{"id":169,"date":"2020-06-18T08:23:19","date_gmt":"2020-06-18T08:23:19","guid":{"rendered":"https:\/\/arms10.org\/android\/?p=169"},"modified":"2020-06-18T08:23:21","modified_gmt":"2020-06-18T08:23:21","slug":"i-bus-mobile-app-for-bus-timing-report","status":"publish","type":"post","link":"https:\/\/arms10.org\/android\/i-bus-mobile-app-for-bus-timing-report\/","title":{"rendered":"i bus : Mobile App For Bus Timing Report"},"content":{"rendered":"\n

i bus : Mobile App For Bus Timing Report<\/a><\/p>\n\n\n\n

Advanced traveler information systems are one component of intelligent transportation systems, and a major component is travel time information. Prediction Model have been adopted by many transit agencies for tracking their vehicles and predicting travel time in real time. It is a very important subject to improve the precision and reliability of the prediction model which can attract additional ridership, reduce passengers\u2019 anxieties and waiting times at bus stop, and increase their satisfaction. Furthermore, it can promote the development of city public transportation. This paper presents an improved approach to predict the public bus arrival time based on location. <\/p>\n\n\n\n

\"Online<\/a><\/figure>\n\n\n\n

i bus : Mobile App For Bus Timing Report <\/em>After analyzing the components of bus arrival time systematically, the bus arrival time and dwell time at previous stops are chosen as the main input variables of the prediction model. At first, the algorithm of data interpolation and processing is designed to get the real-time of bus arrival as the input variables of the prediction models. Secondly, the statistical model is obtained based on the filtering the location, bus route and date of each bus respectively. Thirdly, a hybrid prediction model is proposed to predict the bus arrival time. The results show that the improved model out performs the historical data based model in terms of prediction accuracy.<\/p>\n\n\n\n