superpositions are shown in the schematic diagram on region contour map in hotel group Q；
Figure 11 is that 2766 hotels gather schematic diagram when nature step-wise process falls into 5 types；
Figure 12 is certain city's tourist attraction spatial distribution schematic diagram；
Figure 13 is traffic state analysis main region schematic diagram；
Figure 14 is road network average speed temporal behavior schematic diagram on October 1；
Figure 15 is Zhongshan Road shopping mall main region traffic state analysis schematic diagram；
Figure 16 is tourism day congestion in road situation schematic diagram；
Figure 17 is working day congestion in road situation schematic diagram；
Jam level improves section schematic diagram during Figure 18 is tourism；
Figure 19 is Shopping Malls on Zhongshan Road domain hotel rationality of location spatial distribution schematic diagram.
Specific implementation mode
The present invention is further described in detail with reference to the accompanying drawings and embodiments.
A kind of hotel and Floating Car travelling OD degree of coupling model analysis method based on tourism trip space-time, using road network as
Analysis condition, the room number R of hotel HHThe gathereding degree for representing the hotel, with H to its nearest crossing OintersectionEuclidean away from
Go on a journey by bus as this hotel tourist tolerable maximum walking distance from r, and can obtain centered on H, r is radius
Hotel coverage SH, count and fall into SHIn Floating Car OD quantity NHODInfluence degree I as HH, to which wine be calculated
The degree of coupling C of shop HH.It is as follows：
Step 101) matches the latitude and longitude information in hotel, Floating Car OD data with region contour cartographic information：
The geographical location in each hotel, the position of each passenger getting on/off point are shown on region contour map；
Step 102) as shown in Figure 1, x-axis, two of the hotels yZhou Shi H arest neighbors have intersection crossing Ointersection(angle
{θ|0<θ≤pi/2 }) road, H to its nearest crossing OintersectionEuclidean distance r as tourist by bus go on a journey it is tolerable most
Big walking range, establish centered on H, r for radius hotel coverage SH；
Step 103) chosen distance H vertical lines L is most short and in SHInterior section d is fallen into as research object, statistics on d
Floating Car travelling OD quantity NHODInfluence degree I as hotel HH；
Step 104) sets shared M hotel, repeats step 101) to step 103) and calculates the room number that each hotel possesses
RmWith influence degree Im, wherein m ∈ { 1,2 ..., M }；
Step 105) counts the number R that room number is most in M hotelmax=max { Rm, and find out ImMiddle quantity maximum value
Imax=max { Im}；
Step 106) calculates each hotel's degree of coupling C according to the following formulam, as follows：
Step 107) obtains the degree of coupling result { C in M hotel according to step 106)1、C2…CM}；
Step 108) is by the degree of coupling result { C in M hotel1、C2…CMCarry out Density Estimator；
The cuclear density value expression of M hotel's degree of coupling on region contour map and is overlapped by step 109), then into
Row divides, if being divided into KoClass, KoIf the Ganlei more than class center density value is high degree of coupling region Scoupling。
A kind of travel surge period hotel peripheral path traffic congestion judgment method based on Floating Car GPS data, is based on
Hotel and Floating Car travelling OD degree of coupling model analysis method described in claim 1 based on tourism trip space-time, will be a certain
The high degree of coupling region S in city whole hotelcoupling, whole hotel group gather region J and tourism hot spot areas ZtourIt carries out
Superposition, is met that Floating Car is more, hotel is more, W survey region more than tourist simultaneously, and the GPS information of utilization Floating Car is in W
Compare tourism day and the workaday road degree of crowding in a region, to determine the traffic of travel surge period hotel peripheral path
Whether pressure increases；It is as follows：
Step 201) matches the latitude and longitude information in hotel with region contour cartographic information：On region contour map
Show the geographical location in each hotel；
Step 202) gathers principle by opposite, and all hotels are divided into QSUMA hotel group；
Step 203) sets Q as QSUMOne in a hotel group, include N number of hotel, a for the n-th ∈ { 1,2 ..., N }
The maximum search radius in hotel, Density Estimator is R, indicates the biggest impact range in a hotel, is equipped with search radius r ∈
[0,R], S is shared in rrOther a hotels,For SrThe latitude and longitude coordinates in a hotel,For sr∈ 1,2 ...,
SrTo the shortest distance in this hotel, K () is that (non-negative, integral is 1 to kernel function, meets cuclear density property, and mean value for a hotel
