Cloud-based Detection of Road Bottlenecks Using OBD-II Telematics

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 CO
₂
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 CO
₂
emissions.