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IN DEPTH: TECHNOLOGY

Big Ideas about Big Data

Special Excerpt for Passenger Transport from Xerox

In our personal and professional lives, we’re awash in data. The same is true in transportation. But there’s more than meets the eye—smartphone location data, weather data, social media and more. …

Embrace the data. Learn what’s available and how to use it. Then, learn to integrate it. The whole idea is to recognize patterns so you need to normalize, clean and connect data. Once you get a system in place, you’ll be able to maximize the time analysts are working with data to understand patterns and trends.

Here are several ways to use data to help cities and public transportation work better.

Track Your Travelers
Smartcards and open payment systems have revolutionized our understanding of rider behavior by making data collection easier. For the first time, you can get granular detail on individual journeys that have actually been taken by real people—not based on unreliable estimates, random sampling or surveys. This gives you insight never before available: when people are traveling, where they’re traveling to and from, what modes of transport they’re using and in which combinations, where they’re changing and how long it takes.

And you can get this data every day, every hour, every minute. That gives you a truly accurate picture of the travelers and how they’re using transport services (and, for example, see where the system is inefficient, and which neighborhoods might be over- or underserved).

In addition, integrated ticketing and fleet tracking systems make use of available passenger data to help transit operators track and manage their vehicle fleets. …

Identify and Predict Demand
Historical analytics help you build models that forecast traffic and traveler flows for very specific periods, such as Tuesday evenings in July, on days before bank holidays, etc.—and plan for exceptional situations accordingly. And real-time data can give you a complete view of current usage and demand. …

Optimize Resources
If you base your service capacities and frequency on experience and historical practice, there is a good chance you’re either wasting resources or underserving travelers at any given time.

When you don’t know the exact rider behavior or the current demand and usage of your public transport capabilities look like, it’s close to impossible to allocate resources efficiently—and run profitable operations. Data analytics help transport officials dynamically adjust resources and ‘right-size’ them, improving both service levels and efficiency. …

Protect Your Revenue
Automated fare and toll collection systems have made evasions harder for users. In addition, advanced technology can ensure that drivers and riders always pay the correct fare.

It works the other way, too: [W]ith new digital payment tools, authorities can automatically reimburse travelers if they’ve been overcharged or if a service has been delayed for more than a given time. …

Communicate More Effectively
… Real-time data on electronic message signs and traveler apps help communicate issues that cause delays—and ultimately improve passenger satisfaction by keeping riders informed and advising them on alternative travel options.

At the same time, agencies can tap into social media data to improve services: sentiment analyses from Twitter feeds, for example, to show how riders perceive service—and give officials the opportunity to get in touch with customers directly. And they might even learn about blockages or failures on social media first.

Riders are ready to pay for better tech. Travelers value easy ticketing, reduced delays and better communication from transport authorities. A study reported on Wired.com showed that a majority of U.S. riders say they’d be willing to pay more for completely paperless journeys, smartphone ticketing, and daily updates on prices and delays. …

Encourage Environment-Friendly Behavior
Many transport authorities are making a point of increasing ridership among so-called “choice riders”—people who own a car or have other travel options available to them, but opt for public transport due to convenience, cost or other reasons.

Smartcards have been crucial in these campaigns: making public transport faster and easier to use. Transit planners can also use the rider data to provide personalized incentives that make public transport clearly the better choice over driving.

Get a Complete Overview
… Very few cities around the world are bringing all their data flows together yet, but it’s entirely possible. Once you overcome the silos and get all your data in one place, you can build a visual dashboard that illustrates traffic flows.

That gives you an amazing superpower: the ability to make corrective decisions, improve service and optimize resources—within a mode of transport and across the entire system.

And once you’ve been doing this for a while, you’ll have a treasure trove of accurate historical traffic data that helps you forecast like never before.

Make Data Work
This is the age of the consumerization of technology. Your travelers and all their devices are already incredibly well-connected to all the data sources available to them.

They’re expecting the same from you as a service provider. Even with ­budget constraints, you really can’t afford to ignore the data and the enormous potential for optimization that comes with it.

So start getting systematic about data and leverage all the valuable information that’s available all around you. Use data to turn your traffic flows into a well-choreographed ballet that handles rehearsed performances just as well as improvisations.

You’ll never want to go back.

This article is based on a recent ebook published by Xerox. Find details here. Reprinted and excerpted with permission.


