Traffic incidents are one of the leading causes of non-recurrent traffic congestions. By detecting these incidents on time, traffic management agencies can activate strategies to ease congestion and travelers can plan their trip by taking into consideration these factors. In recent years, there has been an increasing interest in Twitter because of the real-time nature of its data. Twitter has been used as a way of predicting revenues, accidents, natural disasters, and traffic. This paper proposes a framework for the real-time detection of traffic events using Twitter data. The methodology consists of a text classification algorithm to identify traffic related tweets. These traffic messages are then geolocated and further classified into positive, negative, or neutral class using sentiment analysis. In addition, stress and relaxation strength detection is performed, with the purpose of further analyzing user emotions within the tweet. Future work will be carried out to implement the proposed framework in the West Midlands area, United Kingdom.

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It is well known that the optimum ignition timing, which gives the maximum brake torque (MBT) for a given engine design, varies with the rate of flame development and propagation in the cylinder. This depends, among other factors, on engine design and operating conditions, and on the properties of the air-fuel mixture. In modern engines the ignition timing is generally controlled by fixed open-loop schedules as functions of engine speed, load and coolant temperature. It is desairable that this ignition timing can be adjusted to the optimum level producing the best torque to obtain minimum fuel consumption and maximum available power. This paper presents an ignition timing control system based on fuzzy logic theory. A pressure sensor system ws developed for the determination of combustion parameters and ignition control on a Ford 1600cm3 four-cylinder engine fuelled with natural gas. Several tests were carried out in optimizing the pressure detection system. The results obtained provide important information compatible with intelligent control of the engine using fuzzy logic technology. Moreover, tests carried out to date using this technology show good results that fit quite well with the original engine output torque characteristics.