Smart Cities Technology and Data

Artificial intelligence stole the show at 2024’s Smart City Expo USA, held in New York City in May. “You can't swing a dead cat without bumping into someone talking about AI,” Justin Tormey, a director at the tech-focused consulting firm Slalom, said during one panel. 

Here are some key takeaways from conference speakers about what city leaders should know about the AI landscape and how to prepare for what’s next.

When to move fast on AI — and when to exercise caution

What are leaders actually referring to when they talk about AI? There’s a difference between machine learning and generative AI, said Jerele Neeld, interim chief information officer for Chattanooga, Tennessee. Put simply, machine learning uses algorithms to learn from data and make informed predictions or decisions; generative AI is a type of artificial intelligence that can create new content, from text to images.

Many machine-learning applications have already been tested in cities, Neeld said, and cities should move as fast as they can to scale up those use cases.

Applications of generative AI are still in earlier stages of development, however — and cities need to plan now for how they will move forward, said Stephen Caines, chief innovation officer for the city of San Jose, California. “AI is going to work its way into your city at some point,” whether it’s through a vendor or an intern, he said, advising cities to figure out the rules of the road now.

One way to take the initiative is to identify a handful of initial use cases to test AI applications, said Alex Foard, executive director of research and collaboration at the New York City Office of Technology & Innovation.

Making AI a priority

Despite the hype AI has sparked, planning for it is still just one of the many priorities city leaders juggle. That poses a major near-term challenge in scaling up AI use, said Ernie Fernandez, Microsoft’s vice president for state and local government. Fernandez recalled a recent conversation with a “young, really progressive mayor” that shocked him: The mayor told him that he hadn’t had the chance to think about AI because he was so busy. 

“The technologists are going to be deep into this, but the leaders, they're focused on their top-level issues,” Fernandez said. “We're going to have to work on bringing them along.” Early AI use cases governments are exploring include 311 assistance, permitting, policy development, language translation and briefing documents, he said.

There are some action items public sectors need to focus on “like, yesterday,” said John Paul Farmer, president of WeLink Cities, a broadband technology and service provider. First, “get your data house in order,” he said. “There can be all kinds of amazing tools out there. If your data is a mess, you're not going to get the outcomes that you expect.” 

Governments must intentionally upskill their workforce to use AI, rather than leave it to individuals to figure it out in their free time, he said. “Every government agency should be finding time, making it a part of the job to learn how to use these AI tools to do that job better, serve people better.”

Keep ethics and transparency top of mind

AI promises to make cities more efficient, said Little Rock, Arkansas, Mayor Frank Scott, Jr., who described how AI technologies helped cut his community’s recovery time after a tornado from a year to about six months. But he urged cities to be wary of unintended consequences.

“While it may be the new, hip, fad thing to do, we at cities really have to focus on the cost — not only from a financial standpoint, but also from a community and political standpoint,” Scott said. He advised cities to be very upfront with residents about how they are using AI.

Amen Ra Mashariki, the Bezos Earth Fund’s director of AI and data strategies, called for more precise, useful policies and guidelines to ensure AI is used ethically. “Right now, people are putting out policies just to make themselves look like they're thinking about it,” he said. “But it doesn't seem like it's implementable in any real way.” 

WeLink’s Farmer said every government should develop its own artificial intelligence strategy that centers people’s “digital rights,” including privacy, security, equity, accountability and transparency. That might require difficult conversations within the community and city council. 

“Sometimes maximizing privacy actually means less equity or less security,” Farmer said. “Having those hard conversations is how you actually come to understand what the priorities are for your community and make sure that you're using AI responsibly in the context of what that community wants.”

Google is piloting an online mapping tool in 14 U.S. communities to help city leaders understand how trees and highly reflective surfaces, like cool roofs, could help mitigate urban heat, according to a press release issued in September. 

“Cities can determine the neighborhoods with the highest heat exposure and vulnerability, and which cooling interventions could have the most reduction in surface temperatures,” said Mansi Kansal, Google Research product manager, in an email. The tool, which is not publicly available, Kansal said, applies artificial intelligence to satellite and aerial imagery to quantify potential reductions in surface temperatures.

