What Is Urban Mobility?
At its core, urban mobility is about how people and goods move through a city. It seems simple, but as you consider all factors—infrastructure, technology, politics, culture—how we navigate cities is a complex subject with an ancient history and an increasingly sophisticated technology-fueled future.
Urban Mobility Challenges
The fundamental challenges of getting around a city have been with us since humans began living in urban environments. How do we balance congestion, ease of movement, and pollution?
The tensions between innovation in transportation, constantly growing urban populations, and civic government’s ability to keep up with limited resources are timeless. But the pressure today’s cities face is unprecedented.
Urban population growth will continue to strain urban mobility:
- 4.6 billion city dwellers by 20251
- 41 megacities by 20302
- 67.1 trillion passenger kilometers on mass transit by 20502
Time spent sitting in traffic is a major drain for individual commuters and the overall productivity of the world’s cities. According to a 2019 Global Traffic Scorecard from INRIX,3 the US lost an average of 99 hours per driver to congestion in 2019, costing nearly USD 88 billion in lost productivity.
Congested roadways do more than waste time. Traffic accidents kill 1.35 million people around the world each year and leave between 20 and 50 million people with nonfatal injuries. More than half of all road traffic deaths and injuries involve pedestrians, cyclists, and motorcyclists and their passengers.
Congested stop-and-go traffic increases the volume of microscopic particulates in vehicle exhaust.4 According to the World Health Organization, exhaust particulates are “capable of penetrating deep into lung passageways and entering the bloodstream causing cardiovascular, cerebrovascular, and respiratory impacts.”5
Cities everywhere are concerned about environmental sustainability, and transportation is a major contributing factor.
An estimated 22 percent of global carbon dioxide (CO2) emissions are attributed to the transportation sector,6 which will rise as cities grow and individual citizens adopt carbon-burning transportation.
For example, without major investments in public transportation and smart road technologies, CO2 emissions in India are projected to rise from 70 megatons in 2015 to 540 megatons by 2050.7
Meeting Urban Mobility Challenges Today with Smart City Technology
Cities are improving mobility, saving energy, and reducing pollution today with smart city IoT technologies.
Smart cities take in data from every available input—traffic signals, cameras, embedded devices on public transit—then analyze the data with AI and share it through open data pools. This creates a constant awareness of conditions that can be used for traffic management, route planning, public safety, and emergency response.
Smart City Technologies Can Be Embedded Throughout a City’s Infrastructure
- Smart pavement can monitor weather, road conditions, and even road wear and share the information with drivers and traffic controllers.
- Smart parking can alert drivers to available spaces, reducing time spent circling and searching for a spot.
- Smart traffic signals can respond to traffic conditions and retime themselves to relieve congestion.
- Smart streetlights with cameras, microphones, and sensors can dim themselves when no one is around and gather intelligence on traffic, public safety, and air quality.
- Smart cameras can keep an eye on traffic and public safety. When problems arise, they automatically alert transportation systems.
- Smart intersections can optimize vehicle and pedestrian traffic, detect risks, and warn against imminent accidents, reducing crashes and injuries.
- Smart tolling can assess tolls on free-flowing traffic, support specific road congestion pricing or per-mile tolls, and collect fees electronically.
- Smart public transportation can count passengers, track vehicle locations, and automatically share status.
- Smart fleets, including public police vehicles and fire engines plus private delivery trucks, taxis, and trains, share their location, watch over their drivers, and listen to their engines, brakes, and wheels for signs of wear and tear.
Smart traffic systems can save commuters up to 60 hours a year.4
Together, these intelligent sensors, microphones, and cameras can power a model of what’s happening on a city’s streets, highways, and rail lines in near-real time. With the help of AI, this awareness can transform passive traffic management into active intelligent transportation systems.
These systems can anticipate congestion, automatically reroute traffic, retime lights, and apply dynamic tolling to help keep the city moving. The city of Bangkok, Thailand, is saving over 51,000 commuter hours a year and reducing traffic delays up to 24.5 percent with just three smart intersections.8
The Future of Urban Mobility
Multiple technologies are coming online that will fundamentally change how people and goods move through cities. Increasing vehicle intelligence, autonomous systems, and vehicle communications will improve safety, traffic flow, and emissions.
