CN109561386A - A kind of Urban Residential Trip activity pattern acquisition methods based on multi-source location data - Google Patents
A kind of Urban Residential Trip activity pattern acquisition methods based on multi-source location data Download PDFInfo
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- CN109561386A CN109561386A CN201811407211.1A CN201811407211A CN109561386A CN 109561386 A CN109561386 A CN 109561386A CN 201811407211 A CN201811407211 A CN 201811407211A CN 109561386 A CN109561386 A CN 109561386A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/025—Services making use of location information using location based information parameters
- H04W4/027—Services making use of location information using location based information parameters using movement velocity, acceleration information
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/029—Location-based management or tracking services
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/20—Services signaling; Auxiliary data signalling, i.e. transmitting data via a non-traffic channel
- H04W4/21—Services signaling; Auxiliary data signalling, i.e. transmitting data via a non-traffic channel for social networking applications
Abstract
The invention discloses a kind of Urban Residential Trip activity pattern acquisition methods based on multi-source location data include the following steps: that (1) carries out denoising to mobile phone signaling data, identify the trip end points base station of user and by way of base station；(2) it is matched in road network map by the trip end points base station of step (1) acquisition and by way of base station, obtains user's trip route；(3) trip distance, travel time and the average trip speed for calculating user, identify the trip mode of user；(4) using the social network information near trip end points base station, the trip purpose of user is identified.The present invention can effectively utilize mobile phone signaling data and social network data, it identifies various information such as the movable trip origin and destination of Urban Residential Trip, travel time, trip distance, trip mode and trip purpose, provides a kind of novel resident trip activity chain investigation method for Urban Traffic Planning field.
Description
Technical field
The present invention relates to traffic programme technical field, especially a kind of Urban Residential Trip based on multi-source location data is living
Dynamic model formula acquisition methods.
Background technique
Travel Demand Forecasting is the brace foundation of Urban Traffic Planning management and decision.It builds and advises with China big and medium-sized cities
The lasting expansion of mould drives the continuous inflow of population from other places, and the current widely applied needing forecasting method based on trip survey is difficult
To meet new challenge, novel trip information data source and needing forecasting method need be expanded.
Traditional transport need acquisition of information is investigated dependent on city dweller's traffic trip, generally with questionnaire survey, phone
The more complete trip informations such as trip purpose, the trip mode of resident are obtained based on the methods of inquiry, but need to expend a large amount of
Manpower and material resources, and there is the disadvantages of time-consuming, sample size is small, poor in timeliness, meet the city of real current situation to obtain and hand over
Logical demand data is very difficult.
With the fast development of China's mobile communication technology and widely available, the traffic information based on data in mobile phone of mobile phone
Acquisition technique has obtained the new hot spot that Competent Authorities of Transport and Communications payes attention to and becomes transport need academic research, at the same time mobile agency
It hands over network (such as wechat, microblogging) also to absorb a large amount of user, becomes the new sources of acquisition personnel's geographical location information.Mobile phone
The covering crowd of location data is extensive, and nearly close to ecentury, location information real-time is higher, can on a large scale objectively
The space-time characteristic of long-term record User Activity, shortcoming are to be difficult to obtain user's travel activity purpose and individual subscriber society warp
Help attribute information.Mobile social networking user passes through interest point list (the Point of near mobile phone application APP offer
Interest, abbreviation POI), the stop place POI of oneself is selected, individual position is actively shared with good friend.By mobile social
The open source interface of network application, can be with the service type (such as school, office building, residential quarter, restaurant) of a large amount of POI of Free Acquisition
And the temperature of registering of each point of interest.Mobile social networking data can provide the land-use style information of trip destination, but
Compared to data in mobile phone, it is not high that there are sample sizes, it is difficult to obtain the deficiency of the complete Trip chain of single user.
Summary of the invention
Technical problem to be solved by the present invention lies in it is living to provide a kind of Urban Residential Trip based on multi-source location data
Dynamic model formula acquisition methods can effectively utilize mobile phone signaling data and social network data, identify that Urban Residential Trip is living
Various information such as dynamic trip origin and destination, travel time, trip distance, trip mode and trip purpose are handed over for city
Logical planning field provides a kind of novel resident trip activity chain investigation method.