Climate change-driven extreme heat presents a pressing challenge for cities around the world. Local and state governments are beginning to hire chief heat officers and devise heat action plans packed with short-term measures, such as opening cooling centers for the public to find respite on hot days, and long-term strategies, like increasing tree canopy.

Among the solutions that cities are turning to are “cool” pavement and roofs, which are designed to reflect rather than absorb sunlight. Some research has shown these reflective surfaces, deployed at neighborhood scale, can lower the ambient air temperature by several degrees Fahrenheit. However, some experts warn that this cooling effect is marginal compared with the public health benefits of increasing access to shade.

Google’s new tool marks another way city leaders can try to figure out the best heat mitigation measures for their community. It’s available in Austin, Texas; Baltimore; Boston; Boulder and Colorado Springs, Colorado; Los Angeles; Miami-Dade County, Florida; Nashville, Tennessee; New York City; Phoenix; San Antonio; Stockton, California; Tempe, Arizona; and Washington, D.C., Kansal said. 

The tool allows city planners to predict how changes in cool roof areas and tree canopy would change the average summer land surface temperature in their communities. It includes filters to show areas that see the highest temperatures and are most vulnerable, based on factors such as median household income and social vulnerability index, Kansal said.

Powering the tool is AI-driven object detection and models that account for local characteristics including how much green space a city has or how well the roofs on buildings reflect sunlight, the press release says.

The tool measures land surface temperatures rather than air temperature because the former provides higher spatial resolution and is available globally, Kansal said. Air temperature data is much more sparsely available and requires additional weather factors for accurate modeling. 

The land surface temperature data can give cities reliable insights that can enable decisions now on cooling interventions in vulnerable areas, Kansal said. Meanwhile, Google is working on longer-term research to model the effects of cooling interventions on air temperature. The tech company hopes to partner with more cities in the future to inform urban cooling strategies, the press release says.

Sponsored content

By RUBICONSmartCity

Given its location in the Chihuahuan Desert, the City of El Paso, Texas has a perennial sand problem. Sand lines the streets of the City, sometimes up to six inches deep, clogging storm drains and dirtying El Paso’s roads. This makes street sweeping a top priority for the City.

In 2023, the City of El Paso announced a three-year contract with RUBICONSmartCity™ to modernize its street sweeping operations. El Paso implemented the technology suite in ten street sweepers that service 35,000 curb miles of roadway, leveraging exception logging, digital material tickets, digitized routes, and turn-by-turn directions to clean the City’s streets effectively and equitably.

Challenges

Before adopting RUBICONSmartCity, El Paso’s street sweeping operations were entirely paper based, with printouts of daily route sheets, pre- and post-trip inspections, and material tickets. Without a route management system in place, the City struggled with driver accountability and visibility into service. This led to challenges in service confirmation, driver productivity, missed service complaints, training time, and reporting.

Service confirmation

Supervisors used to rely on highlighted paper maps to verify service. Now, with automatic service completion data, supervisors have real-time visibility into where and when drivers have swept, reducing inaccurate service information. According to Solid Waste Operations Coordinator Andres Carrasco, “It was hard to know where drivers were. Now that we have RUBICONSmartCity, we’ve been able to see where drivers are and what time they passed certain roads.”

Driver productivity

With increased supervisor awareness through vehicle tracking and route progress monitoring, driver productivity has increased. Average stationary time has decreased 27% per route since implementation, from 2 hours 9 minutes to 1 hour 34 minutes.

Customer service has also improved. In the Driver App and the Manager Portal, drivers and supervisors can see the last time streets have been swept. They can also more effectively monitor how many passes are needed on certain streets over a set time period to ensure streets stay clean and resident satisfaction remains high.

Drivers have increased the number of passes completed by 28% since the start of the partnership, from an average of 96 passes to an average of 122 passes per route, leading to cleaner streets. Even with an increase in passes, the City observed an 8% decrease in average route mileage. RUBICONSmartCity estimates this translates to $85,170 in annual operating cost savings for the City. These idling and mileage reductions lead to 4,173 fewer gallons of fuel annually and 37 metric tons of avoided CO2e emissions, the equivalent of taking 9 passenger cars off the road each year.

