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Transit Network Design Problem (TNDP) is the most important component in Transit planning and operation, in which the overall cost of the public transportation system highly depends on it. The main purpose of this study is to develop a simple and effective solution methodology for the TNDP, which goes beyond previous traditional sophisticated approaches. The solution methodology adopted in this research for the TNDP is based on partitioning the solution into two consecutive stages; Transit route Network Design Problem “TrNDP” stage and frequency setting stage. In the first stage; a deterministic solution for TrNDP is tackled to construct bus routes. The deterministic manner of the TrNDP solution relies on using linear and integer mathematical formulations that can be solved exactly with their standard solvers. In the second stage; bus frequencies are optimized among bus routes (obtained in stage 1) via Genetic Algorithm, for a total bus fleet size representing operator's main cost. The adopted solution methodology has been tested through Mandl’s benchmark transit network problem. The test results showed that the methodology developed in this research is able to provide and effective solution in terms of the number of constructed routes, the direct demand coverage, and the total travel time

Transit Network Design Problem (TNDP) is the most important component in Transit planning and operation, in which the overall cost of the public transportation system highly depends on it. The main purpose of this study is to develop a simple and effective solution methodology for the TNDP, which goes beyond previous traditional sophisticated approaches. The solution methodology adopted in this research for the TNDP is based on partitioning the solution into two consecutive stages; Transit route Network Design Problem “TrNDP” stage and frequency setting stage. In the first stage; a deterministic solution for TrNDP is tackled to construct bus routes. The deterministic manner of the TrNDP solution relies on using linear and integer mathematical formulations that can be solved exactly with their standard solvers. In the second stage; bus frequencies are optimized among bus routes (obtained in stage 1) via Genetic Algorithm, for a total bus fleet size representing operator's main cost. The adopted solution methodology has been tested through Mandl’s benchmark transit network problem. The test results showed that the methodology developed in this research is able to provide and effective solution in terms of the number of constructed routes, the direct demand coverage, and the total travel time

Transit Network Design Problem (TNDP) is the most important component in Transit planning and operation, in which the overall cost of the public transportation system highly depends on it. The main purpose of this study is to develop a simple and effective solution methodology for the TNDP, which goes beyond previous traditional sophisticated approaches. The solution methodology adopted in this research for the TNDP is based on partitioning the solution into two consecutive stages; Transit route Network Design Problem “TrNDP” stage and frequency setting stage. In the first stage; a deterministic solution for TrNDP is tackled to construct bus routes. The deterministic manner of the TrNDP solution relies on using linear and integer mathematical formulations that can be solved exactly with their standard solvers. In the second stage; bus frequencies are optimized among bus routes (obtained in stage 1) via Genetic Algorithm, for a total bus fleet size representing operator's main cost. The adopted solution methodology has been tested through Mandl’s benchmark transit network problem. The test results showed that the methodology developed in this research is able to provide and effective solution in terms of the number of constructed routes, the direct demand coverage, and the total travel time.

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High-resolution satellite images usually contain a significant amount of shadow cast by elevated objects. Image information is affected by such shadows. Shadows obscure image information partially and/or totally, which leads to interpretation errors. This article aims to develop a complete shadow detection approach using all available bands of a multispectral image. Therefore, the near-infrared band is employed in addition to visible bands while generating a C1C2C3 colour model to achieve a modified C3 index. A comparative study is then applied to the original C1C2C3, hue, saturation, and value colour model and the modified model, to validate the developed index. The results show the superiority of the proposed approach in shadow detection, with overall accuracy around 94%.

High-resolution satellite images usually contain a significant amount of shadow cast by elevated objects. Image information is affected by such shadows. Shadows obscure image information partially and/or totally, which leads to interpretation errors. This article aims to develop a complete shadow detection approach using all available bands of a multispectral image. Therefore, the near-infrared band is employed in addition to visible bands while generating a C1C2C3 colour model to achieve a modified C3 index. A comparative study is then applied to the original C1C2C3, hue, saturation, and value colour model and the modified model, to validate the developed index. The results show the superiority of the proposed approach in shadow detection, with overall accuracy around 94%.