Transit organizations have been challenged to serve ever-increasing population centers on restricted budgets. Over the past several years, the cost of fuel has risen almost 50%. As a consequence, fuel now represents a substantial portion of the annual operating budget for transit organizations.
Fuel reductions of only a few percent can, in larger transit organizations, result in savings of millions of dollars. In a recent article, the U.S. Environmental Protection Agency asserted that:                Fleet managers have estimated that driver training and incentive programs typically results in 15% fuel savings. Two trucking fleets in Canada documented the impact of driver training and found fuel efficiency improvements of 18% and 20%, while a Canadian study estimates that many fleets could achieve a 10% fuel economy improvement through driver training and monitoring. A study of the European Commission estimates that an annual one-day driver-training course will improve truck fuel efficiency by 5%.        
Fuel economy is just one of a number of metrics that can be measured to provide an indication of driver and/or vehicle performance. Other metrics can include, for example, the “jerkiness” of the ride, hard acceleration and braking, and speeding.
It can be desirable to identify, on an ongoing basis, specific drivers who may most benefit from targeted driver training in order to keep training costs low and reduce interruption of the daily operation of the transit organization. The process of identifying drivers that would best benefit from driver training, however, can prove very difficult. Direct attribution of the poor fuel economy of a vehicle to the driver operating the vehicle can result in a number of drivers being incorrectly flagged as being good candidates for driver training. There are, in fact, a number of parameters that impact the fuel economy of transit vehicles, such as the type of vehicle, the route traveled, the fare and traffic load along the route (which is largely dependant on the day and time), the weather conditions, etc. It can be inappropriate to ignore these parameters when examining the fuel economy of a vehicle being operated by a particular driver. Other methods of evaluating drivers for driver training are available, such as having a skilled assessor ride in a vehicle being operated by a driver. Should the driver be aware of the presence of an assessor, however, he may alter his driving style temporarily, thus possibly incorrectly rejecting the driver as a good candidate for driver training.
Similarly, it can also be desirable to identify vehicles that are performing poorly. As local maintenance is costly, it can be desirable to prioritize vehicles in terms of their condition and, thus, candidacy for servicing. Any metrics collected over one or more runs along routes during operation of the vehicle can be influenced, however, by the parameters identified above. For the most part, vehicle condition is reported by drivers when a vehicle exhibiting clear signs of requiring service, such as an engine running very roughly, visible smoke from the exhaust, or a significantly underinflated tire. Otherwise, the condition of the vehicle is generally assessed very infrequently when undergoing a regular scheduled maintenance. As a result, vehicles exhibiting less prominent symptoms may not be quickly identified for servicing.
There are a number of issues associated with carrying out performance analysis on full runs across a route in a single direction. As a vehicle and/or driver's performance is only analyzed after the completion of the full run, their performance during the run cannot be determined. In some cases, it can be desirable to analyze the performance of the vehicle and/or driver more frequently in order to spot issues more quickly. Further, some routes share large common portions, yet it can be inappropriate to compare the performance of a vehicle and/or driver over one route to that of another vehicle and/or driver over a similar route.
It is therefore an object of this invention to provide a system and method for storing performance data in a transit organization.