TNO - Urban Strategy

URBAN STRATEGY DIGITAL TWINS TO OPTIMIZE URBAN MOBILITY LAURENS PEIJS (AMSTERDAM) | JEROEN BORST (TNO)

How to plan multiple roadworks at the same time?

What will be the impact of new spatial developments on accessibility and environment?

What will be the effect of Mobility-as-a-Service?

What is the optimal placement of mobility hubs?

How do we optimize the availability of shared vehicles to contribute to a car light city?

What is the impact of parking tariffs on Public Transport usage?

How will shared automated vehicles (‘robotaxis’) be used, …and how will they affect the use of urban roads?

TNO TRAFFIC & TRANSPORT

URBAN STRATEGY

NEXT GENERATION PLATFORM FOR INTERACTIVE INTEGRAL DIGITAL CITY TWINS Integral planning and management of cities, which is highly complex and extremely time consuming with existing models: Integral urban development User behaviour irt. Urban mobility Environmental impact and community response Energy transition Use city data (IoT, static) to address complex multi-domain challenges Enabling cooperation across multiple domains and levels of decision making Distributed application (across platform) Extremely fast GPU based parallel computing Modular and open for collaboration

Interfaces IMB high speed data exchange

Models

(IoT)Data

URBAN STRATEGY SIMULATION MODULES INTEGRATING TOPICS - NEW MODELS CAN BE ADDED MOBILITY DEMAND

MOBILITY AND LOGISTICS

SUSTAINABLE ENERGY ELECTRIC FLEET AND CONSUMPTION SIMULATION

TRANSPORTATION ITS MICROSIMULATION

AIR POLLUTION (INDUSTRY + ROAD)

INTERACTION WITH GLOBAL WARMING ENERGY SYSTEM (GRID) POTENTIAL

NOISE (TRANSPORT + INDUSTRY)

HEALTH IMPACT EFFECTS

Strategic Cooperation - City of Amsterdam

Integral Urban Planning Challenge The city is growing within its limited boundaries. Newly built-up areas to be developed This will lead to increasing pressure on the mobility system and the environment.

Approach Jointly identify complex challenges and bottlenecks, using Urban Strategy to assess, monitor and evaluate system interventions. Close the “learning cycle” and validate assumptions with the realtime data.

Result Integral Digital City Twin Reduced simulation time from days to minutes Impact of land-use and mobility plans Adopted by city of Amsterdam in workflow

Example: Scenarios for implementation of shared mobility

Shared Mobility Amsterdam Challenge The Municipality of Amsterdam has asked TNO to explore options for shared mobility: Determining optimal parameters, availability, spatial distribution, expected use of shared mobility and its effects (e.g. space gain)

Approach Free floating shared cars have been added as an additional modality in the in Urban Strategy. Data from shared mobility providers is used to validate the model. Various policy options (e.g. availability, pricing) have been modelled.

Result Scenarios to implement of shared mobility in the city of Amsterdam have been calculated, in terms of number of cars, number of trips per car and impact on freeing up parking space.

Example: Scenarios for implementation of Multi Modal Mobility hubs

Multi Modal Hubs Challenge Many cities are considering the implementation of multi modal mobility hubs as means to stimulate the use of shared mobility. Challenge is to find the right location and dimension of these hubs

Approach On the basis of available data on latent transportation demand and the availability of all modes of transportation, a module has been developed to simulate usage of hubs, including first and last mile transportation.

Result Scenarios to implement mobility hubs being implemented in the Digital Twin Amsterdam and made available Rotterdam and in the EU project MOVE21 for the partner cities Oslo, Gothenburg and Hamburg.

NEW MOBILITY MODELER: ADOPTION OF NEW MOBILITY

Impact of new mobility experiences (e.g. MaaS* and CCAM**) On transportation demand and modal split Road network use and environment * Mobility-as-a-Service ** Connected Cooperative Automated Mobility

FUTURE STEPS COOPERATION AMSTERDAM Equity, Social inclusion, target groups Spatial redistribution effects New indicators: broad welfare – city donut

Modelling MaaS – ABM Shared Mobilty Extend link with energy transition Urban logistics: modelling flow of goods Link with traffic management / IoT Traffic Safety …

DIGITAL CITY TWINS WITH TNO’S URBAN STRATEGY PLATFORM COME AND WORK WITH US! Amsterdam Rotterdam Portland

Antwerp

Oslo Gothenburg

Utrecht Kiel Beijing Hamburg Hannover

Curacao

New Delhi

Shenzhen

Eindhoven Heilbronn

Ludwigsburg

Singapore

WANT TO LEARN MORE? L.PEIJS@AMSTERDAM.NL JEROEN.BORST@TNO.NL

Singapore E-bus simulation platform

Bus electrification strategies Challenge Collaboration on assessment of concrete use-cases and (what-if) scenarios for bus electrification in Singapore. Goal: Complete electrification in 2040 of Singapore’s 6,000 buses

Approach Assessment framework, development and application of e-Bus simulation model on different scenarios for E-Bus deployment strategies, taking into account charging strategies, routing, weather, etc. Knowledge transfer to LTA

Result Assessment of use-cases (what-if) scenarios.

Tailored tooling for LTA as a central instrument for informed decision making

Air Quality in Germany (with PTV)

Reducing NO2 car emissions Challenge In many German cities, the legal amounts of NO2 set by the EU were exceeded. That led local courts to act against those municipalities, increasing pressure on authorities to implement clean-air plans with quick results.

Approach City authorities suggest action plans, but also struggle to predict if and how those will affect pollution levels. A study

by TNO and PTV, sponsored by Volkswagen, aimed to do that for four German Cities.

https://blog.ptvgroup.com/en/city-and-mobility/reducing-no2-car-emissions/

Result The study was conducted in four German cities, which struggle to reduce NO2 emissions from cars: Hannover and Kiel in the northern part of the country, and Heilbronn and Ludwigsburg in the south-west.

Digital Twin Antwerp

Real Time Traffic and Air Quality Challenge Urban Traffic is affecting air quality. The challenge in Antwerp was to create a proof of concept of the integration of sensor information and predictive traffic models to enable real-time insight, forecasts and management information.

Approach Together with Imec and PTV, a Digital Twin of the city of Antwerp was built to demonstrate the combination of realtime traffic prediction and the resulting air quality impacts with the use of a sensor network.

Result Successful implementation and demonstration of the Digital Twin Antwerp, integrating sensor information with real-time predictive models on traffic and air quality.