For 0), the kernel function f (r) of the hotels Ze Ci gathereding degree is：
The cuclear density region in one hotel is as shown in Fig. 2 circles：Center of circle triangle indicates hotel, and being indicated in round edge and circle should
The cuclear density region in hotel, is theoretically covered by smooth surface f (r) above, in terms of direction arbitrarily parallel with map as
(dotted line is assumed for principle of specification) shown in Fig. 3, size of the density area from the center of circle to round edge is that kernel function is fallen on map
Size；
Step 204) is performed both by step 203) to the N number of hotel for belonging to Q, and N number of hotel is found out according to the kernel function f (r)
Cuclear density value；
The cuclear density value in N number of hotel is indicated on region contour map and is overlapped by step 205), obtains gathering for Q
Region SQ；
Step 206) is to whole QSUMA hotel group repeats step 203) to step 205), and to the collection of each hotel group
Poly- region is overlapped to obtain whole hotel group aggregation zonesIt is rightNature step-wise process division is carried out, if being divided into
KclassificationClass, then KclassificationIf the Ganlei more than the hotels Lei Zhong cuclear density value is hotel aggregation zone J；
Step 207) is by the high degree of coupling region Scoupling, hotel aggregation zone J and known tourist travel popular area
Domain ZtourIt is overlapped, obtains the main region that W overlapping region between three is traffic state analysis；
Step 208) analyzes the average travel speed of floating car data for day of travelling, obtain Floating Car one day it
Interior road network average speed temporal behavior finds out the trip peak period t of tourism day；
Step 209) chooses a region w ∈ { 1,2 ..., W }, by the latitude and longitude information in hotel, region in w-th of region
Profile cartographic information is matched with the floating car data of the region peak period：The ground in hotel is shown on region contour map
Manage position, the road network in region and Floating Car specific location；
Step 210) calculates tourism day floating car data and obtains each section jam level { LD on w-th of regionw1、
LDw2..., evaluation work day floating car data obtain each section jam level { FD in w-th of regionw1、FDw2..., compare tourism
The mileage and proportion of day different from working day section jam levels, the increase for analyzing tourism day tourist are all for hotel
The influence degree on by-pass road.
It is a kind of based on tourism trip time and space usage hotel gather region hotel addressing appraisal procedure, based on it is described based on
The travel surge period hotel peripheral path traffic congestion judgment method of Floating Car GPS data, is as follows：
Step 301) calculates the traffic behavior of respective distances nearest road in hotel's in w-th of region, compares tourism day and work
The variation for making day traffic jam level, by day section YD more increased than working day traffic jam level of travellingw={ YDw1,YDw2,…
YDwroad... carry out hotel's siting analysis；
Step 302) is for certain a road section YDwroadOn a certain hotel JSIt is analyzed, traffic impact sectionFor on its nearest road, from the midpoint for the road for reaching its left side arest neighbors hotel to reaching its right most
The midpoint of the road in neighbour hotel, as shown in figure 4, triangle indicates hotel JS, square indicate its right space length its most
A close hotel, circle indicate a hotel of its left margin nearest neighbours, point OleftIndicate round hotel and JsDistance exist
Road YDwroadOn projection midpoint, LhalfIndicate round hotel and JsDistance in road YDwroadOn projector distance one
Half, point OrightIndicate square hotel and JSDistance in road YDwroadOn projection midpoint, DhalfIndicate square hotel with
JSProjector distance of the distance on road Road half, JSIn road YDwroadOn YDwroadJsFor from point OleftTo point
OrightSection；
Step 303) seeks YDwThe average value N of upper tourism day OD quantity more increased than working dayaveYDw；
Step 304) compares tourism day than working day in sectionUpper increased Floating Car OD quantityWhenMore than NaveYDw, this hotel JSTo influence the hotel of traffic behavior；
Step 305) is to YDwroadOn whole hotels execute step 304) analysis determine whether it is influence traffic behavior
Hotel；
Step 306) is for day whole section YD more increased than working day traffic jam level of travelling in w-th of regionwWeight
Multiple step 302) is to step 305), and so far whole hotels in region have carried out rationality of location judgement；
The hotel for influencing traffic behavior is identified and is shown on map by step 307).
