At 340 square miles, the City of Memphis is among the largest in the United States in terms of land area. Memphis has over 6,800 lane-miles of city streets, enough to drive back and forth to Los Angeles four times. Keeping these streets well maintained and safe for citizens and visitors is a major priority for the city.

Lots of traffic, lots of roads, and a four-season climate prone to wintertime freeze-thaw-refreeze cycles means the opportunity for potholes. Although the city aims to fill potholes within five business days of notification, it can take longer, especially during winter and early spring. Last year, the city’s Public Works crews repaired some 63,000 potholes, only 20% of which were reported by residents. Approximately 32,000-man-hours each year are spent repairing potholes, with seasonal fluctuations requiring ten to twelve Street Maintenance crews working steadily during the winter months. Still, many went unreported, leading the city to flag pothole request resolution under “needs improvement” on its open data portal website.

Like many large cities, Memphis also struggles with vacant and blighted properties. Nearly 15,000 properties in Memphis are likely vacant, and city officials contend that many are owned by out-of-town investors who live elsewhere and do not take necessary restoration or maintenance steps. These properties can decrease the value of surrounding real estate and discourage new businesses and other residents from moving to an area. Citizen frustration and concerns over the number of blighted properties has made blight eradication a major focus of the City of Memphis.

Historically, residents reported potholes and blighted properties by calling 311, or more recently by using the Memphis 311 app. However, these reports only covered about 20 percent of the problems — often the worst cases. And by the time residents took the initiative to submit a 311 report, they usually weren’t feeling good about the situation.

Recognizing that potholes and vacant properties are often the most visible indicators of whether a city government is doing its job efficiently, Memphis Mayor Jim Strickland and CIO Mike Rodriguez began looking for ways they could apply technology to fix the problems. Mike approached Google for ideas, and Google recommended conducting a machine learning proof-of-concept (POC) with SpringML, a Google Cloud Partner.

“Memphis is focused on easy living, and we want to do everything we can to keep our citizens happy,” says Mike Rodriguez. “Working with Google and SpringML to reduce potholes and urban blight using machine learning and artificial intelligence was an easy decision.”

Bringing machine learning to city operations and budgets

The city’s goal is to detect potholes and abandoned properties by analyzing video footage of roads and residential properties. It wanted to classify potholes by width and depth, and share the information with workers who can repair them. For abandoned properties, it wanted to enable more strategic deployment of resources for homeowners citywide and take action to hold neglectful property owners accountable.

The POC began by training TensorFlow models for ML object detection using preconfigured AI Platform Deep Learning VM Images on Compute Engine. SpringML helped set up cameras and developed a user interface to collect pothole data and automate the 311 ticketing process.

Together, the teams analyzed 30 days of video from a moving city bus and high-resolution video from 360-degree cameras mounted to a code enforcement vehicle, overlaid with data from 311 reports. As the models were refined, accuracy quickly climbed from 50 percent to over 90 percent as models were taught to differentiate a pothole from a manhole cover or other object.

The city also imported routes, potholes, and paving data along with geolocation data from ArcGIS and Google Maps into BigQuery to better understand street conditions and the proximity of potholes to one another. BigQuery also analyzes city property records, tax records, 311 reports, and third-party survey data on-demand to predict where homes are starting to become run down and where neighborhood decay is most likely to occur. The SpringML team created a pilot analysis to begin vacant property protections and developed a user interface tool to interact with the model’s results.

“Google Cloud Platform made it possible for us to experiment with machine learning and artificial intelligence to help solve our city’s problems while working within the budget constraints of a municipal IT organization,” says Mike. “Google turned a ‘nice to have’ into a ‘let’s do this!'”

Identifying 75 percent more potholes

Memphis expects to substantially reduce the number of potholes on its streets, creating a better driving experience for residents and visitors alike. Because drivers won’t be as likely to swerve to miss a pothole, streets will be safer and friendlier to bicycles and scooters. Fewer potholes will also save the city between $10,000 and $20,000 annually in city claims that it pays out in cases where vehicle damage results from a pothole that was not addressed in a timely manner.

“Historically, Public Works has relied primarily upon Street Maintenance crews to proactively locate and fill potholes. As Memphis has over 6,800 lane-miles of public streets, it is a daunting task to reliably survey the entire system in an efficient and systematic way,” says Robert Knecht, Public Works Director for the City of Memphis. “The outcome of the data collected will be invaluable to Public Works so that it can ensure it is managing the city’s street system in a more proactive manner.”

Memphis will be able to better prioritize road maintenance based on condition and impact, increasing the efficiency of its Public Works road crews. Analyzing video of streets also gave the city visibility into issues it wasn’t previously aware of, such as curbs, gutters, and manhole covers that had been mistakenly paved over and need to be excavated. The ML process is easily transferrable to other concerns as well, helping the city identify illegal signs or spools of cable hanging on light posts that could be potentially unsafe.

Helping communities recover and thrive

Memphis is also having success in analyzing predictive trends to combat high rates of abandoned and blighted properties, surpassing 97.5 percent accuracy. “In the past, Public Works experimented with comprehensive, city-wide blight identification by using approximately 200 volunteers to survey and photograph over 237,000 city parcels. This effort was costly, took a long time to complete, and resulted in inconsistent data collection,” says Robert. “Blighted property conditions can change quickly in a city the size of Memphis. Now, with this new technology, Memphis will be able to make a significant difference in the efforts to proactively and comprehensively identify and manage blighted and substandard properties.”

Code Enforcement with better data-driven detection mechanisms enables the city to also identify cases where homeowners are not physically or financially able to keep up with the challenges of homeownership and make them aware of resources that are available to assist them. Memphis Code Enforcement can do a better job of finding people living in derelict properties that pose hazards to inhabitants’ health and safety, and help them fix those problems or find a new place to live.