RideScout: Opportunities to Harness Big Data
REGINA R. CLEWLOW, Director, Transportation Research and Policy

As public transit agencies begin to adopt technologies such as mobile-based payment systems and connected vehicles, they will be presented with numerous opportunities to harness big data to improve the performance of their systems for current and future users. Big data generated by technology-enabled transit services—including anonymized traces of transit payment purchases, geocoded locational information of users and vehicles and the performance and operation of vehicles—can enable transit agencies to make smarter strategic, operational and real-time decisions for improved mobility.

Strategic decisions. Insights harnessed from data on where and when customers start and end their trips, as well as when they opt to use other transportation services, can support transit agencies in their infrastructure and service decisions—such as where to invest in high-capacity rail or bus lines versus where to pilot new services.

Operational improvements. Big data harnessed from vehicles and users can inform tactical route adjustments and vehicle maintenance decisions that will enable agencies to provide more reliable, comfortable service. By connecting more users to transit agencies through mobile phone applications, agencies can schedule and communicate advance notice to users about maintenance and services and facilitate the coordination of new, shared mobility services to fill gaps, when needed.

Real-time decisions. With advances in mobile ticketing, agencies will have the ability to harness real-time data to assess their capacity and reliability. By harnessing and sharing this live information, they will have the opportunity to push out real-time notifications (and potentially real-time incentives) to balance peak demand by delaying trips or encouraging users to utilize alternative services.

The proliferation of GPS-enabled smartphones and future connected vehicle technologies will usher in a new era of ITS. Transit agencies stand to benefit exponentially from harnessing the power of big data to deliver improved mobility services in cities around the world.


Cubic Transportation Systems: Big Data; Big Solutions
BORIS KARSCH, Vice President, Strategy

At the highest level, big data in transportation promises to transform lives, improve services, make communities more efficient and achieve greater safety.

And it can lead to big solutions for public transit agencies by solving one of their most critical issues—funding—by enabling them to operate more efficiently.

Outcomes can be as simple as delivering the optimal level of services to the right locations at the right times. However, no system can operate in isolation.

To understand the impact of planning and service decisions, just look more broadly at the operators, services and infrastructure within a regional network—public and private. Data analytic techniques and technologies enable us to integrate and study data in silos, enabling us to unlock the benefits that come from understanding relationships among data sets. We’re in the midst of this transformation through integration of mobile payments and location-based services.

In the future, digital personal assistants will be providing predictive services based on a rich understanding of our habits and preferences. Travelers who choose additional convenience from these services will need to share more of their data and travel behavior in return for highly tailored information that will save time and cost.

This is a trend we’re well accustomed to when one thinks how we interact with sharing economy apps and services. Amazon, Uber, Citymapper, smart bikes and smartcards already gather transport information that helps commuters make smarter travel decisions, and Cubic Transportation Systems has long been managing this data for our clients.

Gaining the benefits of big data will require trust and transparency in managing data for the benefit of knowing how, when and where to travel—whether it’s based on the consumer’s normal travel or a journey that will maximize current conditions and add the greatest value. In the end, it can be a win-win for agencies and their customers.


Cambridge Systematics: Focus on Predictive Analytics
ERIC ZIERING, Principal, Executive Vice President, Software
ANITA VANDERVALK, Principal, Transportation Operations/Data Management

Ziering: Everyone has used systems to find the best route from here to there, and those systems are now starting to take into account real-time information and becoming responsive in very exciting ways. One is personalized routing using open source platforms that help find the right route for every individual.

Vandervalk: Eric points to the ability to develop software and tools based on the emerging availability of all of these data sources, whether we call it big data or not.

Predictive analytics is an area that we’re definitely getting into. We’re interested in taking a look at how we can develop relationships between data and helping to predict traffic demand, traffic flow, including the impact of weather, incidents and other unpredictable events. The power of those data to help with predicting and enabling transportation operations and provision of travel information—to our cities and within our cities—is important.

We’ve been using visualization to show relationships in data, and transportation agencies will soon be using that information to change their operations. What was originally envisioned as outward facing applications to alert the public to on-time arrival of buses can—through data collection and analytics—help agencies better operate the system through the use of performance measures.

Ziering: I see a dramatic shift underway from a time when agencies thought that data were something to be kept in proprietary systems to a time when every agency realizes that, when they can make their data available, they get back tremendous value and learn things they can’t learn on their own. It’s been a huge change that’s only going to continue.

For some time, only the largest public transit agencies have the manpower and technology to do innovative things with data. With open source software, small and mid-size agencies can do things that they never could have afforded to do before.


Genfare: Data Guides Every Decision
DARREN DICKSON, President

Due to its increased insight, big data has transformed and evolved businesses worldwide, and the very same applies to the transportation industry.

