CN107038858B - Commute private car dynamic share-car recommended method - Google Patents
Commute private car dynamic share-car recommended method Download PDFInfo
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Abstract
The invention discloses a kind of commuting private car dynamic share-car recommended methods, including, license auto-recognition system data are inputted, it is pre-processed, generate track of vehicle collection；A kind of working day commuting private car recognizer is designed in conjunction with the high reproducibility and periodicity of commuting private car trip time and space idea based on city vehicle track collection；In conjunction with family-work address, commuting time window and the working day dynamic effects factor (weather, accident and traffic) of commuting private car, realizes and the key feature for influencing commuting private car share-car is extracted；Based on commuting vehicle space-time characteristic, match the commuting vehicle with similar commuting time and space idea, and combine influence of the dynamic effects factor to driver's share-car wish, these dynamic factors are effectively combined with user in the share-car demand of Spatial dimensionality, realize the dynamic share-car matching based on user intention, a kind of share-car mechanism steady in a long-term is provided for commuting private car, and significantly reduces private car vehicle number in peak period morning and evening.
Description
Technical field
The present invention relates to intelligent transport system field, in particular to a kind of commuting private car dynamic share-car recommended method.
Background technique
With the rapid development based on location-based service and development of Mobile Internet technology, Ride-share service has become alleviation urban transportation
Economic new model is shared in congestion.
Recommend field in share-car, traditional algorithm has two: first, and existing method is mostly to meet the real-time of passenger
Share-car demand is simple target, i.e., how to reduce the waiting time of passenger, and is not yet effectively solved between share-car driver because same
The problem of industry competes and increases private car usage amount.Therefore, existing share-car method is possible to cause more in peak period morning and evening
Serious congestion in road.Second, existing share-car scheme depends on the mobile number of the large-scale crowds such as GPS track, mobile phone signaling mostly
According to, track, abundant but semantic information is lacked, and not yet effectively considers the dynamics such as weather, festivals or holidays and occasion, traffic accident
Great influence of the factor to share-car scheme.Therefore, it is difficult to provide personalized dynamic share-car scheme for user.
Summary of the invention
It is an object of the invention to overcome above-mentioned the deficiencies in the prior art, a kind of commuting private car dynamic share-car recommendation is provided
Method.
To achieve the above object, the invention adopts the following technical scheme:
Commute private car dynamic share-car recommended method, comprising the following steps:
S1, input VLPR data set, pre-process VLPR data set, generate city vehicle track data collection；
S2, the high reproducibility based on commuting private car trip time and space idea, filter out from city vehicle track data concentration
The low non-commuting vehicle of liveness carries out the analysis of family address and work address to remaining vehicle, will possess family-work simultaneously
The vehicle of address is determined as the private car that commutes；
S3, family-work address in conjunction with the private car that commutes, commuting time window and working day dynamic effects factor difference are real
Now space characteristics, temporal characteristics and the dynamic effects factor feature extraction to commuting private car share-car is influenced, the dynamic effects
Factor includes weather, accident and traffic flow conditions；
S4, space characteristics and temporal characteristics based on commuting vehicle match the scheduled bus with similar commuting time and space idea
, and influence of the dynamic effects factor to driver's share-car wish is combined, by these dynamic factors and user in Spatial dimensionality
Share-car demand effectively combines, and realizes the dynamic share-car matching based on user intention.
Further, step S1 specifically includes the following steps:
S11, local private car is extracted, by license plate number and license plate color feature, is extracted from VLPR data set local private
Family's car data；
S12, field are refined, and extract required field from local private savings car data, generate vehicle record data；
S13, redundancy cross vehicle record cleaning, deleted vehicle record data in repetition vehicle record and incorrect recording data；
S14, track of vehicle data set is generated, crosses vehicle record to after cleaning, classifies according to license plate number, identical license plate number note
Vehicle time-sequencing was pressed in record, and identical license plate is then crossed vehicle record, and first place is connected in chronological order, formed track of vehicle data.
Further field needed for step S12 includes VLPR number, VLPR type, the direction VLPR, license plate number, license plate
Color, lane number and mistake vehicle time.
