CN112351394A - Traffic travel model construction method based on mobile phone signaling data - Google Patents
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- H04W4/20—Services signaling; Auxiliary data signalling, i.e. transmitting data via a non-traffic channel
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- G08G1/00—Traffic control systems for road vehicles
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Abstract
The invention discloses a traffic travel model construction method based on mobile phone signaling data, which relates to the technical field of traffic models and comprises the following steps: the method comprises the steps of acquiring mobile phone signaling data in advance, extracting the mobile phone signaling data, acquiring characteristic information of individual trip, judging trip modes, acquiring trip distances and trip speeds, judging trip modes of individual trips, and judging a selected trip mode according to the trip distances and the trip speedsijAnd acquiring the activity type as input information, and establishing a dynamic traffic travel model of the target area as a prediction model. The invention can reduce the cost of obtaining the traffic model data by data mining, the machine signaling data is updated in real time, the timeliness and the accuracy of the model are greatly improved, and the constructed traffic model has wider application rangeThe system can predict future trips and simulate the implementation effect of the traffic policy in advance, and has wide application range.
Description
Technical Field
The invention relates to the technical field of traffic models, in particular to a traffic travel model construction method based on mobile phone signaling data.
Background
The traffic prediction model refers to quantitative description of the relationship between the elements of the traffic phenomenon and the relationship between the traffic phenomenon and the factors of social and economic activities. The method is used for traffic analysis and traffic prediction and is one of important technical methods for traffic planning. The expression form may be one or a set of mathematical expressions, a graph, or a set of mathematical processing procedures. The method is established by a large amount of survey statistical data through mathematical methods such as mathematical statistics and the like. Usually having a person input end and an output end. The input end is a known factor, namely an independent variable; the output end is a traffic phenomenon to be calculated or predicted, namely a dependent variable. The correct traffic model should be able to reproduce the traffic phenomenon, i.e. the known relevant factors of the person who is lost, and to obtain the traffic data corresponding to the actual traffic phenomenon within a certain accuracy range. Common traffic models include traffic demand models, traffic flow models, and the like. The model is generally established through the steps of model form generation, model parameter calibration, model verification and the like. Since the model reflects the history and current state law of the traffic phenomenon, the model should be newly verified and corrected with new examination data as the situation changes.
At present, the data for constructing the traffic model usually comes from organized manual investigation, and the resident travel data is acquired by adopting the manual investigation mode, and the following disadvantages are provided:
1. manual investigation is time-consuming and labor-consuming, investigation contents need to be designed elaborately before investigation begins, and investigation personnel need to be trained intensively. 2. From trip investigation to data sorting, the model construction is satisfied, the period is long usually, the data timeliness is insufficient, and the investigation cannot be carried out for multiple times in a short period. 3. Data needs to be sorted and processed in the later stage of investigation, unqualified data is eliminated, the workload is huge in the stage, and the statistical result is difficult to avoid human errors. 4. Due to the restriction of investigation cost, the sampling rate is usually low, the number of samples is small, and statistical deviation is easy to generate. 5. The workload of investigators is large, the investigation cost is high, and the investigation region range is limited.
The invention patent CN 107103753A for searching chinese discloses a traffic time prediction system, a traffic time prediction method and a traffic model building method. The traffic time prediction system is used for predicting driving time required by a driving route and comprises a model construction module, a model selection module and a prediction module. The model construction module is used for establishing a plurality of candidate prediction models. The candidate predictive models each respectively correspond to one of a plurality of road segments and one of a plurality of distinct mathematical models. The model selection module is used for selecting a prediction model corresponding to a road section from candidate prediction models which are consistent with each road section in the driving route. The prediction module is used for predicting the predicted vehicle speed of each road section according to the prediction model of each road section in the driving route so as to calculate the driving time estimation value. The model selection module selects one of the candidate prediction models corresponding to the road section with a smaller prediction error value as the prediction model of the road section. Because the prediction models selected by each road section are high-accuracy prediction models, the accuracy can be effectively improved. However, the investigation cost is high, the investigation region is limited, the period is long, the data timeliness is insufficient, and the investigation cannot be carried out for many times in a short period.
An effective solution to the problems in the related art has not been proposed yet.
Disclosure of Invention
Aiming at the problems in the related art, the invention provides a traffic travel model construction method based on mobile phone signaling data, so as to overcome the technical problems in the prior related art.