Data is at the core of every public transit authority. It influences route coverage and changes, service and maintenance schedules, revenue service and reporting, adoption of fare media and special programs—every decision made is guided and impacted by the data generated by the back end at each transit agency.

In order to optimize resources, it’s imperative that all agencies are true experts on their ridership and partners’ needs. With increased insight into the behavior of their riders, transit authorities can take a deeper dive into their ridership patterns and trends. Ridership statistics like identifying peak travel times and trends allow public transit authorities to derive enhanced recommendations and more effectively plan for the future.

Having a richer, more complete picture of what’s happening in the field—that comprehensive overview—provides insight that allows the agency to be more efficient. As a result, transit authorities have become far more flexible, and they can manage and leverage change faster, smarter and more effectively.

However, the data itself is of no use without the accompanying analytics—analytics and reporting are the catalysts for how the data becomes actionable to agencies. It’s that magical weaving of raw data into actionable intelligence that spurs change.

Genfare has a thorough understanding of data’s importance to the transit industry based on over 30 years of in the trenches experience, and we have developed a suite of fully integrated, software and hardware-based solutions that can manage multimedia fare collection and customer service across multiple transit authorities and modes of transport.

A truly vertically integrated solution is the future for transit agencies and will be a key driver in moving the industry forward.


Trapeze: Creating Analytical Models
MARSHA MOORE, Chief Technology Officer

Big data enables public transit agencies to store, model and analyze vast amounts of data that were previously considered to be too complex, expensive and time-consuming to process with traditional databases.
This means transit agencies can load and process disparate data from on-board vehicle technologies, transit enterprise systems and third-party solutions to create analytical models and optimize resources. For example, agencies can use big data to:

Understand, compare and enhance driving behavior by analyzing habits and coaching operators to drive more efficiently and safely training to avoid collisions. Further, drivers can be allocated more efficiently to reduce payroll cost. We call this “driver intelligence.”

Optimize vehicle maintenance, reduce costs and improve service by predicting when issues will occur and taking measures to resolve them and by extending the useful life of a vehicle. We call this “vehicle intelligence.”
In terms of single vehicle data, think of on-board diagnostic systems that ­display an alert when the engine needs servicing, like car maintenance systems.

For fleets, think of automatic download and analysis of diagnostic data across all vehicles, regardless of whether there is an issue. This will allow the agency to create predictive models to produce proactive maintenance schedules before an issue becomes a disruption.

To evolve from predictive analytics (knowing when and how a problem might arise) to prescriptive automation (using technology to create real-time solutions) means bridging the gap between business intelligence and tools. Prescriptive methods will soon predict multiple futures and allow agencies to assess several possible outcomes. These techniques will be powered by big data from across the enterprise and incorporate historical, real-time and event-driven data, data from third parties and the passenger’s digital experience.

This will help agencies solve old challenges while creating new opportunities to use intelligence to inform decision-making. Eventually, agencies will have a suite of integrated tools to help measurably improve service, operations and the bottom line.


INIT: Turning Data into Action
ROLAND STAIB, President & CEO

Analyzing big data is not only about saving time, money and resources while delivering better service; it’s also about reacting to, anticipating and managing change more intelligently.

Intermodal Transport Control Systems (ITCS) gather a tremendous amount of real-time data. Three examples illustrate how big data can help make the most of this.

Statistics reports are available to slice and dice recorded data and analyze how to improve service and reliability. By using a data warehouse, a customer with a 1,500-bus system was able to extract and compile data at a 20x increased speed and schedulers were able to remove idle time in the schedule, saving more than 36,000 hours annually.

Predictive maintenance is a key component of increasing the safety and reliability of vehicles. A host of data from various on-board systems is processed by the vehicle health monitoring system, determining which vehicles need attention and when. The benefits are clear: Prevent unexpected equipment failures, conveniently schedule corrective maintenance, provide greater safety and convenience and proactively determine vehicle availability. Trend analysis can even anticipate defects.

Another interesting use is analyzing how incidents are managed and service is restored. This is not only perfectly suited for big data, but would actually be impossible without it. INIT is conducting a research project with university partners and researchers to detect standard operating situations and establish best practices.

The incident management system can be prepared for machine learning. Pattern recognition algorithms find, identify and classify standard situations. The algorithms can even uncover unknown dependencies. The resulting standard operating procedures are transformed into “scenarios” to guide dispatchers and can be run by the ITCS automatically. Now “tribal knowledge” is commonly available, resulting in increased consistency and efficiency.

All of these examples improve passenger service and satisfaction. That’s what it’s all about.

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