EMT_UNIVERSIDAD DEUSTO - Proyecto Momentum

MODELADO DE SOLUCIONES EMERGENTES DE TRANSPORTE PARA MOVILIDAD URBANA MODELLING OF EMERGING TRANSPORT SOLUTIONS FOR URBAN MOBILITY 27th November 2020

MOTIVATION New mobility technologies, services and solutions

OBJECTIVES 1. Identify a set of plausible future scenarios for the next decade to be taken into account for mobility planning in European cities.

2. Characterise changes in travel behaviour with special focus on emerging mobility solutions, profiting from the increasing availability of high-resolution data.

3. Develop data-driven models of the adoption and use of new mobility concepts and transport solutions and their interaction and complementarity with PT. 4. Develop transport simulation and planning support tools able to cope with the new challenges faced by transport planners.

5. Provide guidelines for the practical use of the methods, tools and lessons learnt delivered by the project in the elaboration and implementation of SUMPs and other planning instruments.

Leuven Madrid Regensburg Thessaloniki

CASE STUDIES

• • •

• • •

Leuven

Madrid

Develop a new transport model

Enhance the transport model

Circulation plan Shared mobility – public transport intermodal hubs New mobility solutions in regional mobility strategy

• • •

Modal shifts from private car to new mobility solutions Shared mobility inclusiveness Shared mobility – public transport complementarity

Regensburg

Thessaloniki

Enhance the transport model

Enhance the transport model

Autonomous people mover in public transport Car ownership decreases Emission reductions from new mobility services

• • •

Extension of DRT services Ridesharing role Regulation frameworks for micromobility and bike sharing

More information: D2.2 Specification of the MOMENTUM Test Cases

WORK PLAN

KEY OUTPUTS AND PROGRESS •

MOMENTUM takes advantage of the increasing data availability : –

to include artificial intelligence models that exploit historical data from emerging mobility services

–

to develop more disaggregated approaches to transport simulation that are suitable to emerging mobility services

KEY OUTPUTS AND PROGRESS 1.

Future scenarios + relevant policy questions More information: D2.1 Challenges and opportunities for transport planning and modelling

2.

Data collection and analysis methods More information: D3.1 Data Inventory and Data Quality Assessment D3.3 Data Analytics --> to be delivered in the next weeks

3. Modelling algorithms Work in progress since April 2020

4. Decision support tools 5. Guidelines for policy making

https://h2020-momentum.eu/

WP3 DATA COLLECTION & ANALYSIS •

The main objectives of MOMENTUM WP3 are : –

Collect, harmonize and integrate different data sources

–

Perform an assessment of the strengths and weaknesses of each data source

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Develop new data fusion and artificial intelligence algorithms for the extraction of the mobility patterns and travel choice behavior factors

ORIGIN-DESTINATION MATRIX COMPARISON •

Motivation: identification of differences between travel patterns, estimation of “typical” OD matrices, anomaly detection in OD matrices (e.g. events)

•

Main characteristics of the new methodology: –

Highly efficient and deployable in High-Performance Computing facilities

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Fast processing of large longitudinal/historical series of OD matrices

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Off-the-shelf approach

Day-to-day mobility pattern comparison October 2019:

DEMAND RESPONSIVE TRANSPORT PLANNING Data analysis and processing

•Give the data a form that can revel demand patterns.

•Extract Demand Distributions.

Facility Location algorithms

•Determine the locations of the Line.

•

Motivation: Estimate potential demand that can be served by a Demand Responsive Transport or a Ride-sharing system.

•

Step-wise approach

•

Validated with taxi data in Thessaloniki

Simulation over Pickup and Delivery TSP/VRP solutions

•Extract operational characteristics. .

Estimation of the Demand that served from DRT line.

SHARED-MOBILITY ADOPTION •

Motivation: Analyze socio-demographic characteristics of shared-mobility (SM) users

•

Involved case studies and SM modes:

•

–

Madrid (Spain): car-sharing, moto-sharing and bike-sharing

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Leuven (Belgium): car-sharing

–

Regensburg (Germany): car-sharing

Socio-demographic characteristics: –

Age and Gender

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Education Level and Labor status

–

Income level

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Household size and type

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Car-ownership

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Main transport mode

–

…

SHARED-MOBILITY ADOPTION •

•

Similarities among shared-mobility users in the three case studies: –

Young and highly educated

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They are mostly employed and have highincome levels

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Higher tendency to use soft modes (e.g. bike, public transport)

Differences among the three case studies: –

Predominantly men in Madrid vs Predominantly women in Regensburg and Leuven

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NO lower car-ownership of SM users in Madrid

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Significantly lower car-ownership in Regensburg and Leuven of SM users

TAKE-HOME MESSAGES • Emerging mobility solutions are having/will have an impact in urban travel patterns • Necessity of understanding how this impact will be • The fusion of heterogeneous data sources (e.g. demand, supply, socio-demographic) allows for more complete, accurate and reliable information about this phenomena.

• Artificial Intelligence allows to extract more information and value from the data.

THANKS!

https://h2020-momentum.eu/

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 815069