Wireless Sensors for Traffic Control
The attempt to control traffic was first demonstrated during the Roman empire. However, till the middle of the nineteenth century, nothing concrete was discovered. It was in 1868, near Westminister Abbey, in London, England that J.P Knight, a railway signalling engineer designed the first traffic light. The device unexpectedly exploded resulting in the death of a police officer hence the use of the device was discontinued.
In America, the modern traffic light was discovered. In 1918, New York adopted a three-color system that was manually operated from a tower, which was present in the centre of the street. Some traffic signaling devices were also created by Garrett Morgan, also the inventor of the gas mask. Morgan, on witnessing an accident between a carriage and a car felt the need to design a system that will help stop collisions. Morgan designed an electric traffic light system with a pole having a cross section with the words STOP and GO illuminated.
In London, the first automatic signals were developed in 1926 that were activated using a timer. Vehicle-activated lights were designed in the 1930’s. In this system, the weight of the car rolling over half-buried rubber tubes displaced the air present in the tubes, an increase in pressure occurred, driving an electric contact, which activated the lights.
Following this, an inductive-loop device was developed. The road was embedded with a wire loop, which was linked to a box used for controlling the lights. An electric current was transmitted through the loop, and an activation signal was emitted when a steel car body travelled overhead.
The traffic volume on the highway can be easily determined using a computer activated guidance system. Using that, traffic is automatically routed onto limited access highways. Most of today’s cars are provided with global positioning satellite systems (GPS) that connect to a satellite helping the drivers to easily and quickly identify the routes to reach their desired destinations.
Wireless Sensors for Traffic Control
A research conducted by Yousef MK et al (2010) studied an intelligent traffic light control system using wireless sensor networks.
The design of the wireless sensor network (WSN) used in the proposed traffic light control system is discussed in detail.
In order to enable system components to communicate, two algorithms that include the traffic system communication algorithm (TSCA) and traffic signals time manipulation algorithm (TSTMA) were developed. The successful operation of the control system depends on the interaction of the algorithms with each other as well as with other system components. The process begins with the traffic WSN, the TSA and the TSTMA and ends by the setting an effective time on the traffic signals for specific traffic light durations.
Communication routes between base stations and traffic sensor nodes can be identified and controlled using the TSCA. A direct routing scheme is used by the TSCA wherein the distribution of all TSNs must be within the range of the base station. All the vehicles are identified and counted using the traffic sensor nodes (TSN). This information is periodically transmitted to the base station. Based on the number of TSNs, system operation is segregated into time slots in which each TSN will operate. The aggregated traffic information is then transferred to the TSTMA in order to fix the duration of the time for traffic signals dynamically based on the number of vehicles on each traffic signal.
Installation of the traffic sensor nodes into small holes centered in each lane in the roadbed. These holes are designed such that they are safe, protected from the condition of the roadbed and the environment and do not interfere with the operations of the TSN. Sensefields, a company that focuses on traffic technology have recently developed a wireless system for the detection and monitoring of road traffic as is demonstrated in the video below:
Alonso L et al (2010) conducted a study on ultrasonic sensors in urban traffic driving aid systems. A Hexamite HXN43TR ultrasonic sensor behaving as a transmitter-receiver along with its signal conditioner was used for developing a prototype.
The key features of this sensor are:
4 kHz bandwidth
43.0 ± 5.0 kHz central frequency
Narrow beam pattern of 8.5° at -3 dB.
Stages involved in signal processing are as follows:
Noise level is reduced by filtering via a band-pass filter. This filter has a lower cut off frequency of 42 kHz and upper cutoff frequency of 44 kHz.
The signal’s envelope is obtained by calculating the Hilbert transform
Parts with high amplitude are amplified selectively by increasing the signal envelope to the third power, thus reducing the false echoes which are originated due to occurrence of turbulences or irregularities in the road or in other words, improving discrimination between the noise and the signal
Compensation of the ultrasonic wave’s attenuation via amplification using an exponential gain.
The resulting signal is compared with a preset threshold value for acquiring the distance between the vehicles.
Steingröver M et al (2005) conducted a study on reinforcement learning of traffic light controllers adapting to traffic congestion.
Findings from this research concludes that by allowing traffic lights to take into account the amount of traffic congestion (i.e., adding implicit traffic light co-operation) helps algorithms to deliver better results. Future studies may include evaluating the chances of global co-operation between traffic light junctions. This can be done by allowing a vehicle to not only account the next traffic light but also the others that are on its path. Another avenue for research is the issue of rapidly increasing state spaces. One may use hierarchical methods with a divide-and conquer strategy for dealing with large problems. States can be categorized into higher-level states, causing easier optimization problems and smaller state spaces.
The third future avenue for research is the simulation of accidents. Local congestions caused by accidents can be solved by traffic light controllers. Adaptive traffic controllers need to be designed that can effectively route traffic around accident spots. The ultimate goal is that this and other reinforcement learning methods be studied and implemented in real traffic systems. Before one actually attains that goal, it is important to obtain more results with regards to the robustness of the controllers, and techniques of attaining required sensor capabilities.
Ahmad. BA. Development of a traffic light control system using programmable logic controller. Ahmad. BA. Development of a traffic light control system using programmable logic controller. Pages 1–24.
Yousef MK, AL-karakI JN, Shatnawi MA. Intelligent Traffic Light Flow Control System Using Wireless Sensors Networks. Journal of Information Science and Engineering. 2010; 26: 753–768.
Alonso. L, Milanés. V, Torre-Ferrero C, Godoy J, Oria JP, De Pedro D. Ultrasonic Sensors in Urban Traffic Driving. Sensors. 2010; 11: 662–672.
Steingröver M, Schouten R, Peelen S, Nijhuis E, and Bakker B. Reinforcement learning of traffic light controllers adapting to traffic congestion. Pages 1- 8.