Abstract
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.
Highlights
Nowadays the ever more increasing number of vehicles creates a challenge in the modern urban transportation scenario
The budget constraints are not considered in this test case. 2nd test case works on a 16-link problem adapted from [20]
The 3rd test case is based on Sioux Falls problem adapted from [20, 21]
Summary
Nowadays the ever more increasing number of vehicles creates a challenge in the modern urban transportation scenario. In many cases there is a limitation or unavailability of road junction or it is possible that at a particular instance of time a particular link which seems shorter is unavailable or highly contested Another profitable way to put up with it can be optimizing the wait time at traffic signals. K. Sahana [9, 10] designed a Discrete Evolutionary Model to reduce the waiting time of vehicles at traffic signals within the urban transportation system using level Stackelberg game model 5 test networks with 12, 16, 20, 24, 28 nodes designed using Petri Net. The proposed hybrid technique was solved for optimizing wait time at traffic signals and for SUE.
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