The use of social media in public transit systems: the case of the Gautrain, Gauteng province, South Africa: analysis and lessons learnt

Abstract: The use of public transit systems is still in its infancy in Gauteng, South Africa. Commuters still prefer using private motor vehicles. However the introduction of the first efficient high speed train in Africa (The Gautrain) during the 2010 world cup was thought to change perceptions of the public on transit systems. The Gautrain was also thought to enhance Johannesburg as a smart city. Social media has proved to be useful in proving user information, which can be use to improved services. The study is an exploratory study, which analyses how commuters feel on the effectiveness of the Gautrain by analysing posts on social media before and after completion of construction of the Gautrain system. Emerging findings reflect that although the Gautrain has positively changed the publics’ perception on public transit systems, the Gautrain system still needs to be improved for the South African public to embrace fully public transit systems

Ranking nodes in complex networks : a case study of the Gaubus

Abstract: Connecting points of interest through a well-planned, inter-connected network provides manifold benefits to commuters and service providers. In the South African context, traffic congestion has become of great concern. Given how the South Africa community is slowly developing towards the use of multi-modes of mobility, the Gautrain network can be used to promote the use of multi-modes of mobility, as the Gautrain has been identified as the backbone of mobility within the Gauteng province. Currently commuters have the option to board the Gaubus (a form of Bus Rapid Transit) at their origin points which will take them to the Gautrain station to board the Gautrain. The problem to be solved arises when a commuter wishes to traverse from any bus stop to the Gautrain station, currently he/she only has one option and if the bus network has a shutdown at any point in the network the commuter’s journey will not be possible. In solving this problem, we consider the problem of graph robustness (that is creating new alternative routes to increase node/bus stop connectivity). We initial use Strava data, to identify locations were cyclist prefer to cycle and at what time of day. In graph theory, the nodes with most spreading ability are called influential nodes. Identification of most influential nodes and ranking them based on their spreading ability is of vital importance. Closeness centrality and betweenness are one of the most commonly used methods to identify influential nodes in complex networks. Using the Gaubus network we identify the influential nodes/ bus stops, using the betweenness centrality measure. The results reveal the influential nodes with the highest connectivity as these have cross-connections in the network. Identification of the influential nodes presents an important implication for future planning, accessibility, and, more generally, quality of life

Built-up area and land cover extraction using high resolution Pleiades Satellite Imagery for Midrand, in Gauteng Province, South Africa

Abstract: Urban areas, particularly in developing countries face immense challenges such as climate change, poverty, lack of resources poor land use management systems, and week environmental management practices. Mitigating against these challenges is often hampered by lack of data on urban expansion, urban footprint and land cover. To support the recently adopted new urban agenda 2030 there is need for the provision of information to support decision making in the urban areas. Earth observation has been identified as a tool to foster sustainable urban planning and smarter cities as recognized by the new urban agenda, because it is a solution to unavailability of data. Accordingly, this study uses high resolution EO data Pleiades satellite imagery to map and document land cover for the rapidly expanding area of Midrand in Johannesburg, South Africa. An unsupervised land cover classification of the Pleiades satellite imagery was carried out using ENVI software, whereas NDVI was derived using ArcGIS software. The land cover had an accuracy of 85% that is highly adequate to document the land cover in Midrand. The results are useful because it provides a highly accurate land cover and NDVI datasets at localised spatial scale that can be used to support land use management strategies within Midrand and the City of Johannesburg South Africa

Exploring the potential of crowd sourced data to map commuter points of interest : a case study of Johannesburg

Abstract: Modern African cities are faced with various mobility and transportation challenges. In developing smart sustainable cities, city planners need to create a balance between supply and demand for public transportation. Development of multi-mobility mode models has contemporarily received a special interest in smart cities development. Globally, the use of bike sharing services to complete the first kilometre or last kilometre of the trip has been highly received, with commuters using either rail or road mobility modes for the middle section of their trip. Within the developing world context, the use of multi-mobility modes in daily commuting is still new, and little research has been done to guide this. Notwithstanding the influence of uncertainties and fragmentation over demand and supply in public transportation provision. In the South Africa context, various modes of public transportation have been developed which seek to be smart, sustainable and efficient such as the fast train (Gautrain), Bus rapid transport (Rea Vaya and Gaubus) and Bikes sharing platforms (Upcycles), however most of these modes are currently not spatially connected. Hence the researcher sought to develop a stepping stone in planning for future mobility demand. Using an explorative methodology, the authors collected quantitative and spatial data in the form of land-use data and crowd sourced data (from twitter) to map commuter points of interest in and around the city of Johannesburg. The results reveal hot and cold spots in the city. The hot spots reveal areas where commuters frequently travel to, and when overlaid with transportation data, we are able to identify potential locations to develop new transportation hubs as these will overtime become key points of interest

Tweets and Facebook posts, the novelty techniques in the creation of origin-destination models

