Bus bunching

Bus bunching
Two buses on the same route in the same location

In public transport bus bunching, clumping, or platooning refers to a group of two or more transit vehicles along the same route, such as buses or trains, which are scheduled to be evenly spaced, running in the same location at the same time. This occurs when at least one of the vehicles is unable to keep to its schedule and therefore ends up in the same location as one or more other vehicles of the same route at the same time. The end result can be longer wait times for some passengers on routes that have shorter scheduled intervals.

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Classical theory

A classical theory for a causal model for irregular intervals builds on the premise that a late bus tends to get later and later as it completes its run, while the bus following it tends to get earlier and earlier. Eventually these buses form a pair, one right after another, and the service breaks down as the headway degrades from its nominal value. The buses that are stuck together are called a bus bunch or banana bus and may involve more than two buses. It is often theorized to be the primary cause of reliability problems on bus and metro systems.

Hypothesized causes

Clumping can be caused by heavy usage on any particular vehicle, resulting in it falling behind schedule. The vehicle then ends up in the location of a later scheduled vehicle when that one should be there. In some cases, the later scheduled vehicle gets ahead of its schedule, and they meet in a location in between the scheduled times of the two vehicles. In some cases, a later scheduled vehicle may actually get ahead of an earlier scheduled one.[1][2][3]

When buses clump, one can pass another on the street or road. When trains clump, this is often not possible due to the limitations of the single track.

Abnormal passenger loads

The time taken for a bus to complete its duties is related to the number of people attempting to board or alight at stops. The bus that is already late tends to attract a higher number of riders due to the longer headway between it and the previous bus. The higher number of riders boarding the bus results in delaying it further. At the same time, the following bus tends to collect fewer passengers, because its headway is shorter due to the delay of its predecessor, and hence it spends less than expected time on stops, which further shortens its headway (unless it deliberately idles on stops or slows down).

Speed of individual drivers

Another cause is that some drivers are faster than others. This results in catching up on long or high-frequency routes.

Deliberate acts

According to the article "Progress Has Passed Metrobus" by Lyndsey Layton (Washington Post, December 27, 2005) bus bunching may be deliberately caused by bus drivers, so that the bus ahead of them picks up more passengers and decreases their own workload.

Theory

The classical bus bunching theory is an example of chaos theory. The orderly procession of buses is presumed inherently unstable and buses are presumed to tend towards bunches if left unchecked. According to proponents of this theory, it is impossible to predict from the outset which buses will be bunched and which buses will proceed on schedule to the destination, because bunching is presumed to be caused by random conditions such as traffic, stoplights, and the number of passengers at a stop. The study of the actual practice of bus operations, however, has been shown to be quite different.

Practice

The existence of classical pairwise bunching has not been borne out by vehicle tracking systems data. Studies into metro operations have broadly debunked the theory of pairwise bunching as a major cause of irregular intervals on metro lines, and have tied irregularity largely to problems in other key scheduling and operational processes.[citation needed]

Recent research has demonstrated that simulation models of bus routes based on the classical theory of bus bunching have failed to replicate actual conditions of bus service intervals as captured in bus location tracking databases, even when random external events are incorporated into the model.[citation needed] Though, simulation studies have successfully demonstrated the extent of possible factors influencing bus bunching and they may equally well be used to understand the impact of actions taken to overcome negative effects of bunching.[4]

While station dwell time, as dependent on passenger volumes does influence interval variability, other explanations of bus service unreliability are the following:

  • The lack of ability in resetting scheduled departure times at the start of the line. This is often the case because outer terminals in bus networks are often remote and on an isolated route rather than a convergence of routes, and it is uneconomical to position a supervisor at such a remote site for just one bus route. AVL/CAD systems have been used successfully in some surface transit systems to remotely revise terminal departure times, thus improving overall variability in service intervals from the start of the line.[citation needed]
  • Schedules and service plans that provide very little recovery margin compared to actual running time performance will accumulate lateness. Service control actions may be taken to keep bus drivers within union-agreed contractual work parameters, in many bus systems through the use of unscheduled short-turning. Unscheduled short-turning often occurs in the post-peak period and results in many passengers off-loaded onto the following vehicle, which itself may also be crowded.[citation needed]
  • Bus routes are subject to street closures, which may increase running times, leading to further lateness of drivers and greater levels of intervention necessary to keep drivers within work parameters.[citation needed]
  • Bus operation is also dependent on the aggressiveness of driving. This is partly predictable driver-specific effect which has been quantified by researchers studying the Portland bus network in Oregon.[citation needed]

Prevention

Clumping can be prevented as follows:

  • Scheduling minimum and maximum amounts of time at each stop[2]
  • Scheduling some crowded runs to skip certain stops[1]
  • If, on a popular route with frequent service, a crowded vehicle arrives, passengers can be trained to wait for the next vehicle, which may be less crowded[1]

Adding more vehicles to the schedule has been proven not to be a solution to the problem.[2]

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References

See also


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