In order to solve the above technical problems, the present invention provides a kind of Urban Residential Trip movable mold based on multi-source location data
Formula acquisition methods, include the following steps:
(1) denoising is carried out to mobile phone signaling data, identifies the trip end points base station of user and by way of base station；
(2) it is matched in road network map by the trip end points base station of step (1) acquisition and by way of base station, obtains user's trip
Path；
(3) trip distance, travel time and the average trip speed for calculating user, identify the trip mode of user；
(4) using the social network information near trip end points base station, the trip purpose of user is identified.
Preferably, in step (1), identify that the trip end points base station of user and approach base station specifically comprise the following steps:
(11) it from morning to night sorts to the mobile phone signaling information of mobile phone user according to signaling time stamp, the use after traversal sequence
Family signaling data calculates time interval, distance and speed interval between the signaling record of front and back two, it is excessive to reject speed difference
Data；
(12) time interval t and distance interval d are met into certain threshold condition<T, D>one group of signaling record be set to trip
Endpoint base station wherein the base station of previous signaling record is stop base station, while being also last trip up to point, and latter item is believed
Enable record base station be the starting point gone on a journey next time, remaining, which is discontented with, walks threshold condition<T, D>base station be set to by way of base station.
Preferably, in step (2), road network map is matched to by the trip end points base station of step (1) acquisition and by way of base station
On, it obtains user's trip route and specifically comprises the following steps:
(21) to the base station in each Trip chain of each user, buffer area is established using radius R as range, to fall into buffer area
Interior road section establishes candidate road section collection；
(22) according to the candidate road section collection of each base station of the order traversal of Trip chain, if the candidate road section of former and later two base stations
Collection is then directly added into path set, if it exists multiple same road segments, then chooses length shortest one there is unique identical section
Path set is added in item；If identical section is not present in the candidate road section collection of former and later two base stations, sought with dijkstra's algorithm
Look for shortest path between two base stations；
(23) arranging step (21) (22) as a result, forming connection trip origin base station, by way of base station, trip point so far
The complete trip route collection of base station.
Preferably, in step (3), identify that the trip mode of user specifically comprises the following steps:
(31) matched according to road network in step (2) as a result, concentrate the length in each practical section to be added trip route,
Trip distance is calculated, then further calculates travel time and average trip speed；
(32) since the cellular base station of most of service Underground Rail Transits is independently arranged, in conjunction with route matching knot
Fruit can effectively identify rail traffic trip mode；
(33) difference of average travel speed and trip distance can be used as distinguish motor vehicle, bicycle and walking according to
According to；
(34) in order to further discriminate between the car and bus in motor vehicle, to all users within the scope of near zone
Trip route application level clustering method, discriminated whether to belong to same according to the similarity degree of mobile phone movement properties
Vehicle estimates the vehicle carried mobile phone quantity of each car, to distinguish car and bus.
Preferably, in step (4), using the social network information near trip end points base station, the trip mesh of user is identified
Specifically comprise the following steps:
(41) POI information acquired within the scope of trip end points 1km is write micro- using microblogging open platform programming interface API
Rich data acquisition program of registering: commonly read using location-based service interface place nearby pois, search for survey region range
Each POI is carried out keyword match according to place name, can divide each POI for house, do by interior microblogging interest point list POI
Public building, sight spot, campus, market, food and beverage sevice and the big type of public transit facility seven；
(42) trip purpose of user can be rationally estimated according to the attribute of POI near trip end points, if trip end points 1km model
Enclosing buffer area, there are a plurality of types of POI, then register temperature according to the user of all kinds of POI to calculate the general of corresponding trip purpose
Rate.
The invention has the benefit that the present invention can effectively utilize mobile phone signaling data and social network data, identification
The various aspects such as the movable trip origin and destination of Urban Residential Trip, travel time, trip distance, trip mode and trip purpose out
Information, provide a kind of novel resident trip activity chain investigation method for Urban Traffic Planning field.
Detailed description of the invention
Fig. 1 is method flow schematic diagram of the invention.
Specific embodiment
As shown in Figure 1, a kind of Urban Residential Trip activity pattern acquisition methods based on multi-source location data, including it is as follows
Step:
Step 1: from morning to night sorting to the mobile phone signaling information of mobile phone user according to signaling time stamp, after traversal sequence
Subscriber signaling data calculate time interval, distance and speed interval between the signaling record of front and back two, it is excessive to reject speed difference
Data；Time interval t and distance interval d are met into certain threshold condition<T, D>one group of signaling record be set to trip end points
Base station wherein the base station of previous signaling record is stop base station, while being also last trip up to point, and latter signaling is remembered
The base station of record is the starting point gone on a journey next time, remaining, which is discontented with, walks threshold condition<T, D>base station be set to by way of base station.
