Thesis (MBA)--Stellenbosch University, 2005. / ENGLISH ABSTRACT: In April 2004, the then Transport MEC of the Western Cape, Tasneem Essop unveiled Government's grand vision of converting Klipfontein Road into a pioneering form of public transport called Bus Rapid Transit (BRT). BRT is the public transport system that the provincial government and City of Cape Town have selected to address South Africa's city transport problems, with a future looking perspective. BRT is characterised by dedicated bus lanes, which will be divided from the normal traffic lanes to ensure that 'express' buses can travel unhindered. Government envisages changing the 20 km Klipfontein Road Corridor stretching from the Inner City through Athlone/Gatesville to Nyanga and Khayelitsha into an activity axis of economic transformation, featuring bus stations, convenience stores, supermarkets, coffee shops and kerbside cafes. This vision of turning Klipfontein Corridor into a well of economic prosperity by using rapid bus transport is primarily what this study will focus on. We are asking whether this goal is attainable and what the positive and negative spin-offs are in respect of sustainable economic growth for the city and a reduction in racially rooted inequalities and inequities. Although a lot of the issues relevant for this study have been raised down the years of the MSDF (1993-1996), current literature on the Klipfontein Corridor is very limited as it is a first for the City of Cape Town and the government. In fact, the national and provincial governments in conjunction with the City of Cape Town have chosen the Klipfontein corridor as a pilot project, with a view to a national roll out in other provinces, if this venture proves successful. / AFRIKAANSE OPSOMMING: In April 2004 het die destydse Minister van Vervoer in die Wes-Kaap, Tasneem Essop onthul dat die regering die groot visie het om Klipfontein weg te transformeer deur gebruik te maak van publieke vervoer bekend as BRT. BRT is die publieke vervoersisteem wat die streeksregering en Kaapstad gekies het, om Suid Afrikaanse stede se vervoerprobleme, met 'n toekoms vooruitsig, op te los. BRT word gekenmerk deur spesiale busbane, apart van die normale verkeer, sodat busse ongehinderd kan voortbeweeg. Die regering beoog om die 20 km Klipfontein korridor te verander in 'n aktiewe node van ekonomiese transformasie met busstasies en winkelkomplekse. Die korridor strek vanaf die Binne Stad deur Athlone/Gatesville na Nyanga en Khayelitsha. Die visie van Me. Tasneem Essop om die Klipfonteinweg te ontwikkel in 'n vooruitstrewende ekonomiese gordel, is die fokus vir die studie. Ons vra of die visie haalbaar is, en wat die positiewe en negatiewe moontlikhede is met betrekking tot langtermyn ekonomiese groei en 'n vermindering in rasse-ongelykhede. Alhoewel baie van die relevante probleme reeds deur die Metropolitaanse Ruimtelike Ontwikkelingsraamwerk (MROR) behandel was, is huidige literatuur oor die Klipfontein projek baie beperk. Tans is dit 'n eerste vir Kaapstad en die regering, wat beoog om soortgelyke projekte uit te rol in ander stede reg oor die land, indien die projek suksesvol is.