The technical scheme of the invention is realized as follows:
a traffic travel model construction method based on mobile phone signaling data comprises the following steps:
step S1, pre-obtaining mobile phone signaling data, extracting the mobile phone signaling data, and obtaining characteristic information of the trip individual, wherein the characteristic information comprises passing point information and stop point information;
step S2, judging the travel mode, obtaining the travel distance and the travel speed and judging the travel distance and the travel speedThe travel mode of the individual trip comprises obtaining individual trip speed VijExpressed as:
Vij＝Dtravel*ρ/Ttravelrho is the road network nonlinear coefficient;
step S3, judging the selected travel mode according to the travel distance and the travel speedij；
Step S4, obtaining the activity type, including determining the space attribute of the activity site based on the stop point information;
step S5, obtaining the activity type and the travel modeijAnd as input information, establishing a dynamic traffic travel model of the target area as a prediction model.
Further, the method for acquiring the mobile phone signaling data in advance comprises the following steps:
dividing the big data of the mobile phone signaling, wherein the dividing comprises the steps of sorting the original data according to the ID value and dividing the original data into data packets of which each data file comprises 100 ten thousand pieces of communication information;
and carrying out data cleaning on the acquired data packet information.
Further, the method also comprises the following steps:
obtaining travel time TtravelTime T of the first trip point informationenterAnd the time T of leaving the last trip point informationleaveThe time difference between, expressed as:
Ttravel＝Tleave-Tenter。
further, the method also comprises the following steps:
spatial coordinate value (X) by dwell point informationi，Yi) Determining the space distance between two adjacent stop points, and obtaining the individual space trip DtravelExpressed as:
the invention has the beneficial effects that:
the invention is based onThe method for constructing the traffic travel model of the mobile phone signaling data comprises the steps of obtaining the mobile phone signaling data in advance, extracting the mobile phone signaling data, obtaining characteristic information of individual travel, judging a travel mode, obtaining a travel distance and a travel speed, judging a travel mode of the individual travel, and judging a selected travel mode according to the travel distance and the travel speedijThe activity type is acquired as input information, the dynamic traffic trip model of the target area is established as a prediction model, data mining is achieved, the cost of acquiring traffic model data can be reduced, the time for constructing the model is greatly shortened, the machine signaling data is updated in real time, the timeliness and the accuracy of the model are greatly improved, the traffic model can be updated at any time, in addition, the constructed traffic model has wider usability, the future traffic trip can be predicted, the implementation effect of a traffic policy can be simulated in advance, and the application range is wide.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for constructing a traffic travel model based on mobile phone signaling data according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments that can be derived by one of ordinary skill in the art from the embodiments given herein are intended to be within the scope of the present invention.
According to the embodiment of the invention, a traffic travel model construction method based on mobile phone signaling data is provided.
As shown in fig. 1, the method for constructing a traffic travel model based on mobile phone signaling data according to the embodiment of the present invention includes the following steps:
step S1, pre-obtaining mobile phone signaling data, extracting the mobile phone signaling data, and obtaining characteristic information of the trip individual, wherein the characteristic information comprises passing point information and stop point information;
step S2, judging the travel mode, obtaining the travel distance and the travel speed to judge the travel mode of the individual travel, wherein the step S comprises obtaining the individual travel speed VijExpressed as:
Vij＝Dtravel*ρ/Ttravelrho is the road network nonlinear coefficient;
step S3, judging the selected travel mode according to the travel distance and the travel speedij；
Step S4, obtaining the activity type, including determining the space attribute of the activity site based on the stop point information;
step S5, obtaining the activity type and the travel modeijAnd as input information, establishing a dynamic traffic travel model of the target area as a prediction model.
In addition, the method for acquiring the mobile phone signaling data in advance comprises the following steps:
dividing the big data of the mobile phone signaling, wherein the dividing comprises the steps of sorting the original data according to the ID value and dividing the original data into data packets of which each data file comprises 100 ten thousand pieces of communication information;
and carrying out data cleaning on the acquired data packet information.
