Collection No 5

Insights Sensing

Synchronized Cities: Leveraging Sensors to Optimize Urban Activity

Sensing technologies increase the efficiency of basic services like food distribution, while raising questions about the value of serendipity.

We’ve all experienced the frustration of running late in the morning. Despite best efforts, there are days when you simply can’t get yourself out of the house in time, and you leave knowing that the long commute will end with you walking into work fifteen minutes late.

But some of us have also experienced the delightful phenomena of synchronicity. After running to the subway, you arrive to the platform just as the train is pulling into the station. The ride is prompt and you emerge from the subway to find no line awaits you at your local street vendor. You grab your coffee and maneuver to your office building, and the moment you arrive at the lobby the elevator doors open. You enter the elevator alone and go straight to your floor. As a result of this synchronicity, you gracefully glide into your morning meeting with minutes to spare. You smile to yourself as you entertain the thought that your city understood your morning struggles, and adjusted its services accordingly.

The experience of a synchronous city is quickly becoming a calculated strategy rather than an occasional coincidence. Urban inhabitants use smartphones to navigate streets, find products, coordinate activities with friends, and discover opportunities nearby. The infrastructures of cities are also getting smarter through the use of distributed sensors that monitor activity and usage in real-time. As the urban environment and its people grow more connected, there’s an opportunity to more efficiently assess supply and align it with demand.

A great example of this is the car-sharing service Uber, which makes local drivers aware of people nearby seeking a ride. When multiple requests are made for trips, and several drivers are available for service, the Uber system calculates each pairing so that the network of resources can be optimized. The success of Uber points to a larger trend towards ‘synchronized cities’. Algorithms already determine when streetlights should turn green based on traffic flow. But what happens when the road recognizes your vehicle, scans your personal profile, calculates your approximated arrival, and sends a notification to the local vendor that you’ll be ready for coffee in 10 minutes? The system may have optimized your morning commute but has personal independence been sacrificed? When life in cities is optimized, will serendipity be lost?

A Case Study: Matching Markets

In 2007, while working within MIT’s Senseable City Lab, I developed the Matching Markets program to explore the potential of urban sensing for synchronized cities. Working with a team of designers, data analysts, and engineers, we traveled to the northern territory of Italy to research the business of regional agriculture, and the challenges farmers face in selling their products locally. The territory, known as South Tyrol, is a sprawling mountainous region known for its nearly 25,000 family farms and vineyards, which maintain traditional practices and produce high quality products. Each year, the region sees a heavy influx of tourists who are drawn to the ski slopes in the winter, cottage retreats in the spring, lakeside villages in the summer, and harvest festivals in the fall. Locally produced goods such as wine, cheese, speck, and apples are a main attraction for tourists, but opportunities to purchase these items are limited to small stores on the farmstead or weekly farmers’ markets in the villages.

Most farmers export their goods to sustain their operations. For those distributing locally, heavy competition within the region makes it challenging to determine the right area or market for promoting goods. The mountainous landscape of South Tyrol makes transportation costly and the population distribution, including both tourists and locals, varies significantly throughout the year. In the fall, many people flock to the cities for street festivals and art exhibits. But in the summer and winter months, tourists retreat to the hillsides for skiing and hiking. This fluctuation in activity is representative of a thriving territory with a multitude of offerings, but single-family farms with limited resources for distribution struggle to adapt to such a dynamic clientele.

Sensing Supply and Demand

Following an extensive survey of market patrons and in-depth interviews with local farmers, my team outlined an opportunity for building awareness and transparency around supply and demand of local products. The tourist population in South Tyrol is predominately European and a majority travel with smart phones. They use mobile applications to monitor ski conditions, receive notifications of seasonal events, and publish photos to social media. Given the prevalence of mobile technology in the region, the team hypothesized that through sensing, patterns of movement could be assessed and used to inform farmer’s decisions about where to sell their goods.

To test this hypothesis, we analyzed geo-tagged flickr photos to understand when and where people captured images across South Tyrol over the course of one year. The visualization shows significant changes throughout the seasons, with people flocking to city centers during the spring, clustering around popular lakes in the summer, and spreading out across mountainsides in the winter and fall. In comparison, the location of farmers markets throughout the year is relatively static, with no correlation to the movement of people. The markets are established in relation to local neighborhoods and aren’t designed to adapt to mobile populations.

