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3 Cs Make It Easier to Live with Your 70 Million New Smart City Neighbors Part 3: Cost-Effectiveness

Ender MorgulEnder Morgul

In our two previous blog posts, we discussed connectivity and coverage as essential components of Smart Cities. In this final post, we explore a primary benefit achieved from collecting data through mobile devices: cost-effectiveness.

Challenges of Collecting Traffic Data

According to a survey conducted in 2012, fifty-two transportation agencies across the U.S. reported the primary challenges for collecting traffic data include fixed and variable costs of devices and staffing to set up and perform regular equipment maintenance.

Yet even with these challenges, governments pour millions and millions of dollars into collecting traffic data every year. For example, New York has been using a wireless network of 377 transmitters, called NYCWIN, to collect real-time high-quality transportation data since 2009. The initial system cost is estimated at $500 million. This cost combined with the $49 million annual maintenance fee made the City of New York rethink its value. Despite this massive and ongoing investment, the City considered getting rid of the system, and officials looked for alternatives.

There are of course less expensive, “old-fashioned” automated data collection options. These include RFID transponders, Bluetooth and wireless sensors. Although these options are big improvements compared to someone standing at the corner of an intersection counting cars — which is still one of the most-used data collection techniques — reliability and usability of data obtained from these sensors can be questionable.

For example, Bluetooth data can be used for travel time estimation between two points on a roadway by detecting mobile devices in vehicles. However, the primary challenge with Bluetooth data is that detection rates are usually very low, varying between 3% and 11% of traffic levels. Unfortunately, it’s unlikely for the number of devices with Bluetooth features to increase in the near future. As a result, data obtained from Bluetooth devices will never be an accurate representation of traffic conditions.

Smartphone Sensor Data To The Rescue

When it comes to collecting traffic data, there’s a trade-off between quality and affordability: highly-accurate connected devices are expensive devices. However, Smart Cities are looking at existing technology for cost-effective solutions.

For example, many cities already invest in vehicle tracking systems for fleet safety and management. GPS-equipped probe vehicles from municipal and regulated fleets, such as local buses and taxicabs, offer high-quality data streams for traffic monitoring with almost no additional costs.

Better yet, smartphones have similar sensors, including location data with date and time information. These continuously-active mobile sensors can collect traffic data in real-time.

In order to maintain cost-effectiveness, city and state governments are now establishing public-private partnerships with a number of traffic and navigation apps, where drivers share real-time traffic and road information anonymously via their smartphones. These partnerships are benefiting cities, data-driven organizations and the public. Cities are saving money, as these organizations provide traffic data to the public. In return, the organizations are benefiting by gaining access to city data that can help them develop better driving routes for their users.

Quality Matters Most: The Zendrive Solution

While smartphone sensor data has proven to be a cost-effective solution, cities require accurate data to make roads safe.

At Zendrive, we use smartphone sensor data differently than most organizations. When it comes to driving safety, we have the largest and fastest growing dataset. As a result, cities are able to better understand dangerous road conditions and specific risk factors in a cost-effective manner.

Through smartphone sensors, Zendrive measures high-risk driving behaviors, including:

Measuring and analyzing this data provides cities with location-specific and time-specific information that can help prevent crashes and save lives.

Zendrive recently partnered with NYU’s Tandon School of Engineering to demonstrate how smartphone sensor data can help make roads safe. Based on Zendrive’s data, the study found:

These results show that driving behavior data has great potential to predict when and where traffic crashes are likely to occur. As a result, cities can implement solutions that will deter specific behaviors before they result in collisions.

Driver Safety is “Priceless”

The ability to prevent future crashes, to save lives and to keep families and loved ones together is priceless. Pragmatism reminds us, somewhat unfortunately, that at the end of the day, officials have to balance their budgets. At Zendrive, we see a path forward to solve both challenges.

Smartphone sensor data is an existing solution that is revolutionizing the way Smart Cities collect traffic data. Zendrive is providing accurate data in the most cost-effective manner possible.

As the world’s cities grow by upwards of 70-million people a year, Smart Cities are leveraging connectivity, coverage and cost-effectiveness to save lives and improve quality of life. As more data becomes available, faster, the three C’s will help communities accelerate progress towards solving their top challenges.

Join Zendrive in the Smart City revolution!

Follow us on twitter and contact us if you want to bring our data and analytics to bear on your work.

About

mm

Ender F. Morgul is a data scientist at Zendrive. He received his PhD in Transportation Planning and Engineering from New York University. His research interests include traffic safety, modeling and predicting driver behavior, GIS-based data analysis and transportation economics. He has authored several articles in peer-reviewed journals on a variety of topics, including driver behavior modeling, congestion pricing and urban transportation planning.

Ender F. Morgul is a data scientist at Zendrive. He received his PhD in Transportation Planning and Engineering from New York University. His research interests include traffic safety, modeling and predicting driver behavior, GIS-based data analysis and transportation economics. He has authored several articles in peer-reviewed journals on a variety of topics, including driver behavior modeling, congestion pricing and urban transportation planning.