Transportation
❯
Traffic Flow Analysis
❯Automated travel pattern recognition using mobile network data for applications to mobility as a service
Automated travel pattern recognition using mobile network data for applications to mobility as a service
For:
Mobility service provider, end-user of the transport systemGoal:
Improved Customer Experience, Improve Operation EfficiencyProblem addressed
Phase 1: Attribute trip purpose and mode of transport to multimodal door-to-
door journeys from a mobile phone network dataset using AI and machine
learning techniques (activity-based model)
Phase 2: Generate daily activities for static agents in the agent-based model
Phase 3: Optimize new mobility services in integration with mass transit
Scope of use case
Automatic travel pattern recognition from anonymized and aggregated mobile
phone network data (MND).
Description
Activity-based modelling has the capability to exploit the big
data source generated by smart cities to create a digital twin
of urban environments to test mobility as a service scheme.
Given the rise of location-based data and mobile phone
network data (MND) for transport modelling purposes,
agent based modelling has become a viable tool to explore
the sustainable introduction of mobility services, exploring
integration with mass transit.
AI is used in detecting the purpose and mode of transport in
multimodal round trips and in assigning the purpose and
mode of transport to the trip-chains dataset taken from
MND. The methodology has been developed for the Innovate
UK-funded mobility on demand laboratory environment
(MODLE) project and would undergo a validation process
during the demand modelling and assessment through a
network demonstrator (DeMAND) project for the
Department for Transport (UK).
Machine Learning
AI: Understand