Embodiment 1
By taking the hotels m1604 as an example, the road intersection of periphery arest neighbors is Urban Branch Road (x-axis), city subsidiary road (y
Axis), crossing OintersectionFor the intersection (θ=90 °) of city subsidiary road and Urban Branch Road, the floating of on October 1st, 2014 is chosen
Motor-car OD is sample data, therefore is ridden out as tourist by r=58.2 meters of the Euclidean distance in the hotels m1604 to its nearest crossing
The tolerable maximum walking range of row, is established with r=58.2 meters as radius, the hotels H=m1604 are the buffering area in the center of circle.Selection
Section d=108.62 meters apart from L=20.69 meters of hotel's vertical line shortest distance, and in hotel's buffering area is used as research
Object, statistics fall into the Floating Car OD quantity N on dHOD=169, that is, there is the influence degree I in the hotels m1604H=169.To certain
All hotels of city are analyzed as follows：Certain city shares M=2766 hotel, and the room number in each hotel is as shown in table 1, all
The distribution of hotel room quantity is as shown in figure 5, it can thus be concluded that the largest number of number R of hotel roommax=601.
The influence degree in each hotel is as shown in table 2, the distributions of whole hotel's influence degrees as shown in fig. 6, it can thus be concluded that
Hotel influence degree maximum value Imax=1275.For the m=1 hotel, the room number R that possessesm=9 rooms, hotel
Influence degree Im=94, the degree of coupling C in this hotelmThe degree of coupling kernel function image in the hotel=0.001104, Ze Ci is as shown in Figure 7.
Are carried out by kernel function image superposition and is shown on region contour map for all 2766 hotels, as shown in Figure 8.To Fig. 8 into
Row divides manually, is divided into Ko=5 classes, then it is the preferable region S of the degree of coupling that cuclear density, which is worth more class,coupling。
Table 1
| Serial number | Hotel name | Longitude | Latitude | Room number |
| 0 | m0 | 118.07477 | 24.45818 | 9 |
| 1 | m1 | 118.07049 | 24.45871 | 16 |
| 2 | m2 | 118.07059 | 24.459126 | 18 |
| 3 | m3 | 118.07468 | 24.458097 | 20 |
| 4 | m4 | 118.11213 | 24.439148 | 18 |
| 5 | m5 | 118.07435 | 24.458244 | 5 |
| … | …………………… | … | … | … |
| … | …………………… | … | … | … |
| … | …………………… | … | … | … |
| 2761 | m2761 | 118.18231 | 24.473981 | 291 |
| 2762 | m2762 | 118.16079 | 24.454539 | 60 |
| 2763 | m2763 | 118.1935 | 24.506071 | 11 |
| 2764 | m2764 | 118.15691 | 24.478769 | 105 |
| 2765 | m2765 | 118.15467 | 24.483657 | 100 |
Table 2
Embodiment 2
It is analyzed as follows by taking a certain hotel group in 2017 cities Nian Mou as an example：This hotel group Q has N=299 hotel, for the
N=1 hotel, R=1500 meters of the maximum search radius of Density Estimator, r=1500 meters of search radius share in r
Sr=325 other hotels,For sRThe shortest distance in a hotel to this hotel is as shown in table 3.
Table 3
The image of the kernel function f (r) in the hotels Ze Ci is as shown in Figure 9.Kernel function is carried out for 299 hotels of hotel group
Image superposition simultaneously shows on region contour map, as shown in Figure 10.For 2766 hotels of group of whole hotels with radius R=
1500 meters progress gathereding degree kernel function image superpositions simultaneously shown on region contour map, as shown in figure 11.It is rightInto
Row nature step-wise process divides, if being divided into Kclassification=5 classes, interrupting value is respectively 12.40634066,38.04611134,
73.61095455,130.6801216,210.9077911, then hotel's cuclear density be worth more kmore=3 hotels Lei Wei most assemble
Region.Hotel is most gathered to the best region S of region J (as shown in figure 11), the tourism day degree of couplingcoupling(as shown in Figure 8)
And certain city tourism hot spot areas Ztour(such as Figure 12).The overlapping region W for being overlapped to obtain traffic state analysis is shown in area
On the profile map of domain as shown in figure 13, it chooses and carried out floating car data analysis October 1, obtain Floating Car road network within one day
Average speed temporal behavior is as shown in figure 14, and it is 10 to find out tourism peak hour day t:30-11:30.It is with the w=1 region
For Zhongshan Road shopping mall is as shown in figure 15, tourism day (on October 1st, 2014) and working day (on October 14th, 2014) are calculated
Floating car data obtain each section jam level and shared mileage on the w=1 region, ratio (according to《Beijing's road is handed over
Logical postitallation evaluation index system》The standard divided about road section traffic volume Operation class), as shown in table 4, day very congestion road of travelling
Duan Licheng increases by 1307.03 meters compared with working day, and ratio increases by 8.2%, and moderate congestion mileage increases by 1594.63 meters, and ratio increases
10.01%.Therefore the increase of hotel tourist causes the congestion level aggravation of peripheral path during travelling.
