Reaction time driven profiling of traffic flow with intelligent vehicles
This paper addresses the critical need to characterize traffic flow driven by the reaction time of evolving Intelligent Vehicles (IVs). Macroscopic traffic models play a vital role in understanding traffic conditions, however, the IVs behavior is ignored. Thus, a new traffic model for IVs based on s...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824011992 |
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| author | Waheed Imran Daud Khan Zawar H. Khan Katarzyna Markowska Susilawati Susilawati Luigi Pariota |
| author_facet | Waheed Imran Daud Khan Zawar H. Khan Katarzyna Markowska Susilawati Susilawati Luigi Pariota |
| author_sort | Waheed Imran |
| collection | DOAJ |
| description | This paper addresses the critical need to characterize traffic flow driven by the reaction time of evolving Intelligent Vehicles (IVs). Macroscopic traffic models play a vital role in understanding traffic conditions, however, the IVs behavior is ignored. Thus, a new traffic model for IVs based on safe reaction velocity, reaction time, and braking time is proposed, incorporating the IVs reaction times. The findings demonstrate a trade-off between the reaction time and braking time, significantly explaining traffic dynamics. Specifically, reducing the reaction time improves traffic operations. Results from three distinct traffic scenarios highlighted the significance of reaction time in shaping traffic safety. In the first scenario, smoother traffic flow demonstrates the impact of reaction time on safety. The shorter reaction time showed improved outcomes. In the second scenario, changes in traffic patterns near the ramp highlighted the importance of smaller reaction times in mitigating safety risks. In the third scenario, chaotic traffic conditions emphasized the role of reaction time in ensuring overall safety. The proposed traffic model offers a more realistic characterization of traffic flow. By understanding the relation between reaction time and braking time, this approach contributes to the development of safer and more efficient automated traffic mobility. |
| format | Article |
| id | doaj-art-e2131cc99c6f4d4690ecce2da88a52fb |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-e2131cc99c6f4d4690ecce2da88a52fb2025-01-18T05:03:36ZengElsevierAlexandria Engineering Journal1110-01682025-01-01111283292Reaction time driven profiling of traffic flow with intelligent vehiclesWaheed Imran0Daud Khan1Zawar H. Khan2Katarzyna Markowska3Susilawati Susilawati4Luigi Pariota5Department of Civil, Architectural and Environmental Engineering, University of Naples, Via Claudio 21, Naples, 80125, Italy; Corresponding author.Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, Krasinskiego 8 Street, Katowice, 40019, PolandCollege of Computer Science and Engineering, University of Ha’il, Ha’il, 55476, Saudi ArabiaDepartment of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, Krasinskiego 8 Street, Katowice, 40019, PolandDepartment of Civil Engineering, School of Engineering, Monash University Malaysia & Monash Climate-Resilient Infrastructure Research Hub (M-CRInfra), School of Engineering, Monash University Malaysia Jalan Lagoon Selatan, Bandar Sunway, Selangor, 47500, MalaysiaDepartment of Civil, Architectural and Environmental Engineering, University of Naples, Via Claudio 21, Naples, 80125, ItalyThis paper addresses the critical need to characterize traffic flow driven by the reaction time of evolving Intelligent Vehicles (IVs). Macroscopic traffic models play a vital role in understanding traffic conditions, however, the IVs behavior is ignored. Thus, a new traffic model for IVs based on safe reaction velocity, reaction time, and braking time is proposed, incorporating the IVs reaction times. The findings demonstrate a trade-off between the reaction time and braking time, significantly explaining traffic dynamics. Specifically, reducing the reaction time improves traffic operations. Results from three distinct traffic scenarios highlighted the significance of reaction time in shaping traffic safety. In the first scenario, smoother traffic flow demonstrates the impact of reaction time on safety. The shorter reaction time showed improved outcomes. In the second scenario, changes in traffic patterns near the ramp highlighted the importance of smaller reaction times in mitigating safety risks. In the third scenario, chaotic traffic conditions emphasized the role of reaction time in ensuring overall safety. The proposed traffic model offers a more realistic characterization of traffic flow. By understanding the relation between reaction time and braking time, this approach contributes to the development of safer and more efficient automated traffic mobility.http://www.sciencedirect.com/science/article/pii/S1110016824011992Driving behaviorMacroscopic modelsSimulationReaction and stimuliTraffic safety |
| spellingShingle | Waheed Imran Daud Khan Zawar H. Khan Katarzyna Markowska Susilawati Susilawati Luigi Pariota Reaction time driven profiling of traffic flow with intelligent vehicles Alexandria Engineering Journal Driving behavior Macroscopic models Simulation Reaction and stimuli Traffic safety |
| title | Reaction time driven profiling of traffic flow with intelligent vehicles |
| title_full | Reaction time driven profiling of traffic flow with intelligent vehicles |
| title_fullStr | Reaction time driven profiling of traffic flow with intelligent vehicles |
| title_full_unstemmed | Reaction time driven profiling of traffic flow with intelligent vehicles |
| title_short | Reaction time driven profiling of traffic flow with intelligent vehicles |
| title_sort | reaction time driven profiling of traffic flow with intelligent vehicles |
| topic | Driving behavior Macroscopic models Simulation Reaction and stimuli Traffic safety |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824011992 |
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