Efficient urban planning is crucial for creating liveable and sustainable cities. This involves designing urban areas that maximize economic, social, and environmental viability while minimizing negative impacts. Land use and transport development play a significant role in shaping transport patterns and infrastructure needs. However, applying Land Use and Transport Integrated (LUTI) models in developing countries can be challenging due to limited resources and data availability. This dissertation aims to explore freely, or low-cost high-frequency (HF) data related to land use and transport and develop an integrated model using available data in Sri Lanka as an example for developing countries. The complexity of the urbanisation process, particularly in developing countries, demands sophisticated decision support tools such as the Land Use and Transport Integrated (LUTI) models to assist in the balanced and sustainable development of transport and land use. However, the high cost and time-consuming nature of LUTI models have limited their application in developing countries due to limited resources and funding. The advent of information and communication technology (ICT) has revolutionised data collection and analysis, providing more extensive and high frequency (HF) data, such as real-time traffic data collected from sensors and mobile devices. This data can be used for various purposes, including traffic management, urban planning, public health and safety, and research and innovation. The availability of high-frequency data presents both opportunities and challenges for urban planning and development. However, based on the literature review, application of HF data in developing countries are very limited due to the data availability such as high resolution satellite data, technological constraints, lack of understanding of potential benefits of HF data, data sensitivity and privacy concerns and resource constraints including financial and human capacity. For these cases, researchers and planners can use HF proxy data as a substitute for HF data. The study aims to extend the use of LUTI models by incorporating HF proxy data in Western Province, Sri Lanka, and demonstrates the approach's effectiveness. It utilizes electricity consumption data, GPS point data of customers, and satellite imagery to develop the model. The resulting trip generation model achieves high accuracy in predicting travel demand of 79% in 2013. Additionally, the study uses satellite imagery and supervised classification to obtain land use area information. Separate trip attraction models for general and industrial areas are developed, achieving accuracy of 70% and 79.5% for general and industrial purpose related trip in predicting attracted trips, respectively. A