# System Proposal for Mass Transit Service Quality Control Based on GPS Data
## 4 The Service Quality Control Problem  
As discussed above, quality of service on regular passenger transport by road is evaluated by applying criteria which vary depending on whether line services are planned by frequency or by passing times. In the first case, the criterion used is regularity and the associated metrics to evaluate regularity are HV and EWT. In the case of line services planned by timetabled passing times, the criterion used is punctuality and the associated metrics are AD and RTV. From the mathematical expressions for calculating each of these metrics we may deduce that in order to obtain the values it is necessary to know the time at which the vehicle arrives and stops--when it stops to pick up or set down passengers--or the time at which the vehicle passes the stop--when it does not stop because there are no passengers to pick up or set down at the stop. To obtain these data it is necessary to know the position of the vehicle, and from this position to locate the vehicle on the transport network, an action performed by the vehicle's positioning system. When locating the vehicle on the transport network, if the position is provided by GPS, it should be borne in mind that sometimes the location data are erroneous or may not be 100% accurate, depending on the data error caused by the various factors described in the previous section. When the vehicle passes a stop on the route without stopping, the time of passing is the moment at which it is nearest the stop; the distance between the stop and this point is defined by a preset threshold. By contrast, when the vehicle stops to pick up or drop off passengers, the arrival time at the stop is the moment at which the GPS data indicate that the vehicle is stationary and its distance in relation to the stop is below a defined threshold. When defining this threshold, the aforementioned GPS data error must be taken into account, as well as the variable length of the bus stop parking area; in the case of the transport network of this particular study this distance varied between 12 and 50 m. If vehicles only stopped at bus stops on their route, the arrival time at each stop could be obtained with a high degree of reliability, regardless of errors in GPS data. However, in reality no bus only stops at bus stops; they also stop because of traffic or because of the traffic signals along the route. The problem is how to distinguish the position of a bus that has stopped because of a traffic signal from the position of the same vehicle on the same route that has stopped at a bus stop when the position of the traffic signal and the stop are very close. This is a very common situation on urban bus routes.  
Another challenge that must be addressed when studying the regularity or punctuality of line services on public transport is the reliability of the results. To achieve a high degree of reliability it is necessary to work with integral data sets, i.e., complete and correct sets of data. In the context of public transport, potential sources of error in the data are: failure in position data or in the devices that process the data, occasional errors due to noise or data that, although correct, have been obtained for routes that do not match the corresponding route plan. It is therefore necessary to carry out a filtering process to ensure data integrity when analysing the regularity or punctuality of line services.  
A final challenge when conducting a comprehensive and ongoing evaluation of the punctuality or regularity of public transport services is the handling of the amount of data required, which in the case of medium to large transport networks is massive. Therefore, intelligent data handling is required.