Short-term traffic flow forecasting is a critical function in advanced traffic management systems (ATMS) and advanced traveler information systems (ATIS). Accurate forecasting results are useful to indicate future traffic conditions and assist traffic managers in seeking solutions to congestion problems on urban freeways and surface streets. There is new research interest in short-term traffic flow forecasting due to recent developments in ITS technologies. Previous research involves technologies in multiple areas, and a significant number of forecasting methods exist in literature. However, forecasting reliability is not properly addressed in existing studies. Most forecasting methods only focus on the expected value of traffic flow, assuming constant variance when perform forecasting. This method does not consider the volatility nature of traffic flow data. This paper demonstrated that the variance part of traffic flow data is not constant, and dependency exists. A volatility model studies the dependency among the variance part of traffic flow data and provides a prediction range to indicate the reliability of traffic flow forecasting. We proposed an ARIMA-GARCH (Autoregressive Integrated Moving Average- AutoRegressive Conditional Heteroskedasticity) model to study the volatile nature of traffic flow data. Another problem of existing studies is that most methods have limited forecasting abilities when there is missing data in historical or current traffic flow data. We developed a General Regression Neural Network(GRNN) based multivariate forecasting method to deal with this issue. This method uses upstream information to predict traffic flow at the studied site. The study results indicate that the ARIMA-GARCH model outperforms other methods in non-missing data situations, while the GRNN model performs better in missing data situations.

Intelligent transportation systems (ITS) have garnered considerable attention for providing efficient traffic management solutions. Traffic flow forecasting is a crucial component of it which serves as the foundation for various state-of-the-art deep learning approaches. Initially, researchers recognized that significant temporal changes from traffic flow data for modelling. However, as researchers delved deeper into the underlying correlations within traffic flow data, they discovered that spatial information from the road network also plays a crucial role in accurate forecasting. Consequently, deep learning methods that incorporate Spatio-temporal representation have been employed to address traffic flow forecasting. Although recent solutions to this problem are impressive, it is essential to discuss the reasoning behind the architecture of the model. The expression of each feature relies on selecting appropriate models for feature extraction and designing architectures that minimize information loss during modeling. In this thesis, the work focuses on graph-based Spatio-temporal feature engineering. The experiments are divided into two parts: 1). explores the efficient architecture for expressing spatial-temporal information by considering both different sequential modelling approaches. 2). Based on the result obtained, the second experiment focuses on multi- scale modelling to capture informative Spatio-temporal feature. We propose a model that incorporates sequential modeling and captures multi-scale Spatiotemporal semantics by employing residual connections in different hierarchy. We validate our model using three datasets, each containing varying information for extraction. Taking into account the dataset characteristics and the model structure, our model outperforms the baselines and state-of-the-art models. The experimental results indicate that the performance of sequential modeling and multi-scale semantics, combined with thoughtful model design, significantly contribute to the overall forecasting performance. Furthermore, our work serves as inspiration for expressive data mining methods that rely on appropriate feature extraction models and architecture design, taking into consideration the information content within the dataset.