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Every day throughout the city of Boston, thousands of commuters spend large portions of their drive waiting in traffic. All it takes for traffic to build up is one accident on a main street and cars will be backed up for miles. Part of this issue is due to the volume of commuters entering and leaving the city during rush hour, but part of the blame can also be placed on traffic control. Traffic control, specifically the use of traffic signals, is fairly far behind today's technology. Although traffic signals are monitored and timings are adjusted every five years, the results are still not optimal. There needs to be a way for signals to utilize current technologies in order to optimize commute times and allow for people's already sparse free time to be spent how they like. Along with people's free time being wasted, there is also a large amount of unnecessary fuel consumption happening while waiting in traffic. Something needs to be done about traffic issues throughout high population cities such as Boston.

Activity Report for FY 2013. Rep. Boston Traffic Management Center, n.d. Web.

The Boston Traffic Management Center produces an annual activity report in order to publish the statistics and usage of its department. Currently, the Traffic Management Center (TMC) "has computer control of 544 out of the 845 traffic signals operated by the Boston Transportation Department (64%)"(1). The department made 31,000 real-time manual changes to the timing of traffic signals based on current traffic conditions. The department also owns 195 traffic monitoring cameras throughout the city. These statistics show that the Boston Transportation Department has part of the infrastructure to implement an automated real-time traffic control system. The automated system would be able to make the 31,000 manual changes that employees made throughout the year by itself and reduce traffic.

DeMarco, Peter. "Exploring the Science behind Traffic Lights." Boston.com. N.p., 09 May 2010. Web. 13 July 2016.

An article published by Boston.com discusses the current technology used in traffic signals in the Quincy area. Jack Gillon, a traffic engineer in Quincy, explained how he has total control of the lights from a central location. He also quickly showed how sensors detect cars at traffic signals. Most traffic signals have an electrically charged wire underneath the pavement. When a car passes over this wire, a change in magnetic field is measured and sent to the control box of the light. This tells the light that there is a car waiting to go through the intersection or make a turn. This technology could be implemented in an automated traffic light system by sending the readings from the charged wire to a central network. The central network could then take in these readings and account for the incoming traffic at the next lights that the car would encounter.

Gilmore, John F., Khalid J. Elibiary, and Naohiko Abe. "Neural Network System for Traffic Flow Management." AAAI Technical Report (1993): 85-95. Web.

John Gilmore along with Khalid Elibiary of the Georgia Tech Research Institute produced a paper for Association for the Advancement of Artificial Intelligence regarding the use of neural networks to control traffic in 1993 with the assistance of Naohiko Abe of Mitsubishi Heavy Industries in Japan. Gilmore and his associates go into depth about how neural networks can be implemented into a traffic control system to improve traffic conditions. They introduce the idea of using the Hopfield neural network model in order to allow the network to learn and build off of its knowledge of previous congestion situations to improve traffic conditions. The group forms an energy function which allows them to assign lights and traffic conditions different weights. Higher weighted traits, such as how busy an intersection generally is, allow lights to have a higher priority and change faster. The usage of a neural network as described by Gilmore, Elibiary, and Abe can be used as a base for a city wide traffic control system, where the system learns and adapts based on previous traffic data. This could cut out a large amount of traffic by optimizing the traffic flow.

Santi, Paolo, and Carlo Ratti. "Revisiting Street Intersections Using Slot-Based Systems." PLOS ONE. N.p., n.d. Web. 13 July 2016.

Paolo Santi of the Senseable City Lab and Carlo Ratti of MIT outlined a plan to eliminate the need for traffic lights at all using automation in cars. For Santi's system, called a slot-based system, to work, cars need to be autonomously operated. The cars need to be able to communicate with each other to determine where they are on the road and what their planned route is at each light. The cars would then decide amongst themselves which car is turning when with no need for traffic lights. If all cars could communicate and turn without interfering with each other, it would eliminate the need for stop lights and would greatly decrease traffic given that the cars will harmoniously move through the streets. Although this seems like the optimal solution, it would first require that all cars be autonomous, so this would not be a solution for the near-term.

Srinivasan, Dipti, Min Chee Choy, and Ruey Long Cheu. "Neural Networks for Real-Time Traffic Signal Control." IEEE Trans. Intell. Transport. Syst. IEEE Transactions on Intelligent Transportation Systems 7.3 (2006): 261-72. Web.

Dipti Srinivasan and her colleagues explore the use of real-time neural networks in order to assist with traffic control in this paper for an Institute of Electrical and Electronics Engineers journal on Intelligent Transportation Systems. Srinivasan uses the concept of a neural network to take in real-time traffic statistics and adjust traffic lights accordingly. The current conditions of traffic are available to the system and can be included in calculations made to determine how long to have lights turned green. Her results showed that the implementation of a real-time neural network can allow a greater number of cars to pass through congested intersections. Dipti's real-time neural network could be applied in a city like Boston where sensors could be installed to acquire real-time data used in the program to control the lights. Traffic control based on current conditions would produce much better traffic flow than average timed lights.

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