Forecasting urban sprawl is important for land-use and transport planning. The aim of this study is to model and predict the future urban sprawl in Sylhet Sadar using remote sensing data. The ordinary least square (OLS) regression model and the geographic information system (GIS) are used for modeling urban expansion. The model is calibrated for the years 2014 to 2017 using eight explanatory variables extracted from the regression model. The regression coefficients of the variables are found statistically significant at a 99% confidence level. The cellular automata (CA) model is then used to analyze, model, and simulate the land-use and land-cover (LULC) changes by incorporating the algorithm of logistic regression (LR). The calibrated model is used to predict the 2020 map, and the result shows that the predicted map and the actual map of 2020 are well agreed. By using the calibrated model, the simulated prediction map of 2035 shows an urban cell expansion of 220% between 2020 and 2035.

Traffic delay is a very common phenomenon in urban intersections where the traffic volume on the approach roads is high. Coordinated traffic signals based on real-time traffic can be very useful for minimizing intersection traffic delays. Therefore, this study is aimed to investigate the prospect of a coordinated signal system for the busiest and most closely spaced intersections (Ambarkhana, Chowhatta, and Zindabazar) in the Sylhet city of Bangladesh. This signal system will allow a continuous flow of traffic through moving vehicles between successive coordinated intersections so that the vehicles that running at the design speed can pass through the coordinated intersections without a significant halt. An isolated signal is designed for three intersections and linked up to them based on offset values (time to travel from one intersection to another). Phase splits were adjusted using the Time-Space diagram.