Employing computational intelligence in transportation systems

• Main Author: Obias, Karl Cedric Joel U.
• Format: text
• Language: English
• Published: Animo Repository 2022
• Subjects: Intelligent transportation systems; Computational intelligence; Electrical and Computer Engineering
• Online Access: https://animorepository.dlsu.edu.ph/etdm_ece/20; https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1020&context=etdm_ece
• Institution: De La Salle University

Employing computational intelligence on existing transportation systems allows vehicles and roads to be more intelligent and adaptable, which helps lessen existing traffic systems' limitations. The study considers three factors needed to employ computational intelligence solutions to existing transportation systems—first, the technique to use in the system. Second, understanding the vehicle mobility dynamics of the system. Lastly, the exchange of data within the system. The study on intelligent highway tollgates shows the use of different computational techniques in optimizing traffic flow in expressways. The study results show that both queueing policies could optimize traffic flow in terms of queue length and waiting time at toll booths. However, the fuzzy logic queueing policy performs better than the genetic algorithm queueing policy. The study on vehicle mobility dynamics shows the extraction of mobility dynamics using GPS taxi traces. The study on the neural network-based policy uses extracted vehicle mobility dynamics to improve passenger transportation costs through ridesharing. The policy shows that the neural network-based policy can group passengers and reduces transportation cost for passengers. The study on data exchange between vehicles and infrastructures uses index coding-based transmission to improve communication. The result shows improvement compared to the conventional transmission scheme in terms of the metrics, reducing the number of transmissions, conserving bandwidth, and securing communication which is helpful for data exchange needed in intelligent systems. The study identified the factors needed to employ computational intelligence and showed improvement in the selected transportation systems.