<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Various forecasting schemes have been proposed to manage traffic data, which is collected by videos cameras, sensors, and mobile phone services. However, these are not sufficient for collecting data because of their limited coverage and high costs for installation and maintenance. To overcome the limitations of these tools, we introduce a hybrid scheme based on intelligent transportation system (ITS) and global navigation satellite system (GNSS). Applying the GNSS to calculate travel time has proven efficient in terms of accuracy. In this case, GNSS data is managed to reduce traffic congestion and road accidents. This paper introduces a short-time forecasting model based on real-time travel time for urban heterogeneous road networks. Travel time forecasting has been achieved by predicting travel speeds using an optimized exponential moving Average (EMA) model. Furthermore for speed adaptation in heterogeneous road networks, it is necessary to introduce asuitable control strategy for longitude, based on the GNSS. GNSS products provide worldwide and real-time services using precise timing information and, positioning technologies.</p></abstract>

Various forecasting schemes have been proposed to manage traffic data, which is collected by videos cameras, sensors, and mobile phone services. However, these are not sufficient for collecting data because of their limited coverage and high costs for installation and maintenance. To overcome the limitations of these tools, we introduce a hybrid scheme based on intelligent transportation system (ITS) and global navigation satellite system (GNSS). Applying the GNSS to calculate travel time has proven efficient in terms of accuracy. In this case, GNSS data is managed to reduce traffic congestion and road accidents. This paper introduces a short-time forecasting model based on real-time travel time for urban heterogeneous road networks. Travel time forecasting has been achieved by predicting travel speeds using an optimized exponential moving Average (EMA) model. Furthermore for speed adaptation in heterogeneous road networks, it is necessary to introduce asuitable control strategy for longitude, based on the GNSS. GNSS products provide worldwide and real-time services using precise timing information and, positioning technologies.

Speed Adaptation in Urban Road Network Management

Computer Science Department Al Qasemi Academic College Baqa El Gharbia, Israel

Transport and Telecommunication Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Speed Adaptation in Urban Road Network Management"}

Various forecasting schemes have been proposed to manage traffic data, which is collected by videos cameras, sensors, and mobile phone services. However, these...

Speed Adaptation in Urban Road Network Management

Data publikacji: 06 maj 2016

Zakres stron: 111 - 121

DOI: https://doi.org/10.1515/ttj-2016-0010

Słowa kluczowe
ITS, EMA, traffic, speed adaptation

© Transport and Telecommunication Institute

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Speed Adaptation in Urban Road Network Management

Data publikacji: 06 maj 2016

Zakres stron: 111 - 121

DOI: https://doi.org/10.1515/ttj-2016-0010

Słowa kluczoweITS, EMA, traffic, speed adaptation

© Transport and Telecommunication Institute

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Słowa kluczowe
ITS, EMA, traffic, speed adaptation