Further, step S2 specifically includes the following steps:
S21, low liveness vehicle filtering define labourer hired by the month based on the periodicity and repeatability of commuting private car trip rule
Make to enliven private car of the number of days less than 15 days in day to be the too low vehicle of liveness, the judgement too low vehicle of liveness is non-commuting vehicle
And it is excluded；
S22, potential address for determining potential commuting private vehicle and potential work address, to determine the potential " family of vehicle
Address ", analysis is come off duty to the track of vehicle in work hours next day section, L daily1, L2..., LnFor track VLPR equipment
Address sequence, t1, t2..., tn-1Pass through the time interval sequence per continuous two VLPR equipment, t for vehiclem=max (ti), i
=1,2 ..., n-1, works as tmL is determined greater than given thresholdm+1For its potential address；To determine the potential " place of working of vehicle
Location ", working daily is analyzed to the track of vehicle in next time interval, L1, L2..., LnFor the track device address VLPR sequence
Column, t1, t2..., tn-1Pass through the time interval sequence per continuous two VLPR equipment for vehicle, works as tmIt is greater than given threshold
Determine LmIt is potential " work address " for the vehicle；
S23, vehicle man address and work address are determined, for potential address of each potential commuting private car, calculated
Frequency freq (the PH of each potential addressi), the potential address of mark frequency highest and frequency greater than 0.8 is family address,
For potential work address, its frequency freq (PW from fixed address is calculatedi), while calculating it apart from family
Distance dist (the PW of locationi), determination possesses maximum value max (freq (PWi)×dist(PWi)) potential work address be real
Work address；
S24, it determines commuting private car, determine while possessing family-work address private car for the private car that commutes.
Further, step S3 specifically includes the following steps:
S31, space characteristics extract, and the longitude and latitude of the family address of each commuting private car and work address is private as commuting
Family's vehicle space characteristics；
S32, temporal characteristics extract, using multiple working days be used as observation time range, by departure time every workday with
Arrival time respectively as one-dimensional characteristic, in order to which by time quantization, the departure time is converted to its hour apart from same day 00:00
Number, arrival time is converted to the hourage of its distance 06:00, if the same day morning peak vehicle without commuting track, when setting out
Between with arrival time with 0 replace；
S33, dynamic effects factor feature extraction, it is contemplated that the dynamic effects such as traffic flow, traffic accident and weather condition because
The influence that element recommends share-car, the dynamic effects factor feature for extracting every workday morning peak respectively will be for traffic flow
No Monday, as one-dimensional characteristic otherwise it was 0 that being, which is 1,；For traffic accident, the traffic thing that same day morning peak Xiamen City is occurred
Therefore sum is used as Accident Characteristic；For weather, consider that the visibility of same day morning peak and rainfall are special as bidimensional weather respectively
Sign.
Further, in step S31, consider close starting point and the importance of terminal when share-car, prevent other feature poly-
Excessive interference is generated in class algorithm, and longitude is converted to the difference of itself and all VLPR equipment minimum longitudes, latitude is converted
For the difference of itself and all VLPR equipment minimum latitudes, by the longitude and latitude after conversion simultaneously multiplied by 1000, to increase space characteristics
Weight.
Further, step S4 specifically includes the following steps:
S41, be by the space characteristics of each commuting private car and temporal characteristics description commuting private car commuting it is regular when
Empty feature vector carries out coarseness cluster to commuting private car with k-means algorithm, makes the commuting with similar commuting rule
Vehicle gathers for one kind.
S42, according to dynamic effects because usually determine driver to passenger provide Ride-share service willingness factor IF, specifically
For,
IF=ω1×isMonday+ω2×(-visibility)+ω3×rainfall+ω4× accident wherein,
IsMonday indicates whether Monday, and being is 1, and no is 0；Visibility indicates morning peak visibility；Rainfall indicates early high
Peak rainfall；Accident indicates morning peak traffic accident sum；ω1, ω2, ω3, with ω4Four factors are respectively represented to logical
The influence degree of diligent private car trip rule,Wherein visibility visibili and impact factor are negatively correlated, therefore
Coefficient is negative；
S43, under different willingness factor effects, in conjunction with different shared seating capacity seats ∈ [Isosorbide-5-Nitrae], with recurrence
AGNES algorithm generates the commuting private car share-car combination under different sharing seating capacity.