Vehicle Communication and Orchestrated Traffic
Vehicle communications technologies will turn the current state of unpredictable, “dumb” vehicles into smart, active players within the smart city’s intelligent transportation system.
Vehicle communication—between individual smart vehicles and from smart vehicles to central traffic management systems—will help traffic flow as an intelligent, unified whole that maintains consistent speeds and set vehicle distances. Throughput and fuel efficiency will increase dramatically as accidents drop and pollution eases.
Researchers in Germany have built an experimental two-kilometer test field on the autobahn that communicates with vehicles and orchestrates traffic. It uses overlapping radar sensors to model traffic in near-real time. The system shares this information with oncoming cars, signaling them when it is safe to change lanes, when to reduce speed, and when to avoid critical situations.8
Smarter, More-Flexible Public Transportation
Bus and rail will continue to be fundamental elements of urban mobility. Incremental technological upgrades, using open platforms consolidated onto standard x86 hardware, can support smart security video, monitor vehicle systems, and deliver AI-powered predictive maintenance.
These platforms can also support better rider experiences by providing Wi-Fi, onboard entertainment, and interactive information displays.
Mobility as a Service (MaaS)
Getting around a city today takes planning and effort, whether you are driving your own car, ride sharing, or navigating public transportation. Smart cities with intelligent transportation systems and open data platforms are changing mobility from a chore into a service.
Commuters today can choose from multiple apps, like Moovit, that can plan routes and schedules across public transit. As cities grow smarter, and the number of autonomous, connected vehicles increases, mobility as a service (MaaS) applications will be able to weave every available transportation option into a seamless, multimodal trip. Commuters will save time while the system optimizes traffic flow, energy efficiency, and vehicle use across the city.
Urban Mobility Technology Requirements
Reaching the highest levels of intelligent transportation and smart, energy-efficient urban mobility requires many, many technologies. Thankfully, upgrading a city is not an all-or-nothing proposition. Many of the technologies are already in place; others only need incremental upgrades. The biggest challenges—privacy, data security, data ownership—lie at the intersection of technology and public policy.
Key Technologies for Upgrading Urban Mobility
Embedded Smart City Devices and Systems
Cities already collect massive amounts of data through existing systems like traffic and public safety cameras. Incremental upgrades can transform them into intelligent nodes in a smart city fabric.
To function in near-real time, intelligent transportation systems need immediate insight and analysis. That requires processing AI workloads at the edge, on a smart device itself, or on a nearby AI appliance.
Faster, More-Reliable Connectivity
5G promises to speed cellular data transfer rates and improve stability. Software can help orchestrate and manage edge network services for even better performance.
Open, Integrated Data Pools
Smart city and intelligent transportation data isn’t worth much if citizens, first responders, and businesses can’t access it. Ingesting, cleaning, aggregating, and sharing data through a single shared pool is critical to improving mobility and reducing congestion and pollution.
Hardware-Based Data Security
As intelligent technologies spread and interconnect, cities have to secure thousands of embedded devices and protect public and private data as it moves through the system. Hardware-based security protocols harden systems and help protect data.
Urban Mobility Policies and Standards
Public policies and industry standards are critical for developing smart city technologies for urban mobility and for deploying the IoT technologies that make them possible. Public policy sets the goals and defines the ground rules for the public agencies, private citizens, and businesses that make the city’s vision for a smart, highly mobile community a reality.
- Open standards so that every smartphone, car, traffic cam, and smart pavement monitor can plug and play with any smart city technology.
- Open data platforms so that every player has access to the data they need to do their job.
- Open processes that include all stakeholders so that people buy in and help drive the city’s technological vision.
Intel® Reference Designs for Urban Mobility
Vehicle computers, road sensors, AI, and intelligent cameras are some of the technologies that make intelligent transportation systems possible. Intel and our partners have developed multiple technologies, hardware, and software for urban mobility and smart cities. We provide prevalidated reference designs and reference implementations that can serve as the foundation for a modern smart city.
