## 2 Literature Review
[PERSON] [25] evaluated the setting of bus lanes from the perspective of the traffic environment by studying the development of bus lanes and motor vehicle tailpipe emission models, establishing a quantitative method for the setting of bus lanes on motor vehicle fuel consumption and tailpipe emission. Example analyses show that the fuel consumption, CO2, CO, HC (hydrocarbon), and NOx emissions of road sections are reduced after the implementation of bus-only lanes. While public transport enterprises are committed to providing efficient traveling services to passengers with less economic costs, they bear the social responsibility of reducing transport emissions. Some studies have found that reasonable bus scheduling is conducive to reducing bus tailpipe emissions [26]. Currently, few scholars have considered bus tailpipe emissions as the optimization objective of the scheduling strategy in their studies on bus scheduling optimization. A demand-responsive bus scheduling optimization model was put forward by [PERSON] et al. [27]. Three objectives were chosen: bus emissions, operating costs, and service level. The objective of the model was to identify a scheduling strategy that would reduce bus exhaust emissions significantly, albeit somewhat at the expense of operating costs and passenger travel time. In order to cut down operational expenses and vehicle emissions while purchasing fresh buses under a restricted budget and adhering to strict bus travel timetables, [PERSON] and [PERSON] [28] looked into the bus scheduling challenge. Taking into consideration the variations in the importance of the bus-stopping scheme and the frequency of departure in the optimization process, [PERSON] et al. [29] established a combined skip-stop and inter-area bus scheduling model to minimize the total cost of passenger time, the total cost of bus operation, and the cost of tailpipe emission. It, additionally, suggested a dynamic probabilistic genetic algorithm for optimization with probability varying with the number of iterations to solve for the optimal stopping scheme, along with the frequency of departure. The research on optimizing the combination scheduling of large station express buses aims to propose an optimal scheduling program that satisfies passenger, enterprise, and environmental requirements. [PERSON] [30] prioritizes minimizing passenger time costs and enterprise operating costs as the primary objectives, while considering reducing bus exhaust emissions as a secondary objective. Taking the reduction of bus exhaust emissions as one of the model optimization objectives, [PERSON] [31] studied the combination of two full buses, inter-area buses, buses with big stops, and express buses, considering the benefits to passengers and bus companies, and solved the two models by using genetic algorithms. This work investigates bus scheduling optimization, taking into account bus exhaust emissions and drawing inspiration from previous studies on bus exhaust emissions and scheduling. However, the existing research still has the following problems:1. The full-vehicle scheduling models in the literature are proposed mostly by taking the travel time cost of passengers and the operating cost of public transport enterprises as the optimization objectives, and there are very few researches that have taken bus exhaust emissions into account in the optimal design of scheduling strategies. 2. The proposed scheduling model in the literature has not considered the impact of passenger travel choice behavior on passenger flow distribution and the constraints on the number of buses allocated to a route. 3. Unbalanced dispatching strategies are adopted in most previous studies, but unbalanced scheduling schemes obtained by using intelligent algorithms are not only detrimental to the schedulers' designation of the scheduling plan but also prolong the waiting time of some passengers. This paper uses Nanjing's segmented IC card data as the main database.