Citizen satisfaction

Supervisors address resident complaints using historical service information and can more accurately dispatch sweepers as needed. This has led to increased customer satisfaction and more equitable distribution of service across the city. “RUBICONSmartCity helps put a smile on the customer’s face,” Andres mentions, tying this increased satisfaction to timely service that covers all neighborhoods equally.

Additionally, by providing photographic evidence of blocked cars, the City has seen a decrease in citizen complaints. Andres projects this will reduce the number of 311 calls related to street sweeping, as there is greater accountability at the curb, noting, “As soon as I tell a caller I have a picture of their car parked in the street, the caller closes the issue.”

Training time

Prior to RUBICONSmartCity, training new drivers was an arduous process, as the drivers needed to learn the ins and outs of the City and learn how to use the heavy sweeping machinery. Digitized routes and turn-by-turn navigation have streamlined the training process for new drivers, reducing the onboarding time from 2-3 months to just 3 weeks. The City can save over $20,000 per year for every new driver added to the team by continuing to adopt this efficient training process.

Digital weight tickets

Drivers used to manually fill out weight tickets, leading to incomplete data. Drivers can now log material and water fill-up tickets digitally using their phones, providing more accurate data on material usage. This has streamlined the reporting process for supervisors and enabled the department to report on water usage and debris tonnage to the City Council.

Conclusion

The City of El Paso’s partnership with RUBICONSmartCity has transformed its street sweeping operations, providing greater efficiency, accountability, and improved service to its residents. The City now has the tools and technology it needs to maintain cleaner streets and ensure a better quality of life for its residents. Learn more here. 

Four U.S. cities were recognized in June by Results for America and Bloomberg Philanthropies for their “exceptional” use of data to inform policy, allocate funding, improve services, evaluate programs and engage residents, according to a news release.

The cities newly certified through the What Works Cities Certification program are Dallas; Boise, Idaho; Issaquah, Washington; and Sugar Land, Texas, along with five cities in Latin America. These communities will join the 74 other cities that have accomplished certification since the program launched in 2017. 

What makes the certified cities special is that they are putting practices into action, said Rochelle Haynes, managing director of What Works Cities Certification. “It's not just putting the data out there. It's not saying you have the dashboards, but it's ... saying how are we leveraging those to actually make decisions and decisions that connect to people,” she said.

The newly certified communities used data in distinct ways. For example, Issaquah used data to connect those experiencing homelessness with resources and shared data with local businesses to help reduce burglaries and theft. Boise used data at dozens of community meetings to engage residents in its decision to build a new water treatment facility. Sugar Land analyzed population, housing and code violation data to develop a program that helped residents upgrade more than 166 homes.

Dallas combed through data to make budget decisions that address race- and income-based inequities, according to a news release. That led to $40 million in equity investments, the release says. “Now they're tracking that as well to see if those investments are actually getting the desired outcomes,” Haynes said.

How data-driven cities are evolving

Cities have significantly improved at using data to engage the public since What Works Cities launched, Haynes said.

“Gone are the days where you're a resident of a city and you don't know where to access information,” she said. “Open data portals are common. You have leadership routinely talking about data when they’re thinking about plans or programs that they're rolling out.”

Haynes said many cities could improve at using data to evaluate whether initiatives are working, which can be challenging due to lack of time and resources. “If I have a homeless prevention program, am I doing an evaluation to see if residents are actually staying in their homes because of the prevention services that we're providing?” she said. “Is it actually working, or are we just ... giving money to the problem and not actually solving for the upstream issue?” 

Evaluation should be a consideration from the beginning of a project, not an afterthought, she said. Some cities even use their contracting requests for proposals to outline the outcomes they want to see.

It can also be challenging for cities to figure out how to align the many stakeholders involved in data-driven processes. Part of addressing that is determining who within an administration should be in charge of pulling together all the information to make strategic decisions, Haynes said.

Booming interest in artificial intelligence could also change how cities think about their data. “You have to be able to have the data to feed the AI tool,” Haynes said. “Cities have tons of data in different formats, so how do we help them think about organizing themselves to best use the technology?”

Among the cities Haynes works with, she sees the most excitement for AI around chatbots that can help residents connect with government services as well as to better understand traffic flow. Some cities are also using it for criminal justice applications, “but that's not across the board because of the questions around … bias and equity,” she said.

