Evaluating the Usability and Variability of Data on Transport for Modelling
=A Case Study of Dar Es Salaam, Tanzania
DOI:
https://doi.org/10.48346/IMIST.PRSM/ajlp-gs.v8i4.52650Abstract
Background and context
Modelling transport demand is a vital tool in urban planning, facilitating the prediction of future travel demand and guiding the development of infrastructure and policy decisions.
Goals and objectives
The study aimed at assessing the availability and utilization of data during transport planning processes. The findings and recommendations focus on improvements that would not only make data more accessible and useful for transport modelling but also facilitate more informed decision-making and policy formulation in Tanzania. Furthermore, the findings could have broader implications for other African cities facing similar data challenges, helping to advance data-driven approaches to transport planning across the continent. The objectives of the study are i) to identify gaps on the availability and utilization of modelling data during transport planning; ii) to determine and assess the main sources transport data and information that could lead to recommendations for better data collection, integration, and documentation practices.
Methods
The case study approach was adopted and Dar es Salaam City was selected as the case study area. The surveys were conducted in main transport institutions such as TARURA, TANROADS, DART, Ministry of Transport and Ministry of Lands and Human Settlements Development, and official interviews conducted to government officials to determine the available data, and the level of utilization of data in urban planning processes. Data analysis were conducted using statistical methods.
Results
The results showed that, for trip generation, the frequency variable had a mean of 3.1 (SD = 3.05), and the percentage variable had a mean of 21.9 (SD = 21.8). The dataset exhibited a high skewness (2.46) and a kurtosis of 6.51, indicating the presence of outliers. PCA results revealed that the first principal component (PC 1) explained 100% of the variance, with variables like "Traffic Data" and "GDP" showing strong negative scores of -14.94, while "Household Survey (2017)" and "Paved Roads" had positive scores of 21.12 and 57.18, respectively. The findings highlight substantial variability in data, with traffic counts and road conditions being the most influential factors in modelling. The significant skewness and kurtosis suggested a need for data normalization to reduce the impact of outliers.
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