## 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
(5) Holidays or not: This study further refines and assigns values to the time indicators. Given that the relevant regulations in Xi'an stipulate that the city's vehicles are not affected by traffic restriction measures during holidays, this usually leads to a significant increase in traffic flow during holidays. To enhance the precision of the immediate traffic flow forecasting model for Xi'an, this study particularly distinguishes the temporal attributes of holidays and divides them as an independent time indicator. Such a treatment helps the model to capture and predict traffic flow changes under special dates more accurately. (6) Temperatures: Temperature, as a weather factor of general interest, plays an important role in traffic flow prediction. According to the annual traffic flow report of Xi'an and the existing research literature, traffic flow in Xi'an usually decreases significantly under extremely high- or low-temperature conditions. On the contrary, when the temperature is in the appropriate range, the traffic flow will increase. Given this, this study incorporates temperature indicators into the index system for short-term traffic flow prediction in Xi'an to more comprehensively consider the impact of weather factors on traffic flow. (7) Horizontal atmospheric pressure and ground relative humidity: Several studies in the literature have repeatedly emphasized that barometric pressure and ground humidity have a significant effect on vehicle braking distances, and these changes may indirectly act on the accident rate as well as the operating speed of the road traffic, which may have a chain reaction on the traffic flow. Considering the importance of these factors, this study incorporates barometric pressure and ground humidity as key indicators into the index system for short-term traffic flow prediction in Xi'an city to ensure that the prediction model can more accurately reflect the potential impacts of these environmental factors on traffic flow. (8) Course of events and special weather phenomenon: Changes in wind direction and special weather conditions, such as rain and snow, can cause a reduction in the speed of urban vehicles. According to data provided by the Xi'an Intelligent Transportation System, the overall speed of traffic may decrease by about 15-20% under special weather conditions such as rain and snow. This slowdown in speed tends to aggravate the congestion of urban traffic. Therefore, this study incorporates wind direction and special weather conditions as important indicators into the index system for short-term traffic flow prediction in Xi'an to enhance the model's ability to predict traffic flow dynamics. (9) Horizontal visibility and total cloud capacity: Several studies in the literature have consistently pointed out that horizontal visibility and total cloudiness are the key factors that significantly affect the overall speed of vehicular traffic. In addition, data analysis of the Xi'an intelligent transportation system (XITS) reveals that vehicles generally travel at lower speeds under cloudy weather conditions than in clear, cloudless weather. Therefore, these two indicators are considered important factors affecting traffic flow prediction. Considering their potential exacerbation of urban traffic congestion, this study incorporates horizontal visibility and total cloudiness into the short-term traffic volume forecasting index system of Xi'an to improve the accuracy and practicality of the prediction model. The above is the indicator system of this paper related to the description and selection of the reasons, and in other literature, there are other related indicators, but this paper did not choose them, and the reason is as follows:  
1. The peak period and other time-assigned attribute indicators: In this study, the peak period and other attribute indicators have been reflected in the existing time indicator framework of this study. In view of the differences in peak arrival times on different road sections in Xi'an, it is not appropriate to treat them uniformly.