MULTIMODAL ACTIVITY MODELLING FOR MOBILITY AS A SERVICE

Midlands Intelligent Mobility ConferenceDr Patrizia Franco

Transport Systems Catapult

Nottingham24th January 2018

Tailored around your needs

Flexible Efficient

User Friendly Seamless

THE MOBILITY ON DEMAND LABORATORY ENVIRONMENT (MODLE)

Consumers access the service online via the web, an app or SMS text

Demand Prediction

MODLE platform

• aggregates demand• calculates itineraries• informs passengers and

vehicles• Informed by an Activity

Based Model

OperatorsDeliver services

Funder

• E-hailing minibus service to improve mobility services in an area currently disconnected by public transport and strongly relying on private cars

• The service extends PT services penetration rather than competing and consider social inclusion, providing services to hospital and low income areas

THE MOBILITY ON DEMAND LABORATORY ENVIRONMENT (MODLE)

Synthetic population• The MODLE case study includes five

areas (2 industrial park Severnsideand Avonmouth, Frenchay –University West of England Campus – Glenside with the busiest Park and Ride Rail station in UK, and Staple hill)

• The area of interest includes the Greater Bristol to account for commuting habits

ACTIVITY BASED MODEL FOR GREATER BRISTOL

• Agent based model built using the open platform MatSim. • Demand for the model is coming from a synthetic population

built using anonymised and aggregated Mobile Phone Network Data

• Multimodal public transport network built with publicly available data

ACTIVITY BASED MODEL FOR GREATER BRISTOL

• OD matrices built using anonymised and aggregated Mobile Phone Network data (MND)Segmentation by:– Mode (by rail and road trips)– Purpose– Start zone, end zone– Time period (AM; 3 Inter-peaks;

PM and off-peak)– Trip Chain Data set: Aggregated journeys with intermediate legs per time

period– Distribution of departure times from each area zone from the area of interest

• Demographics to define static agents behaviour• Multimodal Public Transport (bus and rail)• Facilities from OpenStreet map

Activity Based Model for Greater Bristol

• Identify Catchment area for on demand flexible and responsive shared service• Identify demand coming from fixed scheduled public transport services (Bus and

rail) for a first mile and last mile service, delivered in partnership with bus operators

ACTIVITY BASED MODEL FOR GREATER BRISTOL

CONCLUSIONS AND FURTHER WORK

• Activity Based models are flexible tools to model and visualize demand for flexible mobility services (fixed scheduled transport services vs. dynamic agents);

• Newly available big data source, such as MND, are an ideal solution to build a synthetic population for newly developed areas;

• MND can be available at different granularity than classic OD matrices: Static agents maintain the trip chain journeys;

• The simulation identifies pockets of demand for a flexible demand responsive service and a first/last mile service for the fixed schedule public transport, which extend PT provision and patronage (two level of definition: LSOA and MSOA)

• Further work will include data feed from the service to evaluate the performance• Calibration of the model will be improved based on the

revealed preference survey run by UWE

Dr Patrizia FrancoSenior TechnologistTransport Systems Catapultpatrizia.franco@ts.catapult.org.uk