Screen Shot 2014-05-25 at 8.57.42 PM

Flickr data mining by Fabien Girardin. Visualization by Bernd Resch and Jennifer Dunnam.

When examining the movement of farmers in the region, the team took a more active approach and attached GPS trackers to the delivery vehicles of five farm operations. Over the course of five weeks, the team tracked the farmer’s movement and assessed their ability to reach customers throughout the region. The data showed considerable inefficiencies in how, and to whom, products were distributed.

For example, there is a practice of ‘ambulante’ in South Tyrol where vehicles selling products drive slowly through neighborhoods and stop for patrons that flag them down, similar to American ice cream trucks that play music and are eagerly chased by children. The ambulantes drive slowly and rely on locals to be at home and alert when they pass through a neighborhood. While looking at the patterns of the vehicles in transit to farmers’ markets, we found that farmers were passing a number of underserved villages where they could potentially offer home delivery services. By digitally broadcasting the presence of these vehicles and the products they carry, people could track ambulante routes and make requests for home delivery. The data also showed opportunities for collaboration among farmers who shared customers but made separate deliveries. Farmers often spend hours traveling to disperse locations rather than coordinating their operations for shared profits and efficient distribution.


The Matching Markets Model

Following our research into the connectedness of people and producers in South Tyrol, the team developed a model to demonstrate a potential system for synchronization of supply and demand. The Matching Markets system is a real-time communication platform that negotiates between producers and their customers, with the aim of bringing products and people more in line with one another.

There are three parts to the Matching Markets system:
1. A producer is networked among his peers and can access the system using a mobile smart device. Using the GPS feature of the smart device, the vendor’s location is continuously made available to the public. In addition to sharing this location, the producer can also post updates about his products, particular sales he is offering, delivery services, and upcoming market stops. The producer can monitor updates from other vendors in order to decipher more efficient strategies for selling his products.
2. Customers access the system using a smart device or personal computer. From the smart device, location identification is possible if the user chooses to share this information. The customer is able to search through real-time data to find nearby vendors, food events, and market sales. The customer can also browse recommendations from other customers and query the database for details about the products and agricultural practices.
3. The city has access to information generated by the system, which enables city officials to make better decisions about when to close off streets, where to invest in infrastructure development, how to support local producers, and how to better accommodate seasonal tourism.

This model forms a feedback loop, so that exchange between farmers and their customers can be continuously adapted. At any given moment, a customer can browse this ‘Internet of food’ and seek out the closest vendor or make a request for delivery. Farmers can utilize this network to make strategic decisions about where to travel, which people to target, and how to promote their products. And while daily decisions are important in the trade of goods, what is truly powerful about this open flow of information are the insights that come over time and their ability to fuel optimization.

Optimization and Humanization

Sensing technologies promise farmers the ability to respond in real time to the needs of the people they serve. Over time, data analytics allow them to reflect on activities and extract patterns of behavior that may suggest recurring cycles. Through a combination of in the moment responses, and an increasing ability to anticipate activity, the opportunity to allocate resources accordingly grows exponentially. The result is an environment that is optimized for movement, with resources that are calculated for efficient distribution. But what does an optimized city feel like for its inhabitants?

As we design solutions for this type of synchronicity, we must also consider the importance of serendipity. If cities are designed purely on analysis of past behaviors, there is a risk in narrowing experiences to pre-defined interactions. As citizens of the digital city, we must weigh the benefit of a morning commute that proceeds quickly, with the pleasure of meeting someone new while we wait. Through sensing, we can identify and monitor variables within a city, such as supply and demand. Through design, we have the opportunity to create new experiences that grow from intelligent systems and manifest as thoughtful pairings of people and needs.

Jennifer Dunnam

Jennifer Dunnam is an Associate Creative Director at frog where she leads an interdisciplinary team to research and develop innovative products, services, and experiences for today’s leading organizations, and emerging start-ups.

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