## 5 Optimization Design and Control Methods for Non-Conventional Lanes
### Spatiotemporal Collaborative Optimization  
Spatiotemporal collaborative optimization is the core concept of non-conventional lane design, aiming to achieve the best match between spatial layout and temporal control. Spatiotemporal collaboration is manifested not only in the integration of traffic space design and signal control but also in coordination among different traffic participants. [PERSON] et al. [78] proposed an optimization method for dynamic switching and signal control of contraflow reversible lanes. [PERSON] et al. [79] studied the layout optimization problem of variable lanes from a network perspective. The key to spatiotemporal collaborative optimization lies in establishing multi-dimensional optimization models, incorporating spatial design parameters (such as lane width, turning radius, transition section length) and temporal control parameters (such as signal cycle, green ratio, phase difference) into a unified optimization framework. Simultaneously, the passage needs of different transportation modes (motor vehicles, non-motorized vehicles, pedestrians) must be considered to achieve coordinated organization of multi-modal traffic. In selecting optimization objectives, traditional efficiency indicators must be considered alongside factors such as safety and environmental impact. Spatiotemporal collaborative optimization research embodies systems engineering thinking, integrating originally separate design elements into a unified optimization problem. Existing research has made progress in model construction and algorithm development but still faces challenges in handling large-scale networks and real-time optimization. Future research should develop more efficient solution algorithms to apply spatiotemporal collaborative optimization to city-level traffic networks. Simultaneously, the handling of uncertainty factors needs to be strengthened to improve the robustness of optimization schemes. Additionally, dynamic spatiotemporal collaborative strategies should be researched to enable systems to adjust spatial configurations and temporal control parameters in real time according to changes in traffic states. Current optimization models for non-conventional lanes exhibit limitations in addressing temporal uncertainties and real-time responsiveness. Linear and mixed-integer programming approaches, while mathematically rigorous and successful in specific applications ([PERSON] et al. [15] achieving 9.63% delay reduction and 11.48% capacity increase),assume deterministic traffic conditions that inadequately represent real-world variability. These models struggle with temporal uncertainty handling, requiring substantial computational time for re-optimization that limits real-time applicability. Scalability becomes problematic as network complexity increases, creating computational bottlenecks that prevent city-wide implementation. Alternative optimization paradigms offer enhanced robustness and scalability potential. Robust optimization methods, demonstrated by Sun et al. 's [PERSON] et al. (2019) two-stage approach and [PERSON] et al. 's [PERSON] et al. (2019) robust design frameworks, address uncertainty while maintaining computational tractability. Dynamic programming approaches by [PERSON] et al. [PERSON] et al. (2019) show superior performance in oversaturated conditions, suggesting hierarchical optimization strategies for scalability enhancement. Machine learning integration enables predictive optimization that adapts to traffic pattern evolution, while distributed optimization algorithms facilitate real-time network-level coordination without centralized computational constraints. Stochastic programming frameworks accommodate arrival pattern variability, and multi-objective evolutionary algorithms balance competing objectives under uncertainty.