On-time performance


On-time performance

In transportation (such as municipal public transportation), schedule adherence or on-time performance refers to the level of success of the service (such as a bus or train) remaining on the published schedule.

There are many factors that can have an impact on on-time performance. Depending on the situation, the service may face regular delays or a service that usually performs on time may be occasionally behind schedule. Some of these factors include:

  • Traffic:
    • Bus transport: The volume of automobile traffic on the bus's route can impact the bus's ability to keep on the schedule. While a route that is free of obstruction can remain on schedule or even move ahead of schedule, heavy traffic can slow down the bus behind its schedule. This can be due to regular conditions at the time of day, or an occasional or one-time event, such as a sports game or festival that draws a large crowd.
    • Rail transport: The volume of traffic on the route can cause even more delays for rail vehicles, because of tight scheduling at sidings and other meet points. On passenger railroads, freight traffic is especially problematic because of the length and slow acceleration of the freight trains.
  • Accident: A collision that obstructs traffic flow can also tie up vehicles on the route until the accident is cleared and therefore cause them to be behind schedule.
  • Detour: A road closure that forces the bus to temporarily deviate from its regular route may cause it take more time than planned to reach its destination.
  • Weather: Inclement weather may cause a bus that usually remains on schedule to be behind as a result of precautions that must be taken by the vehicle's operator and other vehicles on the road. Trains might have to operate slower as a result of slippery rail during the autumn season, and high wind can affect signal systems.
  • Increased ridership: When a special event takes place, the service may experience higher ridership levels than usual, leading to more time the bus or train may have to spend allowing passengers to board and depart.
  • Breakdown: A disabled bus or train, besides being unable to complete its own route, may slow down others that follow on the schedule. As riders scramble to board a subsequent bus or train on the schedule, the higher ridership levels on that run may slow it down. Additionally, on a rail line, a disabled train may block other trains from passing, or may require track-sharing with opposing trains, thereby slowing service in both directions.
  • Unrealistic scheduling: Many public transportation agencies are tax-subsidized, and therefore are often cash-strapped while attempting to maximize service provided to customers. In such cases, schedules that are written do not provide enough time for operators to travel along the route in the time allotted, and layovers are too short to allow enough recovery time, thereby seriously delaying the service on a regular basis.
  • Clumping: On some bus lines with more frequent service, while one bus falls behind schedule while it is busy picking up and dropping off a large volume of passengers, one or more subsequent buses on the published schedule that pass these already cleared stops may have a nearly empty run, and may actually jump ahead of its scheduled time to the point that two or more buses are within close sight of one another, or in some cases, one bus is able to pass another. This phenomenon is sometimes known as clumping or bunching [1]. When this occurs, the even spacing of buses on the schedule may be severely disrupted, leading to extremely long waits for those attempting to catch a bus with a more frequent schedule, and multiple buses arriving at once.

Improving schedule adherence

Transit agencies often take the following measures in attempts to improve schedule adherence on their routes:

  • Modify schedules by adding running time, known as schedule padding. This is the most common solution, but agencies often add running time by cutting layover time, which adversely affects the ability to recover from unplanned incidents. If layover time is to remain the same, it may require an increased expenditure in the system's budget or a slight reduction in the number of trips.
  • Cutting other under-performing services. The savings generated from eliminating or reducing other services may be used to improve service on other, higher-performing routes.
  • Modifying routes to a route where buses can be less obstructed, when this does not interfere with the ability of riders to reach their buses.
  • Splitting a long route into two or more shorter ones, as shorter routes are more likely to remain on schedule.
  • Some cities have introduced bus rapid transit services or limited stop lines on long, overcrowded routes. This involves the use of part of the route's budget to operate another line on the same route that stops only at key points. While the overall frequency of the original route is reduced, riders traveling over a longer distance have the option of obtaining a quicker trip on the limited-stop line.
  • Some agencies and transport companies have installed GPS devices on their buses and trains to monitor the locations of the vehicles. By linking this real-time information with stop locations and predicted travel-times, the estimated time of arrival of the next service is then displayed at some stops.
  • Customer interfaces: Some agencies and companies have made the GPS tracking data described above available to public transport users online, by a toll free number, or through the mobile web.
  • Accept the principle that A late journey always gets later. It is better for the majority that one journey is significantly delayed or canceled rather than delay many after because of knock-on effects such as clumping. This is more important for transportation such as railoads where vehicles are carefully scheduled to use limited infrastructure such as tracks.

Real-world examples

The following chart shows some examples of real-world on-time performance. The figures are always (unless stated otherwise) per vehicle, not per customer. This means that if 95 % of vehicles have less than 5 minutes of delay, one cannot conclude that 95 % of passengers experience less than 5 minutes of delay. Delays are usually greater during rush hour, therefore the delays per passenger are usually higher than per vehicle.

Operator Mode of transport Period < 1' < 2' < 3' < 5' < 10' < 15' < 20' < 30' Comments Source
SBB All passenger rail 2010 84.3 % 90.7 % 95.6 % [2]
DB Intercity rail September - October 2007 46 % 62 % 72 % 84 % 92 % 96 % Figures disputed by DB AG, no official figures available [3]
Local rail 56 % 76 % 86 % 94 % 98 % 99 %
Amtrak California Zephyr 2010 47.7 % > 550 miles, therefore on time is < 30' delay [4]
Capitol Corridor 93.5 % < 250 miles, therefore on time is < 10' delay
JR Central All passenger rail 2010 Average delay 30 seconds (no distribution given) [5]
Lufthansa Short/medium distance flights November 2008 - March 2009 84.7 % Arrivals [6]
Long distance flights 78.2 %

References


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