“BigDataEurope Mobility Use Case in Thessaloniki”

Brussels, BDE – SC4 Workshop – 14th Sept 2017

The mobility use case in Thessaloniki

Multisource datasets (speed, traffic flow, travel time) are being used in Thessaloniki for the provision of traffic status short-term prediction based on mobility/traffic patterns recognition.

Integration of machine learning techniques using the travel times, traffic counts and speeds as well as the correlations of traffic speed, to train an appropriate Neural Network Model for efficient and robust traffic speed prediction.

The datasets

The main dataset is composed of Floating Car Data o 500 – 2.500 speed measurements per minute o Hundreds of Gb (historical dataset)

Traffic classification and prediction

Start

Historical Link Traffic State (FCD)

Historical Link Traffic State Classification

Historical Link Traffic State (Loop Detectors)

Historical Link Traffic State BT

Compare Traffic States(ML, NN)

Define Current Link Traffic State

Predict (ARIMAX | NN)

Store in Historical States

Validate Prediction

Traffic classification and prediction

Historical Data

Start

Link idDirection

Date time

Fill Missing Values

Train and Test data Preprocessing Train Prediction EndCross

ValidationCheck Input

variablesAppropriate input variables: No

Last step: No

Last step: Yes

Appropriate input variables: No

Predict Value

Last step? No

Yes

Exist model?

No

Yes

Machine learning techniques (NN)

Input Description

path The path with the historical data

Link id The id of the road for whom the prediction will be made

Direction The direction of the road

datetime The specific date and time for the prediction

predict The variable to be predicted

steps How many steps forward the prediction will be

Output Description

Predicted value The predicted value of the road Real value The real value of the road

RMSE The root mean square

Machine learning techniques (NN)

Training Dataset o Speeds (min, max, mean) o Measures (Standard deviation, Kurtosis, Skewness) o Entries (unique, entries)

Preprocess (normalization to [0,1]) The model used is Multilayer perceptron (MLP). MLP

is a feedforward artificial neural network model. MLP utilizes a supervised learning technique called backpropagation.

Machine learning techniques (NN)

Validation o 10-fold cross validation is used to select the

appropriate Neural Network model to predict the traffic speed for a given time.

o The dataset is being splitted in 10 independent subsets, 10 times. Each time one subset is held out and is used as the test set, while the rest 9 (k-1) subsets form the train set.

Model Selection o The model that will be used for prediction is the one

with the minimum average error.

Machine learning techniques (NN)

Machine learning techniques (NN)

Machine learning techniques (NN)

Machine learning techniques (NN)

Machine learning techniques (NN)

Link Date Time Predicted Real RMSE

1 2017-01-12 19:30:00 17.07 16.71 0.35 1 2017-01-12 19:45:00 16.88 16.14 0.74 1 2017-01-12 20:00:00 16.02 15.57 0.45 1 2017-01-12 20:15:00 15.69 15.00 0.69 2 2017-01-14 16:00:00 36.75 37.28 0.53 2 2017-01-14 16:15:00 37.26 37.85 0.59 2 2017-01-14 16:30:00 37.77 38.42 0.65 2 2017-01-14 16:45:00 38.31 39.00 0.68 3 2017-01-06 12:00:00 50.64 51.00 0.351 3 2017-01-06 12:15:00 49.50 47.32 2.17 3 2017-01-06 12:30:00 47.51 44.00 3.51 3 2017-01-06 12:45:00 42.43 38.00 4.43

Mobility services in Thessaloniki

TrafficThess (http://www.trafficthess.imet.gr) o Visual representation of the current as well as past speeds in Thessaloniki, Greece o Email notifications o Historical raw data export per link in open format

TrafficPaths (http://www.trafficpaths.imet.gr) o Descriptive information of the current travel times wherever available (Thessaloniki, Patra,

Irakleon, Serres, Kavala) o Mobile friendly web page

TrafficThess Reports (http://www.trafficthessreports.imet.gr) o Visual and descriptive representation of the current traffic conditions (speeds & travel times)

on the main roads of Thessaloniki, Greece o Highly customizable email notifications o Normalized historical data export per road in open format o Traffic calendar (powered by Google) o Mobile friendly web portal o Sign up required

Mobility services in Thessaloniki

TrafficThess (http://www.trafficthess.imet.gr) Reliable traffic conditions monitoring on a 24/7/365 basis

Traffic conditions in the city of Thessaloniki, Greece during snowfall on 10 & 11 Jan 2017: https://youtu.be/2z12tUkuwaM (credits to anmpout for helping out with the video)

Keep calm! It’s just another congestion on the ring road…

Mobility services in Thessaloniki

Mobility services in Thessaloniki TrafficPaths (http://www.trafficpaths.imet.gr) Calculation of travel times on a 24/7/365 basis

Mobility services in Thessaloniki TrafficThess Reports (http://www.trafficthessreports.imet.gr) A personalized single point of access

Mobility services in Thessaloniki

• Datatank (Back office + restAPIs) • CKAN (front end)

http://opendata.imet.gr/dataset

“Thank you!”

Brussels, BDE – SC4 Workshop – 14th Sept 2017

DR. JOSEP MARIA SALANOVA GRAU JOSE@CERTH.GR +30 2310 498 433