Experience in accordance with some embodiments, time interval threshold value T need to be set as 15 minutes, and distance threshold D need to be set as 500
Rice, i.e., the minimum time once stopped need to be greater than 15 minutes, and the shortest distance once gone on a journey should be greater than 500 meters.
Step 2: being matched in road network map by trip end points base station that step 1 obtains and by way of base station, obtain user and go out
Walking along the street diameter:
(1) to the base station in each Trip chain of each user, buffer area is established using radius R as range, to fall into buffer area
Interior road section establishes candidate road section collection；
(2) according to the candidate road section collection of each base station of the order traversal of Trip chain, if the candidate road section of former and later two base stations
Collection is then directly added into path set, if it exists multiple same road segments, then chooses length shortest one there is unique identical section
Path set is added in item；If identical section is not present in the candidate road section collection of former and later two base stations, sought with dijkstra's algorithm
Look for shortest path between two base stations；
(3) arranging step (1) (2) as a result, form connection trip origin base station, by way of base station, trip up to putting base
The complete trip route collection stood.
Step 3: trip distance, travel time and the average trip speed of user are calculated, identifies the trip mode of user:
(1) matched according to road network in step 2 as a result, concentrate the length in each practical section to be added trip route, count
Calculation obtains trip distance, then further calculates travel time and average trip speed；
(2) since the cellular base station of most of service Underground Rail Transits is independently arranged, in conjunction with route matching knot
Fruit can effectively identify rail traffic trip mode；
(3) difference of average travel speed and trip distance can be used as the foundation for distinguishing motor vehicle, bicycle and walking；
(4) in order to further discriminate between the car and bus in motor vehicle, to all users within the scope of near zone
Trip route application level clustering method, discriminated whether to belong to same according to the similarity degree of mobile phone movement properties
Vehicle estimates the vehicle carried mobile phone quantity of each car, to distinguish car and bus.
Experience in accordance with some embodiments, speed per hour range is 1-6 kilometers/hour on foot, 10-30 kilometers of bicycle speed per hour/
Hour, automobile is 30-50 kilometers/hour in urban district, 50-80 kilometers/hour outside city, 80-120 kilometers/hour of high speed.
Step 4: using the social network information near trip end points base station, identifying the trip purpose of user.
(1) POI information within the scope of trip end points (including going out beginning-of-line and trip up to point) 1km is acquired, is opened using microblogging
Be laid flat platform programming interface API, write microblogging and register data acquisition program: commonly read using location-based service interface place
Nearby pois searches for the microblogging interest point list (POI) within the scope of survey region, each POI is closed according to place name
The matching of key word, can divide each POI for house, office building, sight spot, campus, seven major class of market, food and beverage sevice and public transit facility
Type；
(2) trip purpose of user can be rationally estimated according to the attribute of POI near trip end points, if trip end points 1km model
Enclosing buffer area, there are a plurality of types of POI, then register temperature according to the user of all kinds of POI to calculate the general of corresponding trip purpose
Rate.
The present invention can effectively utilize mobile phone signaling data and social network data, identify that Urban Residential Trip is movable
Various information such as trip origin and destination, travel time, trip distance, trip mode and trip purpose, are advised for urban transportation
The field of drawing provides a kind of novel resident trip activity chain investigation method.
Claims (5)
1. a kind of Urban Residential Trip activity pattern acquisition methods based on multi-source location data, which is characterized in that including as follows
Step:
(1) denoising is carried out to mobile phone signaling data, identifies the trip end points base station of user and by way of base station；
(2) it is matched in road network map by the trip end points base station of step (1) acquisition and by way of base station, obtains user and go out walking along the street
Diameter；
(3) trip distance, travel time and the average trip speed for calculating user, identify the trip mode of user；
(4) using the social network information near trip end points base station, the trip purpose of user is identified.
