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.

换车道是一项日常工作，但又是一项复杂的驾驶任务，对交通流量效率和道路安全都产生若干负面影响，因此，换车道模型已成为微仿真工具必不可少的一部分。现有模型仅考虑成功完成的变道操纵，而在变道模型的校准和验证过程中，失败的变道尝试（即在启动后中止的变道操纵）被大体忽略。这种无知导致换道模型的结构不完整。此外，与成功的变道操作相比，失败的变道尝试更有可能干扰交通流并在当前车道和目标车道上造成安全隐患，因此，进一步保证在换道建模中要考虑它。互联的环境可以通过提供有关周围交通状况和目标车道中可用的后续车间隔的驾驶信息，从而使这些不利影响最小化，驾驶员可以利用这些信息做出明智且安全的车道变更决策。因此，本研究调查了连通环境对失败的换道尝试的影响，并使用三个步骤解决了换道模型结构不完整的问题。首先，采用基于小波变换（WT）的方法从真实数据中检测失败的车道变换尝试。第二，检查失败的换道尝试对交通流效率和安全参数（如平均速度降低和避免碰撞的减速率（DRAC））的影响。此外，还使用随机参数二进制逻辑模型探讨了互联环境如何影响这些参数。最后，将失败的换道尝试合并到现有的换道模型中。第一步，将基于WT的方法应用于NGSIM数据集和本研究中收集的驾驶模拟器数据时，在检测失败的换道尝试中显示出合理的准确性，从而使驾驶员驾驶CARRS-Q Advanced Driving Simulator并未能完成在两种随机驾驶条件下的变道动作：基线（无驾驶信息）和相连的环境（有驾驶信息）。在第二步中，我们发现失败的换道尝试与成功的换道相比，导致当前和目标道的速度降低更大。同样，与失败的换道尝试相比，失败的换道尝试需要更高的DRAC速率，这意味着失败的换道尝试会带来更高的崩溃风险。此外，连接的环境已显示不仅减少了失败的换道尝试的频率，而且还减少了对周围交通的负面影响。此外，尽管已开发的随机参数模型在个体水平上显示出显着的异质性，这表明尽管大多数驾驶员会随着相对速度的增加而中止改变车道的操作，但这并不适用于一小部分驾驶员。最后，通过将失败的车道变换尝试合并到现有的车道变换模型中，可以提高车道变换模型的预测准确性和真实性。