Dynamic vehicle routing model using geometric Brownian motion
Date
2019
Authors
Mungai, Joseph Muroki
Journal Title
Journal ISSN
Volume Title
Publisher
Strathmore University
Abstract
Traffic jams are one of the most common problems associated with urban areas worldwide. The effects of traffic congestion are usually felt during peak hours of the day and in cases of accidents or bad weather. Essentially, Traffic jams occur with or without the aid of traffic Marshalls or traffic lights. However, the traffic jams get worse with absence of these agents especially in developing countries where public transport vehicles have little regard to traffic rules and regulations. As such, other modes of traffic avoidance and mitigation are required to mitigate the effects of traffic jams. A majority of city dwellers have resulted to waking up early in order to beat the traffic or leaving work early or later than usual. Therefore, to mitigate traffic jams there must be the provision of an alternative solution to the traffic menace. Response time by members of the police force would play a key role in reducing traffic congestion during accidents or incidents. The use of applications such as Waze and Google maps have greatly helped in navigation as well as informing the public on traffic situations on the roads. Radio stations and other social platforms like Twitter and Facebook have become a source of information with regards to traffic situations. Essentially, to substantially help reduce the effects of traffic jams in urban areas, more still needs to be done in terms of providing information to both road users and authorities with regards to traffic situations. Therefore, the need for alternative sources of information that aims at providing relevant information in regards to the traffic congestions. Notably, proper information through social media and other channels has led to a reduced response time by traffic Marshalls as well as a way to warn motorists to avoid areas with accidents as well as high traffic volumes. This research work proposes a dynamic vehicle routing model which implements geometric Brownian motion to help divert traffic to routes less congested. Notably, the model requires a large amount of data in order to accurately predict the expected traffic volume at a particular time. The system calculates the expected traffic at a particular time using variables calculated from the historical data of the route. It is therefore important for historical volume data to exist for the model to work. Based on the provided data, the system then calculates expected traffic volume for the routes entered by the user at a particular time and displays the same to the user. The system is able to calculate the expected traffic volume at a time (t) and is able to find an expected average of traffic on a route
Description
Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Information Technology (MSIT) at Strathmore University
Keywords
Traffic congestion, Traffic jam, Traffic volume, divert