2. the Urban Residential Trip activity pattern acquisition methods based on multi-source location data as described in claim 1, feature
It is, in step (1), identifies that the trip end points base station of user and approach base station specifically comprise the following steps:
(11) it from morning to night sorts to the mobile phone signaling information of mobile phone user according to signaling time stamp, user's letter after traversal sequence
Data are enabled, time interval, distance and speed interval between the signaling record of front and back two is calculated, rejects the excessive number of speed difference
According to；
(12) time interval t and distance interval d are met into certain threshold condition<T, D>one group of signaling record be set to trip end points
Base station wherein the base station of previous signaling record is stop base station, while being also last trip up to point, and latter signaling is remembered
The base station of record is the starting point gone on a journey next time, remaining, which is discontented with, walks threshold condition<T, D>base station be set to by way of base station.
3. the Urban Residential Trip activity pattern acquisition methods based on multi-source location data as described in claim 1, feature
It is, in step (2), is matched in road network map by the trip end points base station of step (1) acquisition and by way of base station, obtains user
Trip route specifically comprises the following steps:
(21) to the base station in each Trip chain of each user, buffer area is established using radius R as range, to fall into buffer area
Road section establishes candidate road section collection；
(22) according to the candidate road section collection of each base station of the order traversal of Trip chain, if the candidate road section collection of former and later two base stations is deposited
In unique identical section, then it is directly added into path set, if it exists multiple same road segments, then chooses length shortest one and add
Enter path set；If identical section is not present in the candidate road section collection of former and later two base stations, two are found with dijkstra's algorithm
Shortest path between base station；
(23) arranging step (21) (22) as a result, forming connection trip origin base station, by way of base station, trip point base stations so far
Complete trip route collection.
4. the Urban Residential Trip activity pattern acquisition methods based on multi-source location data as described in claim 1, feature
It is, in step (3), identifies that the trip mode of user specifically comprises the following steps:
(31) matched according to road network in step (2) as a result, concentrate the length in each practical section to be added trip route, calculate
Trip distance is obtained, travel time and average trip speed are then further calculated；
(32) since the cellular base stations of most of service Underground Rail Transits are independently arranged, in conjunction with route matching as a result,
It can effectively identify rail traffic trip mode；
(33) difference of average travel speed and trip distance can be used as the foundation for distinguishing motor vehicle, bicycle and walking；
(34) in order to further discriminate between the car and bus in motor vehicle, to going out for all users within the scope of near zone
The method of walking along the street diameter application level clustering discriminates whether to belong to same vehicle according to the similarity degree of mobile phone movement properties,
The vehicle carried mobile phone quantity for estimating each car, to distinguish car and bus.
5. the Urban Residential Trip activity pattern acquisition methods based on multi-source location data as described in claim 1, feature
It is, in step (4), using the social network information near trip end points base station, identifies that the trip purpose of user specifically includes
Following steps:
(41) POI information acquired within the scope of trip end points 1km writes microblogging label using microblogging open platform programming interface API
To data acquisition program: commonly read using location-based service interface place nearby pois, search within the scope of survey region
Microblogging interest point list POI, by each POI according to place name carry out keyword match, each POI can be divided for house, office building,
Sight spot, campus, market, food and beverage sevice and the big type of public transit facility seven；
(42) trip purpose of user can be rationally estimated according to the attribute of POI near trip end points, if trip end points 1km range is slow
Rushing area, there are a plurality of types of POI, then register temperature according to the user of all kinds of POI to calculate the probability of corresponding trip purpose.