Os sistemas de transporte público por ônibus têm sido cada vez mais relevantes para o desenvolvimento das cidades. Técnicas para melhorar o planejamento e o controle da operação diária dos serviços de ônibus apresentaram melhorias significativas ao longo dos anos, e a previsão do tempo de viagem desempenha um importante papel no planejamento e nas estratégias da operação diária. A antecipação dos tempos de viagem ajuda os planejadores e controladores a evitar os vários problemas que surgem durante a operação diária da linha de ônibus. Ela também permite manter os usuários informados para que eles possam planejar com antecedência a sua viagem. Vários estudos relacionados à previsão do tempo de viagem podem ser encontrados na literatura. Devido a sua dificuldade intrínseca, o problema foi abordado por diferentes técnicas. Resultados numéricos de estudos demonstram o potencial uso de redes neurais em relação a outras técnicas. No entanto, a literatura não apresenta aplicações que incorporem uma retroalimentação das informações contidas em séries temporais, como é feito por redes neuronais recorrentes. A maioria dos estudos na literatura tem sido realizada com dados de cidades específicas e com linhas de ônibus com paradas fixas. A situação que surge em linhas de ônibus sem paradas fixas operadas com micro-ônibus apresenta uma dinâmica diferente dos estudos de caso da literatura Além disso, os estudos existentes não usam o gráfico de marcha como um instrumento de apoio para a previsão do tempo de viagem em ônibus. Nesta tese, estuda-se o problema da previsão do tempo de viagem para linhas de micro-ônibus sem paradas fixas, utilizando as informações básicas do gráfico de marcha. O modelo proposto é baseado em redes neurais recorrentes. Os dados de entrada incluem: (i) a hora de início da viagem do ônibus, (ii) sua posição atual em coordenadas GPS, (iii) o tempo atual e (iv) a distância percorrida após um minuto. As redes são treinadas com dados de uma linha de micro-ônibus da cidade de Porto Alegre, Brasil. Os dados correspondem ao ano de 2015. Os modelos fornecem previsões para a distância percorrida minuto a minuto e para uma janela de tempo de 30 minutos. O modelo desenvolvido foi treinado com um conjunto abrangente de dados de dias úteis, incluindo períodos de pico e fora de pico. Os dados de treinamento não desconsideraram informações de qualquer dia devido à ocorrência de eventos especiais. Concluiu-se que os modelos de redes neurais recorrentes desenvolvidos são capazes de absorver a dinâmica do movimento dos micro-ônibus. A informação produzida apresenta um nível adequado de precisão a ser utilizado para informar os usuários. Também é adequada para planejadores e controladores da operação, pois pode ajudar a identificar situações problemáticas em janelas de tempo futuras. / Public transport systems by bus have been increasingly relevant for the development of cities. Techniques to improve planning and control of daily operation of bus services presented significant improvements along the years, and travel time forecast plays an important hole in both planning and daily operation strategies. Travel times anticipation helps planners and controllers to anticipate the various issues that arise during the daily bus line operation. It also allows keeping users informed, so they can plan in advance for their trip. Several studies related to travel time prediction can be found in the literature. Due to its intrinsic difficulty, the problem has been addressed by different techniques. Numerical results from studies demonstrate the potential use of neural networks in relation to other techniques. However, the literature does not present applications that incorporate a feedback of the information contained in time series as it is done by recurrent neural networks. Most of the studies in the literature have been conducted with data from specific cities and buses lines with fixed stops. The situation that arises in bus lines without fixed stops operated with microbuses present a different dynamics from the literature case studies. In addition, existing studies do not use time-space trajectories as a supporting instrument for bus travel time prediction. In this thesis we study the problem of travel time prediction for microbus lines without fixed stops using the basic information of the time-space trajectories The proposed model is based on recurrent neural networks. The input data includes: (i) the start time of the bus trip, (ii) its current position in GPS coordinates, (iii) the current time and (iv) distance travelled after one minute. The networks are trained with data from a microbus line from the city of Porto Alegre, Brazil. Data corresponds to the year 2015. The model provide forecasts for distance travelled minute by minute, and for a time window of 30 minutes. The developed models were trained with a comprehensive set of data from working days including peak and off-peak periods. The training data did not disregard information from any day due to occurrence of special events. It was concluded that the recurrent neural network model developed is capable of absorbing the dynamics of the microbuses movement. The information produced present an adequate level of precision to be used for users information. It is also adequate for planners and operation controllers as it can help to identify problematic situations in future time windows.

Thesis (Ph.D.) - Carleton University, 2007. / Includes bibliographical references (p. 219-233). Also available in electronic format on the Internet.

