Smart City Infrastructure: A Background for Risk Management (Part 1)

Drew Groth, ACAS, MAAA
Associate Actuary, Milliman
Jonathan Riehl, PhD, PE
Transportation Systems Engineer, University of Wisconsin-Madison
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In the past decade, more cities have been focusing on advanced infrastructure to improve public services. The objective is to make the city services operate more efficiently and safely for all residents and visitors, and to be more equitable for citizens who have traditionally been underserved.

Smart infrastructure systems have the potential to make cities better places to live and more efficient. They also create many new scenarios requiring public risk managers to step into the unknown and develop novel risk management strategies.

Smart City Infrastructure

Smart city infrastructure is a broad term referring to a range of projects, usually centering around the use of a specific technology to support roads, buildings, parking, utilities, and other city-managed systems. Smart city infrastructure includes:

  • Smart data platforms: Open-source data platforms incorporating smart city data for development of third-party applications (e.g., live bus location updates). These platforms serve as the basis for many other smart infrastructure systems.
  • Traffic and transportation management centers: Centralized locations where all transportation feeds are processed, usually utilizing an array of displays to monitor traffic operations.
  • Dynamic traffic signal timing: Traffic signals that utilize methods to measure current traffic flows and adapt to provide more green-light time and optimize travel for platoons of vehicles through a corridor.
  • Connected traffic signal systems: Traffic signals that have a form of wireless communication and are able to communicate directly with surrounding vehicles to optimize traffic flow and exchange safety information (e.g., red-light runner warnings, pedestrian in crosswalk warnings, etc.).
  • City vehicle route optimization: Optimizing routes and schedules for city vehicles such as trash pickup, street cleaners, or snowplows.
  • Mobility-as-a-service (MaaS): Seamless integration of all transportation modes on a common platform to allow point-to-point booking using multiple transportation services to get people to places faster, cheaper, and/or more efficiently. Trips can be paid for individually or through a subscription model. MaaS includes common payment systems and touchless fare collection.
  • Smart mobility hubs: Strategically located transportation centers within the metro area with access to multiple modes of transit including buses, light rail, bikes, and scooters.
  • Micromobility: Lightweight, low-speed, personal vehicles to allow for fast transportation to destinations over a few blocks away and up to a few miles. These vehicles include bike shares, e-bikes, electric scooters, and mopeds.
  • Automated shuttle services: Automated shuttles can be deployed in traditionally underserved areas of the city to provide dynamic routes that connect to the larger transit systems and serve neighborhoods all hours of the day. Other areas for automated shuttle use include campus routes, first-last mile links, and local deliveries.
  • Smart streetlighting: Streetlights that are connected to the city’s management center that can be set to automatically turn on and off and sense pedestrian presence to turn on when needed.
  • Curb-space management: Treating curb space as the valuable asset that it is, these systems charge for use of the curb space based on time of day, location, vehicle size, and vehicle classification.
  • Smart parking garages and spaces: Parking garages that can automatically count spaces available and relay this information to potential parkers. Specific spots can be found and/or reserved through more advanced systems.

The risk management implications of this infrastructure will be discussed in Part 2...

*The views and opinions expressed in the Public Risk Management Association (PRIMA) blogs are those of each respective author. The views and opinions do not necessarily reflect the official policy or position of PRIMA.*

By: Drew Groth, ACAS, MAAA
Associate Actuary, Milliman

Drew is an associate actuary in the Milliman Milwaukee office with expertise in predictive modeling, ratemaking and loss reserving. He has experience in varying lines of business including workers' compensation, personal lines and commercial auto, with clients ranging from self-insured corporations to large insurance providers. Drew also has experience working on projects with start-up companies where out of the box thinking is required to craft customized solutions. Through his diverse experience at Milliman, he has developed a passion for autonomous vehicle technology with respect to risk management and insurance, especially as it concerns commercial uses.

By: Jonathan Riehl, PhD, PE
Transportation Systems Engineer, University of Wisconsin-Madison

Jon is a transportation systems engineer in the Traffic Operations and Safety Lab at the University of Wisconsin-Madison and helps manage the Wisconsin Automated Vehicle Proving Grounds, including the Park Street Connected Corridor and the automated shuttle program. His work responsibilities include research in transportation systems management and operations (TSM & O), connected and automated vehicles and geographic information systems (GIS), and teaching. Jon holds a PhD in civil engineering from Michigan Tech and has master’s degrees in electrical engineering, geography, and business.

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