Abstract:Commercial vehicle operation (CVO) has been a popular application of intelligent transportation systems. Location determination and route tracing of an on-board unit (OBU) in a vehicle is an important capability for CVO. However, large location errors from global positioning system (GPS) receivers may occur in cities that shield GPS signals. Therefore, a highly efficient mobile positioning method is proposed based on the collection and analysis of the cellular network signals of CVO data. Parallel-and cloud-computing techniques are designed into the proposed method to quickly determine the location of an OBU for CVO. Furthermore, this study proposes analytical models to analyze the availability of the proposed mobile positioning method with various outlier filtering criteria. Experimentally, a CVO system was designed and implemented to collect CVO data from Chunghwa Telecom vehicles and to analyze the cellular network signals of CVO data for location determination. A case study found that the average errors of location determination using the proposed method vs. using the traditional cell-ID-based location method were 163.7 m and 521.2 m, respectively. Furthermore, the practical results show that the average location error and availability of using the proposed method are better than using GPS or the cell-ID-based location method for each road type, particularly urban roads. Therefore, this approach is feasible to determine OBU locations for improving CVO.

In recent years, the improvement of cloud computing and mobile computing techniques has led to the availability of a variety of mobile applications ('apps') in the app store. For instance, a garbage truck app that can provide the immediate location of a garbage truck, the location of collection points, and forecasted arrival times of garbage trucks would be useful for mobile users. Since the power consumption of apps on mobile devices if of concern to mobile users, an optimised power-saving mechanism for updating messages, which is based on location information, for a proposed garbage truck fleet management system (GTFMS) is proposed and implemented in this paper. The GTFMS is a threecomponent system that includes the on-board units on garbage trucks, a fleet management system, and a garbage truck app. In this study, an arrival time forecasting method is designed and implemented in the fleet management system, so that the garbage truck app can retrieve the forecasted arrival time via web services. A message updating event is then triggered that reports the location of garbage truck and the forecasted arrival time. In experiments conducted on case studies, the results showed that the mean accuracy of predicted arrival time by the proposed method is about 81.45 per cent. As for power consumption, the cost of traditional mobile apps is 2,880 times that of the mechanism proposed in this study. Consequently, the GTFMS can provide the precise forecasted arrival time of garbage trucks to mobile users, while consuming less power. OPSOMMINGDie verbetering van wolkverwerking en mobiele verwerkingstegnieke het gelei tot die beskikbaarheid van 'n groot verskeidenheid mobiele toepassings. 'n Voorbeeld hiervan is 'n toepassing wat die onmiddellike ligging van 'n vullistrok, die ligging van vullis versamelpunte en die voorspelde aankomstye van die vullistrokke aan die gebruiker verskaf. Die energieverbruik van die toepassings is ook van belang en 'n geoptimeerde energiebesparingsmeganisme vir die opdateer van boodskappe (wat inligting rakende die vullistrok se ligging bevat) word in hierdie artikel ontwerp en geïmplementeer. 'n Opdateringsboodskap rapporteer die vullistrok se ligging en voorspelde aankomstyd. Gevallestudies toon dat die gemiddelde akkuraatheid van die voorspelde aankomstyd 81.45% is. Die energieverbruik van die toepassing is 2880 keer minder as dié van 'n tradisionele mobiele toepassing. Gevolglik kan die voorgestelde vullistrokvlootbestuurstelsel 'n baie akkurate aankomstyd voorspelling aan gebruikers gee terwyl dit min energie verbuik. 33 INTRODUCTIONIn recent years, the improvement of cloud computing and mobile computing techniques has led to the availability of a variety of mobile applications in the app store [1][2][3]. Furthermore, several mobile applications of intelligent transportation systems (ITS), which include bus apps [4], public bicycle systems [5], mass rapid transit systems [6], and railway apps [7], have been developed and implemented to provide convenient transport services for resid...

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