Os sistemas de transporte público por ônibus têm sido cada vez mais relevantes para o desenvolvimento das cidades. Técnicas para melhorar o planejamento e o controle da operação diária dos serviços de ônibus apresentaram melhorias significativas ao longo dos anos, e a previsão do tempo de viagem desempenha um importante papel no planejamento e nas estratégias da operação diária. A antecipação dos tempos de viagem ajuda os planejadores e controladores a evitar os vários problemas que surgem durante a operação diária da linha de ônibus. Ela também permite manter os usuários informados para que eles possam planejar com antecedência a sua viagem. Vários estudos relacionados à previsão do tempo de viagem podem ser encontrados na literatura. Devido a sua dificuldade intrínseca, o problema foi abordado por diferentes técnicas. Resultados numéricos de estudos demonstram o potencial uso de redes neurais em relação a outras técnicas. No entanto, a literatura não apresenta aplicações que incorporem uma retroalimentação das informações contidas em séries temporais, como é feito por redes neuronais recorrentes. A maioria dos estudos na literatura tem sido realizada com dados de cidades específicas e com linhas de ônibus com paradas fixas. A situação que surge em linhas de ônibus sem paradas fixas operadas com micro-ônibus apresenta uma dinâmica diferente dos estudos de caso da literatura Além disso, os estudos existentes não usam o gráfico de marcha como um instrumento de apoio para a previsão do tempo de viagem em ônibus. Nesta tese, estuda-se o problema da previsão do tempo de viagem para linhas de micro-ônibus sem paradas fixas, utilizando as informações básicas do gráfico de marcha. O modelo proposto é baseado em redes neurais recorrentes. Os dados de entrada incluem: (i) a hora de início da viagem do ônibus, (ii) sua posição atual em coordenadas GPS, (iii) o tempo atual e (iv) a distância percorrida após um minuto. As redes são treinadas com dados de uma linha de micro-ônibus da cidade de Porto Alegre, Brasil. Os dados correspondem ao ano de 2015. Os modelos fornecem previsões para a distância percorrida minuto a minuto e para uma janela de tempo de 30 minutos. O modelo desenvolvido foi treinado com um conjunto abrangente de dados de dias úteis, incluindo períodos de pico e fora de pico. Os dados de treinamento não desconsideraram informações de qualquer dia devido à ocorrência de eventos especiais. Concluiu-se que os modelos de redes neurais recorrentes desenvolvidos são capazes de absorver a dinâmica do movimento dos micro-ônibus. A informação produzida apresenta um nível adequado de precisão a ser utilizado para informar os usuários. Também é adequada para planejadores e controladores da operação, pois pode ajudar a identificar situações problemáticas em janelas de tempo futuras. / Public transport systems by bus have been increasingly relevant for the development of cities. Techniques to improve planning and control of daily operation of bus services presented significant improvements along the years, and travel time forecast plays an important hole in both planning and daily operation strategies. Travel times anticipation helps planners and controllers to anticipate the various issues that arise during the daily bus line operation. It also allows keeping users informed, so they can plan in advance for their trip. Several studies related to travel time prediction can be found in the literature. Due to its intrinsic difficulty, the problem has been addressed by different techniques. Numerical results from studies demonstrate the potential use of neural networks in relation to other techniques. However, the literature does not present applications that incorporate a feedback of the information contained in time series as it is done by recurrent neural networks. Most of the studies in the literature have been conducted with data from specific cities and buses lines with fixed stops. The situation that arises in bus lines without fixed stops operated with microbuses present a different dynamics from the literature case studies. In addition, existing studies do not use time-space trajectories as a supporting instrument for bus travel time prediction. In this thesis we study the problem of travel time prediction for microbus lines without fixed stops using the basic information of the time-space trajectories The proposed model is based on recurrent neural networks. The input data includes: (i) the start time of the bus trip, (ii) its current position in GPS coordinates, (iii) the current time and (iv) distance travelled after one minute. The networks are trained with data from a microbus line from the city of Porto Alegre, Brazil. Data corresponds to the year 2015. The model provide forecasts for distance travelled minute by minute, and for a time window of 30 minutes. The developed models were trained with a comprehensive set of data from working days including peak and off-peak periods. The training data did not disregard information from any day due to occurrence of special events. It was concluded that the recurrent neural network model developed is capable of absorbing the dynamics of the microbuses movement. The information produced present an adequate level of precision to be used for users information. It is also adequate for planners and operation controllers as it can help to identify problematic situations in future time windows.