The number of communities using automated speed safety cameras increased from 211 in 2023 to 222 as of March 2024, according to the Insurance Institute for Highway Safety. This growth comes as communities are allowed to use federal funds for such technology: The 2021 infrastructure law allowed states to spend up to 10% of their highway safety funds on non-infrastructure projects such as automated traffic enforcement. Previously, federal funds for speed cameras could only be used for school zones.

According to the National Association of City Transportation Officials, studies show that speed cameras reduce the percentage of speeding vehicles by 14% to 65% and can cut the number of serious injuries and fatal crashes by up to 44%. “Automated speed enforcement, if deployed equitably and applied appropriately to roads with the greatest risk of harm due to speeding, can provide significant safety benefits and save lives,” the U.S. Department of Transportation states in a 2022 report.

California last year authorized six cities to deploy automated speed safety cameras under a pilot program. New York City has over 2,000 speed enforcement cameras in school zones and experienced a 73% decrease in speeding at fixed camera locations from 2019 to 2021, resulting in one-third fewer pedestrian injuries, the city’s transportation department said. 

Meanwhile, the use of red light cameras has declined across the country. As of this month, 338 communities have deployed automated red light enforcement programs, a decrease from over 500 a decade ago, according to the Insurance Institute for Highway Safety.

However, some cities are increasing their use of this technology. Philadelphia, which implemented its red light camera program in 2005, had 34 locations installed as of fiscal year 2023, with others being added. In 2022, violations generated over $15 million for the Pennsylvania Department of Transportation, which distributed the revenue to 22 municipalities in the state for 28 traffic safety projects. New York City saw its average daily violations per red-light camera decline from more than 30 in 1994 to seven in 2021, according to a report by the city’s transportation department.

New York and other cities are also putting automated enforcement to work to protect bus and bike lanes. Camera-equipped transit buses in New York City began issuing $50 tickets to vehicles blocking bus lanes in 2019 and will start issuing tickets to those blocking bike lanes this year. Washington, D.C., began using cameras mounted on Washington Metropolitan Area Transit Authority buses in the city to issue tickets in 2023 to vehicles illegally parked, stopped or standing in a bus zone. Chicago is preparing to launch bus and bike lane enforcement in the city’s downtown in summer 2024 using cameras mounted on Chicago Transit Authority buses, city vehicles and fixed poles.

Drivers should expect more automated enforcement efforts, said Adam Snider, director of communications for the Governors Highway Safety Association. “There’s a number of examples of cities sort of deprioritizing traditional traffic enforcement and then, unfortunately, fatalities surging since then,” Snider said.

While these technologies have proven effective in reducing vehicle speeds and speed-related crashes, some communities may feel they are unfairly targeted or feel the cameras are just there to generate revenue. “More states are using [automated enforcement], but there are still some big public perception and policy obstacles to overcome,” Snider said.

As the private and public sectors look to harness the benefits of artificial intelligence in their workflows, cities nationwide are contemplating what guardrails they need to ensure their AI use is both safe and effective.

New York City is one of the latest to publish AI guidance, with Mayor Eric Adams touting the “The New York City Artificial Intelligence Action Plan” as the first of its kind for a major U.S. city. In the plan, which the city rolled out in mid-October, Adams says this era of artificial intelligence could be “one of the most impactful technological advances of our time” but warns governments to be aware of the risks. 

Simply put, AI has the potential to make the government run better and provide 8 million people with easier access to services and benefits, said Alex Foard, the executive director of research and collaboration for the NYC Office of Technology and Innovation, which was founded in 2022.

Even before it published the plan, New York City was no stranger to AI, Foard said. City agencies already use AI in public health and cyber-resilience projects, and the city has for several years published a public directory of the algorithmic tools it uses. 

Still, Foard said, “an obvious need” arose for a comprehensive framework to guide how city agencies use AI-powered tools and for a commitment to developing robust risk assessment. The newly released plan is the result of at least three years of city inquiry into AI use and ethics informed by public, private and academic partnerships, he added.

The action plan lists seven initiatives with short-, medium- and long-term goals through 2025. The initiatives include building AI knowledge within city departments, developing responsible AI procurement standards and ensuring that measures created are maintained and updated as technologies change. 

