Calculation of Average Road Speed Based on Car-to-Car Messaging
Arrival time prediction provided by most of naviga tion systems is affected by several factors, such as road condition, travel time, weather condition, car speed, etc. These predictions are mainly based on historical data. Systems that provide near real-time road condition updates, e.g. Google Maps,...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , |
| منشور في: |
2019
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://bspace.buid.ac.ae/handle/1234/2923 https://ieeexplore.ieee.org/document/8679294 https://doi.org/10.1109/BIGCOMP.2019.8679294 |
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إضافة وسم
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| _version_ | 1862980618496770048 |
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| author | Ramzy, Ahmed |
| author2 | Awad, Ahmed A. Kamel, Amr Hegazy, Osman Sakr, Sherif |
| author2_role | author author author author |
| author_facet | Ramzy, Ahmed Awad, Ahmed A. Kamel, Amr Hegazy, Osman Sakr, Sherif |
| author_role | author |
| dc.creator.none.fl_str_mv | Ramzy, Ahmed Awad, Ahmed A. Kamel, Amr Hegazy, Osman Sakr, Sherif |
| dc.date.none.fl_str_mv | 2019 2025-05-06T08:17:15Z 2025-05-06T08:17:15Z |
| dc.identifier.none.fl_str_mv | Ramzy, A. et al. (2019) “Calculation of Average Road Speed Based on Car-to-Car Messaging,” in 2019 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 1–8. Electronic ISBN:978-1-5386-7789-6 Print on Demand(PoD) ISBN:978-1-5386-7790-2 2375-9356 https://bspace.buid.ac.ae/handle/1234/2923 https://ieeexplore.ieee.org/document/8679294 https://doi.org/10.1109/BIGCOMP.2019.8679294 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.relation.none.fl_str_mv | IEEE International Conference on Big Data and Smart Computing |
| dc.subject.none.fl_str_mv | Traffic Monitoring, D2D Communication, Crowdsourcing |
| dc.title.none.fl_str_mv | Calculation of Average Road Speed Based on Car-to-Car Messaging |
| dc.type.none.fl_str_mv | Conference Paper |
| description | Arrival time prediction provided by most of naviga tion systems is affected by several factors, such as road condition, travel time, weather condition, car speed, etc. These predictions are mainly based on historical data. Systems that provide near real-time road condition updates, e.g. Google Maps, depend on crowdsourcing GPS data from cars or mobile devices on the road. GPS data thus has a long journey to travel from their sources to the analytics engine on the cloud before a status update is sent back to the client. Between the time taken for GPS data to be broadcast, received and processed, significant changes in road conditions can take place and would still be unreported, leading to wrong decisions on the route to choose. Road condition, especially average speed of cars, monitoring is of a local and continuous nature. It needs to be accomplished near GPS stream data sources to reduce latency and increase the accuracy of reporting. Solutions based on geo-distributed road monitoring, using Fog-computing paradigm, provide lower latency and higher accuracy than centralized (cloud-based) approaches. Yet, they require a heavy investment and a large infrastructure, which might be a limit for its utility in some countries, e.g. Egypt. In this paper, we propose a more dynamic approach to continuously update average speed on the road. The computation is done locally on the client device, e.g. the traveling car or the mobile device of the traveler. We compare, through simulation, our proposed approach to the fog-computing-based traffic monitoring. Simulation results give an empirical evidence on the correctness of our results compared to fog-based speed calculation. Index Terms—Traffic Monitoring; D2D Communication; Crowdsourcing |
| id | budr_d56f1a342e48611115c68010efd15965 |
| identifier_str_mv | Ramzy, A. et al. (2019) “Calculation of Average Road Speed Based on Car-to-Car Messaging,” in 2019 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 1–8. Electronic ISBN:978-1-5386-7789-6 Print on Demand(PoD) ISBN:978-1-5386-7790-2 2375-9356 |
| language_invalid_str_mv | en |
| network_acronym_str | budr |
| network_name_str | The British University in Dubai repository |
| oai_identifier_str | oai:bspace.buid.ac.ae:1234/2923 |
| publishDate | 2019 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Calculation of Average Road Speed Based on Car-to-Car MessagingRamzy, AhmedAwad, AhmedA. Kamel, AmrHegazy, OsmanSakr, SherifTraffic Monitoring, D2D Communication, CrowdsourcingArrival time prediction provided by most of naviga tion systems is affected by several factors, such as road condition, travel time, weather condition, car speed, etc. These predictions are mainly based on historical data. Systems that provide near real-time road condition updates, e.g. Google Maps, depend on crowdsourcing GPS data from cars or mobile devices on the road. GPS data thus has a long journey to travel from their sources to the analytics engine on the cloud before a status update is sent back to the client. Between the time taken for GPS data to be broadcast, received and processed, significant changes in road conditions can take place and would still be unreported, leading to wrong decisions on the route to choose. Road condition, especially average speed of cars, monitoring is of a local and continuous nature. It needs to be accomplished near GPS stream data sources to reduce latency and increase the accuracy of reporting. Solutions based on geo-distributed road monitoring, using Fog-computing paradigm, provide lower latency and higher accuracy than centralized (cloud-based) approaches. Yet, they require a heavy investment and a large infrastructure, which might be a limit for its utility in some countries, e.g. Egypt. In this paper, we propose a more dynamic approach to continuously update average speed on the road. The computation is done locally on the client device, e.g. the traveling car or the mobile device of the traveler. We compare, through simulation, our proposed approach to the fog-computing-based traffic monitoring. Simulation results give an empirical evidence on the correctness of our results compared to fog-based speed calculation. Index Terms—Traffic Monitoring; D2D Communication; CrowdsourcingIEEE2025-05-06T08:17:15Z2025-05-06T08:17:15Z2019Conference PaperRamzy, A. et al. (2019) “Calculation of Average Road Speed Based on Car-to-Car Messaging,” in 2019 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 1–8.Electronic ISBN:978-1-5386-7789-6 Print on Demand(PoD) ISBN:978-1-5386-7790-22375-9356https://bspace.buid.ac.ae/handle/1234/2923https://ieeexplore.ieee.org/document/8679294https://doi.org/10.1109/BIGCOMP.2019.8679294enIEEE International Conference on Big Data and Smart Computing oai:bspace.buid.ac.ae:1234/29232025-06-13T11:22:01Z |
| spellingShingle | Calculation of Average Road Speed Based on Car-to-Car Messaging Ramzy, Ahmed Traffic Monitoring, D2D Communication, Crowdsourcing |
| title | Calculation of Average Road Speed Based on Car-to-Car Messaging |
| title_full | Calculation of Average Road Speed Based on Car-to-Car Messaging |
| title_fullStr | Calculation of Average Road Speed Based on Car-to-Car Messaging |
| title_full_unstemmed | Calculation of Average Road Speed Based on Car-to-Car Messaging |
| title_short | Calculation of Average Road Speed Based on Car-to-Car Messaging |
| title_sort | Calculation of Average Road Speed Based on Car-to-Car Messaging |
| topic | Traffic Monitoring, D2D Communication, Crowdsourcing |
| url | https://bspace.buid.ac.ae/handle/1234/2923 https://ieeexplore.ieee.org/document/8679294 https://doi.org/10.1109/BIGCOMP.2019.8679294 |