Abstract: Social media and big data have emerged to be a useful source of information that can be used for planning purposes, particularly transportation planning and trip-distribution studies. Cities in developing countries such as South Africa often struggle with out-dated, unreliable and cumbersome techniques such as traffic counts and household surveys to conduct origin and destination studies. The emergence of ubiquitous crowd sourced data, big data, social media and geolocation based services has shown huge potential in providing useful information for origin and destination studies. Perhaps such information can be utilised to determine the origin and destination of commuters using the Gautrain, a high-speed railway in Gauteng province South Africa. To date little is known about the origins and destinations of Gautrain commuters. Accordingly, this study assesses the viability of using geolocation-based services namely Facebook and Twitter in mapping out the network movements of Gautrain commuters. Explorative Spatial Data Analysis (ESDA), Echo-social and ArcGis software were used to extract social media data, i.e. tweets and Facebook posts as well as to visualize the concentration of Gautrain commuters. The results demonstrate that big data and geolocation based services have the significant potential to predict movement network patterns of commuters and this information can thus, be used to inform and improve transportation planning. Nevertheless use of crowd sourced data and big data has privacy concerns that still need to be addressed

Implications of land use change for the sustainability of urban areas: A case study of Stellenbosch, South Africa

Abstract: Sustainable development, an objective of urban planning, is difficult to put into practice. Data to monitor sustainable land use management is often lacking, particularly in developing countries. This paper investigates the use of earth observation data for supporting sustainable land use planning. It proposes the use of decision consequence analysis (DCA) as a simple and structured way to put sustainable development into practice. The study demonstrates how land use change (LUC) which also includes land cover, the local land use mix index (LLUM) and land use frequency (LUF) can be used as indicators of objective land use sustainability..

Exploring the potential of open source data to generate congestion and emission trends in developing cities

Abstract: The growth in Intelligent Transportation Systems (ITS) has enhanced the way mobility in contemporary cities is managed. Given the growth in availability of traffic data that contains location-aware datasets, congestion and pollution indexes can be developed. Metropolitan cities such Johannesburg due to their economic activities, attract commuters into the city on a daily basis seeking greener pastures. This has led to major freeways and roads experiencing high levels of congestion. In 2020, due to a global pandemic of an outbreak of Corona Virus (COVID-19), the national government declared a national shutdown with only essential traffic being allowed to operate. Given the scenario of the national lock-down this allows for the statistical analysis of the impact of essential traffic on the overall transportation system. Consequently the aim of the paper was to explore the congestion and C02 emission impact of essential traffic for the City of Johannesburg. Using an exploratory approach, we monitored and collected traffic congestion data from the Tomtom traffic index for the metropolitan city of Johannesburg, South Africa. Using a mathematic model, we develop a relationship between congestion and pollution to visualise the variations in pollution and congestion levels during varies scenarios. We demonstrate this by comparing datasets for variations in congestion levels in two epochs, viz the period without movement restrictions and the period whereby movement is restricted. The results reveal essential traffic on the congestion index to be below 22 percent for both weekends and weekdays. A scenario common only during weekends in 2019. Whilst for the emission index, C02 levels are approximately less than 45 percent throughout the week. The paper concludes the investment into mining and analysing traffic data has a significantly role for future mobility planning in both the developed and developing world and, more generally, improving the quality of commuting trips in the city

The strategically located land index support system for human settlements land reform in South Africa

Abstract: Creating sustainable human settlements is fundamental in fostering spatial and socio-economic integration in South Africa. Policy makers are often faced with the problem of identifying strategically located land for human settlements land reform in South Africa. To date there is no tool or standard framework that assists the government to identify land that is strategically located for land reform. This study proposes the use of Geographic Information Systems (GIS), and Multi-Criteria Decision Making (MCDM) to develop a Strategically Located Land Index (SLLI) deployed in a web viewer to identify land that is smart for human settlements land reform. The study demonstrates that GIS,MCDMand the SLLI are invaluable tools in facilitating streamlined, coordinated, standardised and evidence-based decisions for human settlements land reform. However, there is need for capacity building in government departments responsible for land reform and development planning for the SLLI to be fully utilised

An analysis to investigate spatial cognitive factors which influence cycling patterns in Johannesburg

Abstract: Cycling in most African cities is done as either a mode of commuting or for recreational purposes. Apart from Smart cities encouraging a shift from cars to public transport by providing efficient last-mile connections, commuter cycling can take a significant share of end-to-end short distance trips. The ultimate realization of cycling merits by urban dwellers, (such as in Johannesburg, South Africa) is hindered by a lack of appropriate data to aid in understanding the dynamics of cycling behaviour. This paper seeks to be the first step in building a multi-model to govern the use of multi-modes of mobility in the city by initial focusing on promoting NMT usage as a mode of commuting in the city. Identification of these factors would go a long way in improving cycling uptake as well as inform policy strategies for non-motorized transportation in the city. Using an analytical approach, the authors conducted a survey along pre-known locations were cyclist choose to cycle. One route with newly developed cycling infrastructure and another without cycling infrastructure. A self-reported travel behaviour form, was used for the collection of spatial cognitive and attitudinal data on participants’ travel environment, attitude, behaviour, norm, intention, and habit was utilized to gather data to understand cyclist cognitive reasoning for choosing one path over another. The data collected from the survey was then overlaid with Strava Metro cycling data showing locations were cyclist prefer to cycle in the city. Findings from the analysis suggest perceived safe routes and routes that maximize health benefits are preferred. Based on the findings it is recommended that planners need to use crowd sourced data before developing infrastructure for cycling the city

Developing an urban sustainability toolbox using earth observation data and GIS for monitoring rapid urbanisation in developing countries.

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