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| CN110753307A (en) * | 2019-10-24 | 2020-02-04 | 南京瑞栖智能交通技术产业研究院有限公司 | Method for acquiring mobile phone signaling track data with label based on resident survey data |
| CN111521191A (en) * | 2020-04-20 | 2020-08-11 | 中国农业科学院农业信息研究所 | Mobile phone user moving path map matching method based on signaling data |
| CN111653094A (en) * | 2020-05-29 | 2020-09-11 | 南京瑞栖智能交通技术产业研究院有限公司 | Urban trip mode comprehensive identification method based on mobile phone signaling data and containing road network correction |
| CN111653093A (en) * | 2020-05-29 | 2020-09-11 | 南京瑞栖智能交通技术产业研究院有限公司 | Urban trip mode comprehensive identification method based on mobile phone signaling data |
| CN111737605A (en) * | 2020-07-09 | 2020-10-02 | 南京瑞栖智能交通技术产业研究院有限公司 | Travel purpose identification method and device based on mobile phone signaling data |
| CN112000755A (en) * | 2020-08-14 | 2020-11-27 | 青岛市城市规划设计研究院 | Regional trip corridor identification method based on mobile phone signaling data |
| CN112351394A (en) * | 2020-11-03 | 2021-02-09 | 崔毅 | Traffic travel model construction method based on mobile phone signaling data |
| CN112530166A (en) * | 2020-12-01 | 2021-03-19 | 江苏欣网视讯软件技术有限公司 | Method and system for analyzing and identifying bus station for getting on or off bus during traveling based on signaling data and big data |
| CN113674122A (en) * | 2021-07-22 | 2021-11-19 | 华设设计集团股份有限公司 | Urban resident travel rule rapid extraction method suitable for high-concurrency travel data |
| CN114598733A (en) * | 2020-12-02 | 2022-06-07 | 四川交通职业技术学院 | Resident traffic distribution calculation method and system based on mobile phone signaling data |
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| CN110753307A (en) * | 2019-10-24 | 2020-02-04 | 南京瑞栖智能交通技术产业研究院有限公司 | Method for acquiring mobile phone signaling track data with label based on resident survey data |
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| CN111521191A (en) * | 2020-04-20 | 2020-08-11 | 中国农业科学院农业信息研究所 | Mobile phone user moving path map matching method based on signaling data |
| CN111653093B (en) * | 2020-05-29 | 2022-06-17 | 南京瑞栖智能交通技术产业研究院有限公司 | Urban trip mode comprehensive identification method based on mobile phone signaling data |
| CN111653094A (en) * | 2020-05-29 | 2020-09-11 | 南京瑞栖智能交通技术产业研究院有限公司 | Urban trip mode comprehensive identification method based on mobile phone signaling data and containing road network correction |
| CN111653093A (en) * | 2020-05-29 | 2020-09-11 | 南京瑞栖智能交通技术产业研究院有限公司 | Urban trip mode comprehensive identification method based on mobile phone signaling data |
| CN111737605A (en) * | 2020-07-09 | 2020-10-02 | 南京瑞栖智能交通技术产业研究院有限公司 | Travel purpose identification method and device based on mobile phone signaling data |
| CN112000755A (en) * | 2020-08-14 | 2020-11-27 | 青岛市城市规划设计研究院 | Regional trip corridor identification method based on mobile phone signaling data |
| CN112000755B (en) * | 2020-08-14 | 2024-03-12 | 青岛市城市规划设计研究院 | Regional travel corridor identification method based on mobile phone signaling data |
| CN112351394A (en) * | 2020-11-03 | 2021-02-09 | 崔毅 | Traffic travel model construction method based on mobile phone signaling data |
| CN112530166A (en) * | 2020-12-01 | 2021-03-19 | 江苏欣网视讯软件技术有限公司 | Method and system for analyzing and identifying bus station for getting on or off bus during traveling based on signaling data and big data |
| CN112530166B (en) * | 2020-12-01 | 2021-11-05 | 江苏欣网视讯软件技术有限公司 | Method and system for analyzing and identifying bus station for getting on or off bus during traveling based on signaling data and big data |
| CN114598733A (en) * | 2020-12-02 | 2022-06-07 | 四川交通职业技术学院 | Resident traffic distribution calculation method and system based on mobile phone signaling data |
| CN113674122A (en) * | 2021-07-22 | 2021-11-19 | 华设设计集团股份有限公司 | Urban resident travel rule rapid extraction method suitable for high-concurrency travel data |
| CN114724358A (en) * | 2022-03-01 | 2022-07-08 | 智慧足迹数据科技有限公司 | Travel distance determination method based on mobile phone signaling and related device |
| CN115412857A (en) * | 2022-08-24 | 2022-11-29 | 浙江大学 | Resident travel information prediction method |
| CN115412857B (en) * | 2022-08-24 | 2023-08-18 | 浙江大学 | Resident trip information prediction method |
| CN115442758A (en) * | 2022-09-05 | 2022-12-06 | 广州瀚信通信科技股份有限公司 | User travel mode determination method and device, terminal equipment and storage medium |
| CN116052436A (en) * | 2023-04-03 | 2023-05-02 | 深圳市城市交通规划设计研究中心股份有限公司 | Cross-city travel mode identification method, electronic equipment and storage medium |
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