Os sistemas de transporte público por ônibus têm sido cada vez mais relevantes para o desenvolvimento das cidades. Técnicas para melhorar o planejamento e o controle da operação diária dos serviços de ônibus apresentaram melhorias significativas ao longo dos anos, e a previsão do tempo de viagem desempenha um importante papel no planejamento e nas estratégias da operação diária. A antecipação dos tempos de viagem ajuda os planejadores e controladores a evitar os vários problemas que surgem durante a operação diária da linha de ônibus. Ela também permite manter os usuários informados para que eles possam planejar com antecedência a sua viagem. Vários estudos relacionados à previsão do tempo de viagem podem ser encontrados na literatura. Devido a sua dificuldade intrínseca, o problema foi abordado por diferentes técnicas. Resultados numéricos de estudos demonstram o potencial uso de redes neurais em relação a outras técnicas. No entanto, a literatura não apresenta aplicações que incorporem uma retroalimentação das informações contidas em séries temporais, como é feito por redes neuronais recorrentes. A maioria dos estudos na literatura tem sido realizada com dados de cidades específicas e com linhas de ônibus com paradas fixas. A situação que surge em linhas de ônibus sem paradas fixas operadas com micro-ônibus apresenta uma dinâmica diferente dos estudos de caso da literatura Além disso, os estudos existentes não usam o gráfico de marcha como um instrumento de apoio para a previsão do tempo de viagem em ônibus. Nesta tese, estuda-se o problema da previsão do tempo de viagem para linhas de micro-ônibus sem paradas fixas, utilizando as informações básicas do gráfico de marcha. O modelo proposto é baseado em redes neurais recorrentes. Os dados de entrada incluem: (i) a hora de início da viagem do ônibus, (ii) sua posição atual em coordenadas GPS, (iii) o tempo atual e (iv) a distância percorrida após um minuto. As redes são treinadas com dados de uma linha de micro-ônibus da cidade de Porto Alegre, Brasil. Os dados correspondem ao ano de 2015. Os modelos fornecem previsões para a distância percorrida minuto a minuto e para uma janela de tempo de 30 minutos. O modelo desenvolvido foi treinado com um conjunto abrangente de dados de dias úteis, incluindo períodos de pico e fora de pico. Os dados de treinamento não desconsideraram informações de qualquer dia devido à ocorrência de eventos especiais. Concluiu-se que os modelos de redes neurais recorrentes desenvolvidos são capazes de absorver a dinâmica do movimento dos micro-ônibus. A informação produzida apresenta um nível adequado de precisão a ser utilizado para informar os usuários. Também é adequada para planejadores e controladores da operação, pois pode ajudar a identificar situações problemáticas em janelas de tempo futuras. / Public transport systems by bus have been increasingly relevant for the development of cities. Techniques to improve planning and control of daily operation of bus services presented significant improvements along the years, and travel time forecast plays an important hole in both planning and daily operation strategies. Travel times anticipation helps planners and controllers to anticipate the various issues that arise during the daily bus line operation. It also allows keeping users informed, so they can plan in advance for their trip. Several studies related to travel time prediction can be found in the literature. Due to its intrinsic difficulty, the problem has been addressed by different techniques. Numerical results from studies demonstrate the potential use of neural networks in relation to other techniques. However, the literature does not present applications that incorporate a feedback of the information contained in time series as it is done by recurrent neural networks. Most of the studies in the literature have been conducted with data from specific cities and buses lines with fixed stops. The situation that arises in bus lines without fixed stops operated with microbuses present a different dynamics from the literature case studies. In addition, existing studies do not use time-space trajectories as a supporting instrument for bus travel time prediction. In this thesis we study the problem of travel time prediction for microbus lines without fixed stops using the basic information of the time-space trajectories The proposed model is based on recurrent neural networks. The input data includes: (i) the start time of the bus trip, (ii) its current position in GPS coordinates, (iii) the current time and (iv) distance travelled after one minute. The networks are trained with data from a microbus line from the city of Porto Alegre, Brazil. Data corresponds to the year 2015. The model provide forecasts for distance travelled minute by minute, and for a time window of 30 minutes. The developed models were trained with a comprehensive set of data from working days including peak and off-peak periods. The training data did not disregard information from any day due to occurrence of special events. It was concluded that the recurrent neural network model developed is capable of absorbing the dynamics of the microbuses movement. The information produced present an adequate level of precision to be used for users information. It is also adequate for planners and operation controllers as it can help to identify problematic situations in future time windows.

