# CQFormer: Cyclic Quasi-Dynamic Transformers for Hourly Origin-Destination Estimation
## 5 Experiments  
### Experiment Configuration  
The proposed model is evaluated in a realistic urban road network in Haikou, China. On the widely used microscopic traffic simulator, SUMO, we run two independent simulations in parallel, representing the realistic traffic scenario and the simulation used to generate paired OD-traffic count data, respectively. From the \"realistic\" simulation, the estimation model obtains several prior OD matrices, and real-time traffic counts on the monitored roads, then infers the real hourly OD matrix.  
The experimental road network is selected from downtown Haikou city, including 2328 roads and 1171 junctions, of which 359 major arterials are equipped with traffic count detection devices to measure the number of vehicles passing through these roads. Vehicles entering and exiting these roads every five minutes are aggregated into a traffic count vector for a subperiod. The entire network covers an area of 10 km \(\times\) 6 km and is divided into 31 traffic analysis zones (TAZs), as illustrated in Figure 4. All origin and destination nodes are aggregated in these TAZs, and TAZ might have more than one origin and destination node. Due to the limited size of TAZ, we do not consider the intra-TAZ traffic demand of vehicles, and the OD flows between TAZs make up the elements on the OD matrix. Figure 5 illustrates how traffic demand varies over a day, where blue circles represent the traffic demand in specified hours in each round of the simulation, the orange line represents the hourly average traffic demand on weekdays, and the green line is for weekends.  
In the temporal dimension, traffic demand involves two characteristics: time-of-day and day-of-week. The former describes the intra-day fluctuations including six periods: morning, morning peaks, noon, afternoon, afternoon peaks, and night; and the latter consists of weekdays and weekends. In different periods, different intensities of OD flows are generated between TAZs with different attributes (e.g., the significant traffic flows from the residential area to the workplace in the weekday morning peak). Note that multiple attributes may exist in the same TAZ.  
As discussed in the last chapter, we set up three ways of path selection in realistic traffic scenarios, where the proportion of these three path selection methods varies between different OD methods and changes over time, and this information about path selection remains unknown to the other simulation used to generate the training data augmentation. To reproduce the diversity of vehicle dynamics in the realistic road network, five categories of vehicles with different dynamics parameters are set up in the simulation: normal cars, conservative cars, aggressive cars, buses, and trucks.  
Each round of \"realistic\" simulation lasts 17.3 simulation hours, from 5:20 a.m. to 10:40 p.m. With regard to the simulation stabilization process, the warm-up and cool-down times of 40 m are not used for estimation. The simulation is executed for 600 rounds, generating 600-day data including 172 weekends and 428 weekdays. Additional 4000-round  
Figure 4: **The experimental road network in Haikou City from Open Street Map.** The experimental network consists of 31 traffic analysis zones (TAZs), with an area of 10 km \(\times\) 6 km. The TAZs are selected from the district’s primary areas, such as schools, neighborhoods, commercial districts, and administrative premises.  
paired data are generated by the parallel simulation, of which each round includes 2 h each of the above 6 periods, and the half-hours of warm-up and cool-down are not recorded. The specific parameters of the experiment are given in Table 1.