PTV Group - Data driven Traffic Management

Data-driven traffic management​ combining analytical models & machine learning Dr. Ing. Lorenzo Meschini, Vice President Product Management & Engineering Real-Time MSc.-Ing. Nora Szabo, Senior Sales Manager

About PTV Group Active customers >50,000 use PTV Software (>2,500 Cities) Global presence

“Empowering transportation and mobility – for a cleaner and smarter future.”

with 28 locations on all continents Founded

1979 as a spin-off of the of

Transportation Karlsruhe Institute Technology 45% Mobility Colleagues 38% ~900 worldwide

Headquarters in Karlsruhe, Germany

Real-time traffic management: Data-driven simulation approach

Network models

Real-time data

Real-time traffic & mobility simulation

Benefits: •

Space propagation: Estimating the current traffic situation not only where data are available, but across the whole network

•

Time propagation: Forecasting the future traffic situation which will arise in the absence of remedial actions

•

Proactive decision-making and management: Evaluation of future traffic situation as it would be influenced by remedial actions

PTV methodology for real-time mobility management TM Functionality

Traffic Estimation

Traffic Forecast

Scenario Evaluation & Decision Support

“What is going on?“

“What is going to happen?“

“What would happen if?“ “What should we do?“

Observed data approach

Maybe (with extensive measures)

X

X

Statistical approach

✓

“Usual“ conditions only

X

Simulation Approach

✓

✓

✓

Real-time traffic management installations European cities

European countries

Non-European cities

•

Paris

•

Slovenia

•

Abu Dhabi, UAE

•

Strasbourg

•

Czech Republic

•

Sydney, Australia

•

Vienna

•

Romania

•

Taichung, Taiwan

•

Salzburg

•

Erfurt

•

Ingolstadt

•

Regensburg

•

Gdansk

•

Krakow

•

Torino

•

Moscow

•

York

Best practice: Regional Traffic Supervisor Piemonte Region

•

Coverage: 25,000 sq. Km, 5M inhabitants, 34,000 km of roads

• Dynamic model • 2,000 zones • hourly OD matrices for 3 demand segments and 6 day types • Supply: 76,000 links • +0’, +15’, +30’, +45’, +60’ forecasts produced every 5 minutes

Best practice: Regional Traffic Supervisor Piemonte Region

• Real-time traffic monitoring • Real-time traffic estimation and short-term prediction • Generate alarms in presence of traffic disruptions (congestion, accidents, …)

• Traffic control and supervision • Info mobility

Best practice: Taichung PTV Optima (Short-term traffic forecast) + PTV Balance (Adaptive singal control) = Proactive Adaptive network signal control PTV Optima for Taichung

-9,4%

+7,6%

+8,4%

Total travel time

Total Flows

Average speed

PTV Balance for Taichung

PTV methodology for real-time mobility management – can we do better?

TM Functionality

Observed data approach Statistical approach Simulation Approach … What‘s next?

Traffic Estimation

Traffic Forecast

Scenario Evaluation & Decision Support

“What is going on?“

“What is going to happen?“

“What would happen if?“ “What should we do?“

X

X

✓

“Usual“ conditions only

X

✓ ?

✓ ?

✓ ?

Maybe (with extensive measures)

Data-driven modelling INPUT

OUTPUT

• Graph • Zones • Floating Car Data

• Model

DATA FUSION

Data-driven outputs

Trip Origins and destinations

Distribution of Paths & Flow

Speed Profiles and Patterns

Improved forecast of traffic conditions using machine learning (AI) Problem: •

Up to now, forecast of traffic conditions is usually based on static approach.

•

Current traffic conditions are not taken into account.

Solution & added value: •

Artificial Intelligence/ Machine Learning uses historical data to automatically detect typical daily time profiles in an unsupervised, fully automatic approach.

•

This data is clustered and provides the fundament for a forecast of up to 60 minutes in advance.

?

Technical Implementation: Machine Learning for short-term forecast of traffic conditions 1/3

Used Data: • Data coming in time profiles, e.g. traffic flow at count locations, speeds at count locations, occupancy of parking lots • The AI algorithms need historic time profiles for training and real-time data to forecast under current conditions Data provider:

• Owner of count locations, e.g. urban / regional road authorities • Their data providers (e.g. Materna)

Historical Data 15’ Intervals

Forecast: Identified time profiles (typical days) as input for the short-term forecast

Algorithms of Machine Learning

Normalisation

Technical Implementation: Machine Learning for short-term forecast of traffic conditions 2/3 AUTOMATIC DAY TYPES CLUSTERISATION

Generic Weekdays (as Monday, Tuesday, Wednesday) are recognised as similar

Unsupervised learning

Technical Implementation: Machine Learning for short-term forecast of traffic conditions 3/3 AUTOMATIC DAY TYPES CLUSTERISATION Unsupervised learning

Some days are special.

For example: Pre- & post-holidays in Christmas period

Machine learning application – Traffic forecast service for the German Mobility Data Space (MDS) developed

by Acatech End client: Acatech, a thinktank advising the German government about technological trends

Production year: 2021 Real-time Traffic Data from Materna •

3,234 Count Locations on the NRW Trunk Road Network (mainly on Motorways)

•

Live traffic speed

Challenge: Improve traffic using the “Mobility Data Space” (a platform for sharing data between different transportation industry players) in the following way: - Read data.

- Process data.

- Put back added-value data.

Solution: PTV Optima Machine Learning Forecast uses real-time data to automatically detect typical daily time profiles in an unsupervised, fully automatic approach.

Impact: - Easy KPIs monitoring.

- Provide solution as fast as possible.

- Decrease operational and services costs.

- Self-learning approach.

Use case demonstration

Combination of analytical + ML forecast •

104 streets covered by data

•

~105 total streets

•

Forecast up to 60’ ahead in 1’

•

Error on flows reduced by 23% (GEH 6.9 → 5.3)

•

Error on speeds reduced by 55% (MAPE 16.2 → 7.3)

L

PTV methodology for real-time mobility management TM Functionality

Traffic Estimation

Traffic Forecast

Scenario Evaluation & Decision Support

“What is going on?“

“What is going to happen?“

“What would happen if?“ “What should we do?“

X

X

X

Simulation Approach

✓ ✓✓

“Usual“ conditions only

Data + Models + ML

✓✓

✓✓

Observed data approach Statistical approach

Maybe (with extensive measures)

✓

✓ ✓✓

Non-scientific methodology for real-time mobility management

Machine learning statistical approach

Data Statistical forecast

Model

Models Data

Data-driven models

Forecasting techniques combination

Model-based forecast

Conclusion

• Data-driven methodologies allows to streamline definition and calibration of tarditional transport models.

• Clear advantages in using combine analytical and machine learning approaches for traffic and mobility forecast.

• Availability of significant, clean, normalized and labeled datasets in the mobility field is still an open issue.

Questions