Table 4
Embodiment 3
By taking the hotels m1604 as an example, the road intersection of periphery arest neighbors is Urban Branch Road (x-axis), city subsidiary road (y
Axis), crossing OintersectionFor the intersection (θ=90 °) of city subsidiary road and Urban Branch Road, the floating of on October 1st, 2014 is chosen
Motor-car OD rides as sample data, therefore by r=58.2 meters of the Euclidean distance in the hotels m1604 to its nearest crossing as tourist
Go on a journey tolerable maximum walking range, establish with r=58.2 meter as radius, the hotels H=m1604 for the center of circle buffering area.Choosing
It selects apart from L=20.69 meters of hotel's vertical line shortest distance, and section d=108.62 meters in hotel's buffering area is used as and grinds
Study carefully object, statistics falls into the Floating Car OD quantity N on dHOD=169, that is, there is the influence degree I in the hotels m1604H=169.It is right
All hotels of certain city are analyzed as follows：Certain city shares M=2766 hotel, and the room number in each hotel is as shown in table 1, entirely
The distribution of portion's hotel room quantity is as shown in figure 5, it can thus be concluded that the largest number of number R of hotel roommax=601.Each hotel
Influence degree it is as shown in table 2, the distribution of whole hotel's influence degrees is as shown in fig. 6, it can thus be concluded that hotel's influence degree is maximum
Value Imax=1275.For the m=1 hotel, the room number R that possessesm=9 rooms, the influence degree I in hotelm=94, this
The degree of coupling C in hotelmThe degree of coupling kernel function image in the hotel=0.001104, Ze Ci is as shown in Figure 7.For all 2766 wine
Shop carries out kernel function image superposition and is shown on region contour map, as shown in Figure 8.Fig. 8 is divided manually, is divided into Ko
=5 classes, then it is the best region S of the degree of coupling that cuclear density, which is worth 3 more classes,coupling。
It is analyzed as follows by taking a certain hotel group in 2017 cities Nian Mou as an example：This hotel group Q has N=299 hotel, for the
N=1 hotel, R=1500 meters of the maximum search radius of Density Estimator, r=1500 meters of search radius share in R
SR=325 other hotels,For sRThe shortest distance in a hotel to this hotel is as shown in table 3.The kernel function f in the hotels Ze Ci
(r) image is as shown in Figure 9.Kernel function image superposition is carried out for 299 hotels of hotel group and in region contour map
Upper displaying, as shown in Figure 10.For 2766 hotels of group of whole hotels with the kernel function of R=1500 meters of progress gathereding degrees of radius
Image superposition simultaneously shows on region contour map, as shown in figure 11.It is rightNature step-wise process division is carried out, if being divided into
Kclassification=5 classes, interrupting value is respectively 12.40634066,38.04611134,73.61095455,130.6801216,
210.9077911 then hotel's cuclear density is worth more kmoreThe region that=3 hotels Lei Wei most assemble.Hotel is most gathered to region J
The best region S of (as shown in figure 11), the tourism day degree of couplingcouplingTravel hot spot areas Z for (as shown in Figure 8) and certain citytour
(as shown in figure 12).The overlapping region W for being overlapped to obtain traffic state analysis is shown on region contour map such as Figure 13 institutes
Show, choose and carried out floating car data analysis October 1, obtains Floating Car road network average speed temporal behavior such as figure within one day
Shown in 14, it is 10 to find out tourism peak hour day t:30-11:30.Using the w=1 region as Zhongshan Road shopping mall such as Figure 15 institutes
It is shown as example, tourism day (on October 1st, 2014) is calculated and obtains w=1 with working day (on October 14th, 2014) floating car data
Each section jam level and shared mileage on a region, ratio (according to《Road Transportation in Beijing postitallation evaluation index system》
The standard divided about road section traffic volume Operation class), as shown in table 4, tourism day, very congested link mileage was compared with working day increase
1307.03 meters, ratio increases by 8.2%, and moderate congestion mileage increases by 1594.63 meters, and ratio increases by 10.01%.Therefore the tourism phase
Between hotel tourist increase cause peripheral path congestion level aggravation.