The city aims to create an AI steering committee within the next year and expand public awareness of city AI tools. It also wants to create an AI risk assessment and project review process that checks for “reliability, fairness, bias, accountability, transparency, data privacy, cybersecurity and sustainability,” the action plan says.

Lessons from across the country

New York isn’t the only city planning for AI. Over the last few months, other cities have rolled out guidelines or policies with similar objectives.

In November, Seattle published its policy for generative AI use. A press release about the new policy states that systems capable of generating text, images, video or audio have the potential to support many of the city’s services. The policy creates a vetting process around the acquisition or use of software services that make use of generative AI and requires due diligence from city employees around intellectual property, attribution of content created by AI and protecting sensitive data.

Tempe, Arizona, approved an AI policy in June, with a focus on being “intentional” in its adoption of different tools. At the time, the city had not yet used AI in government operations, but the policy mentioned chatbots and automated reviews of employment applications to gauge qualifications as potential use cases. Notably, the city requires government workers to “clearly define the problem the AI technology would solve.”

In September, San Jose, California, published its generative AI guidelines, including a requirement that employees record their use of generative AI through the city’s reporting form.

Framing the conversation about AI

New resources for local leaders have been created in response to rapidly proliferating questions about how cities should use AI. 

During its annual Mayors Innovation Studio in October, Bloomberg Philanthropies and the Center for Government Excellence at Johns Hopkins University launched City AI Connect, a peer-based digital platform that anyone with a government email address can use. A survey of 80 mayors around the world by Bloomberg Philanthropies found that more than 75% are interested in AI tools, but just 2% of the cities surveyed had begun using them for government work. 

Claudia Juech, one of Bloomberg Philanthropies’ senior leads on City AI Connect, said that the platform’s users are currently in an “exchange” phase: They are sharing what’s been useful or successful for them and their cities so far. Juech said government leaders are finding that generative AI tools make city operations more effective by, for example, streamlining reporting of issues raised via 311 calls or communicating with residents through chatbots. 

Bloomberg Philanthropies’ research showed that generative AI has helped cities mitigate severe weather events, respond to emergencies and speed up paperwork processing. The survey showed that mayors are most interested in using AI for improving traffic and transportation, followed by infrastructure, public safety, environmental and climate projects and education. 

Juech and the government officials who spoke to Smart Cities Dive for this article emphasized that they see artificial intelligence as an opportunity to grow their employees’ abilities, not downsize their workforce. 

Cities are looking for “how this can help with upskilling and … training the people that they have,” Juech said. 

Boston Chief Information Officer Santiago Garces agrees. His Department of Innovation and Technology published interim guidelines for city generative AI use in May not just because of the pervasiveness of AI tools but because users were finding them widely helpful in their day-to-day work. 

“It wasn't just random hype,” Garces said of some early use cases. “With very little training, you could start doing things that would benefit you as an employee or as a community member — helping draft letters, helping write job descriptions.”

Garces says he knows, however, that AI technologies also bring risks such as phishing attacks or fake letters from constituents. Boston’s interim guidelines include some key principles, such as transparency, accountability, respect, innovation and risk management. The city also published three main rules city employees should follow when interacting with AI: 

1. Fact-check all AI-generated content. While chatbots can create some clear sentiments and sentences, they may generate outdated or simply fabricated information, Garces said. 
2. Disclose when you have used AI and which model you’ve used.
3. Do not put sensitive or private information into generative AI prompts because the data is likely being shared with the companies powering the AI. 

“By creating this environment in which people could experiment — but doing it in a way that was safer, that restricted the risks — we could truly be able to start preparing our workforce to understand the opportunities, the possibilities,” Garces said.

ShotSpotter, a system U.S. police departments use to detect gunfire, resulted in New York Police Department officers spending hundreds of hours in a single month investigating incidents the officers could not confirm were in fact shots fired, according to an audit the New York City comptroller released in June.

But ShotSpotter contends the audit is focusing on the wrong metrics. 

The NYPD will have spent more than $54 million on the gunshot-detection service from the time the department started using it in 2014 until December, the end of its current contract with SoundThinking, the California company that owns the system.

