Artificial Intelligence - Intelligent Transportation.


The use of advanced technology, artificial intelligence, and control systems to manage highways, cars, and traffic is known as intelligent transportation.

Traditional American highway engineering disciplines such as driver routing, junction management, traffic distribution, and system-wide command and control inspired the notion.

Because it attempts to embed monitoring equipment in pavements, signaling systems, and individual automobiles in order to decrease congestion and increase safety, intelligent transportation has significant privacy and security considerations.

Highway engineers of the 1950s and 1960s were commonly referred to as "communications engineers," since they used information in the form of signs, signals, and statistics to govern vehicle and highway interactions and traffic flow.

During these decades, computers were primarily employed to simulate crossings and calculate highway capacity.

S. Y. Wong's Traffic Simulator, which used the facilities of the Institute for Advanced Investigate (IAS) computer in Princeton, New Jersey, to study traffic engineering, is one of the early applications of computing technology in this respect.

To show road systems, traffic regulations, driver behavior, and weather conditions, Wong's mid-1950s simulator used computational tools previously established to investigate electrical networks.

Dijkstra's Algorithm, named for computer scientist Edsger Dijkstra, was a pioneering use of information technology to automatically construct and map least distance routes.

In 1959, Dijkstra created an algorithm that finds the shortest path between a starting point and a destination point on a map.

Online mapping systems still use Dijsktra's routing method, and it has significant economic utility in traffic management planning.

Other algorithms and automated devices for guidance control, traffic signaling, and ramp metering were developed by the automotive industry during the 1960s.

Many of these devices, such as traffic right-of-way signals coupled to transistorized fixed-time control boxes, synchronized signals, and traffic-actuated vehicle pressure detectors, became commonplace among the public.

Despite this, traffic control system simulation in experimental labs has remained an essential use of information technology in transportation.

Despite the engineers' best efforts, the rising popularity of vehicles and long-distance travel stressed the national highway system in the 1960s, resulting in a "crisis" in surface transportation network operations.

By the mid-1970s, engineers were considering information technology as a viable alternative to traditional signaling, road expansion, and grade separation approaches for decreasing traffic congestion and increasing safety.

Much of this work was focused on the individual driver, who was supposed to be able to utilize data to make real-time choices that would make driving more enjoyable.

With onboard instrument panels and diagnostics, computing technology promised to make navigation simpler and maximize safety while lowering journey times, particularly when combined with other technologies like as radar, the telephone, and television cameras.

Automobiling became more informed as computer chip prices dropped in the 1980s.

Electronic fuel gauges and oil level indicators, as well as digital speedometer readouts and other warnings, were added to high-end automobile models.

Most states' television broadcasts started delivering pre-trip travel information and weather briefings in the 1990s, based on data and video collected automatically by roadside sensing stations and video cameras.

These summaries were made accessible at roadside way stations, where passengers could get live text-based weather forecasts and radar pictures on public computer displays, as well as as text on pagers and mobile phones.

Few of these innovations have a significant influence on personal privacy or liberty.

However, in 1991, Congress approved the Intermodal Surface Transportation Efficiency Act (ISTEA or "Ice Tea"), which allowed $660 million for the creation of the country's Intelligent Vehicle Highway System (IVHS), which was coauthored by Secretary of Transportation Norman Mineta.

Improved safety, decreased congestion, greater mobility, energy efficiency, economic productivity, increased usage of public transit, and environmental cleanup are among the aims set out in the act.

All of these objectives would be achieved via the effective use of information technology to facilitate transportation in the aggregate as well as on a vehicle-by-vehicle basis.

Hundreds of projects were funded to provide new infrastructure and possibilities for travel and traffic management, public transit management, electronic toll payment, commercial fleet management, emergency management, and vehicle safety, among other things.

While some applications of intelligent transportation technology remained underutilized in the 1990s—for example, carpool matching—other applications became virtually standard on American highways: for example, onboard safety monitoring and precrash deployment of airbags in cars, or automated weigh stations, roadside safety inspections, and satellite Global Positioning System (GPS) tracking for tractor-trailers.

Private enterprise had joined the government in augmenting several of these services by the mid-1990s.

OnStar, a factory-installed telematics system that utilizes GPS and cell phone communications to give route guidance, summon emergency and roadside assistance services, track stolen cars, remotely diagnose mechanical faults, and access locked doors, is included in all General Motors vehicles.

Automobile manufacturers also started experimenting with infrared sensors coupled to expert systems for autonomous collision avoidance, as well as developing technology that allows automobiles to be "platooned" into huge groups of closely spaced vehicles to optimize highway lane capacity.

The computerized toll and traffic management system, which was launched in the 1990s, was perhaps the most widely used implementation of intelligent transportation technology (ETTM).

By placing a radio transponder on their cars, ETTM enabled drivers to pay their tolls on highways without having to slow down.

Florida, New York State, New Jersey, Michigan, Illinois, and California were all using ETTM systems by 1995.

Since then, ETTM has expanded to a number of additional states as well as internationally.

Because of its potential for government intrusion, intelligent transportation initiatives have sparked debate.

