CN110264706A - A kind of unloaded taxi auxiliary system excavated based on big data - Google Patents
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Abstract
The invention discloses a kind of unloaded taxi auxiliary systems excavated based on big data, comprising: taxi data acquisition module, for collecting taxi big data；Data processing module by traffic network grid, calculates all grids in the passenger carrying capacity and carrying probability of each period, and be converted to passenger carrying capacity after carrying out data prediction to the taxi car data of acquisition；Carrying hotspot prediction module, for predicting the carrying hot spot of next period according to history passenger carrying capacity data；Unloaded taxi is cruised path recommending module, and for the unloaded taxi path optimization model and carrying hot spot according to foundation, recommendation is cruised path.The present invention utilizes big data, predict the carrying hot spot region of following a period of time, recommend to prepare for unloaded taxi path of cruising, then according to cruising minimization of cost principle, recommends a unobstructed path of cruising by more carrying hot spot region for unloaded taxi.
Description
Technical field
The present invention relates to artificial intelligence technologys more particularly to a kind of unloaded taxi excavated based on big data to assist system
System.
Background technique
With the development of the social economy, the trip requirements of people are continuously increased, taxi exists by its convenience, rapidity
It is occupied an important position in the vehicles.But since the information exchange degree between passenger and taxi is low, taxi " carrying is difficult ",
Cause taxi no-load ratio high, the economic well-being of workers and staff of taxi driver reduces.And the appearance of net about vehicle in recent years is to taxi
Garage's industry produces enormous impact, so that taxi no-load ratio rises again, China various regions taxi driver, which goes on strike, to be protested, seriously
Affect the daily trip of the public.
The high no-load ratio of taxi not only reduces the economic well-being of workers and staff of taxi driver, has also seriously affected taxi trade
It develops in a healthy way.In order to reduce taxi no-load ratio, taxi driver cruises without purpose, and due to can not accurate perception carrying hot spot
Distribution, can only by virtue of experience fuzzy Judgment, accuracy rate is low, can not be effectively reduced taxi no-load ratio.China is rare at present grinds
Study carefully or system focuses on unloaded taxi and cruises the recommendation in path, does not find that a kind of effective means reduce taxi no-load ratio.
Summary of the invention
The technical problem to be solved in the present invention is that for the defects in the prior art, providing a kind of based on big data excavation
Unloaded taxi auxiliary system.
The technical solution adopted by the present invention to solve the technical problems is: a kind of unloaded taxi excavated based on big data
Auxiliary system, comprising:
Taxi data acquisition module, for collecting taxi big data, the taxi big data includes vehicle ID, vehicle
Present position longitude and latitude, vehicle passenger carrying status, Vehicle Speed information,；Wherein, vehicle passenger carrying status indicates are as follows: gets on the bus
Time and passenger carrying status or time getting off and light condition；
Data processing module, by traffic network grid, calculates after carrying out data prediction to the taxi car data of acquisition
Passenger carrying capacity and carrying probability of all grids in each period, and be converted to passenger carrying capacity；
Carrying hotspot prediction module, for predicting the carrying hot spot of next period according to history passenger carrying capacity data；
Unloaded taxi is cruised path recommending module, for according to the unloaded taxi path optimization model of foundation and carrying
Hot spot recommends path of cruising；
The zero load taxi path optimization model is specific as follows:
The objective function of unloaded taxi path optimization model is as follows:
Wherein: N is that unloaded vehicle has been cruised dot grid and carrying hot spot grid set；α is traffic resistivity；For vehicle
TkFrom the desired distance of starting dot grid i to final carrying hot spot grid j；S is the fixed cost of a vehicle, and K is all taxis
The quantity of vehicle, C are the transportation cost of taxi unit distance.
According to the above scheme, in the data processing module, traffic network grid is to carry out grid dividing to existing road network, is divided
Principle be to meet an only motor road in grid.
According to the above scheme, in the data processing module, it is using the unit time as the period, when sequence divides that the period, which divides,
Between section, period period according to taxi in road network region averagely receive lodgers interval time determine.
According to the above scheme, in the carrying hotspot prediction module, history passenger carrying capacity data are the prediction period previous time
Section taxi quantity and the previous day, the last week with the period taxi incremental data.
According to the above scheme, it in the carrying hotspot prediction module, using history passenger carrying capacity data as prediction data, sets up
The convolutional neural networks of three-layer coil lamination extract local feature, export the prediction taxi passenger carrying capacity of road network grid.
