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A South Beach local bus. (Image: Miami Dade Transit)

A local bus in Miami’s South Beach. (Image: Miami Dade Transit)

By Rahul Nair

Miami Dade Transit (MDT) serves the seventh largest urban population in the U.S. As you might expect, public transportation for 2.7 million people can be challenging, but especially when asked to operate at a budget deficit that’s projected to reach $81.4M by 2023.

For MDT, operating as efficiently as possible is an imperative. That’s why in 2014 the organization turned to IBM to help identify and eliminate costly inefficiencies. In particular, it asked my team and me to help solve a growing dilemma on the streets of Miami’s South Beach: bus bunching.

Bus bunching occurs when two or more busses serving the same route arrive at a stop at the same time. Such phenomena results in longer wait times for passengers and dramatically lowers the utilization of the busses, costing cities millions of dollars a year. 

Bus bunching occurs in cities around the world.

Bus bunching occurs in cities around the world.

To figure out how to get control of the situation, we launched an eight-week pilot program aimed at reducing delays and improving services. The pilot targeted four main routes along the South Beach corridor and provided real-time predictive analytics of system performance.

Our prediction system provided alerts of potential bunching events to the operations control up to an hour in advance, expanding their ability to anticipate, manage, and ultimately prevent bunching. A key secondary use of such operational data provided data insights that led to better planning, scheduling, and performance management.

For large transit systems, the volume of real-time data can grow quickly, on the order of hundreds or thousands of position updates per second. Key measures for network-wide awareness that require complex analytics have large real-time computational needs. In contrast to “data-at-rest” applications, where data is stored, loaded in memory, and then processed, a streaming data model processes data as it is received.

IBM Infosphere Streams middleware platform allows for parallel and high performance stream processing that scales over a range of hardware environments. Based on computational requirements, applications are distributed over any sized server cluster, and analytics are executed in memory for low latencies and high data throughput.

Bus bunching in India. (

Bus bunching in India. (

The pilot used our Intelligent Transit Analytics based on IBM Infosphere Streams. It analyzed more than 1.4 million bus positions, representing 19,974 bus runs between December 4, 2013 and March 18, 2014. The operational data allowed a rich characterization of service characteristics, its variability, and pinpointed bottlenecks within the system.

As the IBM Intelligent Transit Analytics solution received bus position updates, it performed rigorous quality checks including on-route/off-route analysis through map-matching, and de-noising of the position data to ensure consistency. It then recorded how far a bus was from the start terminal and the time it reported its position. A collection of the bus’s positions creates a trajectory – a path in space and time defined by a bus as it periodically reports its position.

The distance from a start terminal, also called offset, effectively measures bus position as if the route were laid out as a straight line. A plot of a bus’s trajectories, known as a space-time diagram, was generated to represent the results and assist in a visual manner to examine bus performance. Each line of this space-time diagram shows an individual bus’s journey alongside all bus journeys observed for the day. The slope of the trajectory denotes the speed – with steeper lines indicating faster bus speeds. When bus trajectories meet on the diagram, it indicates bus bunching locations and times, along with mid-day slowdowns for several buses.

Rahul Nair, Transportation Analytics Manager, IBM Research-Ireland

Rahul Nair, Transportation Analytics Manager, IBM Research-Ireland

Buses were prematurely returning to their scheduled routes which originated in the downtown terminal. This meant that drivers were only able to stop at the terminal for a few minutes. Consequently, their journey times were doubled as they were not able to stop for a break. Due to this shortened stoppage time, a 35 km route with 113 stops becomes 70 km with 226 stops. Delays from 6 to 11 minutes on these bus routes ultimately cost the city more than $4 million in operational costs.

At the end of the pilot, our report to MDT provided key performance indicators of bus bunching, assessed accuracy of prediction of bus arrivals and bunching event alerts, and provided a detailed assessment of MDT service performance and produced schedule refinements. The operational real-time recommendations were verified and used by the MDT operations team – and they are now armed with predictive capabilities to help keep the busses on schedule.

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August 12, 2016
5:11 am

Great post, it is very helpful, keep it up, thanks for share here with us.

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Posted by: imo for pc
July 15, 2015
3:27 am

This is all about in the benefit of commuters & off course they will appreciate it.

Posted by: Sonu
April 20, 2015
5:05 am

I think old model bus should be changed with the latest one. it is for both to control pollution or escape from jampacked traffic, our government must hide upon on it

Posted by: sant singh
April 20, 2015
12:49 am

What’s up mates, fastidious article and good urging commented here,
I am genuinely enjoying by these.

Posted by: school
February 16, 2015
10:12 am

Can this be shared with customers?

Posted by: Mardi Donahoe
February 16, 2015
6:00 am

I would have liked to hear how they stop the bunching? Do they send off the later buses later? Or do they send the early buses earlier? Seems to me that either would mean annoyed travellers – buses running early or late looks bad. Then again, I guess the travellers are going to be annoyed anyway, because bus bunching is really annoying.

Perhaps the ideal would be to send a bus directly to the point where things start to slow down, so it can service, for example, the second half of the route that would otherwise have had to wait longer for their bus.

Interesting problem.

Posted by: womble
February 12, 2015
4:24 am

India Government should opt this analysis for minimizing the traffic and it is really helpful to commuters..

Posted by: Kalki
February 11, 2015
9:33 pm

Has this type of study ever been done in the Trucking Industry? Do Trucks “bunch” at Ports of Call / shipping docks?

This would be a great use case!

Posted by: Bill
February 11, 2015
5:14 am

Practical, real-life, at scale, focus on the (operational) business problem …. EXCELLENT !

Posted by: Hans Van Mingroot
February 10, 2015
12:22 pm

I’m sure the commuters appreciate this!

Posted by: Pressure Switch
February 10, 2015
3:25 am

Nice use of techonlogy and also minimizes traffic on road

Posted by: Rahul
February 9, 2015
11:36 pm

application of analytics to maximize service quality.
inspiration for me because i am also interested in real life application of data

Posted by: GemPundit
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