The traffic behavior of the nearest road of hotel's respective distances in zoning compares tourism day and working day traffic congestion etc.
Tourism day is picked out than the section that working day traffic jam level improves as shown in Figure 16,17 and carries out hotel by the variation of grade
Siting analysis is as shown in figure 18.With JSFor the hotels=m203, traffic impact section is Kai Yuanlu, the hotel point of the right and left
It Wei not the hotels m227 and the hotels m18.The half L of projector distance between the hotels m227 and the hotels m203half=8.36 meters, m18 wine
The half D of projector distance between shop and the hotels m203half=39.34 meters, YD of the hotels m203 on the road of KatyuanwroasJs=47.70
Rice.Find out the average value N of tourism day peak hour on all congested links shown in Figure 18 OD quantity more increased than working dayaveYDw
=0.61, then the hotels m203 tourism day than working day the increased Floating Car OD numbers on 47.70 meters of influence section on the road of Katyuan
Amount is 2, is more than average value 0.61, then the hotels m203 affect traffic behavior.Tourism days all to Zhongshan Road shopping mall compare work
The increased section of day traffic jam level carries out the judgement of hotel's rationality of location, hotel's rationality of location of Zhongshan Road shopping mall
As a result as shown in figure 19, circle represents the hotel for influencing traffic behavior, and five-pointed star represents the hotel for not influencing traffic behavior.
Above-described embodiment is intended merely to illustrate the present invention, and is not used as limitation of the invention.As long as according to this hair
Bright technical spirit is changed above-described embodiment, modification etc. will all be fallen in the scope of the claims of the present invention.
Claims (3)
1. a kind of hotel and Floating Car travelling OD degree of coupling model analysis method based on tourism trip space-time, it is characterised in that：
Using road network as analysis condition, the room number R of hotel HHThe gathereding degree for representing the hotel, with H to its nearest crossing
OintersectionEuclidean distance r go on a journey by bus as this hotel tourist tolerable maximum walking distance, obtain centered on H,
R is the hotel coverage S of radiusH, count and fall into SHIn Floating Car OD quantity NHODInfluence degree I as HH, to
The degree of coupling C of hotel H is calculatedH；It is as follows：
Step 101) matches the latitude and longitude information in hotel, Floating Car OD data with region contour cartographic information：In region
The geographical location in each hotel, the position of each passenger getting on/off point are shown on profile map；
Step 102) enables angle { θ |0<θ≤pi/2 } road be two of hotel's H arest neighbors and have intersection crossing OintersectionRoad,
H to its nearest crossing OintersectionEuclidean distance r go on a journey by bus as tourist tolerable maximum walking range, establish with H
Centered on, r be radius hotel coverage SH；
Step 103) chosen distance H vertical lines L is most short and in SHInterior section d falls into the floating on d as research object, statistics
Vehicle travelling OD quantity NHODInfluence degree I as hotel HH；
Step 104) sets shared M hotel, repeats step 101) to step 103) and calculates the room number R that each hotel possessesmAnd shadow
The degree of sound Im, wherein m ∈ { 1,2 ..., M }；
Step 105) counts the number R that room number is most in M hotelmax=max { Rm, and find out ImMiddle quantity maximum value Imax
=max { Im}；
Step 106) calculates each hotel's degree of coupling C according to the following formulam, as follows：
Step 107) obtains the degree of coupling result { C in M hotel according to step 106)1、C2…CM}；
Step 108) is by the degree of coupling result { C in M hotel1、C2…CMCarry out Density Estimator；
