## 5 Practical Example and Experimental Evaluation  
### Practical Example  
In this paper, we provide a practical example on how to use our LSTAR model to predict future traffic flow of the Shanghai Century Park area. To evaluate the prediction approach of LSTAR, we adopted the widely used traffic simulation tools Simulation of Urban Mobility (SUMO) [39] and OpenStreetMap (OSM) [40], which are recognized as promising candidates for traffic simulations, and the simulation results are commonly accepted as a replacement of real data. Additionally, plenty of works exist that have adopted SUMO and OSM as tools to generate traffic data for research [41; 42]. In this example, we demonstrate the model-building procedure for our proposed LSTAR model in the context of traffic flow prediction on a road network. First, we downloaded the OSM format road network map of the area near Shanghai Century Park, as shown in Figure 2. The OSM format map not only included the geography topology of the road network, but also the road type, lane number, speed limitation, traffic light duration, and so on, according to real-world information. Then, the SUMO NetConvert tool was used to convert the OSM format map to a SUMO format map. SUMO was then used to simulate the traffic flow of this area according to the road network information converted from the OSM map. In the simulation, trip demands were generated randomly every two seconds according to the edge length. The \"Fringe factor\" was set to 4, which means that roads with no successor or no predecessor had four times the possibility of being selected as the start or end of a trip when compared to other roads. The speed limitations, traffic light durations of each road, and so on were obtained from the real-world data of the OSM map. The simulation duration was one week. The detailed simulation parameters are listed in Table 1.  
After we obtained traffic flow data generated from SUMO, they were used to conduct traffic flow prediction with different prediction models. The prediction intervals were five minutes, 15 min, and 30 min, as normally a prediction interval over 30 min has less significance to real-time route planning or vehicle navigation. The SUMO format map converted from the OSM map is shown in Figure 3. The roads were renamed as Rn-m for easy usage in the following discussion. In the following section, road R7-3 in a north-to-south direction was selected as the example road to demonstrate the LSTAR prediction procedure. Furthermore, we conducted traffic flow prediction for roads R7-2, R3-3, and R3-4 with the same procedure used for road R7-3. Figure 2: OpenStreetMap (OSM) map of Shanghai Century Park area. Construction of a Dynamic Spatial Weight Matrix  
Step 1. Build a spatial adjacency matrix. The first step was to build a spatial adjacency matrix based on the topological structure of the network, which appears in Figure 1. In this paper, spatial adjacency matrices of spatial orders up to three were constructed as per Reference [13]. The spatial neighborhood information can be found in Table 2 with the first, second, and third order neighbors separated. Step 2. Determine the dynamic spatial order and weights. The second step was to determine the dynamical spatial order and weights for every road link with the method proposed in this paper (Section 3.2. Weight Matrix Construction). In this simulation, only road R7-3 in a north-to-south direction was selected to show how the weight matrix was determined. According to weight matrix definition, only upstream road sections of R7-3 in the north-to-south direction were considered. The dynamic spatial weights calculation results of road R7-3 in a north-to-south direction with a five-minute time step are shown in Table 2.