Further, one is defined first to acquire most preferably shared seating capacity under Different Dynamic influence factor in step S43
Energy function E, formula is as follows,
E (IF, seats)=D (IF, seats)+α S (IF, seats)
In formula, IF is willingness factor, and seats is to be ready shared amount of seats, and data item D (IF, seats) is to subtract after share-car
Few vehicle number, smooth item S (IF, seats) are share-car accuracy and influence of passenger's average latency to final share-car effect,
α is balance factor, α ∈ [0,1]；
The calculation formula of data item D (IF, seats) and smooth item S (IF, seats) are as follows,
D (IF, seats)=N-M (IF, seats)；
In formula, N is total vehicle number, and M (IF, seats) is vehicle combination number, and WT (IF, seats) is passenger's average waiting
Time, RA (IF, seats) are average share-car accuracy,It is adjustable parameter with β, for determining share-car accuracy and passenger etc.
Weight to time effects share-car effect；
To each willingness factor IF, taking seats is so that energy function E (if, seats) is the shared seat of maximum value
Number, then have,
Seats=argmaxE (if, seats), seats ∈ [Isosorbide-5-Nitrae].
After adopting the above technical scheme, compared with the background technology, the present invention, having the advantages that and on the one hand improving passenger's
On the other hand share-car satisfaction, i.e. reduction passenger waiting time and guarantee share-car accuracy guarantee the comprehensive society of Ride-share service
Benefit significantly reduces share-car vehicle number in peak period morning and evening.
Detailed description of the invention
Fig. 1 is flow chart of the present invention；
Fig. 2 is VLPR data set, and wherein Fig. 2 (a) is Xiamen City VLPR device distribution situation, and Fig. 2 (b) is Xiamen City VLPR
Facility information exemplary diagram, Fig. 2 (c) are that VLPR crosses vehicle record exemplary diagram, and Fig. 2 (d) is that VLPR equipment saves example of fields figure；
Fig. 3 is Xiamen City's weather history and casualty data exemplary diagram, and wherein Fig. 3 (a) is Xiamen City's 1 day to 5 May in 2016
Month weather condition exemplary diagram on the 4th, Fig. 3 (b) is Xiamen City's part traffic accident data instance figure in 2016；
Fig. 4 is original VLPR data set by pretreatment generation track of vehicle exemplary diagram；
Fig. 5 is part commuting vehicle " family address " and " work address " exemplary diagram；
Fig. 6 is part commuting private car key feature exemplary diagram；
Fig. 7 is to carry out coarseness to part commuting private car to cluster exemplary diagram；
Fig. 8 is the private car cluster that commutes to some coarseness, presses and does not carry out the thin cluster instance graph of dynamic on the same day；
Fig. 9 is commuting private car dynamic share-car recommended method analysis of experimental results figure, and wherein Fig. 9 (a) is that each working day is flat
Time distribution map is waited, Fig. 9 (b) is each working day share-car accuracy distribution map, and Fig. 9 (c) is to reduce vehicle number on each working day
Distribution map.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.
Embodiment
As shown in Fig. 1 flow chart of the invention mainly includes following four step:
S1, input license auto-recognition system (Vehicle License Plate Recognition, abbreviation VLPR) data
Collection, pre-processes VLPR data, generates city vehicle track data collection；
S2, the high reproducibility based on commuting private car trip time and space idea, filter out from city vehicle track data concentration
Apparent non-commuting vehicle, the analysis of family address and work address is carried out to remaining vehicle, will possess family-work address simultaneously
Vehicle be determined as the private car that commutes；
S3, family-work address in conjunction with the private car that commutes, commuting time window and working day dynamic effects factor difference are real
Now space characteristics, temporal characteristics and the dynamic effects factor feature extraction to commuting private car share-car is influenced, the dynamic effects
Factor includes weather, accident and traffic flow conditions；
S4, space characteristics and temporal characteristics based on commuting vehicle match the scheduled bus with similar commuting time and space idea
, and influence of the dynamic effects factor to driver's share-car wish is combined, by these dynamic factors and user in Spatial dimensionality
Share-car demand effectively combines, and realizes the dynamic share-car matching based on user intention.
