Geographic modelling and financial review of ticketing zones

Our client needed to understand the options for changes to their tickets and ticket zones, and the possible financial implications of each, for both passengers and operators.
Payment

Project facts

  • Client
    Commercially confidential 
  • Location
    North UK  
  • Date
    2020 - 2024
  • Challenge
    To estimate financial impacts of fare product restructuring and changing of ticketing zones
  • Solution
    Comprehensive revenue estimation model built from survey origin-destination data, including using geographic breakdown of passenger travel. 

The challenge

Existing ticketing systems and fare zones had, for historic reasons, resulted in an array of options for the travelling public across the city. It was felt locally that too many options might be a discouragement to travel, despite widespread capital investment on other aspects of the public transport network. Our role was to investigate the different ways to harmonise and simplify the ticketing zones and understand the financial impacts of different changes. 

The solution

We carried out exploratory data analysis, to understand the datasets available locally, and 'set the scene' with high-level sociodemographic research. It was particularly important that fare changes did not create further travel costs for passengers from less-affluent areas - but other constraints were considered, for example distances travelled and local commuting patterns within towns in the region.   
Ticket machine on station
We identified from a full data cataloguing exercise that comprehensive survey data existed, allowing us to complete more detailed origin-destination modelling across the client's network. The results of these surveys were scaled up to cover the whole network, and tested within a series of hypothetical zone scenarios set by our client, to help them understand impacts on revenue. This allowed them to understand the impact of possible future zone-to-zone ticketing options, and target fare setting scenarios for different local priorities. It considered both revenue neutrality (i.e. trying to avoid extra costs for either passengers or operators) and patronage maximising options (i.e. trying to grow passenger numbers). These scenarios included demand estimation according to best-practice economic modelling, for example using elasticities from the Department for Transport’s Transport Analysis Guidance and the Passenger Demand Forecasting Handbook. 

This project is a great example of how our data science and financial modelling expertise can be applied to projects to meet a client's needs, and in this case, giving stakeholders confidence in potential zonal fare changes.

Denise FaberDirector

The result

The level of detail and sophistication of our financial model, and the intuitive presentation of the outputs in terms of 'winners and losers', gave our client the confidence to take particular options forward through its planning with internal and external stakeholders. This included the possible advocating for an ambitious, patronage-maximising approach, working in parallel with major capital investments locally to secure affordable fares and successful public transport for the region's passengers. 

Our experts  are happy to assist you - Experts team Public transport planning

Our experts are happy to assist you

Experts team Public transport planning