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Today bike sharing systems exists in many cities around the globe after a recent growth and popularity in the last decades. It is attractive for cities and users who wants to promote healthier lifestyles; to reduce air pollution and gas emission as well as improve traffic. One major challenge to docked bike sharing system is redistributing bikes and balancing dock stations. There are studies that propose models that can help forecasting bike usage; strategies for rebalancing bike distribution; establish patterns or how to identify patterns. Some of these studies proposes to extend the approach by including weather data. Some had limitations and did not include weather data. This study aims to extend upon these proposals and opportunities to explore on how and in what magnitude weather impacts bike usage. Bike usage data and weather data are gathered for the city of Washington D.C. and are analyzed by using a clustering algorithm called k-means. K-means is suitable for discovering patterns within the data by grouping (clustering) similar instances, which literature review also advocated. In this project, the k-means algorithm managed to identify three clusters that corresponds to bike usage depending on weather. The results show that weather impact on bike usage was noticeable between clusters. It showed that temperature followed by precipitation weighted the most, out of five weather variables. Results also supported that the use of k-means was appropriate for this type of study.

Atmospheric pollution resulting from an open-cast coal mine situated 10 km southwest of Witbank (Mpumalanga, South Africa) was investigated during summer and winter 2004. Industrial and urban activities in and around Witbank release large amounts of toxic and criteria pollutants into the atmosphere. Spontaneous combustion from the many collieries in the Witbank area contributes to this problem. Direct, automated, and continuous in situ measurements of trace gas concentrations and prevailing meteorological parameters were carried out by a mobile monitoring unit and an automatic weather station. The data collected show that spontaneous combustion is a source of CO, NO, SO2 and H2S. Summer daily averages of SO2, NO, NO2 and O3 concentrations ranged between 1 and 18 ppb, 0.3 and 40 ppb, 12 and 75 ppb and 0.9 and 19 ppb respectively. Winter daily concentrations of SO2 and O3 were much higher, ranging between 15 and 180 ppb and 14 and 30 ppb respectively. NO and NO2, in contrast, were lower in winter (0.8 to 15 ppb and 2 to 28 ppb for daily means). Winter daily average concentrations of H2S, CO and CO2 ranged between 16 and 217 ppb, 2100 and 5100 ppb and 322 and 436 ppm). Synoptic circulations over the Highveld were found to affect pollutant concentrations. During winter, temperature inversions played a significant role in increasing the pollutant concentrations in the early morning hours until about 10:00. Although considerable amounts of NO, NO2 and O3 were captured; their concentrations were within the South African Department of Environmental Affairs and Tourism’s permissible levels as contained in the National Environmental Management: Air Quality Act (2004). SO2 concentrations during winter 2004 exceeded the allowed standards. Elevated concentrations of pollutants were mostly observed when the wind blew from the SE, SSE, S and WSW directions, implicating the 2A south pits of the open-cast mine investigated as the major source of the emissions.

The development of the Severe Weather Impact Forecasting System (SWIFS) for flash flood hazards in South Africa is described in this thesis. Impact forecasting addresses the need to move from forecasting weather conditions to forecasting the consequential impact of these conditions on people and their livelihoods. SWIFS aims to guide disaster managers to take early action to minimise the adverse effects of flash floods focussing on hotspots where the largest impact is expected. The first component of SWIFS produced an 18-hour probabilistic outlook of potential occurrence of flash floods. This required the development of an ensemble forecast system of rainfall for small river basins (the forecasting model component), based on the rainfall forecast of a deterministic numerical weather prediction model, to provide an 18-hour lead-time, taking into account forecast uncertainty. The second component of SWIFS covered the event specific societal and structural impacts of these potential flash floods, based on the interaction of the potential occurrence of flash floods with the generalised vulnerability to flash floods of the affected region (the impact model component). The impact model required an investigation into the concepts of regional vulnerability to flash floods, and the development of relevant descriptive and mathematical definitions in the context of impact forecasting. The definition developed in the study links impact forecasting to the likelihood and magnitude of adverse impacts to communities under threat, based on their vulnerability and due to an imminent severe weather hazard. Case studies provided evidence that the concept of SWIFS can produce useful information to disaster managers to identify areas most likely to be adversely affected in advance of a hazardous event and to decide on appropriate distribution of their resources between the various hotspots where the largest impacts would be. SWIFS contributes to the current international research on short-term impact forecasting by focussing on forecasting the impacts of flash floods in a developing country with its limited spatial vulnerability information. It provides user-oriented information in support of disaster manager decision-making through additional lead-time of the potential of flash floods, and the likely impact of the flooding. The study provides a firm basis for future enhancement of SWIFS to other severe weather hazards in South Africa. / Thesis (PhD)--University of Pretoria, 2015. / gm2015 / Geography, Geoinformatics and Meteorology / PhD / Unrestricted

The research presented in this thesis, studies driver gap acceptance behavior for permissive left turn movements at signalized intersections. The thesis attempts to model the gap acceptance behavior using three different approaches, a deterministic statistical approach, a stochastic approach, and a psycho-physical approach. First, the deterministic statistical modeling approach is conducted using logistic regression to characterize the impact of a number of variables on driver gap acceptance behavior. The variables studied are the gap duration, the driver's wait time in search of an acceptable gap, the time required to travel to clear the conflict point, and the rain intensity. Considering stochastic gap acceptance, two stochastic approaches are compared, namely: a Bayesian and a Bootstrap approach. The study develops a procedure to model stochastic gap acceptance behavior while capturing model parameter correlations without the need to store all parameter combinations. The model is then implemented to estimate stochastic opposed saturation flow rates. Finally, the third approach uses a psycho-physical modeling approach. The physical component captures the vehicle constraints on gap acceptance behavior using vehicle dynamics models while the psychological component models the driver deliberation and decision process. In general, the three proposed models capture gap acceptance behavior for different vehicle types, roadway surface conditions, weather effects and types of control which could affect the driver gap acceptance behavior. These findings can be used to develop weather responsive traffic signal timings and can also be integrated into emerging IntelliDrive systems. / Master of Science

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