“Using SpringML and Google Cloud Platform to detect indicators of vacant or blighted properties will help Memphis create safer neighborhoods that will be more attractive to businesses and home buyers,” says Mike. “Property values and employment will go up, crime will go down, and social services can be more focused and effective.”

Revolutionizing service delivery for citizens

Memphis is proving the viability of a cost-effective, cloud-based machine learning model that other cities can follow. The city is already looking into new applications of AI and ML that will further improve city services and help it build a better future for its 652,000 residents.

As part of his commitment to a transparent government, Memphis Mayor Jim Strickland created an open data policy that commits to releasing raw data and sharing it with citizens in a variety of downloadable formats. Going forward, this transparency will help citizens understand how their needs are being served and uncover new, innovative use cases for AI and ML.

“Our goal is to become a smart city, and technologies such as Google Cloud Platform and SpringML put us ahead of the game,” says Mayor Strickland. “Google understands data, and there isn’t a better company to help us analyze our data resources for actionable insights.”

Editor’s note: We invited partners from across our retail ecosystem to share stories, best practices, and tips and tricks on how they are helping retailers transform during a time that continues to see tremendous change. This original blog post was published by OnlineSales.ai. Please enjoy this updated entry from our partner.

Tata’s online healthcare entity, Tata 1mg, aims to revolutionize how a consumer finds the right healthcare professional for their needs. The platform offers e-pharmacy services, diagnostics, and health content and has more than 200,000 active brands in its rapidly expanding list of suppliers, sellers and manufacturers. Several of these brands would need regular involvement from the marketplace team in order to set up and run ad campaigns. As one of the largest platforms, Tata 1mg approached OnlineSales.ai to address their growing needs for an efficient Retail Media Monetisation suite to help scale up their existing monetisation efforts. 

Tata 1mg needed more data-driven insights and customizability for their monetisation efforts

Tata 1mg saw several challenges that they needed to address.

1. Manual effort: Tata 1mg marketplace team’s monetization process was largely manual and required heavy time and resource investment to activate advertisers, which subsequently ate into their overall ad revenues.
2. Non-scalable framework: Given the fast-growing number of advertisers on their marketplace – well into the hundreds of thousands – it became increasingly clear that their existing monetization framework was not scalable.
3. Inadequate reporting & analytics: The existing framework facilitated only simple reporting, and brands were not able to draw the deep insights they needed to make data-led decisions and refine their advertising ROIs.
4. Steep learning curve: Several brands lacked dedicated advertising experts who could bring the experience and skills needed to plan successful ad campaigns and optimize budgets. The involved learning curve led to increased advertiser-churn.
5. Lack of personalized offerings: The manual nature of Tata 1mg’s monetization framework left little room for customizability, and account managers could offer very little in terms of tailored ad-buying experiences to different types of advertisers.

All of these issues led to regular revenue leakage. Tata 1mg realized the need for a central platform that addressed the above-mentioned issues and would also be able to cater to new ad channels and the requirements thereof. Developing such a solution in house was evaluated, but was found to be expensive and would require a lot of time to build.

OnlineSales.ai’s AI/ML-powered solution on Google Cloud offered the automation and flexibility to support modern omnichannel advertising needs

Therefore, Tata 1mg looked to OnlineSales.ai Monetize, an AI/ML-powered retail media monetisation suite on Google Cloud, to help address their needs for a cloud-based monetisation platform that offered the flexibility to support omnichannel advertising models, had automation built in to reduce manual tasks, provided detailed intelligence and analytics, and delivered quick, reliable scalability.

1. White labeled self-serve platform: OnlineSales.ai’s retail media platform was implemented within a short 4 weeks. After thorough testing and implementation, the platform was successfully launched, and enabled sellers to run ads on a fully self-serve platform. This required zero involvement from the Tata team.
2. Unified omni channel buying: The team at OnlineSales.ai worked closely with the platform’s core teams to activate and mobilize various advertising products through a self-serve platform, while simultaneously addressing any unique requirements they had.
3. Omnichannel analytics with AI-led suggestions: Sellers are now also able to generate detailed reports that provide them critical insights into campaigns, and help them make better use of their budgets – enhancing their experience and interest in advertising on the platform.
4. Personalized buying experiences: The tailored UX offered by OnlineSales.ai’s Monetize helped Tata engage brands of all levels in technical aptitude to use the platform easily. This also allowed admins to configure what types of inventories were available to different brands or advertiser segments.

OnlineSales.ai makes use of Google Cloud Dataflow to facilitate computations on real-time streaming. Microservices are deployed on the Google Kubernetes Engine (GKE) platform due to the solution’s well-known ability to handle scale.

In addition, Dataproc is used by the company to run batch processing apps while Cloud Load Balancing helps manage traffic across different regions; all at scale. Onlinesales.ai also uses Memorystore as a database for their ad servers in order to have very low latency, and deploys BigQuery to run analytical applications and facilitate powerful reporting. The bulk of their office applications are deployed on different VMs on Compute Engine, and the company also makes use of Cloud Storage for data storage and transfers between applications.

Learn more about OnlineSales.ai available on the Google Cloud Marketplace.

^{About OnlineSales.ai}

^{OnlineSales.ai, offers a fully white labeled retail media platform which helps retailers unlock additional revenue by activating advertising real estate on their platform. With its AI/ML-powered solution, OnlineSales.ai allows retailers to turn brands of all sizes into advertisers – at scale. Its self-serve platform simplifies the ad buying process, and enhances outcomes with smart automation, boosting ad retailers’ ad revenues by multiples.}