Shortest Path Algorithm for Transportation Networks
Loading...
Date
2009-08-12
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
In a metropolis such as Kumasi, the transport network is massive, dynamic, and complicated, and therefore route finding is not an easy task, especially with routes comprising several modes of transport vehicles. This problem is even more important for e-tourism planners and users, security services and moving workforces, who may need to visit an unfamiliar part of the metropolis. To find a route that is the most cost-effective is even harder and time-consuming.
Traffic congestion is becoming a serious environmental threat that must be resolved quickly. Traditionally, travel information systems have been specific to a particular mode of transport. For instance, traffic information (road conditions broadcast) has been directed at drivers. Instead, travel information systems are now being developed, which incorporate route guidance systems to divert drivers away from the congested areas either by change of travel mode or travel route. The mobile travel information system developed at the Kumasi Metropolitan Assembly (KMA) enables progression from a passive mode of interaction between traffic control systems and road-users (one-way flow of information) to an active mode. The integration of data concerning traffic flows and individual journey plans thus makes it possible to perform optimisation of travel. This project focuses on the issue of provision of real-time information about urban travel and assistance with planning travel. KMA traffic-light control system provides real-time information about the traveler distance (time) within certain areas of the city. However, rather than using link travel times at the time of the request, it is more effective to predict the link travel times for the time of travel along the particular links. The future link travel times depend upon the historical travel time of the link (for the specific time step in the day) as well as the current link travel time. Consequently, the link weights are a combination of real-time data, historical data and static data. The prediction method will be validated in the context of Kumasi urban road network.
Description
A Thesis Submitted to the Department of Mathematics,
Kwame Nkrumah University of Science and Technology, Kumasi
in partial fulfillment of the requirement for the degree Master of Science.