BigDataEurope Mobility Use Case in Thessaloniki

Transcription

1 BigDataEurpe Mbility Use Case in Thessalniki Brussels, BDE SC4 Wrkshp 14 th Sept 2017

2 The mbility use case in Thessalniki Multisurce datasets (speed, traffic flw, travel time) are being used in Thessalniki fr the prvisin f traffic status shrt-term predictin based n mbility/traffic patterns recgnitin. Integratin f machine learning techniques using the travel times, traffic cunts and speeds as well as the crrelatins f traffic speed, t train an apprpriate Neural Netwrk Mdel fr efficient and rbust traffic speed predictin.

3 The datasets The main dataset is cmpsed f Flating Car Data speed measurements per minute Hundreds f Gb (histrical dataset)

4 Traffic classificatin and predictin Start Histrical Link Traffic State BT Histrical Link Traffic State (FCD) Histrical Link Traffic State (Lp Detectrs) Histrical Link Traffic State Classificatin Stre in Histrical States Define Current Link Traffic State Cmpare Traffic States (ML, NN) Predict (ARIMAX NN) Validate Predictin

5 Traffic classificatin and predictin Link id Directin Date time Predict Value Last step: N Start Check Input variables Apprpriate input variables: N Fill Missing Values Last step? N Train and Test data Exist Crss Preprcessing N Train Last step: Yes Predictin End mdel? Validatin Histrical Data Yes Yes Apprpriate input variables: N

6 Machine learning techniques (NN) Input Descriptin path Link id Directin datetime predict steps The path with the histrical data The id f the rad fr whm the predictin will be made The directin f the rad The specific date and time fr the predictin The variable t be predicted Hw many steps frward the predictin will be Output Predicted value Real value RMSE Descriptin The predicted value f the rad The real value f the rad The rt mean square

7 Machine learning techniques (NN) Training Dataset Speeds (min, max, mean) Measures (Standard deviatin, Kurtsis, Skewness) Entries (unique, entries) Preprcess (nrmalizatin t [0,1]) The mdel used is Multilayer perceptrn (MLP). MLP is a feedfrward artificial neural netwrk mdel. MLP utilizes a supervised learning technique called backprpagatin.

8 Machine learning techniques (NN) Validatin 10-fld crss validatin is used t select the apprpriate Neural Netwrk mdel t predict the traffic speed fr a given time. The dataset is being splitted in 10 independent subsets, 10 times. Each time ne subset is held ut and is used as the test set, while the rest 9 (k-1) subsets frm the train set. Mdel Selectin The mdel that will be used fr predictin is the ne with the minimum average errr.

9 Machine learning techniques (NN)

10 Machine learning techniques (NN)

11 Machine learning techniques (NN)

12 Machine learning techniques (NN)

13 Machine learning techniques (NN) Link Date Time Predicted Real RMSE :30: :45: :00: :15: :00: :15: :30: :45: :00: :15: :30: :45:

14 Mbility services in Thessalniki TrafficThess ( Visual representatin f the current as well as past speeds in Thessalniki, Greece ntificatins Histrical raw data exprt per link in pen frmat TrafficPaths ( Descriptive infrmatin f the current travel times wherever available (Thessalniki, Patra, Iraklen, Serres, Kavala) Mbile friendly web page TrafficThess Reprts ( Visual and descriptive representatin f the current traffic cnditins (speeds & travel times) n the main rads f Thessalniki, Greece Highly custmizable ntificatins Nrmalized histrical data exprt per rad in pen frmat Traffic calendar (pwered by Ggle) Mbile friendly web prtal Sign up required

15 Mbility services in Thessalniki TrafficThess ( Reliable traffic cnditins mnitring n a 24/7/365 basis Traffic cnditins in the city f Thessalniki, Greece during snwfall n 10 & 11 Jan 2017: (credits t anmput fr helping ut with the vide) Keep calm! It s just anther cngestin n the ring rad

16 Mbility services in Thessalniki

17 Mbility services in Thessalniki TrafficPaths ( Calculatin f travel times n a 24/7/365 basis

18 Mbility services in Thessalniki TrafficThess Reprts ( A persnalized single pint f access

19 Mbility services in Thessalniki Datatank (Back ffice + restapis) CKAN (frnt end)

20 Thank yu! DR. JOSEP MARIA SALANOVA GRAU Brussels, BDE SC4 Wrkshp 14 th Sept 2017