Implementation of Adaptive Traffic Light in Kuwait

Implementation of Adaptive Traffic Light in Kuwait

Technology has been a crucial factor that drives the rapid developments witnessed in the contemporary world. The technological environment has been highly volatile, thereby presenting rapid changes, and for this reason, all processes and systems have to adjust themselves to these changes to retail their importance within the current society (Sotra, 2019). Governments are also realigning their systems and processes to suit the technological demands in the contemporary world to maintain their relevancy in service delivery to their citizens, and the Kuwait government has not been left behind in this. There has been the adoption of e-government across the globe. E-government is an electronic platform in which a country’s population can access relevant services offered by the government. This system helps in areas such as e-procurement that comes in suit with platforms that facilitate tendering and payment electronically.

The rapid rise in the urban population has also been presenting several challenges, and one of these challenges is traffic congestion in these respective urban areas. This has been facilitated further by technological development in the automotive industry, which has resulted in different car brands to help in easing transportation (Smartertransport.Uk, 2019). Traffic congestion in Kuwait city has been a long-standing problem that calls for immediate arrest by devising viable solutions. There are several options that can be deployed by the responsible authorities in handling this problem. These may include legislative means by enforcing existing traffic laws, deploying CCTV in monitoring traffic conditions, deploying adaptive traffic signals, deploying autonomous vehicle technology, and deploying v21 smart corridors, among other solutions (Sotra, 2019). This paper proposes the use of adaptive traffic lights in managing traffic congestion within Kuwait.

Literature Review

According to Zhou, Cao & Wu (2011), an intelligent transport system (ITS) entails a system that integrates electronic technologies and communication-based information into the transport infrastructure to reduce transportation time, improve safety, ease traffic congestion, and reduce fuel consumption. In implementing the best adaptive traffic light control cost and effectiveness must be considered. Though there are several adaptive traffic light control systems that have been implemented such as Split Cycle and Offset Optimization Technique (SCOOT) and Sydney Coordinated Adaptive Traffic (SCAT), wireless sensor network-based adaptive traffic light control is the best among them as it is cost-effective and more efficient compared to other adaptive traffic light control.

Adaptive traffic light control helps in traffic management through its features, which include green light sequence, real-time data detection, and light length determination. The green light sequence algorithm uses algorithmic calculations to determine the number of times a vehicle has stopped and in determining which case it should assign the green light. Real-time data collection helps in detecting and calculating traffic conditions in real-time as it unfolds. Light length determination takes into account both local and neighboring traffic condition requirements in estimating the length of time that should be assigned to a current green light and to the next green light.

Barlović, Huisinga, Schadschneider, & Schreckenberg (2005) observed that the structural elements of a city network exert a given level of pressure on the traffic dynamics within the city. There are three global strategies applicable to traffic management through adaptive light control. These include synchronized strategy, random strategy, and green wave strategy. Considering the synchronized strategy, all traffic lights switch uniformly to green (red) for all vehicles which are headed to the east (north) respectively, and vice versa. In consideration of the green wave strategy, traffic lights switch with regards to the free flow travel time of the selected traffic jam. In the case of a randomized strategy, traffic lights switch randomly.

Additionally, there are three different adaptive strategies these include traffic light switch based on the queue length, switch based on the waiting time, and switching based on comparison to a neural network. Switching based on a queue length, the traffic light will automatically switch if the queue length aligned to red exceeds a given limit. And switching based on waiting time; the traffic light will switch to red if the green light has not been utilized by a vehicle for a given time. In the case of traffic light switching based on comparison to a neural network, the number of the vehicle that has passed through the traffic light determines the cycle time of a traffic light. The cycle time is reset to zero after the switching process.

Mannion, Duggan & Howley (2016) observed adaptive traffic light control has a significant role in the development of smart cities and in addressing the problem of traffic congestion. A reinforcement learning algorithm is key in developing an adaptive traffic light control system, and it entails a class of algorithms with the ability to learn through experience. Since other agents in the traffic environment always influence the effects of a single agent within the environment through their actions, a challenge always arises in the implementation of proper coordination and information sharing between these agents.

The Three-Phase Traffic Theory

The three-phase traffic theory explains traffic flow in three phases, which include free flow, synchronized flow, and moving jam.  I free traffic flow; there is always a positive correlation between the flow rate in terms of the vehicle per unit time and vehicle density in terms of vehicles per unit distance. The relationship always stops at the maximum free flow (Kerner, 2009). In congested traffic, the relationship between flow rate and vehicle density is always weak. This relationship construct cannot be used to describe the complex dynamics of vehicle traffic. The congestion created in free flow instigates synchronize flow. In synchronized flow, vehicle speed drops significantly, but change occurs in the flow rate. This is caused by the increase in the density of vehicles, which causes the product of speed and density to remain the same, resulting in a wide moving jam. In a wide moving jam, the velocity and flow rate significantly drops, resulting in uniformity in the flow and density (Kerner, 2009).

