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Big data in transportation: 6 tips for building an effective data program

November 19, 2020

By David Filiatreau

How creativity, advocacy, and consistent analysis can inform better decisions and more efficient spending

The era of “Big Data” has infused nearly every industry in some form, providing us with new ways to evaluate business practices. Over the past decade, the transportation sector has seen an influx of new data sources. They often promise to aid in project selection and performance evaluations. While data options have become cheaper and more plentiful, building a successful data program is not always simple. 

To help tackle this challenge, our team helps our clients implement data programs and get the most value from data choices. Let’s look at six tips for building an effective data program below. 

The era of “Big Data” has infused nearly every industry in some form, providing us with new ways to evaluate business practices.

Tip #1: Get creative with what you have

While the cost of major data sources has decreased in recent years, it can still be out of reach for many smaller municipalities. What I always recommend to a city or government when funding may not be available is: Get creative. Like a parent creating a child’s Halloween costume out of household items rather than buying something off the shelf, being creative with existing data can sometimes be just as effective and informative. Compiling citizen comments (or complaints) from a 311 system, tracking collision data collected by local law enforcement, or utilizing traffic counts from a Department of Transportation (DOT) can offer telling insights, as well as solutions.

Tip #2: Use the “free” stuff

Crowdsourced data, which refers to information that is voluntarily submitted by someone, has become popular among the travelling public through applications like Waze. Waze uses crowdsourced data for real-time updates on traffic, accidents, and even roadway obstructions. Some companies are willing to share a wealth of crowdsourced information (such as travel time data for vehicles, bicycles, and pedestrians) on the promise of information in return, like the dates of future road closures. 

Waze and Strava Metro, which focuses on bike and pedestrian activity, are two applications that offer information with such data sharing requirements. An added bonus? This data doesn’t require additional IT infrastructure since the data providers support the back end. This type of data sharing would have been unheard of a decade ago. Now, even the smallest municipality can have access to transportation data.

While data options have become cheaper and more plentiful, building a successful data program is not always simple.

Tip #3: Find the RIGHT data

The free stuff is great, but there is a limitation on the depth and breadth of information available. If you feel that you have hit a data limitation, that may be the time for a paid provider. When you decide to join the world of Big Data there will be some questions to answer. Which provider offers the best value for your situation? Which methods of data collection make the most sense? With all the data choices available, it can be difficult to choose exactly what type of data to use. Whatever you choose, there is one outcome to avoid: Spending on sources that do not give the needed answers. Dedicate time to research, vet, and meet with data providers during pre-procurement. Hurrying through the research stage can result in overpaying and underutilizing a data program.

Tip #4: Evangelize the data

After procurement, your program now has that all-important ingredient, data. The difficult part is over, right?  Not exactly. 

If a data program is going to fail, it will likely fail at this step. Often, one of the most important but overlooked factors for success is getting buy-in. Neglecting to get others excited about the positive impacts can result in distrust of the data or worse, having it ignored altogether. In some cases, hundreds of man hours and thousands of dollars can go wasted simply because the project champion did not get buy-in from the end users. If the end users do not trust the data sources, they may continue their work without considering how the new information can aid decision-making processes. 

Identifying and promoting data use cases before procurement will help get buy-in. It will also give ownership to those who may use the information the most. Pinpointing data sources and use cases can sometimes be overwhelming. But our team, with extensive global experience with data from all types of industries, can help focus your search and better ensure the success of your data program.

Tip #5: Share the wealth…of information

Many data providers will allow the data to be shared with other government entities, if this is negotiated during procurement. A successful data program should consider how other entities can use the data and how they can best get involved. Can municipal policing agencies or fire departments utilize transportation data to evaluate accident clearance times? Can municipalities improve travel times for their sanitation department by altering their trash collection routes? What about consultants or quasi-government agencies working on a public project—will they have access to this data? Building a successful data program requires getting answers to these types of questions upfront, while also ensuring that access to data is properly shared with eligible groups.  

Consider how data like accident clearing times can be shared with other government entities like fire departments or policing agencies. 

Tip #6: Always look to improve

The manager of a successful data program should continually examine the usefulness of the information collected and discuss ways to improve how the data is applied. Furthermore, getting locked into one data source for too long without considering alternatives can be problematic. The world of Big Data is evolving fast and it is entirely possible that a previously good value source is no longer the case. 

There isn’t a set of criteria that can be uniformly applied to show when a data program is considered “effective.” Each city, metropolitan planning organization, or state entity has a different set of circumstances and challenges. Developing goals during data procurement, and documenting when those goals have been achieved, will dictate whether your data program is effective. 

More data, more solutions

With an effective data program, you should now be able to answer many questions quantitatively. How is traffic impacted by a road closure? Where should the next bike or pedestrian pathway be built? Where are the worst bottlenecks and how can the success of a solution be measured?

Removing some subjectivity from a decision-making process involving public funds should result in better answers and more efficient spending. If you are a decision-maker and have been wanting to start a data program for your city, county, or state, reach out to our team. We will apply these strategies and more to help you realize the benefits brought by Big Data.

  • David Filiatreau

    David is a senior traffic engineer traffic signal management and intelligent transportation experience. He prepares traffic signal designs, conducts traffic studies, and implements ITS solutions to improve the safety and efficiency of roadways.

    Contact David
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