Research Progress and Prospects of Transit Priority Signal Intersection Control Considering Carbon Emissions in a Connected Vehicle Environment

In terms of sub-area division control methods, Ma et al. [14] proposed three automatic division methods, namely, equal division, division by mean value, and division by distance; they used a spectral method to take the correlation degree of adjacent intersections as the basis of division. With the help of complex network theory, Wang et al. [15] researched the control sub-area optimization method based on a cohesive community discovery algorithm, using community modularity as an indicator. Shen et al. [16] analyzed the influence of road section distance, traffic flow density, and signal cycle on intersection correlation and established a sub-area division method based on a fuzzy algorithm. Xu et al. [17] proposed a dynamic division method for road networks based on different saturation degrees for different states of intersections, established the correlation and similarity model between intersections within each sub-area, and utilized the spectrogram theory to dynamically divide road sections and intersections. Ambühl et al. [18] proposed a control sub-area division method based on the Monte Carlo algorithm, according to the traffic characteristic parameters set by fixed sensors. Ding et al. [19] proposed a continuous division method for controlling sub-areas based on the heterogeneity difference theory of road networks. Fang et al. [20] proposed a spectral clustering algorithm for homogeneous partitioning of heterogeneous networks, taking into account road similarity, cluster values, and associations between adjacent roads. By combining the existing research results, the division methods of the control sub-areas are summarized, as shown in Table 2. Based on the division methods of the control sub-areas, the design of the traffic control sub-area divisions process is shown in Figure 3.