Wherein, step S1 specifically includes the following steps:
S11, local private car is extracted.By license plate number and license plate color feature, extracted from all VLPR record data
We need type of vehicle, i.e., local private car；
S12, field are refined.Original VLPR record includes 22 fields, 7 fields needed for refining this method, including,
VLPR number, the direction VLPR, license plate number, license plate color, lane number, spends the vehicle time at VLPR type；
S13, redundancy cross vehicle record cleaning.Due to the defect of license auto-recognition system equipment, VLPR equipment, which exists, to be repeated to record
The case where license board information, be subject to for the first time shooting cross vehicle information, delete repeat record.In addition, incorrect recording data also can
It is cleaned；
S14, track of vehicle data set is generated.Vehicle record is crossed to after cleaning, is classified according to license plate number, identical license plate number note
Vehicle time-sequencing was pressed in record, and identical license plate is then crossed vehicle record, and first place is connected in chronological order, formed track of vehicle data.
Fig. 2 is VLPR data set exemplary diagram, and wherein Fig. 2 a is Xiamen City VLPR device distribution situation, and Fig. 2 b is Xiamen City
VLPR facility information exemplary diagram, Fig. 2 c are that VLPR crosses vehicle record exemplary diagram, and Fig. 2 d is that VLPR equipment saves example of fields figure；Fig. 4
Track of vehicle exemplary diagram is generated by pretreatment for original VLPR data set.
On the basis of step S1 obtains track of vehicle data set, step S2 is carried out, to identify city commuting private car,
In include:
S21, low liveness vehicle filtering.In view of the periodicity and repeatability of commuting private car trip rule, liveness
Too low vehicle can be excluded, therefore filter the private of low liveness (i.e. month 20 working days enlivened number of days less than 15 days)
Family's vehicle, to exclude apparent non-commuting vehicle.
S22, potential address and work address are determined.In order to determine potential address of vehicle, (afternoon every night is analyzed
6 points to 9 time intervals in morning next day) track of vehicle, L1, L2..., LnFor the track device address VLPR sequence, t1, t2...,
tn-1Pass through the time interval sequence per continuous two VLPR equipment, t for vehiclem=max (ti), i=1,2 ..., n-1 work as tm
> 6h, then it is assumed that Lm+1For its potential address；Work address potential for vehicle analyzes (6:00 AM to afternoon 9 on daily daytime
Point time interval) track of vehicle, L1, L2..., LnFor the track device address VLPR sequence, t1, t2..., tn-1For vehicle process
In view of a part commuting vehicle Work break (ratio can occur at noon for the time interval sequence per continuous two VLPR equipment
Such as eat out, go home halfway), if tm> 2h, and LmThe vehicle record moment is spent earlier than 11 points of the morning, it is believed that LmFor the vehicle
Potential work address.
S23, family address and work address are determined.For potential address of each potential commuting private car, calculate each
Frequency freq (the PH of potential addressi), the potential address of mark frequency highest and frequency greater than 0.8 is family address.For
Potential " work address " calculates it from the frequency freq (PW of determining family addressi), while it is calculated apart from family address
Distance dist (PWi), i.e. oneself address to VLPR number of devices between potential work address track, finally, determination possesses maximum
Value max (freq (PWi)×dist(PWi)) potential work address be real work address.
S24, commuting private car is determined.Commuting private car is the private car for possessing " family-work address " pair simultaneously, oneself
Location to the track between work address is the track that commutes.
Fig. 5 is part commuting vehicle man address and work address exemplary diagram.
On the basis of step S2 acquisition commuting private car, step S3 is carried out, to realize to influence commuting private car share-car
Key feature extract, specifically includes the following steps:
S31, space characteristics extract.By " family address " and " work address " longitude and latitude of each commuting private car, as logical
Diligent private car space characteristics, it is contemplated that the importance of close terminus when share-car prevents other feature from generating in clustering algorithm
Excessive interference, this method increase the weight of space characteristics: longitude is converted to the difference of itself and all VLPR equipment minimum longitudes
Latitude, is converted to the difference of itself and all VLPR equipment minimum latitudes by value, by the longitude and latitude after conversion simultaneously multiplied by 1000, with
Increase weight.