According to the above scheme, the convolutional neural networks include input layer, three-layer coil lamination and output layer, and data input is three
Matrix is tieed up, wherein the period interval of grid dividing and time dimension comprising longitude and latitude level, wherein first layer convolutional layer
Convolution kernel comprising 5 3*3 sizes, the second layer include the convolution kernel of 10 5*5 sizes, for the third time include the volume of 5 3*3 sizes
Product core.
According to the above scheme, the unloaded taxi path optimization model constraint condition is using one or more in the following conditions
It is a:
The constraint of grid passenger carrying capacity: the quantity for the unloaded taxi vehicle that each grid may pass through is not more than the load of the grid
Objective ability；
The constraint of potential income grid: the sum of passenger carrying capacity of each carrying hot spot grid is greater than unloaded taxis quantity；
Distance restraint: desired distance no more than the average unloaded cruising time of urban taxi and taxi average speed per hour it
Product；
According to the above scheme, the value of the traffic resistivity is as follows:
α=1, the road is clear
α=1.3, road are crowded
α=1.7, road congestion.
The beneficial effect comprise that: the present invention utilizes history big data, constructs simultaneously training convolutional neural networks, in advance
The carrying hot spot region of following a period of time is surveyed, recommends to prepare for unloaded taxi path of cruising, then according to cost of cruising
Minimumization principle establishes unloaded taxi path optimization model, recommends one to pass through more carrying hot zone for unloaded taxi
The unobstructed path of cruising in domain.
Detailed description of the invention
Present invention will be further explained below with reference to the attached drawings and examples, in attached drawing:
Fig. 1 is the structural schematic diagram of the embodiment of the present invention；
Fig. 2 is the neural network structure schematic diagram of the embodiment of the present invention；
Fig. 3 is the method flow diagram of the embodiment of the present invention.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to embodiments, to the present invention
It is further elaborated.It should be appreciated that described herein, specific examples are only used to explain the present invention, is not used to limit
The fixed present invention.
As shown in Figure 1, a kind of unloaded taxi auxiliary system excavated based on big data, comprising:
Taxi data acquisition module, for collecting taxi big data, the taxi big data includes vehicle ID, vehicle
Present position longitude and latitude, vehicle passenger carrying status, Vehicle Speed information,；Wherein, vehicle passenger carrying status indicates are as follows: gets on the bus
Time and passenger carrying status or time getting off and light condition；
In the present embodiment, having collected Wuhan, the traffic control department in two city of Chengdu and Di Di company, more than totally 3.6 hundred million taxis are big
Data, including information of vehicles such as vehicle ID, longitude and latitude, passenger carrying status, travel speeds.Select Hadoop ecosystem for
The data processing of magnanimity, and ultra-large data set is read and write in real time, the storage of data is being divided using building at random
The non-relationship distributed data base HBase of cloth storage system HDFS.Due to big data because some environmental factors cause data to go out
Mistake will repeat using Rye up to rule to data prediction and the GPS data of location information mistake screen out.
Data processing module, by traffic network grid, calculates after carrying out data prediction to the taxi car data of acquisition
Passenger carrying capacity and carrying probability of all grids in each period, and be converted to passenger carrying capacity；
Carrying hotspot prediction module, for predicting the carrying hot spot of next period according to history passenger carrying capacity data；
To history carrying data use the distributed computing framework MapReduce based on Hadoop, using Mahout to point
Cloth data realize clustering algorithm, obtain required hot spot data collection.And visualize hot spot region using ArcGIS, it can be found that
Regularity variation is presented in the passenger carrying capacity of net region at any time.Since convolutional neural networks are in the prediction in terms of time series
Performance preferably, can effectively excavate big data inherent laws.Therefore building convolutional neural networks excavate the prediction of big data inherent law
Carrying hot spot region.
History passenger carrying capacity data are the taxi quantity of prediction period previous time period (one month) and previous
It, the last week with the taxi incremental data of period set up the convolutional Neural of three-layer coil lamination as prediction data
Network, such as Fig. 2 extract local feature, export the prediction taxi passenger carrying capacity of each road network grid.
