## 3 Operational Mechanisms and Traffic Flow Characteristics of Non-Conventional Lanes
### Mesoscopic Traffic Flow Characteristics  
The mesoscopic traffic flow characteristics of non-conventional lanes are mainly reflected in vehicle queuing and shockwave propagation. [PERSON] et al. [52] analyzed the performance of CLL based on stationary conditions, considering the influence of upstream signalized intersections. The stochastic nature of CLL capacity improvements represents a fundamental characteristic that distinguishes non-conventional lanes from deterministic conventional designs. [PERSON] et al. 's research [9] demonstrates that while CLL can significantly improve left-turn capacity, the actual capacity gains exhibit considerable randomness due to the stochastic arrival patterns of left-turning vehicles. This randomness manifests through temporal clustering and gap patterns that affect contraflow lane utilization efficiency. During high-arrival periods, the borrowed space achieves maximum utilization, while irregular arrival patterns may result in underutilization and reduced system benefits. Probabilistic modeling techniques address these challenges through sophisticated frameworks, including binary logit models for driver behavior prediction, cellular automata simulation for stochastic vehicle movement analysis, and stationary condition models that account for arrival distribution variability. The implications extend beyond simple capacity calculations to fundamental questions about system reliability and performance guarantees under varying traffic conditions. Shockwave theory and queue length models provide fundamental analytical frameworks for optimizing non-conventional lane design parameters and signal timing strategies. [PERSON] et al. [53] applied shockwave theory to analyze vehicle queuing formation-dissipation processes, proposing calculation methods for maximum generalized queue length with 6.46% average relative error, supporting the precise determination of overflow safety dis tances and detector placement optimization. [PERSON] et al. [11] developed binary logit models incorporating driver decision-making uncertainty by estimating stopping probabilities at pre-signals when vacant spaces exist in conventional left-turn lanes. [PERSON] et al. [15] established mixed-integer linear programming frameworks integrating geometric parameters with signal timing optimization using a cell transmission model, achieving a 9.63% delay reduction and 11.48% capacity increase through coordinated contraflow lane length and phase timing optimization. Cellular automata simulation reveals distinct performance patterns between conventional and non-conventional lanes under varying operational parameters. [PERSON] et al. [13] demonstrated that vehicle injection probability (\(\alpha\)) and lane-changing area length (LB) create different sensitivity patterns: small injection probabilities (\(\alpha\leq 0.1\)) make conventional lane density more sensitive to lane-changing area length, while larger probabilities (\(\alpha>0.1\)) shift sensitivity to CLL lanes. When both parameters are substantial (\(\alpha>0.1\), LB \(\geq 20\) cells), departure ratios between CLL and conventional lanes approach 50%, matching real-world peak hour observations and validating cellular automata modeling accuracy for design optimization applications. Research on mesoscopic traffic flow characteristics reveals the influence mechanisms of non-conventional lanes on queue formations, shockwave propagation, and traffic flow stability. Existing research has established some basic models, but most are limited to specific types of non-conventional lanes. Future research should establish more universal mesoscopic traffic flow models that can adapt to characteristics of different types of non-conventional lanes; simultaneously, there is a need for in-depth study of traffic flow characteristics under oversaturated conditions in non-conventional lanes, as well as resilience and recovery capabilities under sudden events.