Applied Intelligence and Computing
Dynamic Transportation Problem
Authors: Rahul Raj Singh, Kartikesh Thakur, Saurabh Kasaudhan and Gurwinder Singh
Publishing Date: 10-11-2024
ISBN: 978-81-955020-9-7
Abstract
Rising urbanisation and population expansion present difficulties for urban transportation networks. All of these constraints worsen traffic-related problems: unforeseen congestion, poor route planning, and underuse of resources. Dynamic Transportation Optimisation (DTO) technologies have consequently become intriguing means to increase the sustainability and efficiency of metropolitan transportation networks. Dynamic programming (DP), Simulated Annealing (SA), Ant Colony Optimisation (ACO), Swarm Intelligence (SI), Genetic Algorithms (GA), Reinforcement Learning (RL), Machine Learning Models (ML), Geographic Information Systems (GIS), and Integer Linear Programming (ILP) are just a few of the wide range of approaches DTO methods apply. These technologies generate complete traffic analysis and dynamic routing solutions by using real-time data from security cameras, traffic sensors, and GPS devices. This paper gives a complete evaluation of DTO approaches coupled with assessments of their usefulness, performance, and constraints in the framework of urban transportation. By way of a detailed comparison analysis, the study intends to highlight the advantages and disadvantages of every method, so supporting stakeholders in choosing and deploying DTO solutions customised to unique urban transport concerns. Furthermore highlighted in light of developments in artificial intelligence, big data analytics, and predictive modelling is the future prospects of DTO techniques. The integration of these technologies within present urban contexts aims to develop resilient transportation networks, thereby contributing to the continuing discourse on the evolution of urban mobility solutions. The subsequent sections of this article delve into the intricacy of each DTO technique, offering insights into their capabilities, actual implementations, and projected repercussions on urban transportation networks. By combining empirical evidence with theoretical frameworks, the research presents a holistic understanding of the role and significance of DTO techniques in defining the future of urban mobility.
Keywords
Dynamic transportation optimization, urban mobility, transportation management, DTO methodologies, comparative analysis, artificial intelligence, big data analytics.
Cite as
Rahul Raj Singh, Kartikesh Thakur, Saurabh Kasaudhan and Gurwinder Singh, "Dynamic Transportation Problem", In: Mukesh Saraswat and Rajani Kumari (eds), Applied Intelligence and Computing, SCRS, India, 2024, pp. 145-156. https://doi.org/10.56155/978-81-955020-9-7-16