America’s roads, streets and highways are rapidly becoming clogged with snarled traffic as more and more vehicles take to our roadways. We pollute the air, waste time and energy and put up with ever-increasing frayed nerves. The solution is complex and will take decades and trillions of dollars to implement. We need more and better roads, more fuel-efficient vehicles and a greater use of public transportation. Another important element in solving our growing vehicular transportation dilemma is that of Smart Traffic Management (STM).

     A major part of Smart Traffic Management involves an adaptive computer-controlled system that takes advantage of the advances in Artificial Intelligence (AI) and computer technology that have taken place since the limited computer-controlled traffic signal systems that were hurried into place during the Arab oil embargoes of the 1970’s.

     Such systems combine traditional traffic lights with arrays of sensors and artificial intelligence to efficinetly route vehicle and pedestrian traffic. These systems continually adapt to current traffic conditions as opposed to the static traffic management systems in use today. Most current traffic controls utilize a fixed set of timing rules that do not adapt to changing traffic conditions. A modern Smart Traffic Management system will reduce pollution, reduce fuel consumption, and speed up traffic flow. It will do so by reducing traffic congestion, by smoothing traffic flows, and by prioritizing traffic in response to demand. It will eliminate stop-start driving, which is inefficient and polluting.

     As opposed to the environment which existed an the 1970’s, today we have GPS, cellphone communications, smartphones, video surveillance cameras, infinitely more powerful and cheaper computer power, a burgeoning artificial intelligence technology, and many more technological advances. Shortly, we will add intervehicle communications to the panoply of technologies available. In-ground sensor systems have been installed in some locations around the world, allowing intelligent traffic systems to wirelessly communicate with software in a secure cloud infrastructure. This can provide data on daily traffic, volume and capacity, and vehicle speeds, plus signal timing tools to better manage traffic.[1]

     Starting with Arab oil embargoes of the 1970’s, the federal government began to take action to reduce the fuel consumption of vehicles on the highways of America. A side effect of this effort was the beginning of the reduction of environmental pollutants created in the exhausts of these vehicles. Speed limits on our highways were imposed and some feeble attempts at traffic light synchronization were made. Large trucks added air flow streamlining panels and auto makers began to develop more fuel-efficient vehicles. “When Congress enacted the CAFÉ {Corporate Average Fuel Economy} statute in 1975, it {simply} sought to push the automakers to build high-mileage vehicles to defend against serious fuel shortages and skyrocketing pump prices caused by the Arab oil embargo of 1973.” (Ref. 2)

     Over the past 4 decades, the U.S government has mandated fuel efficiency standards for automakers – at first to reduce fuel consumption and today, primarily to reduce the emission of greenhouse gasses. The Obama administration was demanding that auto makers increase average gas mileage to 54.5 miles per gallons by 2025 in order reduce hydrocarbon emissions with the goal of reducing or slowing global warming and improving the environment. Realistically however, Obama's fuel economy mandate was little more than a thinly disguised electric car mandate, since hitting that level would have required a substantial increase in electric vehicle sales. As it stands, there are only a handful of gasoline-powered cars that could potentially go more than 54 miles on a gallon of gas and consumers would almost certainly not like the performance, comfort or safety of those vehicles.[3]

     But, in August of 2018, the Trump White House announced that it was “moving ahead on its much-anticipated plan to roll back the fuel economy mandate set by the Obama administration. . .
     “The previous guidelines, which were reached during Obama's first term, {called} for automakers to steadily reach a fleet average of 54.5 miles per gallon by 2025 — though with credits and other modifications, the actual figure {was} expected to wind up in the low to mid-40 mpg range. . . . the new standard would be frozen at 2020 levels, around 37 mpg.” (Ref. 4) This standard is both realistic and attainable.

     Instead of publishing unrealistic feel-good fuel economy standards, the U.S. government needs to mandate realistic fuel economy standards AND do its part by investing money in such efforts as the mandating AND funding of STM systems throughout the country.

