Abstract:
Addressing the persistent issue of traffic congestion in Pakistan, particularly in Islamabad,
remains a critical challenge impacting both productivity and quality of life. Past efforts have
focused on congestion prediction using parameters such as road conditions, traffic speed, and
holidays, employing algorithms like Random Forest, DBSCAN, and GRU. While past research
has shown promise, a significant research gap exists concerning integrating weather data
specific to Islamabad's roads. This study proposes a novel approach to predict traffic congestion
levels by combining 2022 Islamabad Roads Traffic Data obtained through Google APIs with
weather data from the Open-Meteo website. The roads are divided into two junctions, each
comprising "Cross" and "Reach" sections, with traffic flow classified into three categories: low
congestion (Class 0), mild congestion (Class 1), and congested (Class 2). The predictive
modelling primarily relies on Long Short-Term Memory (LSTM) networks. Two
comprehensive studies were conducted: one incorporated traffic and weather data, and the other
utilised only traffic data. The evaluation metrics encompassed F1 scores, precision, recall, and
overall accuracy for each junction and congestion class. Notably, integrating weather data
yielded superior results across all metrics, indicating a more accurate prediction of congestion
levels. For instance, at Junction 1 Cross, incorporating traffic and weather data achieved an
overall accuracy of 0.98, compared to 0.78 when using traffic data alone. Similarly, at Junction
1 Reach, the combined dataset yielded an overall accuracy of 0.96, outperforming the 0.88
accuracy with traffic data alone. Junction 2 Reach also demonstrated improved accuracy with
the combined dataset, achieving 0.94 compared to 0.75 with traffic data alone. Future research
avenues include incorporating additional parameters such as road conditions, public holidays,
signals, traffic flow, density, and speed. These enhancements could further refine the predictive
capabilities, ultimately contributing to more effective traffic management strategies in
Islamabad and beyond.