Hong Kong's government deployed electronic road pricing (ERP) in the mid-1980s, with radar transmitter-receivers triggered when cars went through tolled tunnels or highway checkpoints.

This system's billing bills supplied drivers with a full record of where they had gone and when they had been there.

The system was put on hold before the British turned Hong Kong over to the Chinese in 1997, due to concerns about potential human rights violations.

On the other hand, the basic purpose of political surveillance is sometimes broadened to include transportation goals.

The UK government, for example, placed street-based closed-circuit television cameras (CCTV) in a "Ring of Steel" around London's financial sector in 1993 to defend against Irish Republican Army terror bombs.

Extreme CCTV enhanced monitoring of downtown London ten years later, in 2003, to incorporate several infrared illuminators enabling the "capture" of license plate numbers on autos.

A daily usage tax was imposed on drivers entering the crowded downtown area.

Vehicles with a unique identification like the Vehicle Identification Number (VIN) or an electronic tag may be tracked using technologies like GPS and electronic payment tollbooth software, for example.

This opens the door to continuous monitoring and tracking of driving choices, as well as the prospect of permanent movement records.

Individual toll crossing locations and timings, the car's average speed, and photos of all passengers are routinely obtained by intelligent transportation surveillance.

In the early 2000s, state transportation bureaus in Florida and California utilized comparable data to send out surveys to individual drivers who used certain roads.

Several state motor vehicle agencies have also contemplated establishing "dual-use" intelligent transportation databanks to supply or sell traffic and driver-related data to law enforcement and marketers.

Artificial intelligence methods are becoming an increasingly important part of intelligent transportation planning, especially as large amounts of data from actual driving experiences are now being collected.

They're being used more and more to control vehicles, predict traffic congestion, and meter traffic, as well as reduce accident rates and fatalities.

Artificial neural networks, genetic algorithms, fuzzy logic, and expert systems are among the AI techniques already in use in various intelligent transportation applications, both singly and in combination.

These methods are being used to develop new vehicle control systems for autonomous and semiautonomous driving, automatic braking control, and real-time energy consumption and emissions monitoring.

Surtrac, for example, is a scalable, adaptive traffic control system created by Carnegie Mellon University, which uses theoretical modeling and artificial intelligence algorithms.

The amount of traffic on particular roadways and intersections may change dramatically throughout the day.

Traditional automated traffic control technology adapts to established patterns on a set timetable or depends on traffic control observations from a central location.

Intersections can communicate with one another, and automobiles may possibly exchange their user-programmed travel paths, thanks to adaptive traffic management.

Vivacity Labs in the United Kingdom employs video sensors at junctions and AI technology to monitor and anticipate traffic conditions in real time during an individual motorist's trip, as well as perform mobility assessments for enterprises and local government bodies at the city scale.

Future paths in intelligent transportation research and development may be determined by fuel costs and climate change consequences.

When oil costs are high, rules may encourage sophisticated traveler information systems that advise drivers to the best routes and departure times, as well as expected (and costly) idling and wait periods.

If traffic grows more crowded, cities may use smart city technologies like real-time traffic and parking warnings, automated incident detection and vehicle recovery, and linked surroundings to govern human-piloted vehicles, autonomous automobiles, and mass transit systems.

More cities throughout the globe are going to use dynamic cordon pricing, which entails calculating and collecting fees to enter or drive in crowded regions.

Vehicle occupancy detection monitors and vehicle categorization detectors are examples of artificial intelligence systems that enable congestion charging.


Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also: 

Driverless Cars and Trucks; Trolley Problem.

Further Reading:

Alpert, Sheri. 1995. “Privacy and Intelligent Highway: Finding the Right of Way.” Santa Clara Computer and High Technology Law Journal 11: 97–118.

Blum, A. M. 1970. “A General-Purpose Digital Traffic Simulator.” Simulation 14, no. 1: 9–25.

Diebold, John. 1995. Transportation Infostructures: The Development of Intelligent Transportation Systems. Westport, CT: Greenwood Publishing Group.

Garfinkel, Simson L. 1996. “Why Driver Privacy Must Be a Part of ITS.” In Converging Infrastructures: Intelligent Transportation and the National Information Infrastructure, edited by Lewis M. Branscomb and James H. Keller, 324–40. Cambridge, MA: MIT Press.

High-Tech Highways: Intelligent Transportation Systems and Policy. 1995. Washington, DC: Congressional Budget Office.

Machin, Mirialys, Julio A. Sanguesa, Piedad Garrido, and Francisco J. Martinez. 2018. “On the Use of Artificial Intelligence Techniques in Intelligent Transportation Systems.” In IEEE Wireless Communications and Networking Conference Worshops (WCNCW), 332–37. Piscataway, NJ: IEEE.

Rodgers, Lionel M., and Leo G. Sands. 1969. Automobile Traffic Signal Control Systems. Philadelphia: Chilton Book Company.

Wong, S. Y. 1956. “Traffic Simulator with a Digital Computer.” In Proceedings of the Western Joint Computer Conference, 92–94. New York: American Institute of Electrical Engineers.

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