Addressing traffic congestion and throughput through optimization.
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Date
2021
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Abstract
Traffic congestion experienced in port precincts have become prevalent in recent years
for South Africa and internationally [1, 2, 3]. In addition to the environmental impacts
of air pollution due to this challenge, economic effects also weigh heavy on profit
margins with added fuel costs and time wastages. Even though there are many
common factors attributing to congestion experienced in port precincts and other areas,
operational inefficiencies due to slow productivity and lack of handling equipment to
service trucks in port areas are a major contributor [4, 5].
While there are several types of optimisation approaches to addressing traffic
congestion such as Queuing Theory [6], Genetic Algorithms [7], Ant Colony
Optimisation [8], Particle Swarm Optimisation [9], traffic congestion is modelled based
on congested queues making queuing theory most suited for resolving this problem.
Queuing theory is a discipline of optimisation that studies the dynamics of queues to
determine a more optimal route to reduce waiting times.
The use of optimisation to address the root cause of port traffic congestion has been
lacking with several studies focused on specific traffic zones that only address the
symptoms. In addition, research into traffic around port precincts have also been
limited to the road side with proposed solutions focusing on scheduling and
appointment systems [25, 56] or the sea-side focusing on managing vessel traffic
congestion [30, 31, 58]. The aim of this dissertation is to close this gap through the
novel design and development of Caudus, a smart queue solution that addresses traffic
congestion and throughput through optimization. The name “CAUDUS” is derived as
an anagram with Latin origins to mean “remove truck congestion”.
Caudus has three objective functions to address congestion in the port precinct, and
by extension, congestion in warehousing and freight logistics environments viz.
Preventive, Reactive and Predictive. The preventive objective function employs the use
of Little’s rule [14] to derive the algorithm for preventing congestion. Acknowledging
that congestion is not always avoidable, the reactive objective function addresses the
problem by leveraging Caudus’ integration capability with Intelligent Transport
Systems [65] in conjunction with other road-user network solutions. The predictive
objective function is aimed at ensuring the environment is incident free and provides
an early-warning detection of possible exceptions in traffic situations that may lead to
congestion. This is achieved using the derived algorithms from this study that identifies
bottleneck symptoms in one traffic zone where the root cause exists in an adjoining
traffic area.
The Caudus Simulation was developed in this study to test the derived algorithms
against the different congestion scenarios. The simulation utilises HTML5 and
JavaScript in the front-end GUI with the back-end having a SQL code base. The entire
simulation process is triggered using a series of multi-threaded batch programs to
mimic the real-world by ensuring process independence for the various simulation
activities. The results from the simulation demonstrates a significant reduction in the
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duration of congestion experienced in the port precinct. It also displays a reduction in
throughput time of the trucks serviced at the port thus demonstrating Caudus’ novel
contribution in addressing traffic congestion and throughput through optimisation.
These results were also published and presented at the International Conference on
Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD
2021) under the title “CAUDUS: An Optimisation Model to Reducing Port Traffic
Congestion” [84].
Description
Masters Degree. University of KwaZulu-Natal, Durban.