Incorporating external factors into the TL-GPSTGN model can significantly enhance its predictive accuracy. Key external factors include:
Weather Conditions: Weather data, such as temperature, precipitation, and visibility, can greatly influence traffic patterns. Integrating real-time weather data can help the model account for variations in traffic flow due to adverse weather conditions, such as rain or snow, which often lead to reduced speeds and increased congestion.
Special Events: Data regarding local events, such as concerts, sports games, or festivals, can be crucial for predicting traffic surges. By incorporating event calendars and attendance estimates, the model can better anticipate increased traffic volumes in specific areas.
Social Media Data: Analyzing social media trends and public sentiment can provide insights into traffic conditions. For instance, spikes in social media activity related to traffic incidents or road closures can be integrated into the model to improve real-time predictions.
Traffic Incidents: Real-time data on accidents, road construction, and other incidents can be integrated to provide immediate context for traffic flow changes. This data can be sourced from traffic management systems or crowdsourced applications.
Public Transport Schedules: Incorporating data from public transportation systems can help the model understand how public transit usage affects road traffic. For example, bus schedules and ridership data can indicate potential congestion points during peak hours.
By integrating these external factors, the TL-GPSTGN model can achieve a more holistic understanding of the traffic ecosystem, leading to improved accuracy in predicting road conditions and traffic flow.