The convolutional neural networks that the present invention establishes include input layer, three-layer coil lamination and output layer, and data input is three-dimensional
Matrix, wherein the period interval of grid dividing and time dimension comprising longitude and latitude level, wherein first layer convolutional layer packet
Convolution kernel containing 5 3*3 sizes, the second layer include the convolution kernel of 10 5*5 sizes, for the third time include the convolution of 5 3*3 sizes
Core.
The convolutional neural networks use input-three-layer coil lamination-export structure, and convolutional layer reduces the complexity of network,
Reduce weight quantity；Neural network structure can map linear character, and activation primitive can map nonlinear characteristic, convolution mind
Complex characteristic therein can be preferably extracted through network.It is gone forward side by side using Keras+TensorFlow platform architecture convolutional neural networks
Row training, using GPU operation, the training process greatly speeded up can quickly adjust parameter therein, quickly learn therein
Rule.
Unloaded taxi is cruised path recommending module, for according to the unloaded taxi path optimization model of foundation and carrying
Hot spot recommends path of cruising；
The zero load taxi path optimization model is specific as follows:
The objective function of unloaded taxi path optimization model is as follows:
Wherein: N is that unloaded vehicle has been cruised dot grid and carrying hot spot grid set；α is traffic resistivity；For vehicle
TkFrom the desired distance of starting dot grid i to final carrying hot spot grid j；S is the fixed cost of a vehicle, and K is all taxis
The quantity of vehicle, C are the transportation cost of taxi unit distance.
Unloaded taxi path optimization model constraint condition is as follows:
The constraint of grid passenger carrying capacity: the quantity for the unloaded taxi vehicle that each grid may pass through is not more than the load of the grid
Objective ability；
The constraint of potential income grid: the sum of passenger carrying capacity of each carrying hot spot grid is greater than unloaded taxis quantity；
Distance restraint: desired distance no more than the average unloaded cruising time of urban taxi and taxi average speed per hour it
Product；
The value of traffic resistivity is as follows:
α=1, the road is clear
α=1.3, road are crowded
α=1.7, road congestion.
The coefficient is used to adjust the transportation cost C of taxi unit distance.
For desired distance,
1) it is obtained according to objective function, path optimizing is the shortest path of desired distance, i.e. taxi zero load cruises cost most
Small path Wk=(Gi,g1k,g2k,……,gmk),gmk∈N-, Gi→gmkFor unloaded taxi TkIt travels to potential income grid
The grid passed through in the process；
2) grid GiFor unloaded taxi starting point, Gi→gmkFor unloaded taxi TkDriving path defines ErkTo hire out
Vehicle TkIn path g1k→gmkCarrying event；
3)E∞kFor taxi TkThe event of carrying, taxi T are unable on this pathkPath of cruising all possibility
Property is Ωk={ Erk∪E∞k}r,k∈m,
4) unloaded taxi TkIt is as follows in the probability calculation formula that grid carrying event occurs:
It 5), may the arbitrary mess carrying in path of cruising during unloaded taxi is cruised, it is also possible in potential income
Grid carrying then it is expected that zero load is cruised distance,
Wherein, d (gnk, g(n+1)k) it is adjacent mesh gnkAnd g(n+1)kThe distance between.
In the present embodiment, ant colony optimization for solving is used to the model.The mode that other traversals can also be used solves.
Fig. 3 ant group algorithm process.
1) Step1:nc ← 0 (for searching times or the number of iterations)；Each τijWith Δ τijInitialization；M ant is put
It sets on n vertex；
2) Step2: the starting point of every ant is placed on current solution and is concentrated；By every ant k (k=1,2,3,
4........, m) according to probabilityMove to the next position point j；Point j is placed on current solution to concentrate；
3) Step3: counting and calculates the length L in the path that every Ant Search is crossedk=(k=1,2,3,4........,
M) and current optimal solution is recorded；
4) Step4: track intensity is modified according to renewal equation；
5) Δ τ Step5: is set to each side arc (i, j)ij← 0, nc ← nc+1；
6) it Step6: if the phenomenon that nc does not reach preset the number of iterations, but algorithm has no degeneration, that is, looks for
Be to solution it is identical, then turn Step2；
7) Step7: current optimal solution is exported.
The present invention is based on current position, according to prediction hot spot region, recommends optimal patrol for taxi driver
Path is swum, and is visualized and is used for driver.
It should be understood that for those of ordinary skills, it can be modified or changed according to the above description,
And all these modifications and variations should all belong to the protection domain of appended claims of the present invention.