S32, temporal characteristics extract.In order to reflect the commuting rule of commuting private car comprehensively, using multiple working days as sight
Time range is surveyed, using departure time every workday and arrival time as one-dimensional characteristic, in order to by time quantization, we
Departure time is converted to its hourage apart from same day 00:00 by method, and arrival time is converted to the hour of its distance 06:00
Number, if the same day morning peak vehicle is replaced without commuting track, departure time and arrival time with 0.
S33, dynamic effects factor feature extraction.In view of the dynamic effects such as traffic flow, traffic accident and weather condition because
The influence that element recommends share-car, this method extract the dynamic effects factor feature of every workday morning peak respectively.For traffic
Stream, since all a pair of of traffic impacts are larger, will whether Monday is as one-dimensional characteristic, otherwise it is 0 that being, which is 1,；For traffic thing
Therefore using same day morning peak, (the traffic accident sum that 7:00 to the Xiamen City 9:00) occurs is as Accident Characteristic；For weather, respectively
The visibility and rainfall for considering same day morning peak are as bidimensional weather characteristics.
Fig. 6 is part commuting private car key feature exemplary diagram.
On the basis of step S3 is extracted commuting private car key feature, step S4 is carried out, is anticipated with realizing based on user
The dynamic share-car of hope matches, specifically includes the following steps:
S41, be by the space characteristics of each commuting private car and temporal characteristics description commuting private car commuting it is regular when
Empty feature vector (if observing time range is d working day, space-time characteristic total (4+2d) dimension), with k-means algorithm to logical
Diligent private car carries out coarseness cluster, gathers the commuting vehicle with similar commuting rule for one kind.
S42, for the private car that commutes, different dynamic effects factors (weather, the magnitude of traffic flow, traffic accident etc.) item
Under part, size is not to the wish (being ready shared amount of seats) of passenger (Riders) offer Ride-share service by driver (Drivers)
Together, the share-car combination finally generated is also different.First, it would be desirable to it defines driver and the willingness factor of Ride-share service is provided, and
According to dynamic effects because usually determining that willingness factor IF, formula are as follows:
IF=ω1×isMonday+ω2×(-visibility)+ω3×rainfall+ω4× accident wherein,
IsMonday indicates whether Monday, and being is 1, and no is 0；Visibility indicates morning peak visibility；Rainfall indicates early high
Peak rainfall；Accident indicates morning peak traffic accident sum.ω1, ω2, ω3, with ω4Four factors are respectively represented to logical
The influence degree of diligent private car trip rule,Wherein visibility visibility and impact factor are in negative
It closes, therefore coefficient is negative.
S43, under different willingness factor effects, in conjunction with different shared seating capacity seats ∈ [Isosorbide-5-Nitrae], with recurrence
AGNES algorithm generates the commuting private car share-car combination under different sharing seating capacity.
To acquire most preferably shared seating capacity under Different Dynamic influence factor in step S43, an energy function is defined first
E, formula is as follows,
E (IF, seats)=D (IF, seats)+α S (IF, seats)
In formula, IF is willingness factor, and seats is to be ready shared amount of seats, and data item D (IF, seats) is to subtract after share-car
Few vehicle number, smooth item S (IF, seats) are share-car accuracy and influence of passenger's average latency to final share-car effect,
α is balance factor, α ∈ [0,1]；
The calculation formula of data item D (IF, seats) and smooth item S (IF, seats) are as follows,
D (IF, seats)=N-M (IF, seats)；
In formula, N is total vehicle number, and M (IF, seats) is vehicle combination number, and WT (IF, seats) is passenger's average waiting
Time, RA (IF, seats) are average share-car accuracy,It is adjustable parameter with β, for determining share-car accuracy and passenger etc.
Weight to time effects share-car effect；Share-car target is to guarantee that D (if, seats) and RA (IF, seats) is big as far as possible, WT (IF,
Seats) small as far as possible, therefore WT (IF, seats) coefficient is made to be negative.
To each willingness factor IF, taking seats is so that energy function E (if, seats) is the shared seat of maximum value
Number, then have,
Seats=argmaxE (if, seats), seats ∈ [Isosorbide-5-Nitrae].
Fig. 3 is Xiamen City's weather history and casualty data exemplary diagram, and wherein Fig. 3 a is Xiamen City's May 1 to May in 2016
Weather condition exemplary diagram on the 4th, Fig. 3 b are Xiamen City's part traffic accident data instance figures in 2016.
