Service Load Estimation and Real-Time Crowding Prediction for Melbourne Trams

Category

Excellence in Transport Data Award

Submitting Organisation

Australia Integrated Multimodal Ecosystem (AIMES)

Collaborating Partners

The University of Melbourne

Department of Transport Victoria

Yarra Trams

Cubic Transportation Systems iMOVE Australia

Detailed knowledge of service utilisation and passenger load profiles for public transport service is the basis for the design and optimisation of public transport service plans and operations.

The research team has developed an integrated suite of data fusion software and machine learning models to digest very big datasets that are usually underutilised (including farecard data, tram location data, pedestrian sensor data, traffic sensor data, population, land use, census data, etc.) and make the most accurate estimates of service utilisation for Melbourne trams, especially inside the free tram zone which is currently unobserved, and to make real-time prediction of tram loads for operational applications. This data allows operators to monitor day-to-day variability of travel demand and understand demand responses to service disruptions, special events, and restrictions (e.g. COVID-19).

The outcomes of this project allow operators to reduce the number of required devices and monitor service utilization cost-efficiently, especially in public transport networks where farecard data coverage is usually incomplete and negatively skewed. This will not only help operators to accommodate the variability in passenger demand and provide more efficient service, but also assist passengers in journey planning to avoid service overcrowding.

In terms of sustainability impacts, the developed models and software packages outcomes have the potential to facilitate:

• Optimisation of service plans, frequency, efficiency and utilisation- minimisation of operational cost and fuel/energy consumption.

• Reduce the number of required data collection devices (on-board sensors).

The advanced models and methodologies produced in this project are directly applicable to service load estimation for trains, and buses, and crowding prediction at train stations and platforms which have significant safety implications. The proposed method are also transferable to big data approaches from other modes of transport that are widely and automatically monitored.