It is often acknowledged that movement exists in multiple, interdependent forms and that we live in an Information Age. However, mobilities perspectives on contemporary cycling tend to neglect the a) interconnections between transport (physical mobility of people and objects) and communication (mobility of symbolic information) b) paradigmatic shifts in modernity that affect how and why we communicate about transport. This thesis responds to such neglect. Firstly, it places urban cycling in an internet context by examining practices and perceptions of policy blogging, asking why do individuals blog about cycling-related transport policy and to what effect? Secondly, it analyses the answers to these questions through the theoretical lens of the risk society and reflexive modernisation theses. Empirical data is the result of 46 semi-structured interviews with bloggers and expert system representatives, mostly in London, New York and Paris. Blogging about cycling-related transport policy is shown to be an individualised response to the perceived failings of expert systems, as well as in Giddens’ words, a ‘reflexive project of the self’. Citizens who may otherwise only be policy subjects or passive consumers of transport, emerge as policy, media and civil society actors by virtue of their ability to publish information, which forms the basis of social relations. Through blogging, they produce and mobilise knowledge. Knowledge claims mediated by blogging interact with expert systems responsible for transport, which in turn adapt; routine institutional practices evolve; a new order emerges; blogging makes a difference. That difference is however limited, not least because the public remains reliant on expert systems. Ultimately, despite the obvious importance of physical mobility to cycling, this thesis seeks to move beyond it. Information and communication technologies have radically altered how we - researchers, the public, expert system representatives - communicate about and understand cycling, and as such, this project argues for a renewed emphasis on mobilities in a genuinely plural sense of the word as being about more than physically moving from A to B.

<p> This project presents the design, implementation and experimental demonstration of an intensity control system for highway lighting. High Intensity Discharge lights (HID) are replaced by the White Light Emitting diodes (LED) in highway lighting systems in order to incorporate the dimming feature and conserve energy. The intensity is controlled by utilizing a micro controller that belongs to the 8051 family by generating pulse width modulated signals that energize a metal-oxide-semiconductor field effect transistor (MOSFET) to switch the LEDs to accomplish the desired operation. </p><p> A working model using ATMEL has been developed in which an array of LEDs are connected in series and parallel to control the light intensity applicable to the highway lights, which makes it a real time system. The micro controller is programmed to keep light intensity high during peak traffic hours, and gradually reduce it as traffic decreases, until dawn when lights are turned off to save energy.</p>

<p> The increasing trend in crashes and consequent fatalities due to distracted driving is a growing safety concern in our road network. With rapid advancement in cellphone and in-vehicle technologies along with driver&rsquo;s inclination to multitasking, the number of crashes due to distracted driving are further on the rise. Some previous studies attempted to detect distracted driving behavior in real-time to mitigate this issue. However, these studies mainly focused on detecting either visual or cognitive distractions, while most of the real-life distracting tasks involve driver&rsquo;s visual, cognitive, and physical workload, simultaneously. Additionally, previous studies frequently used eye, head, or face tracking data, although current vehicles are not equipped with technologies to acquire such data. To address above issues, this driving simulator study focused on developing algorithms for detecting specific distraction tasks using only vehicle control and driving performance measures. Specifically, algorithms were developed to detect two distracting tasks &ndash; texting and eating/drinking. Three data mining techniques were explored &ndash; Linear Discriminant Analysis (LDA), Logistic Regression (LR), and Support Vector Machine (SVM). SVM algorithms found to outperform LDA and LR, which detected texting and eating/drinking distraction with an accuracy of 84.33% and 79.53%, respectively. This study may provide useful guidance to successful implementation of distracted driver detection algorithm in Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) communication, as well as to auto manufacturers interested in integrating distraction detection systems in their vehicles. </p>

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published_or_final_version / Urban Planning / Master / Master of Science in Urban Planning

published_or_final_version / Urban Planning / Master / Master of Science in Urban Planning

published_or_final_version / abstract / Transport Policy and Planning / Master / Master of Arts in Transport Policy and Planning

published_or_final_version / Transport Policy and Planning / Master / Master of Arts in Transport Policy and Planning

published_or_final_version / Transport Policy and Planning / Master / Master of Arts in Transport Policy and Planning

published_or_final_version / Urban Planning / Master / Master of Science in Urban Planning