     Today, we are wasting billions of dollars driving on our highways. To fix the problems will be very expensive. It has been reported that “42 percent of America’s major urban highways remain congested, which costs an estimated $101 billion in wasted time and fuel annually. [Emphasis mine]
     “. . . in order to improve US roadways, the nation would have to invest $3.6 trillion, which could save resources and create jobs and safer roads.” (Ref. 5)

     Smart Traffic Management systems are currently being introduced throughout the world on a trial basis and on relatively small scales. What the United States needs is a national Smart Traffic Management program to upgrade vehicular transportation throughout the country. We need a program similar to the Eisenhower Interstate Road Program of the 1950’s and a concerted effort similar to the Apollo Moon Project of the 60’s and 70’s.

     Examples of STM programs that have been initiated are summarized in the following: (Ref. 6)

     The city of Toronto, Canada recently announced the launch of two smart traffic signal pilot projects to independently adjust to real-time traffic conditions.
     Unlike standard traffic signals that are fixed to a set timing cycle for morning and afternoon rush hours and off-peak times, the new smart signals will be able to adjust traffic signals independently to respond to real-time traffic patterns at any time of the day. They can also communicate and synchronize with other smart signals in the vicinity to alleviate congestion.
     Toronto will test two technologies during 2018 to determine which works best for Toronto. The city will pilot a technology which makes decisions based on video-analysis camera detection that measures queue lengths on the approach to the intersection and relays that data a control point.
     The second smart traffic system that will be tested is a technology that makes decisions using radar detection to measure traffic flow at intersections.

     In 2017, Miami-Dade County approved a project to install smart traffic signals at some of the county’s roadways. It used a new technology to operate traffic lights according to the changing flow of vehicles.
     The traffic controller used next-generation, web-based user interface traffic control software. It was designed to expand traffic control capabilities and prepare agencies for the upcoming demands of connected and autonomous vehicle (CAV) and smart city traffic control systems.

     Another example of Smart Traffic Management initiatives comes from Shenzhen, China and is described below: (Ref. 7)

     Shenzhen in China has a population of approximately 21 million, and like many of the world’s other large cities, it faces growing pains, particularly traffic congestion. Because it has the highest vehicle density in China, the conflict between people, vehicles, and roads was an increasing problem.
     Shenzhen adopted "Big Data" methods of translating data into solid plans and encouraged public participation As a result, a series of standardized service innovations, such as the "zipper merge" and reversible lanes was introduced.
     A more efficient and intelligent traffic management technology architecture — a city-wide traffic brain – was introduced. This traffic brain is based on high-level design principles that help to comprehensively plan the urban traffic system. The plan includes cutting-edge technologies such as video cloud, “Big Data”, and Artificial Intelligence (AI) in order to build a unified, open, and intelligent traffic control system.
     "Big Data" was used to create a unified data collection, analysis, and processing platform to achieve higher levels of information resource sharing, integration, and utilization. As a result, the analysis of traffic data made it possible to deliver more high-quality and efficient traffic services.
     The use of this "Big Brain" technology has resulted in: 1) an ultra-broadband traffic network that uses a high-speed optical communication network, 2) full awareness of city-wide traffic using a road monitoring system that can detect traffic conditions through license plate identification, video surveillance, and other methods with a detection accuracy rate of up to 95%, 3) AI-assisted law enforcement technology which permits a response to checkpoint data within seconds, 4) Improved crime fighting efficiency based upon traffic-analysis modeling to create multiple reports of disqualified driving, drunk driving, and cars with multiple violations. Vehicles with false license plates, scrapped vehicles, and vehicles with multiple violations have almost disappeared from the streets of Shenzhen, 5) Improved travel experience because of the use of a real-time surveillance system for all signal-equipped intersections. Also, a precise traffic signal control model based on the traffic time-space software engine was developed, which precisely establishes lanes through intersections, and organizes traffic flow.