Fig. 7 is to carry out coarseness to part commuting private car to cluster exemplary diagram.
Fig. 8 is the private car cluster that commutes to some coarseness, presses and does not carry out the thin cluster instance graph of dynamic on the same day.
Fig. 9 is commuting private car dynamic share-car recommended method analysis of experimental results figure, and wherein Fig. 9 a is that each work is per day
Serving-time distribution figure, average daily waiting time are 7.45 minutes, and Fig. 9 b is each working day share-car accuracy distribution map, average daily share-car
Accuracy is that 83%, Fig. 9 c is to reduce vehicle number distribution map each working day, average daily to reduce 5478, accounts for about commuting private car total amount
30%.
From being can be seen that in above-mentioned figure using after share-car recommended method proposed by the present invention, performance has been obtained significantly
Promotion: on the one hand ensure that the share-car satisfaction (lesser waiting time and higher share-car accuracy) of user, another party
Face significantly reduces the commuting private car usage quantity of peak period morning and evening.
More than, it is merely preferred embodiments of the present invention, but scope of protection of the present invention is not limited thereto, it is any
In the technical scope disclosed by the present invention, any changes or substitutions that can be easily thought of by those familiar with the art, all answers
It is included within the scope of the present invention.Therefore, protection scope of the present invention should be subject to the protection scope in claims.
Claims (6)
1. commute private car dynamic share-car recommended method, which comprises the following steps:
S1, input VLPR data set, pre-process VLPR data set, generate city vehicle track data collection；
S2, the high reproducibility based on commuting private car trip time and space idea, filter out active from city vehicle track data concentration
Low non-commuting vehicle is spent, the analysis of family address and work address is carried out to remaining vehicle, family-work address will be possessed simultaneously
Vehicle be determined as the private car that commutes；
S3, it is realized respectively pair in conjunction with family-work address, commuting time window and the working day dynamic effects factor of commuting private car
Influence space characteristics, temporal characteristics and the dynamic effects factor feature extraction of commuting private car share-car, the dynamic effects factor
Including weather, accident and traffic flow conditions；
S4, space characteristics and temporal characteristics based on commuting vehicle match the commuting vehicle with similar commuting time and space idea, and
Influence in conjunction with dynamic effects factor to driver's share-car wish needs these dynamic factors and user in the share-car of Spatial dimensionality
Effective combination is asked, realizes the dynamic share-car matching based on user intention；
Wherein, step S4 specifically includes the following steps:
It S41, is that the regular space-time of description commuting private car commuting is special by the space characteristics of each commuting private car and temporal characteristics
Vector is levied, coarseness cluster is carried out to commuting private car with k-means algorithm, makes the commuting vehicle with similar commuting rule
Gather for one kind；
S42, according to dynamic effects because usually determine driver to passenger provide Ride-share service willingness factor IF, specifically,
IF=ω1×isMonday+ω2×(-visibility)+ω3×rainfall+ω4× accident wherein,
IsMonday indicates whether Monday, and being is 1, and no is 0；Visibility indicates morning peak visibility；Rainfall indicates early high
Peak rainfall；Accident indicates morning peak traffic accident sum；ω1, ω2, ω3, with ω4Four factors are respectively represented to logical
The influence degree of diligent private car trip rule,Wherein visibility visibility and impact factor are negatively correlated,
Therefore coefficient is negative；
S43, under different willingness factor effects, in conjunction with different shared seating capacity seats ∈ [Isosorbide-5-Nitrae], with recurrence AGNES
Algorithm generates the commuting private car share-car combination under different sharing seating capacity；
Wherein, an energy function is defined first to acquire most preferably shared seating capacity under Different Dynamic influence factor in step S43
E, formula is as follows,
E (IF, seats)=D (IF, seats)+α S (IF, seats)
In formula, IF is willingness factor, and seats is to be ready shared amount of seats, and data item D (IF, seats) is to reduce vehicle after share-car
Number, smooth item S (IF, seats) are share-car accuracy and influence of passenger's average latency to final share-car effect, and α is
Balance factor, α ∈ [0,1]；
The calculation formula of data item D (IF, seats) and smooth item S (IF, seats) are as follows,
D (IF, seats)=N-M (IF, seats)；
In formula, N is total vehicle number, and M (IF, seats) is vehicle combination number, when WT (IF, seats) is passenger's average waiting
Between, RA (IF, seats) is average share-car accuracy,It is adjustable parameter with β, for determining that share-car accuracy and passenger wait
The weight of time effects share-car effect；
To each willingness factor IF, take seats be so that energy function E (IF, seats) is the shared seating capacity of maximum value,
Then have,
Seats=argmaxE (IF, seats), seats ∈ [Isosorbide-5-Nitrae].