The cuclear density value of M hotel's degree of coupling is indicated on region contour map and is overlapped by step 109), then is drawn
Point, if being divided into KoClass, KoIf the Ganlei more than class center density value is high degree of coupling region Scoupling。
2. a kind of travel surge period hotel peripheral path traffic congestion judgment method based on Floating Car GPS data, based on power
Profit requires hotel and the Floating Car travelling OD degree of coupling model analysis method based on tourism trip space-time described in 1, feature to exist
In：By the high degree of coupling region S in whole hotelscoupling, whole hotel group gather region J and tourism hot spot areas ZtourIt carries out
Superposition, is met that Floating Car is more, hotel is more, W survey region more than tourist simultaneously, and the GPS information of utilization Floating Car is in W
Compare tourism day and the workaday road degree of crowding in a region, to determine the traffic of travel surge period hotel peripheral path
Whether pressure increases；It is as follows：
Step 201) matches the latitude and longitude information in hotel with region contour cartographic information：It is shown on region contour map
The geographical location in each hotel；
Step 202) gathers principle by opposite, and all hotels are divided into QSUMA hotel group；
Step 203) sets Q as QSUMOne in a hotel group, include N number of hotel, for a hotel the n-th ∈ { 1,2 ..., N },
The maximum search radius of its Density Estimator is R, indicates the biggest impact range in a hotel, is equipped with search radius r ∈ [0,
R], S is shared in rrOther a hotels,For SrThe latitude and longitude coordinates in a hotel,For sr∈{1,2,…,Sr}
A hotel is to the shortest distance in this hotel, and K () is kernel function, and the kernel function f (r) of the hotels Ze Ci gathereding degree is：
Step 204) is performed both by step 203) to the N number of hotel for belonging to Q, and the core in N number of hotel is found out according to the kernel function f (r)
Density value；
The cuclear density value expression in N number of hotel on region contour map and is overlapped by step 205), and obtain Q gathers region
SQ；
Step 206) is to whole QSUMA hotel group repeats step 203) to step 205), and to the Nesting Zone of each hotel group
Domain is overlapped to obtain whole hotel group aggregation zonesIt is rightNature step-wise process division is carried out, if being divided into
KclassificationClass, then KclassificationIf the Ganlei more than the hotels Lei Zhong cuclear density value is hotel aggregation zone J；
Step 207) is by the high degree of coupling region Scoupling, hotel aggregation zone J and known tourist travel hot spot areas
ZtourIt is overlapped, obtains the main region that W overlapping region between three is traffic state analysis；
Step 208) analyzes the average travel speed of the floating car data for day of travelling, and obtains Floating Car road within one day
Net average speed temporal behavior finds out the trip peak period t of tourism day；
Step 209) chooses a region w ∈ { 1,2 ..., W }, by the latitude and longitude information in hotel, region contour in w-th of region
Cartographic information is matched with the floating car data of the region peak period：The geographical position in hotel is shown on region contour map
It sets, the specific location of the road network in region and Floating Car；
Step 210) calculates tourism day floating car data and obtains each section jam level { LD on w-th of regionw1、LDw2..., meter
It calculates working day floating car data and obtains each section jam level { FD in w-th of regionw1、FDw2..., compare tourism day and work
The mileage and proportion of the different jam levels in day section analyze the increase of tourism day tourist for hotel's peripheral path
Influence degree.
