A Research Approach to Study Human Factors in Transportation Systems

This thesis proposes a new general-purpose methodology to conduct studies on Human Factors in Transportation Systems.A full-fledged setup and implementation of the methodology is provided for validation. This setup, which uses real data to perform the simulation, includes a traffic micro-simulator, a driving simulator, a traffic control centre and an Advanced Driver Assistance System, providing an experimentation laboratory, in which empirical research can be conducted. The communication between the simulation components is made interchangeably using both the European standard Datex II and the SUMO TraCI protocols.Several usage scenarios are implemented and indications on how to extend the methodology to accommodate different requirements are provided; as to prove its usability and feasibility. A simple Human Factors study was conducted using the implemented setup. This study uses naturalistc data and evaluates the network performance gain by using an Advanced Driver Assistance System that recommends new routes to drivers in congestion situations and provides a final validation of the methodology.In conclusion, the methodology has been proved usable to effectively conduct Human Factors research and also to develop Advanced Driver Assistance Systems applications in a controlled, yet realistic environment.This thesis proposes a new general-purpose methodology to conduct studies on Human Factors in Transportation Systems.A full-fledged setup and implementation of the methodology is provided for validation. This setup, which uses real data to perform the simulation, includes a traffic micro-simulator, a driving simulator, a traffic control centre and an Advanced Driver Assistance System, providing an experimentation laboratory, in which empirical research can be conducted. The communication between the simulation components is made interchangeably using both the European standard Datex II and the SUMO TraCI protocols.Several usage scenarios are implemented and indications on how to extend the methodology to accommodate different requirements are provided; as to prove its usability and feasibility. A simple Human Factors study was conducted using the implemented setup. This study uses naturalistc data and evaluates the network performance gain by using an Advanced Driver Assistance System that recommends new routes to drivers in congestion situations and provides a final validation of the methodology.In conclusion, the methodology has been proved usable to effectively conduct Human Factors research and also to develop Advanced Driver Assistance Systems applications in a controlled, yet realistic environment

Transportation evacuation strategies based on vehicular disaster management system in urban network environment Zubaida

The importance of emergency response systems have grown tremendously in the recent times due to the many manmade and natural disasters in recent years such as September 2001, July 2005 London bombings and the 2011 Japan earthquake and tsunami disaster. Disasters cost huge human, social and financial losses. For example, in Typhoon Haiyan, as of November 2013, the official death toll from Philippines‟s devastating storm has passed 10,000 people. In addition, based on early estimates, the reconstruction costs could come to as much as $20bn (£12.3bn). Conventional methods for disaster management have shown little prospects of realizing the true potential of current and emerging technologies.This PhD research aims to propose and evaluate a disaster management system based on the emerging ICT technologies with a focus on transportation in urban environments. This work is presented on an Intelligent Disaster Management System based on Vehicular Ad hoc Networks (VANETs) and Cloud Computing. Our research objective is to increase the safety and system efficiency, to reduce the accidents, congestion, and manage the emergencies and disasters. The effectiveness of the intelligent system has been demonstrated through modelling the impact of disaster on real city transport environments and compares it with the case where the intelligent proposed system was in place, and ability of generalizing the concept was increased through applying the proposed system on different cities. By applying our system, substantial benefits have been achieved in terms of improved and balanced traffic flow and smooth evacuation rates.Furthermore, a micro-simulation software model has been developed which employs the vehicular disaster management system in order to investigate the transportation evacuation strategies potential in reducing the human and economic losses.The particular contribution of my thesis is in the modelling and simulation of the traffic for disaster and evacuation scenarios. To this end, this project uses a range and mix of modelling and simulation technologies including macroscopic and microscopic simulation models; OmniTRANS and S-Paramics transport planning software.xixDuring the course of this PhD, disaster scenarios of varying scales involving 2-3 different cities of various sizes and characteristics have been modelled and analysed, thereby presenting a system which deliver advanced services in managing disasters which results in lower losses.Also, the Average Vehicle Occupancy impact on the evacuation process time has been investigated. Literally, it represents the higher number of car occupancy which means less number of trips required to the evacuation process. The results have shown that AVO contributes effectively in evacuation plans that are in place.Additionally, two different evacuation strategies have been applied and evaluated simultaneously and isolated. Subsequently, either continues the processes or perhaps there is a need to change the strategy where applicable and appropriate. In other words, after propagating the evacuation strategy, the traffic situation has been assessed and observed the effectiveness of the disaster management system on the network by comparing the performance of the proposed system against the traditional system. To sum up, the comparison between both scenarios shows the ability to secure more of vehicles, up to double the number, and hence improve the network performance in terms of safety. Moreover, there is an improvement in flow rate of many critical links. Many blocked links are turned into some reds and blues which means an improvement seemed to occur to the whole network