2. commuting private car dynamic share-car recommended method according to claim 1, it is characterised in that: step S1 is specifically included
Following steps:
S11, local private car is extracted, by license plate number and license plate color feature, local private car is extracted from VLPR data set
Data；
S12, field are refined, and extract required field from local private savings car data, generate vehicle record data；
S13, redundancy cross vehicle record cleaning, deleted vehicle record data in repetition vehicle record and incorrect recording data；
S14, track of vehicle is generated, crosses vehicle record to after cleaning, classify according to license plate number, when identical license plate number record pressed vehicle
Between sort, then identical license plate crossed to vehicle record is the first in chronological order to be connected, form track of vehicle data.
3. commuting private car dynamic share-car recommended method according to claim 2, it is characterised in that: needed in step S12
Field include VLPR number, VLPR type, the direction VLPR, license plate number, license plate color, lane number and cross the vehicle time.
4. commuting private car dynamic share-car recommended method according to claim 1, which is characterized in that step S2 is specifically included
Following steps:
S21, low liveness vehicle filtering define moon working day based on the periodicity and repeatability of commuting private car trip rule
In to enliven private car of the number of days less than 15 days be the too low vehicle of liveness, determine that the too low vehicle of liveness is non-commuting vehicle and to give
To exclude；
S22, potential address for determining potential commuting private vehicle and potential work address, to determine the potential " family of vehicle
Location ", analysis are come off duty to the track of vehicle in work hours next day section, L daily1, L2..., LnFor track VLPR equipment
Location sequence, t1, t2..., tn-1Pass through the time interval sequence per continuous two VLPR equipment, t for vehiclem=max (ti), i=
1,2 ..., n-1, works as tmL is determined greater than given thresholdm+1For its potential address；To determine that vehicle is potential " work address ",
Analysis is gone to work daily to the track of vehicle in next time interval, L1, L2..., LnFor the track device address VLPR sequence,
t1, t2..., tn-1Pass through the time interval sequence per continuous two VLPR equipment for vehicle, works as tmDetermine greater than given threshold
LmIt is potential " work address " for the vehicle；
S23, vehicle man address and work address are determined, for potential address of each potential commuting private car, calculated each
Frequency freq (the PH of potential addressi), the potential address of mark frequency highest and frequency greater than 0.8 is family address, for
Potential work address calculates its frequency freq (PW from fixed addressi), while it is calculated apart from family address
Distance dist (PWi), determination possesses maximum value max (freq (PWi)×dist(PWi)) potential work address be real work
Make address；
S24, it determines commuting private car, determine while possessing family-work address private car for the private car that commutes.
5. commuting private car dynamic share-car recommended method according to claim 1, which is characterized in that step S3 is specifically included
Following steps:
S31, space characteristics extract, using the longitude and latitude of the family address of each commuting private car and work address as commuting private car
Space characteristics；
S32, temporal characteristics extract, using multiple working days as observation time range, by departure time every workday and arrival
Time respectively as one-dimensional characteristic, in order to which by time quantization, the departure time is converted to its hourage apart from same day 00:00,
Arrival time is converted to the hourage of its distance 06:00, if the same day morning peak vehicle without commuting track, the departure time with
Arrival time is replaced with 0；
S33, dynamic effects factor feature extraction consider traffic flow, traffic accident and weather condition dynamic effects factor to share-car
The dynamic effects factor feature of every workday morning peak is extracted in the influence of recommendation respectively, will whether Monday is made for traffic flow
For one-dimensional characteristic, otherwise it is 0 that being, which is 1,；For traffic accident, the traffic accident sum that same day morning peak is occurred is as accident
Feature；For weather, the visibility and rainfall for considering same day morning peak respectively are as bidimensional weather characteristics.