Claims (8)
1. a kind of unloaded taxi auxiliary system excavated based on big data characterized by comprising
Taxi data acquisition module, for collecting taxi big data, the taxi big data includes vehicle ID, vehicle institute
Locate position longitude and latitude, vehicle passenger carrying status, Vehicle Speed information；Wherein, vehicle passenger carrying status indicate are as follows: pick-up time and
Passenger carrying status or time getting off and light condition；
Data processing module, by traffic network grid, calculates all after carrying out data prediction to the taxi car data of acquisition
Passenger carrying capacity and carrying probability of the grid in each period, and be converted to passenger carrying capacity；
Carrying hotspot prediction module, for predicting the carrying hot spot of next period according to history passenger carrying capacity data；
Unloaded taxi is cruised path recommending module, for according to the unloaded taxi path optimization model of foundation and carrying heat
Point generates the path of cruising for recommending to unloaded taxi；
The zero load taxi path optimization model is specific as follows:
The objective function of unloaded taxi path optimization model is as follows:
Wherein: N is that unloaded vehicle has been cruised dot grid and carrying hot spot grid set；α is traffic resistivity；For vehicle TkFrom
Desired distance of the initial point grid i to final carrying hot spot grid j；S is the fixed cost of a vehicle, and K is the number of all taxis
Amount, C are the transportation cost of taxi unit distance.
2. the unloaded taxi auxiliary system according to claim 1 excavated based on big data, which is characterized in that the number
According in processing module, traffic network grid is that grid dividing is carried out to existing road network, and the principle of division is to meet in a grid only
There is a motor road.
3. the unloaded taxi auxiliary system according to claim 1 excavated based on big data, which is characterized in that the number
According in processing module, it is using the unit time as the period that the period, which divides, and sequence divides the period, and period period is according to road
In web area taxi averagely receive lodgers interval time determine.
4. the unloaded taxi auxiliary system according to claim 1 excavated based on big data, which is characterized in that the load
In objective hotspot prediction module, history passenger carrying capacity data are the taxi quantity of prediction period previous time period and previous
It, the last week with the period taxi incremental data.
5. the unloaded taxi auxiliary system according to claim 4 excavated based on big data, which is characterized in that the load
In objective hotspot prediction module, using history passenger carrying capacity data as prediction data, the convolutional neural networks of three-layer coil lamination are set up,
Local feature is extracted, the prediction taxi passenger carrying capacity of road network grid is exported.
6. the unloaded taxi auxiliary system according to claim 5 excavated based on big data, which is characterized in that the volume
Product neural network includes input layer, three-layer coil lamination and output layer, and data input is three-dimensional matrice, wherein horizontal comprising longitude and latitude
Grid dividing and time dimension period interval, wherein first layer convolutional layer includes the convolution kernel of 5 3*3 sizes, the
Two layers of convolution kernel comprising 10 5*5 sizes include the convolution kernel of 5 3*3 sizes for the third time.
7. the unloaded taxi auxiliary system according to claim 1 excavated based on big data, which is characterized in that the sky
Path optimization model constraint condition of hiring a car is set out using one or more of the following conditions:
The constraint of grid passenger carrying capacity: the quantity for the unloaded taxi vehicle that each grid may pass through is not more than the carrying energy of the grid
Power；
The constraint of potential income grid: the sum of passenger carrying capacity of each carrying hot spot grid is greater than unloaded taxis quantity；
Distance restraint: the product of desired distance average no more than urban taxi unloaded cruising time and taxi average speed per hour.
8. the unloaded taxi auxiliary system according to claim 1 excavated based on big data, which is characterized in that the friendship
The value of logical resistivity is as follows:
α=1, the road is clear
α=1.3, road are crowded
α=1.7, road congestion.
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Cited By (8)
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| CN110852354A (en) * | 2019-10-22 | 2020-02-28 | 上海中旖能源科技有限公司 | Vehicle track point identification method and device |
| CN112070529A (en) * | 2020-08-24 | 2020-12-11 | 贵州民族大学 | Passenger carrying hotspot parallel prediction method, system, terminal and computer storage medium |
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| CN113868553A (en) * | 2021-09-18 | 2021-12-31 | 湖南科技大学 | Hierarchical taxi passenger-carrying recommendation method and system |
| CN113888141A (en) * | 2021-11-10 | 2022-01-04 | 安徽达尔智能控制系统股份有限公司 | Taxi network taxi appointment service information management method and system |
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