Outdated travel prediction systems are getting in the way of more efficient and equitable transportation. Urban planners Claire Fram and Richard de Cani of Arup believe there’s a solution.
Assumptions regarding how people move and behave have grown increasingly inaccurate in the past decade, but it took Covid-19 for people to truly pay attention to these flaws. Yet these assumptions continue to form the foundation of global transport systems.
Complexity and variability have become the new normal as a result of the pandemic. But current systems for predicting passenger travel fail to allow for that. Take for example the day of a working parent. Existing outdated modelling assumes they drop off their child at the nursery and then head to the office. But in reality, the typical nine-to-five has disappeared, and their day might involve a nursery drop-off, followed by a trip to the grocery store before an afternoon of telecommuting. How do we plan transit around that trajectory?
Uncertainty triggered by Covid-19 has created a magnitude of challenges. Fewer trips are being taken overall, and despite lockdowns, traffic is near pre-coronavirus levels. Fewer people are taking trains and buses, which points to a potential mode shift towards cars. In fact, according to data from the Google Mobility Report, public transport use in London was 29% of pre-Covid 19 levels as of mid-December 2020. In the US, the picture is even worse, with ridership down 62% from pre-pandemic levels as of the third quarter of 2020.
As the vaccine programme gains momentum and restrictions ease, we face unprecedented levels of uncertainty, with little understanding of future demand. But ambiguity triggered by the pandemic is just one of the industry’s many challenges. Emerging technologies and transport modes, such as electric vehicles and electric scooters, as well as the need to decarbonise to meet net-zero commitments, and address social inequality, are all contributing to an unprecedented strain on the network.