In addition, the method also comprises the following steps:
obtaining travel time TtravelTime T of the first trip point informationenterAnd the time T of leaving the last trip point informationleaveThe time difference between, expressed as:
Ttravel＝Tleave-Tenter。
in addition, the method also comprises the following steps:
passing through the stopping pointSpatial coordinate value (X) of informationi，Yi) Determining the space distance between two adjacent stop points, and obtaining the individual space trip DtravelExpressed as:
by means of the technical scheme, the traffic trip model construction method based on the mobile phone signaling data acquires the characteristic information of individual trips by acquiring the mobile phone signaling data in advance and extracting the mobile phone signaling data, performs trip mode judgment, acquires the trip distance and the trip speed to judge the trip mode of the individual trips, and judges the selected trip mode according to the trip distance and the trip speedijThe activity type is acquired as input information, the dynamic traffic trip model of the target area is established as a prediction model, data mining is achieved, the cost of acquiring traffic model data can be reduced, the time for constructing the model is greatly shortened, the machine signaling data is updated in real time, the timeliness and the accuracy of the model are greatly improved, the traffic model can be updated at any time, in addition, the constructed traffic model has wider usability, the future traffic trip can be predicted, the implementation effect of a traffic policy can be simulated in advance, and the application range is wide.
In addition, specifically, in one embodiment, the following are included:
the method comprises the following steps: and (4) carrying out segmentation processing on the mobile phone signaling big data. Desensitization mobile phone signaling data acquired from a communication operator is large in data volume, usually in dozens of GB levels, sometimes reaches TB level with the expansion of a research area and the increase of data collection time, and the huge data volume needs to be divided firstly.
Step two: and cleaning the data. The original handset signaling data usually contains much interference data, which is usually generated under bad communication signal conditions, including signal drift, ping-pong handover, and signal discontinuity.
Step three: and (5) data mining. On the basis of eliminating the error data, determining the 'passing point' and 'stopping point' of each individual trip through clustering analysis.
The ' passing point ' is that in the process of travelling, the time of each passing base station signal point is usually short in the ' passing point ' and passes through a series of points in a short time, if the characteristics are met, the individual is judged to be in the process of travelling by calculating the time T of entering the first ' travelling pointenterAnd time T of leaving the last "trip pointleaveThe time difference between the two can obtain the travel time Ttravel；
Ttravel＝Tleave-Tenter
The "staying point" is a destination of a trip, and an individual usually needs to spend a certain time at the "staying point" to perform a certain activity, namely, if the staying time at a certain base station is long, the point is determined as the "staying point". Spatial coordinate value (X) through "dwell pointi，Yi) Calculating the space distance between two adjacent stop points to obtain the individual space trip Dtravel。
Step five: and judging a trip mode. And judging the trip mode adopted by the individual trip through the calculation of the trip distance and the trip speed. According to the existing research result of travel, the travel distances which can be achieved by adopting different travel modes are obviously different, and accordingly, the travel mode selected by the individual travel can be judged.
First, the individual traveling speed V is calculatedijI.e. by
Vij＝Dtravel*ρ/Ttravel
Where ρ is a road network nonlinear coefficient.
Secondly, a travel mode selected by travel distance and travel speed judgment is selectedij，
(1) When D is presenttravelWhen the speed is more than or equal to 0.5km, the mode of going out is determinedijWalking;
(2) when D is more than 0.5travelWhen the length is less than or equal to 1.5 km:
if VijWhen the speed is less than or equal to 5km/h, modeijWalking;
if 5 < VijWhen the speed is less than or equal to 10km/h, modeijAs a bicycle;
if 10 < VijWhen the speed is less than or equal to 20km/h, modeijElectric bicycles and motorcycles;
if 35 < VijWhen the speed is less than or equal to 60km/h, modeijA car;
if VijWhen the speed is more than 60km/h, then modeijOther.
(3) When 1.5km < DtravelWhen the speed is less than or equal to 5km,
if VijWhen the speed is less than or equal to 5km/h, modeijWalking;
if 5 < VijWhen the speed is less than or equal to 10km/h, modeijAs a bicycle;
if 10 < VijWhen the speed is less than or equal to 20km/h, modeijElectric bicycles and motorcycles;
if 20 < VijWhen the speed is less than or equal to 35km/h, modeijGetting the bus as public transport;
if 35 < VijWhen the speed is less than or equal to 60km/h, modeijA car;
if VijWhen the speed is more than 60km/h, then modeijOther.