The audit, led by New York City Comptroller Brad Lander, analyzed ShotSpotter’s accuracy over several months in 2022 and 2023. When the system was performing at its best, 20% of ShotSpotter’s alerts related to “confirmed shootings,” the audit found. But its performance was often below that level: Of the 940 alerts officers responded to in June 2023, for instance, only 13% corresponded to confirmed shootings.

That’s not the measure of accuracy the contract uses, however. The contractual standard the NYPD set was that ShotSpotter would detect and identify the location of 90% of outdoor gunfire incidents in the coverage areas, with some exceptions based on weapon caliber and suppression. (Suppressors, also known as silencers, muffle the sound of gunfire.) ShotSpotter reached its 90% target “in almost all boroughs except Manhattan, but when measured against the number of confirmed shootings, performance is far lower,” the audit states.

“It was surprising to me to learn how ShotSpotter wastes thousands of hours of NYPD officers’ time every year, and it doesn’t necessarily make New York safer for cops to, say, chase after a car backfiring,” Lander said in an interview.

In its four-page response, the NYPD said it “is limited in what it can consider a ‘confirmed shooting’ in conjunction with a ShotSpotter alert by the nature of police work and alerts which don’t result in the recovery of evidence (i.e. ballistics, property damage, shell casings/live ammunition, firearms, video, ear or eyewitnesses and/or victims).”

The audit criticizes NYPD for not collecting data the comptroller and the public could use to evaluate the tool’s effectiveness and economy. Without that evaluation, the audit “therefore does not currently support renewal of the contract,” it states. 

If NYPD renews the contract, Lander said, he would like to see a clause with consequences for the system providing too many false positives, including a structure to reduce the fee the city pays to SoundThinking if, say, more than half of all alerts sent to officers do not relate to a confirmed shooting.

How ShotSpotter works

In more than 160 cities, ShotSpotter has placed sensors in areas that are known to have high crime rates. The devices listen for sharp, loud sounds and transmit data to company employees who determine whether they sound like gunshots. If so, they notify the police. Due to how the sensors triangulate in a given area, the system can provide an exact location to police in about a minute.

What is often misunderstood is the human element at play here, says Tom Chittum, senior vice president of forensic services at ShotSpotter. “Our staff don’t just use their ears, but they have other elements to help them determine if a sound is a gunshot,” he notes. For example, they can listen for audio cues such as whistling associated with fireworks or look at the waveform of the audio data to determine whether that sound has a sharp rise and fall, or its distance from the nearest sensor.

In the NYPD audit report, the comptroller “is being disingenuous about what he found,” Chittum said. When NYPD records an unconfirmed shooting, “our system could have detected a real shooting, but the perpetrators may have fled, they may have collected casings, and witnesses don’t often stick around after calling 911.”

Chittum added that because finding evidence in low light can be challenging, when clients deploy “daytime follow-up” after a nighttime shooting, the recovery rate goes up.

Chittum and other SoundThinking executives have long said that ShotSpotter isn’t meant to replace law enforcement efforts or services such as 911; it’s only complementary. ShotSpotter can’t detect indoor shootings, for one, and the company doesn’t plan on updating the system to do so.

Other cities evaluating the technology

ShotSpotter has both advocates and critics. In February, Chicago Mayor Brandon Johnson said he is ending the city’s six-year contract with ShotSpotter, which had been one of his campaign pledges, saying that “Chicago spends $9 million a year on ShotSpotter despite clear evidence it is unreliable and overly susceptible to human error.”

In May, Houston announced it wants to end its four-year contract with ShotSpotter before it runs out in January 2027; Houston media reports found that only 5% of alerts from December 2020 through September 2022 led to an arrest.

After a one-year pilot program in 2023 in Durham, North Carolina, the City Council voted not to continue using the system. Philip Cook, professor emeritus of public policy and economics at Duke University, led an audit of ShotSpotter’s success rate in Durham. He shared three conclusions:

• For the 1,447 total gunshot notifications in the pilot area, 57% had only a ShotSpotter notification, 15% had both a 911 call and a ShotSpotter notification, and 28% had only a 911 call. 
• For the 282 confirmed gunshots, 26% had only a ShotSpotter notification, 34% had both a 911 call and a ShotSpotter notification, and 40% had only a 911 call. 
• The notifications only detected by ShotSpotter led to an additional 2.3 police deployments per day in the pilot area.

