Modelling active travel with efficient, future-proof tools

Robin Lovelace

Introduction

“The role of data analysis is to make people change their mind, based on the data.” Kruschke (2010)

The role of transport modelling is to make people change their mind, and investment decisions, based on evidence.

“Those among us who are unwilling to expose their ideas to the hazard of refutation do not take part in the scientific game.” Popper (1934)

The more people who can run, modify, and see the outputs of transport models, the larger the impact.

What do we mean by impact?

“Propensity to Cycle Tool helped us to assess the potential future demand for travel on these routes” (Leicestery County Council, 2023, one of 90+ local authority network plans using the tool).

Work in the Civil Service

Department for Transport's Data Science for Transport conference

In 2024 I completed a 2 year contract in new agency Active Travel England (ATE), part of the UK Department for Transport (DfT).

My roles:

  • Recruit the team
  • Lead Data Scientist
  • Projects: plan.activetravelengland.gov.uk (formerly ATIP), SchoolRoutes

Source: photo taken May 2023 at the Department for Transport’s Data Science for Transport conference

Premises

  1. There are often trade-offs between level of detail and simplicity in transport models.
  2. Simplicity has advantages: transparency, reproducibility, flexibility, number of scenarios that can be run, and explainability.
  3. Results at the route segment level are the most useful output of transport models for many applications, including active travel planning.
  4. For models to be future-proof, they need to be easy for others to understand, reproduce, and extend.

I’m going to start with some general observations about the role of transport planning and what we’re trying to achieve, before going into a case study of building a transport model from first principles using open tools.

  1. There is no one-size-fits-all transport model. Different contexts require different models.

The 4-stage model

A transport models from first principles can be expressed in 4 stages, according the classic four-step model:

  1. Trip generation
  2. Trip distribution
  3. Mode choice
  4. Route assignment

The 4-stage model as a DAG

Trip Generation

Trip Distribution

Mode Choice

Route Assignment

Figure 1: The 4-stage transport modelling framework presented as a linear Directed Acyclic Graph (DAG)

A more realistic model?

The dependency structure may be more realistic, with trip generation, distribution and mode choice all affected by the network.

Network

Route Assignment: s4

Other Factors

Mode Choice: s3

Trip Distribution: s2

Trip Generation: s1

Figure 2: A 6-stage transport modelling framework with recursive dependencies between Trip Distribution and Route Assignment stages.

Why the 4-stage model?

  • Simplicity
  • Lack of data
  • Lack of methods
  • Epistemic bias

We should not throw the baby out with the bathwater, but reform, rebuild or retrofit transport modelling for the 21st Century.

So let’s see what we can do with the 4-stage model before moving on to more complex models.

Schematic diagram illustrating the modelling process, geographic analysis and the four-stage model in the context of the wider transport planning process Lovelace (2021), building on Ortúzar S. and Willumsen (2011).

Aggregate models for DRT