Roadside Unit Reference Design
Our reference design for roadside edge computing can be attached to streetlights and other fixtures. This roadside unit is ideal for real-time video analytics and other performance-hungry tasks. Cities can deploy these units as part of a solution for smart streetlights, smart traffic lights, smart parking, or e-tolling stations. They deliver the processing capabilities needed to detect license plates, spot pedestrians, and monitor traffic congestion. These edge nodes can even provide public Wi-Fi coverage. Supported by Intel® Vision Products with integrated security features, the AAEON Atlas edge computing node is a ready-to-deploy solution based on this reference design.
Intel’s Converged Edge Reference Architecture (CERA)
Converged Edge Reference Architecture (CERA) is a platform approach for IoT and networking workload convergence. With this architecture, our partners can design solutions for roadside equipment to process sensor modalities and perform sensor fusion. This brings intelligence to the edge while hosting 5G network capabilities and microservices.
Solutions built on this platform can be set up at intersections or on-premises for near-edge computing and data processing for multiple IoT devices. Solutions can be optimized using the Intel® Distribution of OpenVINO™ toolkit and OpenNESS toolkit.
With 5G connectivity, CERA provides networking capabilities that allow IoT devices to communicate with each other at the edge or send data to the cloud. It processes the information from cameras, radar, and a wide range of other sensors.
Intel’s reference implementations offer preconfigured software for a complete sample application.
Our intelligent traffic management reference implementation monitors intersections via IP cameras and optimizes traffic flow. It is hosted in an OpenNESS edge node, which contains all the necessary software stacks to host a 5G RAN.
Our driver management reference implementation uses computer vision to track driver behavior and fatigue. It can help avert accidents by alerting drivers in real time and can provide long-term metrics and analysis for fleet managers.
|IoT and Embedded Intel® Processors||Intel® processors come in a range of performance and power profiles for intelligent cameras, sensors, and embedded computers for public safety.|
|Intel® Xeon® Scalable Processors||Intel® Xeon® Scalable processors deliver high performance for edge servers, ideal for performing real-time analytics and AI on smart road sensor data.|
|AI and Computer Vision|
|Intel® Movidius™ VPUs||Intel® Movidius™ VPUs enable computer vision for specific use cases, such as finding or “seeing” license plates and vehicles at smart intersections.|
|Intel-Supported 5G Networks||Intel-supported 5G networks will improve real-time traffic data at the edge while also advancing connectivity and transmission to and from wireless networks.|
|Mobileye||Mobileye® technologies power Advanced Driver Assistance Systems (ADAS) in over 60 million vehicles and 300+ car models. Mobileye’s camera-centric self-driving systems—ADAS— and high-definition mapping technologies are paving the way to fully autonomous vehicles. Mobileye is an Intel company.|
|Mobility as a Service (Maas)|
|Moovit||Moovit is a leading MaaS solutions provider and maker of the No. 1 urban mobility app. Moovit is an Intel company.|
|Intel® Edge Software Hub||Find software to accelerate the development of smart road infrastructure solutions, including referenced implementations for intelligent traffic management.|
|Intel® Distribution of OpenVINO™ Toolkit4||The Intel® Distribution of OpenVINO™ toolkit streamlines the development of vision applications on Intel® platforms, including VPUs and CPUs. This portfolio enables computer vision to locate pedestrians, cars, and street signs.|
|OpenNESS||OpenNESS open source software simplifies the complex orchestration and management of edge services across diverse network platforms and access technologies.|
|Intel® DevCloud for the Edge||Reduce time and costs in determining the right hardware for optimal AI application performance. Intel® DevCloud for the Edge provides instant performance feedback via a virtual AI prototyping tool.|
|Open Visual Cloud||This collection of open source stacks and pipelines includes optimized ingredients for encode, decode, inference, and render. This reusable environment makes it easier to test, evaluate, and deploy video on demand (VOD) including live streaming with SVT-AV1.|