Dynamic Route Guidance Using Improved Genetic Algorithms

Abstract

This paper presents an improved genetic algorithm (IGA) for dynamic route guidance algorithm. The proposed IGA design a vicinity crossover technique and a greedy backward mutation technique to increase the population diversity and strengthen local search ability. The steady-state reproduction is introduced to protect the optimized genetic individuals. Furthermore the junction delay is introduced to the fitness function. The simulation results show the effectiveness of the proposed algorithm.

Floyd-A∗Algorithm Solving the Least-Time Itinerary Planning Problem in Urban Scheduled Public Transport Network

“…If Inequality (20) is satisfied, we deem this a consistency assumption for the Itinerary Finder. The definition of this assumption helps to explain why the Itinerary Finder never re-OPENs a CLOSED node.…”

“…Chabini and Lan adapted A * for the fastest path in deterministic discrete-time dynamic networks [18]; they proposed three estimating strategies. Subsequently, numerous works appeared in the literatures that primarily applied A * -based algorithms to LT path problems in unscheduled time-dependent road networks (see [19,20]). Chen et al proposed an A * -based integrated approach combining offline precomputation and online path retrieval for a road navigation system [21].…”

Rapid Operational Access and Maneuver Support platform for military logistics and operation planning in water environments

“…Genetic algorithms have been employed to solve vessel weather routing, often constraining the spatial parameter space through the introduction of waypoints. 14,21,22 Penalty/barrier methods provide the most flexibility for this type of problem as step size, number of nodes, and constraints as applied, needed, or adapted on the fly to change route properties. 23 However, they have seen little application in this area.…”

Analysed potential of big data and supervised machine learning techniques in effectively forecasting travel times from fused data

“…It is widely used in many intelligent transportation system (ITS) services as dynamic route guidance [1,2,3], traveller information system [4,5,6] or traffic management system [7,8]. Another characteristic of travel time information, which distinguishes it from other data on the traffic flow, is its level of relevance and understanding among different groups of the stakeholders such as decision makers, transport system users, transportation planners, etc.…”