## 2 Short-Term Traffic Flow Prediction Model Construction
### Construction of SVR Short-Time Traffic Flow Prediction Model Based on Associated Roads  
#### 2.2.1 Selection of Indicators for Short-Term Traffic Flow Prediction  
The indicators used in the current traffic flow prediction module in Xi'an city are only three, i.e., time, location, and traffic flow, resulting in the prediction accuracy not being able to meet the growing demand for traffic prediction, and the accuracy is even more unable to meet the demand for short-term traffic flow prediction. In this way, based on the existing indicator system, this paper through a large number of short-time traffic flow prediction-related literature readings increased the weather factors, and other scholars at home and abroad recognized the greatest impact on the short-time traffic flow indicators; at the same time, the data collection time interval is narrowed to meet the needs of the city's short-term traffic flow prediction, and collated indicators are shown in Table 3. \begin{table}
\begin{tabular}{c c} \hline
**Index** & **Formats (Examples)** \\ \hline Time & 2022.320-15:00 \\ Location & Jandong Street (East) \\ Traffic flow & 76 \\ Average speed & 42 km/h \\ Holidays or not & Yes/No \\ Temperature & 173 3°C \\ Horizontal atmospheric pressure & 723.5 \\ Ground relative humidity & 54 \\ Course of events & Wind from the northeast \\ Special weather phenomenon & No/rain/snow/sand/fog \\ Horizontal visibility & 0-10/\textgreater{}10 \\ Total cloud capacity & No apparent cloud/sparse cloud/less cloud/more cloud \\ \hline \end{tabular}
\end{table} 
Table 3: Short-time traffic flow prediction index system in Xi’an. After summarizing a large number of studies related to traffic flow prediction, the description of each index and the reasons for selection in this paper are as follows:  
(1) Time: In this study, a refined time recording method was used to collect data every 10 min as a time unit. According to the common classification criteria in academia, traffic flow forecasts can be categorized into three types depending on the time unit of data collection: long-term forecasts, medium-term forecasts, and short-term forecasts. Long-term forecasting usually uses days as the unit of traffic counts, and medium-term forecasting uses hours as the unit of measurement, while short-term forecasting focuses on finer time scales and usually collects data on a minute-by-minute basis. This thesis focuses on the field of short-term forecasting and chooses 10 min as the time interval for data collection, aiming to enhance the precision and reliability for enhancing the accuracy of immediate traffic volume predictions in Xi'an by shortening the data collection period. (2) Location: It is the location of the road where the traffic flow information is collected, and the information for this study comes from the intelligent traffic management system in Xi'an. The data collection devices are deployed in the middle segments of the roadway; therefore, the location metrics in this study focus on roadway segments rather than intersections. (3) Traffic flow: The database for this study is the traffic flow information from the Xi'an intelligent transportation system. The data collection mechanism used in this system is for monitoring the two-way traffic flow on a road; therefore, the traffic flow data used in this study are the sum of the two-way traffic flow on a specific road during a given time interval. These aggregated data provide a comprehensive view of the traffic flow analysis. (4) Average speed: In this study, the collection of traffic flow information includes the recording of the average speed of road vehicles during a specified period of time. In the case of experiencing road congestion, the measured traffic flow data may be lower than the flow data when the road is open. In this case, the average speed indicator is particularly important and provides a basis for a comprehensive assessment of road congestion. By analyzing the changes in average speed, we can more accurately determine the actual traffic conditions on the road.