The document discusses using data fusion techniques to merge mobile phone mobility data with other data sources to gain a more complete understanding of transportation patterns and mobility. It provides examples of how fusing mobile phone data with toll plaza data, land use data, transportation surveys and traffic counts has improved vehicle classification, analysis of toll road demand, and creation of origin-destination matrices for transportation modeling. The challenges of modeling new transportation options like mobility as a service and connected autonomous vehicles are also discussed.

1. Mobile phone mobility data
Practical merging with other data sources
Presented at the AITPM Global Webinar 25th September 2019
Luis Willumsen

2. Opportunities from new data sources
Different data sources provide relevant information on
different aspects of mobility…
…but none of them provides the full picture:
need for data fusion considering the error and bias of each
source…. and good transport knowledge
GPS Navigation
Road network
Speed profiles
Apps and App
Aggregators
Door-to-door trips, high
spacio-temporal
resolution, bias
ANPR, Bluetooth, WiFi
Speed profiles
Local OD matrices
Smart Cards
Public transport
demand, stop-
to-stop trips
Mobile Phone Data
Door-to-door trips, high
sample size, high
representativity

3. What we would like to have
• Origin Destination & Production Attraction matrices segmented by:
 Mode, including active and new mobility
 Purpose (how many?)
 Person characteristics, e.g. vehicle ownership and socio-economic group
 Day and Time of Travel
 Recurrent/Non-recurrent
• Routes used
• Flows (classified)
• Travel times/speeds
• Preferences (generalised cost weights)
• Presence/exposure and permanence
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Data Fusion works better with raw data plus an understanding of transport models

4. Some examples

5. Improving vehicle classification
• Data fusion (e.g. logistics centres, classified traffic counts, timetables..)
• Longitudinal behavioural patterns (e.g. trip distances, mileage)
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6. Analysis of Toll Road Potential Demand
 Toll road users vs users of alternative roads (potential demand)
 Blending with toll plaza data and obtained excellent correlation with rest of traffic counts
 Calibration of a VISUM model for the modelling of different revenue optimisation strategies
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7. Analysis of Interurban Mobility in Spain
 Objectives:
 OD matrices at NUTS-3 (59) level: trips, legs, tours
 Mobility of residents and non-residents
 Mode: road (private vehicle/bus), rail, air, maritime
 Route choice for road trips
 2 periods of study: July-August, October
 Data sources:
 CDRs and Cell Map Orange Spain
 Land use (SIOSE, a Land Use GIS)
 Population (Census, Padrón)
 Tourism statistics (FRONTUR)
 Transport network (Ministerio, APIs travel planners)
 Transport supply (transport operators)
 Ticketing (transport operators)
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8. Analysis of Interurban Mobility in Spain
Fri 14th July Average Thu of October Sun 15th October
+ nº trips
 Main flows > 50 km
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9. Analysis of Interurban Mobility in Spain
 Mode split
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Monday - Friday Sunday
OctoberJuly-
August

10. OD matrices for Málaga
 OD matrices by mode (private vehicles vs public transport) obtained through
the fusion of mobile phone records with data from Málaga public transport
smart card
 Data used for the calibration of the Málaga transport model
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11. OD matrices for Málaga
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12. Traffic analysis in Barcelona Ring Roads
Fusion of:
 Mobile phone data
 PT surveys and ticketing
 GPS data
 Traffic counts
 Roadside interviews
An Activity Based Model was developed from this
data using MATSim to test pricing strategies:
https://www.sciencedirect.com/science/article/pii/S09658564
1830380X
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13. New Challenges:
The complexities of modelling CAVs & MaaS
 MaaS is a new mode, more akin to Taxi or Demand Responsive Public Transport
 Logistic and Dispatcher modelling of service supply
 Natural for agent based micro-simulation
 Identify each user: time, origin, destination, preferences
 How best served ride-share or single use
 Service constraints on wait and detour time
 Estimate best route for ride share
 Produce performance indicators including Level of Service (LOS), Congestion and
Emissions and Impact on Operator: fleet, costs & revenues.
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14. Virtual Mobility Lab Barcelona
http://compass.ptvgroup.com/2018/01/new-virtual-mobility-lab-for-barcelona/?lang=en
 Multimodal Hybrid (part aggregate, part-agent based) Transport model of Barcelona
 Evaluation of new mobility concepts (MaaS, DRT, shared mobility…)
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15. Barcelona by Virtual Mobility Lab
An aggregate Demand Model using Kineo data and VISUM software
New modes, akin to taxi, are introduced and demand estimates from estimated levels of service
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GPS data
Traffic counts
Ticketing data
Some RSI
Zone based ODs for demand model with
estimated new mobility services and LOS

16. Barcelona by Virtual Mobility Lab
PTV’s MaaS Modeller; synthetic population is developed to handle trip requests and the logistics of
scheduling services: new LOS for MaaS are calculated and fed back to Demand
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Agent-based model with
synthetic population and
scheduling of MaaS services
to minimum standards

17. Thanks
www.kineo-analytics.com
kineo@kineo-analytics.com
luis.willumsen@kineo-analytics.com