Understanding Traveller Decision Making---a Crowd Sourced Big Data Analysis of the London Travel Demand Survey

Abstract

Engineers have begun to take interest in the interface between the structures they build and the people that use them. This has resulted in a need to better understand behavioural decision making at the micro scale. An analysis was carried out on the London Travel Demand Survey in order to see what behavioural rules could be derived from the survey data. This analysis attempted to relate the financial cost and time cost of a journey to the socio-economic status of a traveller in order to see what relationship existed. Relating this empirical exhibited data to real-world data is challenging. Crowd sourced big-data avenues were explored in order to derive realistic information that considers factors such as traffic congestion, service time tables and the underlying complexity of the London transport network. It was hypothesised that the socio-economic status of the traveller, the distance and the motivation of the journey would impact on traveller decision making. However, no statistically significant results were found. It is concluded that the use of crude small sample size survey data to analyse fine grained crowd-sourced data is inappropriate and highlights a critical need to find other means of recording actual traveller decisions in order to understand the decision making process better. Such insights are critical if better strategic decisions are to be made on transport infrastructure.

Gerry Casey

Principal Research Fellow, UCL & Associate, Arup

Transport modeller and data scientist building city-scale simulations to help governments and cities make better decisions on transport, climate, and equity.