Introducing a Novel Method to Solve Shortest Path Problems Based on Structure of Network Using Genetic Algorithm

Behzadi, Saeed; Kolbadinejad, M.

doi:10.5194/isprs-archives-xlii-4-w18-201-2019

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…One of the problems with the Dijkstra algorithm was that to find the shortest path, it checked all the vertices in all directions and after this long process, reached the top of the destination (Behzadi and Alesheikh, 2008b;Behzadi and Alesheikh, 2014b;Behzadi et al, 2008;Behzadi and Kolbadinejad, 2019). To solve this problem, algorithm A* was proposed.…”

Section: Algorithm A*mentioning

confidence: 99%

Designing a Commercial Location-Based System to Serve Customers Based on Gis

Behzadi

2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Finding the desired products and achieving them at the lowest possible cost has always been one of the concerns of in-person buyers. Finding stores containing the desired products is one of the most important services that can be provided to this part of the buyer community. It provided that the costs are minimal, as well as determining the order of visiting the stores and guiding the buyer by routing between store and buyer positions. Transportation is also one of the most important applications of spatial information systems in the field of optimal management of facilities. In this study, routing was performed by Algorithm A*, which was slightly different from the Dijkstra algorithm in terms of accuracy, but in terms of response time, recorded an average reduction of 70%.

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Section: Algorithm A*mentioning

confidence: 99%

Designing a Commercial Location-Based System to Serve Customers Based on Gis

Behzadi

2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Tehran has an area of about 730 square kilometers. Tehran Municipality has divided the city into 22 municipal districts as seen in Figure 1 (Behzadi & Alesheikh, 2008;Behzadi & Kolbadinejad, 2019;Jafarian & Behzadi, 2020).…”

Section: The Study Areamentioning

confidence: 99%

Predicting Traffic Data in GIS using Different Neural Network Methods

Ghasempoor

Behzadi

2022

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Traffic is one of the most influential factors in choosing the route to reach the destination. It can be said that a large percentage of people prefer a long but low traffic route than a short route with heavy traffic. Therefore, traffic is a very determining factor in societies, especially in metropolitan areas. The issue of traffic forecasting is another important factor in the field of traffic. In such a way that the traffic of the coming days can be predicted based on the traffic of the previous days. In this paper, traffic forecasting in the coming days is done using a neural network algorithm based on the collected traffic data. Traffic forecasting is performed using Basic Neural Network methods, Feed-forward Levenberg-Marquardt, Conjugate Gradient Neural Network and Bayesian Neural Network. The results of the forecast are then compared with real observations. The results show that the Feed-forward Levenberg-Marquardt method predicts traffic data with 81.59% accuracy, which is the most accurate method among the others. The accuracy of Bayesian Neural Network, Conjugate Gradient Neural Network and Basic Neural Network methods is 81.55, 81.50 and 75%, respectively. Regression values of 24 hours a day were also estimated and it was found that the proximity of input and output values in the Basic Neural Network method is approximately 80%. This parameter was obtained 69.69%, 69.71% and 69.87% for three Feed-forward Levenberg-Marquardt, Conjugate Gradient Neural Network and Bayesian Neural Network respectively.

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Introducing a Novel Method to Solve Shortest Path Problems Based on Structure of Network Using Genetic Algorithm

Cited by 2 publications

References 26 publications

Designing a Commercial Location-Based System to Serve Customers Based on Gis

Designing a Commercial Location-Based System to Serve Customers Based on Gis

Predicting Traffic Data in GIS using Different Neural Network Methods

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