Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Cloud-based detection of road bottlenecks using obd-ii telematics
AU - Sohail, Anwar Mehmood
AU - Khattak, Khurram S.
AU - Iqbal, Adil
AU - Khan, Zawar H.
AU - Ahmad, Aakash
PY - 2019/11/29
Y1 - 2019/11/29
N2 - Transportation's share in energy consumption worldwide stands at 25%, contributing approximately 28% of greenhouse gas emissions. Road bottlenecks are major source of inefficiency in road networks that lead to traffic flow congestions caused by faulty road design and deteriorated road conditions. Data analytics can be exploited for better road network planning, design and development. In this paper, a cloud-based platform named 'BotlnckDectr' has been proposed for data aggregation and analysis for detecting road inefficiencies caused by bottlenecks, all assessing its time/cost-inefficiency, fuel consumption and CO2 emissions. The proposed platform is implemented based on (android-based) mobile application for reading vehicle's sensor data such as speed, RPM, mass air flow, air-to-fuel-ratio and fuel density using Bluetooth enabled OBD-II adapter. The vehicle's sensor data in addition to Smartphone's GPS data is transmitted to cloud platform (A WS) for storage, processing and analysis. The system is field tested on a specific route, detecting three bottleneck points on aforementioned route. The bottlenecks are due to flawed road infrastructure design and inefficiencies of the bottlenecks were calculated in terms of time consumption, fuel consumption and CO2 emissions.
AB - Transportation's share in energy consumption worldwide stands at 25%, contributing approximately 28% of greenhouse gas emissions. Road bottlenecks are major source of inefficiency in road networks that lead to traffic flow congestions caused by faulty road design and deteriorated road conditions. Data analytics can be exploited for better road network planning, design and development. In this paper, a cloud-based platform named 'BotlnckDectr' has been proposed for data aggregation and analysis for detecting road inefficiencies caused by bottlenecks, all assessing its time/cost-inefficiency, fuel consumption and CO2 emissions. The proposed platform is implemented based on (android-based) mobile application for reading vehicle's sensor data such as speed, RPM, mass air flow, air-to-fuel-ratio and fuel density using Bluetooth enabled OBD-II adapter. The vehicle's sensor data in addition to Smartphone's GPS data is transmitted to cloud platform (A WS) for storage, processing and analysis. The system is field tested on a specific route, detecting three bottleneck points on aforementioned route. The bottlenecks are due to flawed road infrastructure design and inefficiencies of the bottlenecks were calculated in terms of time consumption, fuel consumption and CO2 emissions.
KW - Big data analytics
KW - Cloud computing
KW - Mobile computing
KW - Smart transportation
U2 - 10.1109/INMIC48123.2019.9022754
DO - 10.1109/INMIC48123.2019.9022754
M3 - Conference contribution/Paper
AN - SCOPUS:85082658128
T3 - Proceedings - 22nd International Multitopic Conference, INMIC 2019
BT - Proceedings - 22nd International Multitopic Conference, INMIC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd International Multitopic Conference, INMIC 2019
Y2 - 29 November 2019 through 30 November 2019
ER -