# Adaptive Traffic Signal Control Method Based on Offline Reinforcement Learning
## 5 Experiments
### Results  
The average travel time (ATT) is selected as the evaluation metric, which is commonly used in this field. Lower ATT values indicate better control performance. Table 1 below compares the performance of the proposed model with various existing signal control models and methods. In the tests, Jinan provided three traffic flow files, Hangzhou provided two, and New York provided two. We tested our model under each different traffic flow scenario, with the numbers indicating the file sequence for each city. The results demonstrate that the proposed model achieves a significant improvement in ATT, indicating more efficient traffic flow management. An alternative metric, the destination-arrival average travel time (DATT), is an improvement over the traditional average travel time metric. This enhancement is based on our observation in simulated environments that vehicles with incomplete journeys by the end of the specified simulation period are often erroneously included in the calculation of the overall average travel time, which can lead to inaccurate assessments. Therefore, the DATT metric focuses exclusively on vehicles that have completed their destination journeys, calculating the average travel time for this subset. The following Table 2 presents a comparative analysis of the relevant methods and models based on the DATT evaluation criterion:  
To provide a more vivid demonstration of the efficiency of current road traffic, we have chosen another metric, the destination-arrival rate (DAR), as a benchmark for comparing experimental outcomes. The DAR is calculated by determining the ratio of vehicles that successfully reach their destinations to the total number of vehicles within a specified simulation timeframe. The following Table 3 presents a comparative analysis of the relevant methods and models based on the DAR evaluation criterion: \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Jian**} & \multicolumn{2}{c}{**Hangzhou**} & \multicolumn{2}{c}{**New York**} \\ \cline{2-7}  & **1** & **2** & **3** & **1** & **2** & **1** & **2** \\ \hline Fixed Time & 587.6 & 524.8 & 498.4 & 502.2 & 524.4 & 1750.5 & 1542.3 \\ Max Pressure & 339.5 & 348.6 & 329.5 & 465.7 & 477.3 & 1529.4 & 1423.9 \\ Efficient-MP & 320.6 & 343.4 & 299.3 & 398.1 & 376.3 & 1409.3 & 1402.3 \\ \hline CoLight & 313.8 & 298.3 & 295.8 & 348.6 & 318.4 & **1278.6** & 1354.7 \\ AttentionLight & 314.6 & 290.6 & 286.7 & 336.5 & 328.2 & 1322.7 & 1405.9 \\ DynamicLight & 301.3 & 295.2 & 287.8 & 329.7 & 321.6 & 1389.4 & 1323.6 \\ DataLight & 293.4 & 279.3 & 277.1 & 413.6 & 317.9 & 1343.2 & 1333.1 \\ \hline SD3-Light & **286.2** & **269.7** & **266.7** & **310.4** & **306.5** & 1283.7 & **1117.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overall performance. For the average travel time, the lower the better. Bold results for best results. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Jian**} & \multicolumn{2}{c}{**Hangzhou**} & \multicolumn{2}{c}{**New York**} \\ \cline{2-7}  & **1** & **2** & **3** & **1** & **2** & **1** & **2** \\ \hline Fixed Time & 593.2 & 536.3 & 501.6 & 513.5 & 536.1 & 1768.7 & 1568.2 \\ Max Pressure & 346.5 & 363.7 & 342.2 & 472.3 & 450.5 & 1543.3 & 1440.2 \\ Efficient-MP & 330.2 & 348.2 & 302.3 & 400.5 & 382.3 & 1426.2 & 1417.3 \\ \hline CoLight & 323.4 & 303.2 & 302.1 & 352.4 & 326.3 & 1389.6 & 1366.7 \\ AttentionLight & 325.7 & 298.3 & 295.2 & 342.3 & 332.9 & **1332.2** & 1412.3 \\ DynamicLight & 312.4 & 302.3 & 293.2 & 346.2 & 332.6 & 1395.3 & 1338.7 \\ DataLight & 299.2 & 282.4 & 287.4 & 424.3 & 324.8 & 1359.5 & 1342.5 \\ \hline SD3-Light & **294.4** & **274.9** & **275.0** & **325.5** & **312.4** & 1336.4 & **1126.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overall performance. For the destination–average travel time, the lower the better. Bold results for best results.