Throughout the world, major cities are experiencing ever-increasing urbanization and mobility challenges. Current estimates of 55 of the worlds population residing in cities and towns is set to increase to 68 by 2050. An ever-growing population is adding much pressure on existing infrastructure, forcing city leaders to look towards technologies such as Internet of Things to aid in improving municipal quality of life, transforming cities into Smart Cities. An important subset of Smart Cities are the connected vehicles operating within its proximity. Aggregated vehicle data can provide data blanketing of every street throughout the city, while monitoring vehicle parameters such as vehicle speed, RPM, idle time, fuel usage, location and CO2 emissions. Gathering such data is referred to as Vehicle Telematics, and can be obtained through On-Board Units and AVL (Automatic Vehicle Location) devices. This work aims to compare and evaluate two well-known standards for extracting this vehicle information. Both OBDII and FMS standards are widely used for fleet management and monitoring driving behavior. Through statistical analysis, we propose two algorithms for monitoring instantaneous fuel economy via OBDII. The objective of this research is to determine the best standard for retrieving vehicle sensor data to use when monitoring fuel economy, CO2 emissions, and other attributes that may affect quality of life in Smart Cities.
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