In an interview, Cook said that “The ShotSpotter system made it easier for officers to locate where the gunfire occurred, since when they get 911 calls, the caller could be blocks away and may not know exactly [where] the shooting occurred, and human error can also happen with those calls.” 

For the ShotSpotter notifications for which police did not confirm a gunshot, Cook suggested one possible reason: “The area where the sensors were installed [has] a high rate of drive-by shootings, so obviously it will be challenging for officers arriving five minutes later to find any perpetrators on the scene or to locate evidence.”

The Durham audit said researchers were unable to conclude if ShotSpotter’s technology led to a reduction in gun violence.

Beyond efficacy in crime prevention, other concerns about the technology’s use in Chicago have come from the Northwestern University School of Law’s MacArthur Justice Center and the ACLU. The two groups cite the Chicago inspector general’s report on ShotSpotter, which says the perceived aggregate frequency of ShotSpotter alerts in some regions encourages officers to engage in more stops and pat downs in those regions. 

Also, as the ACLU writes, “ShotSpotter’s methodology is used to provide evidence against defendants in criminal cases, but isn’t transparent and hasn’t been peer-reviewed or otherwise independently evaluated. That simply isn’t acceptable for data that is used in court.”

What does it take to quickly and efficiently clear the roads of winter precipitation in America’s snowiest major city? 

Syracuse, New York, which claims that title, uses dozens of snow removal vehicles, plows, and no fewer than three software systems, the city’s operations professionals say. The team has a big job: The city receives an average of 127 inches of snow, or nearly 11 feet, every year. 

Syracuse has been implementing Internet of Things and geographic information systems technologies for its snow removal since 2018, said Corey Driscoll Dunham, Syracuse’s chief operations officer. The overhaul of the system was prompted by a huge storm that January, she said.

“Between the mayor, the deputy mayor, our chief policy officer and myself, we spent the entire weekend driving around the city, getting messages through Facebook or text messages or phone calls or, you know, just chasing down complaints left and right about the roads,” Dunham said about the 2018 snowstorm, which lasted for 66 hours, according to news reports.

Dunham said it was clear the city needed a better way to respond to residents and provide some transparency about snow removal. Over the last few years, she; Conor Muldoon, the city’s deputy chief data and innovations officer; and the rest of the operations team have developed the interactive, real-time Syracuse Snow Operations Map. 

The city first tried building something in-house, Muldoon said, but by summer 2021, the team looked outward. The three-pronged software approach uses GIS company ESRI’s ArcGIS Velocity product paired with Samsara’s cloud-based location technology, which updates the Snow Operations Map with a plow’s location every few seconds. The third vendor is Rubicon, which offers a platform Syracuse uses year-round for management and routing of snow and waste removal vehicle fleets. 

“The Samsara is the underlying platform for knowing where the plows are, ESRI is a platform for ingesting all that information into a dashboard and communicating it out to the public, and then Rubicon is the platform for the drivers to dispatch communications,” Muldoon said. 

The real-time tracker allows residents to see which roads have been plowed and when. They can more easily see if the route to work or school is cleared, budget more time for travel, or decide to stay home. The Snow Operations Map also reports when there’s an impediment, like an illegally parked car, that is preventing a plow from going down a certain street. 

New York City adds bike lanes, salting progress to its plow tracking

Although New York City has had a map of snow plow progress, PlowNYC, for more than 10 years, the Department of Sanitation (DSNY) updated its operations software in 2023, ahead of predictions that the Northeast would receive higher-than-average levels of precipitation this winter. Its Bladerunner 2.0 platform, launched in December, helps the sanitation department streamline and centralize its prep and response to snow and ice and provides tracking data to help determine how and when to deploy staff. 

The Bladerunner software was first launched in 2015, a Department of Sanitation press release said. The city uses it year-round for waste collection and street cleaning operations. The recently released version 2.0 features better GPS tracking of snow removal fleet vehicles than its predecessor, Google Maps integration, real-time salting data, information about the status of protected bike lanes and a cloud-based system that handles more concurrent users with faster response times, the city said in a Dec. 14 press release. 