6. commuting private car dynamic share-car recommended method according to claim 5, it is characterised in that: in step S31, consider
Close starting point and the importance of terminal, prevent other feature from generating excessive interference in clustering algorithm when share-car, and longitude is turned
It is changed to the difference of itself and all VLPR equipment minimum longitudes, latitude is converted to the difference of itself and all VLPR equipment minimum latitudes
Value, by the longitude and latitude after conversion simultaneously multiplied by 1000, to increase the weight of space characteristics.
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| CN108022012A (en) * | 2017-12-01 | 2018-05-11 | 兰州大学 | Vehicle location Forecasting Methodology based on deep learning |
| CN108320503A (en) * | 2018-01-19 | 2018-07-24 | 江苏本能科技有限公司 | Vehicle traveling querying method and system based on point identification |
| CN109949068A (en) * | 2019-01-09 | 2019-06-28 | 深圳北斗应用技术研究院有限公司 | A kind of real time pooling vehicle method and apparatus based on prediction result |
| CN110134865B (en) * | 2019-04-26 | 2023-03-24 | 重庆大学 | Commuting passenger social contact recommendation method and platform based on urban public transport trip big data |
| CN110148298B (en) * | 2019-06-24 | 2022-03-18 | 重庆大学 | Private car regular travel behavior discovery method based on motor vehicle electronic identification data |
| CN111523562B (en) * | 2020-03-20 | 2021-06-08 | 浙江大学 | Commuting mode vehicle identification method based on license plate identification data |
| CN113112331A (en) * | 2021-04-25 | 2021-07-13 | 中山大学 | Private car carpooling matching method based on commuting scene |
| CN114331617B (en) * | 2021-12-29 | 2024-05-31 | 重庆大学 | Commuting private car pooling matching method based on artificial bee colony algorithm |
| CN115440040B (en) * | 2022-09-02 | 2023-09-22 | 重庆大学 | Commuter vehicle identification method based on expressway traffic data |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104751625A (en) * | 2013-12-25 | 2015-07-01 | 上海博泰悦臻网络技术服务有限公司 | Carpooling method and carpooling system based on trajectory analysis |
| CN105674995A (en) * | 2015-12-31 | 2016-06-15 | 百度在线网络技术（北京）有限公司 | Method for acquiring commuting route based on user's travel locus, and apparatus thereof |
| CN105701560A (en) * | 2015-12-31 | 2016-06-22 | 百度在线网络技术（北京）有限公司 | Method and device for determining commuting route information |
| CN106027637A (en) * | 2016-05-18 | 2016-10-12 | 福建工程学院 | Car-pooling method and system based on trajectory information |
| KR20160150212A (en) * | 2015-06-19 | 2016-12-29 | (주)연결해 | Techniques for matching the movement path of the carpool driver and rider |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9612127B2 (en) * | 2014-07-25 | 2017-04-04 | GM Global Technology Operations LLC | Carpool finder assistance |
| US9904900B2 (en) * | 2015-06-11 | 2018-02-27 | Bao Tran | Systems and methods for on-demand transportation |
-
2017
- 2017-05-25 CN CN201710380104.3A patent/CN107038858B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104751625A (en) * | 2013-12-25 | 2015-07-01 | 上海博泰悦臻网络技术服务有限公司 | Carpooling method and carpooling system based on trajectory analysis |
| KR20160150212A (en) * | 2015-06-19 | 2016-12-29 | (주)연결해 | Techniques for matching the movement path of the carpool driver and rider |
| CN105674995A (en) * | 2015-12-31 | 2016-06-15 | 百度在线网络技术（北京）有限公司 | Method for acquiring commuting route based on user's travel locus, and apparatus thereof |
| CN105701560A (en) * | 2015-12-31 | 2016-06-22 | 百度在线网络技术（北京）有限公司 | Method and device for determining commuting route information |
| CN106027637A (en) * | 2016-05-18 | 2016-10-12 | 福建工程学院 | Car-pooling method and system based on trajectory information |
Non-Patent Citations (1)
| Title |
|---|
| 基于时间序列聚类方法分析北京出租车出行量的时空特征;程静 等;《地球信息科学学报》;20160930;第18卷(第9期);第1227-1239页 |
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