(4) When 5km < DtravelWhen the length of the water is less than or equal to 10km,
if 10 < VijWhen the speed is less than or equal to 20km/h, modeijElectric bicycles and motorcycles;
if 20 < VijWhen the speed is less than or equal to 35km/h, modeijGetting the bus as public transport;
if 35 < VijWhen the speed is less than or equal to 60km/h, modeijA car;
if VijWhen the speed is more than 60km/h, then modeijOther.
(5) When 10km < DtravelWhen the length of the water is less than or equal to 30km,
if 20 < VijWhen the speed is less than or equal to 35km/h, modeijGetting the bus as public transport;
if 35 < VijWhen the speed is less than or equal to 60km/h, modeijA car;
if VijWhen the speed is more than 60km/h, then modeijOther.
(6) When 30km < DtravelWhen the temperature of the water is higher than the set temperature,
if 35 < VijWhen the speed is less than or equal to 60km/h, modeijA car;
if VijWhen the speed is more than 60km/h, then modeijOther.
Step six: the type of activity is determined. And performing inverse geographic coordinate operation on the coordinates of the 'stopping points', acquiring the spatial attributes of the activity places, and judging the activity type of the individual according to the spatial attributes. And accessing an API (application program interface) of the Gaode map through a VAB (virtual access bus) program, judging the activity type through returned spatial position information, and totally dividing 11 types of activities, namely home, business, shopping, work, medical treatment, school, 7 relatives and friends visiting, 8 leisure, farming and others.
Step seven: and converting the data into XML format data, and performing simulation operation by means of an MATSim platform to establish a dynamic traffic travel model of the research area.
In summary, with the aid of the above technical solutions of the present invention, the traffic trip model construction method based on the mobile phone signaling data obtains the characteristic information of the individual trip by obtaining the mobile phone signaling data in advance and extracting the mobile phone signaling data, performs trip mode determination, obtains the trip distance and the trip speed to determine the trip mode of the individual trip, and determines the selected trip mode according to the trip distance and the trip speedijAnd the activity type is acquired as input information, a dynamic traffic travel model of the target area is established as a prediction model, data mining is realized, the cost of acquiring traffic model data can be reduced, and the construction model is greatly shortenedThe model time and the machine signaling data are updated in real time, so that the timeliness and the accuracy of the model are greatly improved, the traffic model can be updated at any time, and the constructed traffic model has wider usability, can predict future traffic trips and simulate the implementation effect of the traffic policy in advance, and is wide in application range.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (4)
1. A traffic travel model construction method based on mobile phone signaling data is characterized by comprising the following steps:
the method comprises the steps of obtaining mobile phone signaling data in advance, extracting the mobile phone signaling data, and obtaining characteristic information of a trip individual, wherein the characteristic information comprises passing point information and stop point information;
judging a trip mode, acquiring a trip distance and a trip speed to judge the trip mode of the individual trip, wherein the trip mode comprises acquiring an individual trip speed VijExpressed as:
Vij＝Dtravel*ρ/Ttravelrho is the road network nonlinear coefficient;
travel mode selected by travel distance and travel speed judgmentij；
Obtaining the activity type, including determining the spatial attribute of the activity site based on the dwell point information;
the obtained activity type and the travel mode areijAnd as input information, establishing a dynamic traffic travel model of the target area as a prediction model.
2. The method for constructing a traffic travel model based on mobile phone signaling data according to claim 1, wherein the method for obtaining the mobile phone signaling data in advance comprises the following steps:
dividing the big data of the mobile phone signaling, wherein the dividing comprises the steps of sorting the original data according to the ID value and dividing the original data into data packets of which each data file comprises 100 ten thousand pieces of communication information;
and carrying out data cleaning on the acquired data packet information.
3. The method for constructing a traffic travel model based on mobile phone signaling data according to claim 1, further comprising the following steps:
obtaining travel time TtravelTime T of the first trip point informationenterAnd the time T of leaving the last trip point informationleaveThe time difference between, expressed as:
Ttravel＝Tleave-Tenter。
4. the method for constructing a traffic travel model based on mobile phone signaling data according to claim 3, further comprising the following steps:
spatial coordinate value (X) by dwell point informationi，Yi) Determining the space distance between two adjacent stop points, and obtaining the individual space trip DtravelExpressed as:
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Application publication date: 20210209 |
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