     “Although adaptive signal control has demonstrated economic and customer satisfaction benefits—reducing travel time, delays, and stops—and has been used abroad for more than three decades, most jurisdictions in the United States still use fixed-length, time-of-day traffic control systems. A major barrier to wider adoption of ‘smart’ traffic control systems has been cost: initial investments in signal control hardware, communication networks, and comprehensive traffic studies, as well as the cost of periodic updates to adjust systems to changing traffic conditions. {For this reason}, it is essential that the universal introduction of smart traffic controls in the United States, be driven and financed (in large part) by the federal government. [Emphasis mine]
     “Both conventional and adaptive systems require periodic traffic studies and recalibration. Improvements in technologies associated with adaptive traffic control have paved the way for a next generation of adaptive systems that may spur broader implementation. Hardware memory and processors, including add-on processor boards for legacy hardware, now offer more powerful computing resources at a reasonable cost. Detector technology is also presenting new possibilities, including small, wireless, individual detectors. Finally, the presence of high-bandwidth communications networks in more locales creates new communications possibilities for transportation agencies.
    - - -
     “The goal . . . is an intelligent system that continuously adapts its operations to changing traffic conditions via high-speed communications with vehicles and infrastructure. This system {would use} self-adaptive algorithms to integrate the position, speed, and queue data received from vehicles and infrastructure sensors and transmitters, accurately perform high speed computations, make predictions, and continuously adjust its critical parameters based on incoming data—a strategy of monitor, learn, predict, and respond optimally.
    - - -
     “Ultimately, it is envisioned, self-adaptive control systems that communicate with moving vehicles and intelligent infrastructure will perform many functions to improve mobility and safety:

• Gather coordinates, class, speed, and service requests from . . . vehicles.
• Send traffic advisories directly to vehicles and . . . provide in-vehicle control, improving safety.
• Communicate planned signal durations, levels of congestion, and incident information to traffic operations centers for use by incident management teams, traffic information systems, and maintenance crews.
• Provide selective priority to and exchange information with emergency and transit vehicles and . . . provide appropriate service and priority direction to all vehicles.
• Collect data that can be used in local and regional planning models and traffic forecasts.” (Ref. 8)

1. Drive improved traffic flow with smart traffic technology., enterprise.verizon.com, Accessed 15 November 2018.
2. John McClaughry: The fuel economy mandate battle, John McClaughry, VTDIGGER, 13 August 2018.
3. Trump Dismantles Another Obama 'Achievement' — Just One More To Go, Investor's Business Daily,
   3 April 2018.
4. Trump administration moves to revoke Obama-era fuel economy standards, Paul A. Eisenstein, NBC News,
   2 August 2018.
5. What will America’s roads and highways look like in the future?, Sam Francis, Robotics and Automation,
   16 July 2017.
6. Three smart traffic initiatives in North America, Juan Pedro Tomás, Enterpriseiotinsights.com, 12 February 2018.
7. Shenzhen Creates ‘Traffic Brain’ for Safe City Driving, Li Qiang, https://e.huawei.com,
   Accessed 15 November 2018.
8. Next-Generation Smart Traffic Signals, www.fhwa.dot.gov: Publication No. FHWA-HRT-09-063 HRTM-04/08-09(1M)E, Accessed 13 November 2018.
9. Next Generation Traffic Control Systems , Tom Morton, fhwa.dot.gov/publications/research/ear/15085/15085.pdf, Accessed 15 November 2018.
10. North America Intelligent Traffic Management System (ITMS) Market Analysis Report By Solution Type (Traffic Monitoring System, Traffic Signal Control System, Intelligent Driver Information System), And Segment Forecasts, 2018 - 2024, Grand View Research: Report ID: GVR-2-68038-343-0, March 2018.
11. Smart road technology could turn highways into crash-sensing 'touchpads', David Cox, NBC News,
    26 June 2018.
12. What The Roads Of The Future Could Look Like, Amanda Macias, Business Insider, 3 December 2014.