The requirement of the road services and transportation network development planning came into existence with the development of civilization. In the modern urban transport scenario with the forever mounting amount of vehicles, it is very much essential to tackle network congestion and to minimize the travel time. This work is based on determining the optimal wait time at traffic signals for the microscopic discrete model. The problem is formulated as a bilevel model. The upper layer optimizes the travel time by reducing the wait time at traffic signal and the lower layer solves the stochastic user equilibrium. Soft computing techniques like Genetic Algorithms, Ant Colony Optimization, and many other biologically inspired techniques prove to give good results for bilevel problems. Here this work uses Bat Intelligence to solve the transport network design problem. The results are compared with the existing techniques.
The transport network and road services are the foundation for the development of human civilization. It is immensely essential to manage network congestion as well as to minimize the travel time of the growing traffic load on the road network. Traffic signals may play an important role in managing the mounting traffic. This work relies on reducing the total time lag at the traffic signals, thus reducing the overall travel period. The model is designed on a bi-level framework. The overall wait time is optimized at the traffic signals by the upper level while the User Equilibrium (UE) is estimated by the lower level. Biologically inspired metaheuristic methods like Bat Algorithm (BA), Genetic algorithms (GA), Ant Colony Optimization (ACO), and many others demonstrated optimized outcomes for bi-level problems. To improve the desirability of the metaheuristic techniques an innovative method encapsulating the desirability of both BA and GA is proposed to evaluate the traffic optimization problem (TOP). While BA helps in faster convergence GA diversifies the search space. A comparative analysis has been carried out with the parent algorithms as well as an existing ACO-GA-based model. It was observed that the proposed BA-GA method performs better than the rest of the techniques.
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