The disruptive nature of autonomous vehicles on traffic forecasting

Delivering a reasonably accurate forecast is an important component to estimating the feasibility of a particular project or the viability of a comprehensive transport plan. In most cities, the benefits of the project or plan will depend upon an estimate of travel times on alternative modes, routes and modal shares. 

We have been doing this for many years (with variable accuracy) under a broad assumption of “business as usual”; that is travel trends, capacities and mode preferences (defined by the parameters in our models) will be retained into the future.

However, over the last couple of years, we have learnt that this is unlikely to be the case. Several trends that were once considered trivial are becoming increasingly significant, e.g. ‘peak car’, online shopping, variable fuel prices. The most disruptive of these trends is likely to be the introduction of autonomous vehicles (AV), or self-driving cars.

It is expected that autonomous vehicles will impact a number of things in the near future, including reduction of traffic accidents, increase in road capacity, trip induction and public transport usage, amongst other things, therefore affecting traffic modelling and long-term forecasting.

Serbjeet Kohli, Director at Steer Davies Gleave India, recently co-authored a paper for the 2016 European Transport Conference exploring the potential impacts of autonomous vehicles and how to incorporate them into traffic modelling and forecasting. 

The paper presents the results of a Delphi exercise, undertaken by the authors, with 45 transport modelling experts to collate their professional views on the best way to approach transport and traffic forecasting in a future with autonomous vehicles in circulation.

Read the full paper.