Detecting, analysing, and modelling failed lane-changing attempts in traditional and connected environments

Lane-changing is a routine yet a complex driving task that has several negative impacts on both traffic flow efficiency and road safety, and thus, lane-changing models have become an indispensable part of microsimulation tools. The existing models only consider lane-changing manoeuvres that are successfully completed while failed lane-changing attempts (i.e., a lane-changing manoeuvre that is aborted after its initiation) are by and large ignored during calibration and validation of lane-changing models. This ignorance leads to structural incompleteness of lane-changing models. In addition, compared with successful lane-changing manoeuvres, failed lane-changing attempts are more likely to disrupt traffic flow and create safety hazards in both the current lane and the target lane, and thus, further warranting its consideration during lane-changing modelling. A connected environment can minimise these adverse effects by providing driving messages about surrounding traffic and subsequent gaps available in the target lane that drivers can utilise to make informed and safe lane-changing decisions. As such, this study investigates the impact of a connected environment on failed lane-changing attempts and addresses the issue of structural incompleteness of lane-changing models using three steps. First, a Wavelet Transform (WT)-based method is employed to detect failed lane-changing attempts from the real data. Second, the impact of failed lane-changing attempts is examined on both traffic flow efficiency and safety parameters such as average speed reduction and Deceleration Rate to Avoid a Crash (DRAC). Moreover, how a connected environment influences these parameters is also explored using a random parameters binary logistic model. Finally, failed lane-changing attempts are incorporated into the existing lane-changing models. At the first step, the WT-based method shows a reasonable accuracy in detecting failed lane-changing attempts when applied to NGSIM dataset and the driving simulator data collected in this study whereby drivers drove the CARRS-Q Advanced Driving Simulator and failed to complete a lane-changing manoeuvre in two randomised driving conditions: baseline (without driving messages) and connected environment (with driving messages). At the second step, we find that failed lane-changing attempts cause a higher speed reduction in both the current lane and the target lane compared to the successful ones. Similarly, a higher DRAC rate is required during failed lane-changing attempts compared to the successful lane-changing attempts, implying a higher crash risk during failed lane-changing attempts. Furthermore, the connected environment has shown to reduce not only the frequency of failed lane-changing attempts but also their negative impacts on surrounding traffic. Moreover, although the developed random parameters model reveals a significant heterogeneity at the individual level, suggesting that while the majority of the drivers tend to abort lane-changing manoeuvres with increase in the relative speed, this does not hold for a small portion of drivers. Finally, by incorporating failed lane-changing attempts into the existing lane-changing models, the predictive accuracy and realism of the lane-changing models have been enhanced.