- University of Leeds
|Title of host publication||International Choice Modelling Conference 2019|
|Publication status||Published - 15 Jul 2019|
|Event||International Choice Modelling Conference 2019 - Kobe International Conference Center, Kobe, Japan|
Duration: 19 Aug 2019 → 21 Aug 2019
|Conference||International Choice Modelling Conference 2019|
|Period||19/08/19 → 21/08/19|
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
The aim of this paper is to focus on modelling drivers’ speeding behaviour, as a function of the of the road environment. The analysis is based on data collected as a part of the UK-funded HumanDrive project, the main purpose of which is the development of human-like controls for AVs. The data collection was conducted at the University of Leeds Driving Simulator, where participants drove four simulated scenarios of varying risk composition. Each scenario consisted of several 250m long segments and the variability in risk was applied through changes in the road type (urban/rural), lane width, curvature, contextual lateral risk (e.g. hard, soft, and raised roadside, parked cars etc.), oncoming traffic and persistence of risks along segments. On top of the driving tasks, participants also completed a series of questionnaires (locus of control, sensation seeking etc.). In previous analysis of the data, significant effects of the risk levels were found on longitudinal and lateral vehicle control , while speeding behaviour was found to correlate with participants’ sensation-seeking  as it was derived from the Arnett Inventory of Sensation Seeking items .
This paper builds on the aforementioned data analyses and suggests a modelling framework to combine the effects of road environment and drivers’ characteristics on speed choice. The latter has been averaged across individuals for each 250m segment, and treated as a dependent variable in a time-series model, where the road characteristics have been used as explanatory variables. As an individual’s speed choice is expected to be influenced by past behaviour, speed from the previous time step (road segment) has been lagged and included as an independent variable. Additionally, the model accounts for unobserved heterogeneity, using a standard normal disturbance component. To address the potential correlation between the disturbance term and the lagged speed dependent variable, the approach suggested by Wooldridge  has been applied and the model has been estimated conditionally on the initial observation of each individual. Moreover, an additional correlation parameter has been introduced to incorporate disturbances that follow an autoregressive process, similar to the specification for maximum likelihood estimation described in . Finally, sensation-seeking has been considered as a latent variable using indicators and an ordinal variable specification has been used to define their likelihood.
The main findings show that speed decreases significantly as road radius reduces, while the same pattern is seen for lane width. Moreover, the lateral risk level producing the highest negative impact on speed is the presence of parked cars on the road. Also, past speeding behaviour has a positive effect. Finally, sensation-seeking was also found to significantly contribute to the speed increase. In summary, the findings denote that the road environment has a major impact on speed choice, however there are more aspects to be considered. For instance, the significant effect of sensation-seeking implies that different drivers may have different expectations about the most preferred driving style of an AV controller. Also, the significant effect of the random disturbance term consists an indication that speed choice is a complex issue that varies across individuals. Based on the current findings, there is scope for further research to investigate additional metrics of longitudinal and lateral driving performance, that could lead to the determination of their acceptable boundaries and thus the development of more human-like AV controllers that will positively contribute to the acceptance of this technology.