The Investigation of Queuing Models to Calculate Journey Times to Develop an Intelligent Transport System for Smart Cities
Intelligent transport systems are a major component of smart cities because their deployment should result in reduced journey times, less traffic congestion and a significant reduction in road deaths, which will greatly improve the quality of life of their citizens. New technologies such as vehicula...
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MDPI AG
2025-07-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/17/7/302 |
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| author | Vatsal Mehta Glenford Mapp Vaibhav Gandhi |
| author_facet | Vatsal Mehta Glenford Mapp Vaibhav Gandhi |
| author_sort | Vatsal Mehta |
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| description | Intelligent transport systems are a major component of smart cities because their deployment should result in reduced journey times, less traffic congestion and a significant reduction in road deaths, which will greatly improve the quality of life of their citizens. New technologies such as vehicular networks allow more information be available in realtime, and this information can be used with new analytical models to obtain more accurate estimates of journey times. This would be extremely useful to drivers and will also enable transport authorities to optimise the transport network. This paper addresses these issues using a model-based approach to provide a new way of estimating the delay along specified routes. A journey is defined as the traversal of several road links and junctions from source to destination. The delay at the junctions is analysed using the zero-server Markov chain technique. This is then combined with the Jackson network to analyse the delay across multiple junctions. The delay at road links is analysed using an M/M/K/K model. The results were validated using two simulators: SUMO and VISSIM. A real scenario is also examined to determine the best route. The preliminary results of this model-based analysis look promising but more work is needed to make it useful for wide-scale deployment. |
| format | Article |
| id | doaj-art-0c9b791bac76439c8df3eeafd2f26560 |
| institution | Kabale University |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Future Internet |
| spelling | doaj-art-0c9b791bac76439c8df3eeafd2f265602025-08-20T03:32:26ZengMDPI AGFuture Internet1999-59032025-07-0117730210.3390/fi17070302The Investigation of Queuing Models to Calculate Journey Times to Develop an Intelligent Transport System for Smart CitiesVatsal Mehta0Glenford Mapp1Vaibhav Gandhi2Department of Design, Engineering and Mathematics, Middlesex University, The Burroughs, Hendon NW4 4BT, UKDepartment of Computer Science, Middlesex University, The Burroughs, Hendon NW4 4BT, UKDepartment of Design, Engineering and Mathematics, Middlesex University, The Burroughs, Hendon NW4 4BT, UKIntelligent transport systems are a major component of smart cities because their deployment should result in reduced journey times, less traffic congestion and a significant reduction in road deaths, which will greatly improve the quality of life of their citizens. New technologies such as vehicular networks allow more information be available in realtime, and this information can be used with new analytical models to obtain more accurate estimates of journey times. This would be extremely useful to drivers and will also enable transport authorities to optimise the transport network. This paper addresses these issues using a model-based approach to provide a new way of estimating the delay along specified routes. A journey is defined as the traversal of several road links and junctions from source to destination. The delay at the junctions is analysed using the zero-server Markov chain technique. This is then combined with the Jackson network to analyse the delay across multiple junctions. The delay at road links is analysed using an M/M/K/K model. The results were validated using two simulators: SUMO and VISSIM. A real scenario is also examined to determine the best route. The preliminary results of this model-based analysis look promising but more work is needed to make it useful for wide-scale deployment.https://www.mdpi.com/1999-5903/17/7/302intelligent transport systemvehicular networkscalculation of journey timeszero-server Markov chain |
| spellingShingle | Vatsal Mehta Glenford Mapp Vaibhav Gandhi The Investigation of Queuing Models to Calculate Journey Times to Develop an Intelligent Transport System for Smart Cities Future Internet intelligent transport system vehicular networks calculation of journey times zero-server Markov chain |
| title | The Investigation of Queuing Models to Calculate Journey Times to Develop an Intelligent Transport System for Smart Cities |
| title_full | The Investigation of Queuing Models to Calculate Journey Times to Develop an Intelligent Transport System for Smart Cities |
| title_fullStr | The Investigation of Queuing Models to Calculate Journey Times to Develop an Intelligent Transport System for Smart Cities |
| title_full_unstemmed | The Investigation of Queuing Models to Calculate Journey Times to Develop an Intelligent Transport System for Smart Cities |
| title_short | The Investigation of Queuing Models to Calculate Journey Times to Develop an Intelligent Transport System for Smart Cities |
| title_sort | investigation of queuing models to calculate journey times to develop an intelligent transport system for smart cities |
| topic | intelligent transport system vehicular networks calculation of journey times zero-server Markov chain |
| url | https://www.mdpi.com/1999-5903/17/7/302 |
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