# System Proposal for Mass Transit Service Quality Control Based on GPS Data
## 2 Related Studies  
In this section we shall review the studies published about quality of service in regular road passenger transport, with particular reference to publications that deal with criteria and metrics associated with, and systems used to obtain the data required for, quality control. [PERSON] et al. [6] ranked the factors that influence public transport users' perception of quality according to different levels of importance. On the first level is safety and reliability, on the second level, the speed of the journey, and on the third level, convenience, comfort and finally, the experience. According to [PERSON] [7], three factors have a particularly negative effect on the quality of public transport: unforeseen increases in waiting times for travellers, the time spent by the traveller in situations of overcrowding due to an overloaded public transport network, and delays in arrival times at destinations due to the variability of travel times. From the point of view of the traveller, the first two factors influence the feeling of comfort offered by the public transport service and the third factor influences their convenience-based decision on whether to use public or private transport. [PERSON] et al. [8] carried out an exhaustive review of the various methods that have been used to improve and optimise public passenger transport, which included the evaluation of adherence to public transport timetables. Timetable adherence means arriving at stops and stations on time, a key aspect in the user's perception of public transport service quality. It is therefore essential that timetables are created using techniques that schedule departure and arrival times as accurately as possible. In this vein, the problems in predicting public transport bus arrival times at stops have been addressed using different techniques. For example, [PERSON] et al. [9] used Kalman filters, [PERSON] et al. [10]proposed a solution based on non-parametric regressions, and [PERSON] et al. [11] used neural networks. Once the route timetables have been established, various criteria and associated metrics have been used to evaluate adherence to those timetables. [PERSON] [12] identified two strategies for planning regular road passenger transport routes: the first consists in planning routes by frequency, and the second, by timetable. The first planning strategy is used for urban transport and the criterion applied is regularity. The second planning strategy is used for long-distance transport and the criterion applied is punctuality. A starting point of reference is the study by [PERSON] [13], in which he proposed the following indicators for arterial routes: overall travel time, congestion index, overall travel speed and overall delay. Subsequently, and making use of technological advances in onboard systems that enable a larger volume of data to be recorded, timetable adherence has been analysed at a far greater level of detail in the transport network: at a single stop, or on a given section of the route. [PERSON] [14], [PERSON] et al. [15] and [PERSON] et al. [16] proposed using the following parameters: on-time performance (OTP), run time variation (RTV), headway variation (HV) and excess waiting time (EWT). The first two are used to measure punctuality and the last two, to measure regularity: [PERSON] et al. [17] proposed another indicator, called adjacent site punctuality rate. To obtain all these indicators it is necessary to gather data on vehicle location and the times that the vehicles pass through each of the control points on the route. Continuous monitoring of all the stops on the route gives rise to the challenge of managing a massive amount of data, more so when the transport network contains a considerable number of stops, which is the case of medium to large metropolitan areas. According to [PERSON] et al.