3. a kind of hotel based on tourism trip time and space usage gathers region hotel addressing appraisal procedure, it is based on claim 2 institute
The travel surge period hotel peripheral path traffic congestion judgment method based on Floating Car GPS data stated, is as follows：
Step 301) calculates the traffic behavior of respective distances nearest road in hotel's in w-th of region, compares tourism day and working day
The variation of traffic jam level, by day section YD more increased than working day traffic jam level of travellingw={ YDw1,YDw2,…
YDwroad... carry out hotel's siting analysis；
Step 302) is for certain a road section YDwroadOn a certain hotel JSIt is analyzed, traffic impact sectionFor
On its nearest road, its right arest neighbors hotel from the midpoint for the road for reaching its left side arest neighbors hotel to arrival
The midpoint of road；
Step 303) seeks YDwThe average value N of upper tourism day OD quantity more increased than working dayaveYDw；
Step 304) compares tourism day than working day in sectionUpper increased Floating Car OD quantityWhenMore than NaveYDw, this hotel JSTo influence the hotel of traffic behavior；
Step 305) is to YDwroadOn whole hotels execute step 304) analysis determine whether it is influence traffic behavior wine
Shop；
Step 306) is for day whole section YD more increased than working day traffic jam level of travelling in w-th of regionwRepeat step
302) to step 305), so far whole hotels in region have carried out rationality of location judgement；
The hotel for influencing traffic behavior is identified and is shown on map by step 307).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810403329.0A CN108710996A (en) | 2018-04-28 | 2018-04-28 | Gather region hotel addressing appraisal procedure in hotel based on tourism trip time and space usage |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810403329.0A CN108710996A (en) | 2018-04-28 | 2018-04-28 | Gather region hotel addressing appraisal procedure in hotel based on tourism trip time and space usage |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108710996A true CN108710996A (en) | 2018-10-26 |
Family
ID=63867521
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810403329.0A Pending CN108710996A (en) | 2018-04-28 | 2018-04-28 | Gather region hotel addressing appraisal procedure in hotel based on tourism trip time and space usage |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108710996A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111445309A (en) * | 2020-03-26 | 2020-07-24 | 四川旅游学院 | Social network-based travel service recommendation method |
| CN112988847A (en) * | 2021-04-20 | 2021-06-18 | 广东智九信息科技有限公司 | Scenic spot people number prediction system and method based on big data |
| CN113077102A (en) * | 2021-04-16 | 2021-07-06 | 合肥工业大学 | Landscape route optimization method |
-
2018
- 2018-04-28 CN CN201810403329.0A patent/CN108710996A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111445309A (en) * | 2020-03-26 | 2020-07-24 | 四川旅游学院 | Social network-based travel service recommendation method |
| CN111445309B (en) * | 2020-03-26 | 2023-05-30 | 四川旅游学院 | Tourism service recommendation method based on social network |
| CN113077102A (en) * | 2021-04-16 | 2021-07-06 | 合肥工业大学 | Landscape route optimization method |
| CN113077102B (en) * | 2021-04-16 | 2022-11-08 | 合肥工业大学 | Landscape route optimization method |
| CN112988847A (en) * | 2021-04-20 | 2021-06-18 | 广东智九信息科技有限公司 | Scenic spot people number prediction system and method based on big data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108564226A (en) | A kind of public bus network optimization method based on taxi GPS and mobile phone signaling data | |
| CN103955479B (en) | The implementation method and device of electronic map | |
| CN103927873B (en) | Floating Car and section matching process and the parallel method obtaining real-time road | |
| CN109556622B (en) | Interest point searching method and device | |
| CN105844362B (en) | Urban traffic comprehensive trip decision-making device | |
| CN108629974A (en) | Take the traffic circulation index method for building up of urban road traffic network feature into account | |
| CN109686091B (en) | Traffic flow filling algorithm based on multi-source data fusion | |
| US20180292224A1 (en) | System and method for traffic volume estimation | |
| CN104320789B (en) | A kind of car networking RSU optimal configuration methods based on game theory | |
| CN108710996A (en) | Gather region hotel addressing appraisal procedure in hotel based on tourism trip time and space usage | |
| CN110414795B (en) | Newly-increased high-speed rail junction accessibility influence method based on improved two-step mobile search method | |
| CN110413855B (en) | Region entrance and exit dynamic extraction method based on taxi boarding point | |
| CN113112068A (en) | Method and system for addressing and layout of public facilities in villages and small towns | |
| CN104303013B (en) | Location-based numerical data method and product | |
| CN106952472A (en) | Congestion level sort method and system between city based on multivariate data | |
| Feng et al. | Association of the built environment with motor vehicle emissions in small cities | |
| WO2016123867A1 (en) | Method and device for position search cognition | |
| CN111680930A (en) | Electric vehicle charging station site selection evaluation method based on characteristic reachable circle | |
| CN111612223A (en) | Population employment distribution prediction method and device based on land and traffic multi-source data | |
| CN109520499B (en) | Method for realizing regional real-time isochrones based on vehicle GPS track data | |
| CN111708859B (en) | Business circle boundary identification method based on spatial data | |
| CN109800903A (en) | A kind of profit route planning method based on taxi track data | |
| Regehr et al. | Traffic pattern groups based on hourly traffic variations in urban areas | |
| CN117196197A (en) | Public transportation site layout optimization method | |
| CN116542709A (en) | Electric vehicle charging station planning analysis method based on traffic situation awareness |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181026 |