DSNY also is at its highest head count in two decades, the city said, and it has invested nearly $500 million in new sanitation trucks in the past two years, for a total of about 5,000 vehicles across all five boroughs. The city previously categorized streets in three tiers to prioritize snow clearing, but the investments and software have allowed it to eliminate that system, which had resulted in “different communities receiving different levels of service,” the city said in the press release. Now, every route and bike lane will be cleared when snow conditions warrant it, it said. 

New York City got its first meaningful snow in more than two years in January, and a second storm dropped about 3 inches of snow on Central Park Tuesday, but Belinda Mager, DSNY director of communications, said snowfall has still been limited compared with seasons past. 

“The system did give us more real-time information and situational awareness, allowing us to more effectively manage the storms we have experienced,” she said.

Startup gives Toronto snow plows traffic priority

Traffic optimization startup LYT, which uses cloud-based software to help manage the flow of vehicles in cities, recently expanded its services to snow plows in Toronto.

It seemed like a “no-brainer” that LYT’s platform, which works with existing technology on the roads, like traffic lights and cameras, could be applied to snow removal routes to make them more efficient and environmentally friendly, said founder and CEO Tim Menard. 

LYT’s platform already was using machine learning and artificial intelligence, along with real-time traffic data from smart sensors, to move emergency vehicles through traffic quickly. It’s now applying the same technology to Toronto’s snow plows: Sensors in the plows allow the city’s operations department to adjust the flow of green lights so that plows move more quickly through intersections and neighborhoods. 

Prioritizing snow plows in the flow of traffic offers financial and environmental benefits to cities, Menard said. Less stopping at intersections reduces wear and tear on snow removal vehicles, and it helps the plows clear main roads faster. In addition, snow removal vehicles are often also spreading salt on roads as they travel, so more continuous movement allows for more equal distribution of salt. Salt build-up is a large contributor to potholes, Menard said.

Less stopping also means vehicles use less fuel and produce fewer emissions. Two of the cities LYT works with report they have saved a combined $600,000 in fuel last year and reduced carbon dioxide emissions levels by 1,000 metric tons, the CEO said.

The value of transparency

Three years into the Snow Operations Map project, Syracuse officials said its use by residents continues to grow, showing that the demand for information transparency remains strong. 

The city prioritizes clearing snow from the main roads and roads that provide access to hospitals, but the real-time information shows residents that it aims to be equitable in its snow removal, not favoring certain neighborhoods or areas over others, Dunham said. 

“These decisions are being made by data, which will, hopefully … either restore or maintain the trust that residents have in local government,” Dunham said. “These decisions are being made with the limited resources that we have, and [the service is] going to the greatest areas of need.” 

The city of Stockton, California, plans to use vehicle-mounted cameras and artificial intelligence software to help its understaffed police department detect and report code violations like overgrown lawns, improper parking, peeling paint, boarded windows and graffiti.

Stockton’s city council unanimously voted in June 2024 to enter into a $237,600-per-year contract with City Detect, after running a five-day pilot program using the company’s technology in early 2024. The contract will run for a year with optional second and third years.

The city hopes the technology will allow code enforcement officers to spend more time proactively finding violations and educating the community rather than primarily reacting to complaints, according to the legislation text.

Booming interest in AI has left cities grappling with how to use it to make local government more efficient while minimizing negative unintended consequences. 

“AI is going to work its way into your city at some point,” whether it’s through a vendor or an intern, said Stephen Caines, chief innovation officer for the city of San Jose, California, at Smart City Expo USA. He advised cities to figure out the rules of the road now. 

Stockton will be the first California city to use City Detect’s software, officials told CBS News. The company uses vehicle-mounted cameras to analyze scenarios throughout the city, with its machine learning system analyzing the built environment “exponentially faster than traditional reporting methods,” according to the city’s legislation text. 

Locations are given a relative blight score, which reflects their blight or deterioration compared with other areas, the document says. That score allows code enforcement officers to prioritize resources and interventions.

The city says its five-day pilot with the technology in January identified 4,000 code violations at over 2,000 locations. 

Some other cities are using AI technology similarly. Memphis, Tennessee, for example, is using vehicle-mounted cameras to spot potholes. Columbia, South Carolina, is also testing City Detect’s technology with cameras mounted on garbage trucks. However, some residents have expressed privacy concerns, local newspaper The State reports.