Driver's response to a pedestrian crossing requires braking, whereby both excess and inadequate braking is directly associated with crash risk. The highly anticipated connected environment aims to increase drivers’ situational awareness by providing advanced information and assisting them during critical driving tasks such as braking. Focussing on this crucial behaviour and combined with the promise of a connected environment, the objective of this study is to examine the braking behaviour of drivers in response to a pedestrian at a zebra crossing in a connected environment. Seventy-eight participants from diverse backgrounds performed this driving task in the CARRS-Q Advanced Driving Simulator in two randomised driving scenarios: a baseline scenario (without driving aids) and a connected environment (with driving aids) scenario. A Weibull accelerated failure time duration modelling approach is adopted to model the braking behaviour of drivers. In particular, this duration model is specified to capture the panel nature of the data and unobserved heterogeneity through correlated grouped random parameters with heterogeneity-in-the-means in the Bayesian framework. Results indicate that, for most drivers in the connected environment, it takes longer to reduce their speed with less speed variation and a larger safety margin. In addition, a decision tree analysis for the braking time suggests that for older drivers, when the distance to the zebra crossing is larger in the connected environment than that in the baseline scenario, braking time is likely to increase. The model also reveals that the braking time of female drivers is longer in the connected environment compared to that of male drivers. Overall, the connected environment is associated with increased braking time by providing advanced information, giving drivers additional time to smoothly reduce their speed in response to a pedestrian at a zebra crossing, and ultimately making the vehicle–pedestrian interaction safer.

The availability of advisory warnings via Vehicle-to-Vehicle and Vehicle-to-Infrastructure communication in the connected environments is expected to gradually increase over the next few years. Much of the research on advisory warning systems have examined driving behaviour in response to unexpected driving hazards; however, very little research has been conducted on common driving interactions such as interacting with pedestrians at pedestrian crossings. Therefore, the aim of this study is to investigate the effects of the connected environment on driving behaviour at pedestrian crossings. The connected environment was designed within the CARRS-Q advanced driving simulator. A combination of auditory (beep sound) and imagery message was simultaneously displayed on the windscreen to advise the driver on the presence of a pedestrian entering from a sidewalk. Seventy-eight licensed drivers drove the simulator in two driving conditions, namely, baseline and connected environment. The participants were 18–65 years old, and a third of them were females. Drivers' response to the driving aids and the braking behaviour were analysed in the latent response phase and the observable response phase, and the corresponding response times were modelled using the hazard-based duration modelling approach. In particular, this study applied the Weibull accelerated failure time model with shared frailty accounting for multiple observations from the same driver. Results showed that the time taken to respond to the pedestrian in the latent response phase was longer when the advisory warning was provided to the drivers, but the corresponding time in the observable response phase was shorter, indicating that drivers take an informed decision in the connected environment. Moreover, the safety margin—measured in terms of time-to-collision—was higher in the connected environment than the traditional driving environment, indicating a safer behavioural adaptation towards the connected environment.

The connected environment provides real-time information about surrounding traffic; such information can be helpful in complex driving manoeuvres, such as lane-changing, that require information about surrounding vehicles. Lane-changing modelling in the connected environment has so far received little attention. This is due to the novelty of connected environment, and the consequent scarcity of data. A behaviourally sound lane-changing model is not even available for the traditional environment; that is, an environment without driving aids. To address this need, this study develops a game theory-based mandatory lane-changing model (AZHW model) for the traditional environment and extends it for the connected environment. The CARRS-Q advanced driving simulator is used to collect high-quality vehicle trajectory data for the connected environment. The developed models (for traditional environment and connected environment) are calibrated using NGSIM and simulator data in a bi-level calibration framework. The performance of the models has been rigorously evaluated using various performance indicators. These include the true positive, false positive, detection rate, false alarm rate, time prediction error, and location prediction error. Results consistently show that the developed game theory-based models can effectively capture mandatory lane-changing decisions with a high degree of accuracy. Furthermore, the performance of the developed AZHW models is compared with representative game theory-based lane-changing models in the literature. The comparative analysis reveals that the AZHW models developed in this study outperform existing models.