## 3 Methodology and Data Description
### Data Description
#### 3.5.2 Summary of Data Sources, Accuracy, and Limitations  
The data sources for this analysis draw from multiple structured datasets across three models--Markov processes, max-flow problems, and traffic flow models--and are provided by key Tanzanian authorities. For Markov processes, data include port operation data (e.g., equipment downtime, cargo processing times) sourced from Tanzania Port Authority (TPA)records, and historical delay data (e.g., delays due to congestion or equipment failure) derived from Land Transport Regulatory Authority (LATRA) logs and GPS tracking. Transition probabilities are built using these historical data to model state changes such as \"Waiting at Port\", \"In Transit\", \"Delayed\", and \"Delivered\". For max-flow problems, transportation network data (e.g., road and rail maps), road capacity data (maximum vehicle throughput), and railway capacity data (cargo capacity per train and daily trips) are sourced from LATRA and the Tanzania Railways Corporation (TRC). Additionally, cargo demand data from the Tanzania Shipping Agencies Corporation (TASC) provide insights into real-time demand fluctuations at hinterland destinations. Traffic flow models use real-time traffic volume data, travel time data, and speed/density data, all provided by LATRA traffic monitoring systems, GPS trackers, and fleet management systems. Environmental disruptions, such as road closures due to weather or construction, are also tracked by LATRA and national weather agencies.  
The accuracy of these datasets is generally reliable, given that they are sourced from official Tanzanian institutions with robust data collection systems. Port operation data and historical delay data provide a strong foundation for understanding trends in cargo flow and delays, while real-time traffic volume and GPS-based travel time data offer detailed insights into current network performance. However, some challenges exist. Historical data may not fully capture rare disruptions or rapid demand changes, and static transition probabilities in the Markov model may not reflect short-term variability. Real-time data accuracy depends on the functionality of monitoring systems and GPS trackers, which can experience occasional downtimes. Similarly, road and railway capacity data assume consistent operational conditions, but temporary closures, maintenance, or unexpected traffic surges may affect the network's capacity.  
Despite its robustness, the dataset has limitations that must be addressed. Markov processes simplify cargo flow into broad states, which may not capture more nuanced transitions, while static probabilities fail to account for dynamic real-world conditions. The max-flow model, while useful for optimizing transportation routes, does not always factor in unpredictable external factors like labor strikes or political disruptions. Traffic flow models rely heavily on real-time data, which may suffer from system gaps or inaccuracies. To address these challenges, future iterations should incorporate dynamic transition probabilities, more granular cargo flow states, and machine learning algorithms to better handle rare or unpredictable events. By integrating these enhancements and leveraging the comprehensive data from Tanzania authorities, the models can provide a more precise and efficient solution for improving cargo movement and hinterland connectivity at Dar es Salaam Port.  
Figure 1 illustrates the model flowchart, providing a structured representation of the process and key components involved in the system