Abstract

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence Intelligent traffic planning, the promotion of public transport and the improved interconnectedness of all road users in a city-wide communication infrastructure comprise the mobility characteristics of a smart city. Living in an era of Smart Cities emerging, we can make use of all the data available nowadays to better serve our lives with the best services. All the data generated every day by the Vehicles can be used to get a better insights to improve the quality of the available service as the creation of new routes, increase supervision on problematic areas, among others. This document intends to detail the project carried out at Carris, a public transportation company which operates Lisbon's buses, trams, and funiculars. The master thesis project aims to create a Business Intelligence solution to support planning decisions though the use of a Dashboard by presenting information about all relevant data collected on a bus daily route by the driver in order to get a better understating of a public transportation service and how can the organization improve them to rebuild schedules, create or eliminate routes, do some inspection on tickets validations inside the vehicles, etc. This project is supported by a Microsoft tool called Power BI that enables all the extraction, transformation and loading of data processes, based on an Azure SQL Data Warehouse based on Cloud services. The Business Dashboards are divided in 4 main areas inside the organization and will support Onboard Sales, Ticket validations, Route Planning and Onboard issues reported by the driver.

Abstract

Road congestion results in a huge waste of time and productivity for millions of people. A possible way to deal with this problem is to have transportation authorities distribute traffic information to drivers, which, in turn, can decide (or be aided by a navigator) to route around congested areas. Such traffic information can be gathered by relying on static sensors placed at specific road locations (e.g., induction loops and video cameras) or by having single vehicles report their location, speed, and travel time. While the former approach has been widely exploited, the latter has come about only more recently; consequently, its potential is less understood. For this reason, in this paper, we study a realistic test case that allows the evaluation of the effectiveness of such a solution. As part of this process, (a) we designed a system that allows vehicles to crowd-source traffic information in an ad hoc manner, allowing them to dynamically reroute based on individually collected traffic information; (b) we implemented a realistic network-mobility simulator that allowed us to evaluate such a model; and (c) we performed a case study that evaluates whether such a decentralized system can help drivers to minimize trip times, which is the main focus of this paper. This study is based on traffic survey data from Portland, OR, and our results indicate that such navigation systems can indeed greatly improve traffic flow. Finally, to test the feasibility of our approach, we implemented our system and ran some real experiments at UCLA's C-Vet test bed.