## 3 AMO Model Construction
### Agent-Based Multimode Dynamic Traffic Simulation Model
#### 3.2.2 During-Trip Decision Model  
The during-trip decision model in this study establishes basic driving rules and environmental parameters for each vehicle agent within the traffic network. This model includes a car-following model, an intersection decision model, and traffic signal control parameters. Consequently, individual travelers adjust their route decisions based on real-time traffic information and the implemented incentives and penalty mechanisms, aiming for real-time maximum utility rather than a global optimum due to the dynamic nature of traffic state and incomplete information.  
1. Car-following model:  
The car-following model is a traffic flow model that describes how following vehicles react based on the behavior of leading vehicles. It is used to simulate the interaction between vehicles and the decision-making process of drivers. This study utilized the General Motors (GM) model to depict these interactions, with specific details and parameter settings as described in the literature [45]. The GM model incorporates factors such as the desired following distance, the current speed, and the vehicle's acceleration or braking behavior. This model allows for a more realistic simulation of traffic dynamics, especially under congested conditions, where the behavior of individual vehicles can significantly affect overall traffic flow.
2. Traffic signal control parameters:  
Traffic signal control plays a crucial role in traffic simulation, significantly impacting the simulation results and bringing them closer to real traffic conditions. In this study, each road is set up as a two-way four-lane road, with simplified signal parameters at intersections: the green light duration for going straight and left turns is set at 60 s and 45 s, respectively, with a yellow light duration of 3 s.
3. Intersection decision model:  
In real-world driving scenarios, travelers often rely on smartphones or electronic information boards for real-time traffic information and adjust their travel routes based on this information to maximize utility. This study adopted the method of [PERSON] et al., where vehicles only make and adjust their travel direction at intersections [45]. This means that when a vehicle reaches an intersection, it decides its next direction based on the current, albeit incomplete, real-time information. The probability of a traveler choosing a particular road is closely related to the total cost of the remaining route from that road to their destination. This includes the shortest travel distance of the new route, the queuing and waiting time at the intersection due to traffic flow or signal control, and additional costs associated with a specific route. The probability of a vehicle traveling from intersection \(i\) to intersection \(j\) is calculated accordingly.  
\[C_{ij(t)}=v_{t}*\big{(}\frac{d_{ij}+d_{jl}}{y_{ij(t)}}+w_{ij(t)})+l*(d_{ij}+d_ {jl})+f \tag{13a}\] \[Pij(t)=\frac{exp\big{(}-\theta_{3}C_{ij(t)}\big{)}}{\sum_{j}exp\big{(}-\theta_ {3}C_{ij(t)}\big{)}} \tag{13b}\]  
where \(C_{ij(t)}\) represents the total cost of traveling from intersection \(i\) to intersection \(j\); \(d_{ij}\) is the distance from intersection \(i\) to intersection \(j\); \(d_{jl}\) denotes the shortest travel distance from intersection \(j\) to the destination \(j\) after selecting a new route; \(y_{ij(t)}\) refers to the average speed; and \(w_{ij(t)}\) represents the waiting time for vehicles queued at the intersection.