Implementation Of Adaptive Traffic Light In Kuwait

Since implementing adaptive traffic light in Kuwait involves changing from the traditional traffic control system, it should be done in line with good change management strategies. There are different change management models, such as the McKinsey 7 S Model, Kotter’s change management theory, and Lewin’s change management model, among others (Anastasia, 2019).  In this case, we will adopt Lewin’s change management model in implementing this change.

Lewin’s change management model comprises three phases, which include the unfreezing phase, change phase, and freezing phase. The unfreezing phase is the first phase of this change model. The unfreezing phase involves preparing the stakeholders for change (Anastasia, 2019). Considering how the majority of individuals are always resistant to change, this phase is crucial in persuading the majority to embrace the proposed change. At this level all the stakeholders in the Kuwait transport industry will be enlightened on the reason as to why changing from the traditional traffic light control to the digital traffic control system is necessary for the country, including the benefits Kuwait as a country, will derive from employing this

The second phase in Lewin’s change management model is the actual implementation of the proposed change. This phase involves the transition from the old processes and systems into the proposed process and systems (Anastasia, 2019). At this stage, the new digital traffic control systems will be installed on major roads that always witness traffic congestions. Support activities that will support the success of the change are also necessary. One of such activities is training the stakeholders on how to interact with the newly installed digital traffic control system.

The third face in this change management model is the freezing phase. This phase is all about aligning the implemented change to the long-term objectives of the country (Anastasia, 2019). It involves proper evaluation of the implemented change and discerning if there are any form of shortcomings within the implemented change. Formulation of robust policies that can address the shortcomings from the implemented change is crucial to ensure optimal functionality of the implemented change. At this stage, the effectiveness and efficiency of the newly installed digital traffic light control will be accessed and establish if there is a need for formulating new policies that can enhance the optimal functionality of the system.

Advantages and disadvantages

The digital traffic light control system has numerous advantages, which include the recording of crucial data, environmentally friendly. Digital traffic light control system has unique features such as a camera system which helps the system in recording critical traffic data and also store and transmit these data to the traffic control system. This helps the traffic control department in planning and forecasting necessary traffic policies that can further enhance the traffic. In comparison with the traditional street lights, digital traffic lights a very minimal amount of carbon IV oxide into the atmosphere, which makes it relatively environmentally friendly as compared to the traditional street light systems (Sotra, 2019).

One disadvantage that the digital traffic light control system has is that it requires robust training to help stakeholders in familiarizing themselves with the system. This may result in additional extra costs in educating the stakeholders. Additionally, it involves a relatively higher maintenance cost to ensure the system functions optimally at all times.

Conclusion

The future of traffic control in smart cities is the digital traffic control system. The digital traffic control system is essential in easing traffic congestion in smart cities. In implementing a digital traffic light control system, several factors such as cost-effectiveness and efficiency must be considered. In consideration of these factors, the wireless sensor network-based digital traffic light control system is the best when compared to other systems such as the Split Cycle and Offset Optimization Technique (SCOOT) and Sydney Coordinated Adaptive Traffic (SCAT).

There are three different adaptive strategies these include traffic light switch based on the queue length, switch based on the waiting time, and switching based on comparison to a neural network. The implementation of digital traffic control in Kuwait is an essential step in steering the country’s transport system in the right direction and in correspondence to the technological demands in the contemporary world.

References

Smartertransport.Uk. (2019, November 5). Reducing Traffic Congestion and Pollution in Urban Areas. Retrieved from https://www.smartertransport.uk/smarter-cambridge-transport-urban-congestion-enquiry/

Mahendra, A., & Pawan, M. (2018, September 26). When it Comes to Reducing Car Congestion, India’s Cities Can Learn from its Businesses. Retrieved from https://www.wri.org/blog/2015/06/when-it-comes-reducing-car-congestion-india-s-cities-can-learn-its-businesses

Zhou, B., Cao, J., & Wu, H. (2011, May). Adaptive Traffic Light Control of Multiple Intersections in WSN-Based ITS. In VTC Spring (pp. 1-5).

Barlović, R., Huisinga, T., Schadschneider, A., & Schreckenberg, M. (2005). Adaptive traffic light control in the ChSch model for city traffic. In Traffic and Granular Flow’03 (pp. 331-336). Springer, Berlin, Heidelberg.

Mannion, P., Duggan, J., & Howley, E. (2016). An experimental review of reinforcement learning algorithms for adaptive traffic signal control. In Autonomic Road Transport Support Systems (pp. 47-66). Birkhäuser, Cham.

Anastasia. (2019, September 20). Major Approaches & Models of Change Management. Retrieved from https://www.cleverism.com/major-approaches-models-of-change-management/

Sotra, M. (2019, June 14). 7 Smart city solutions to reduce traffic congestion. Retrieved from https://www.geotab.com/blog/reduce-traffic-congestion/

Kerner, B. S. (2009). Introduction to modern traffic flow theory and control: the long road to three-phase traffic theory. Springer Science & Business Media.