Transit trips include four parts: the trip from the front door to the transit stop; the wait at the transit stop for the transit vehicle; the transit ride; and the trip from the transit drop off point to the final destination. This thesis explores methods of improving the pedestrian trips to and from the transit stop and the waiting period at the transit stop. People are not satisfied with their transit trips. People want better quality waiting areas, increased safety, comfortable surroundings, transit information, and convenience during the transit trip. This thesis explores the positive relationship between the quality of public streets and transit facilities, and ridership satisfaction. The thesis proposes that the transit trip can be improved by improving transit waiting areas, and the paths people take arriving at and departing from transit stops. BC Transit's Vancouver Regional Transit System's transit facilities are the focus of the study. Transit facilities include: bus stops, bus loops, bus exchanges, SkyTrain stations, and SeaBus terminals. The study reviews people's attitudes towards transit facilities and discusses the items that people consider important to a transit trip. This review includes a survey conducted by the author and a review of surveys conducted for BC Transit. A review of the literature provides further evidence on the basic requirements for transit facilities and a comparison is made with the local situation. The thesis explores the potential for land use planning, urban design and on-site design to improve the safety, comfort, and convenience of transit facilities. The role of BC Transit, in providing adequate transit facilities, is discussed along with the roles and responsibilities of other associated organizations including: the Province, the Greater Vancouver Regional District, municipal governments located within the Vancouver Region, private enterprise, and business improvement districts. The study concludes BC Transit should give more thought to the transit customer in the design and location of transit facilities. And that municipal governments must take action to improve the quality of streets and transit facilities in their own communities.

Transit trips include four parts: the trip from the front door to the transit stop; the wait at the transit stop for the transit vehicle; the transit ride; and the trip from the transit drop off point to the final destination. This thesis explores methods of improving the pedestrian trips to and from the transit stop and the waiting period at the transit stop. People are not satisfied with their transit trips. People want better quality waiting areas, increased safety, comfortable surroundings, transit information, and convenience during the transit trip. This thesis explores the positive relationship between the quality of public streets and transit facilities, and ridership satisfaction. The thesis proposes that the transit trip can be improved by improving transit waiting areas, and the paths people take arriving at and departing from transit stops. BC Transit's Vancouver Regional Transit System's transit facilities are the focus of the study. Transit facilities include: bus stops, bus loops, bus exchanges, SkyTrain stations, and SeaBus terminals. The study reviews people's attitudes towards transit facilities and discusses the items that people consider important to a transit trip. This review includes a survey conducted by the author and a review of surveys conducted for BC Transit. A review of the literature provides further evidence on the basic requirements for transit facilities and a comparison is made with the local situation. The thesis explores the potential for land use planning, urban design and on-site design to improve the safety, comfort, and convenience of transit facilities. The role of BC Transit, in providing adequate transit facilities, is discussed along with the roles and responsibilities of other associated organizations including: the Province, the Greater Vancouver Regional District, municipal governments located within the Vancouver Region, private enterprise, and business improvement districts. The study concludes BC Transit should give more thought to the transit customer in the design and location of transit facilities. And that municipal governments must take action to improve the quality of streets and transit facilities in their own communities. / Applied Science, Faculty of / Community and Regional Planning (SCARP), School of / Graduate