Network modelling for road-based Faecal Sludge Management

Improvements in the collection and treatment of sewage are critical to reduce health and environmental hazards in rapidly-urbanising informal settlements. Where sewerage infrastructure is not available, road-based Fecal Sludge Management options are often the only alternative. However, the costs of fecal sludge transportation are often a barrier to their implementation and operation and thus it is desirable to optimise travel time from source to treatment to reduce costs. This paper presents a novel technique, employing spatial network analysis, to optimise the spatio-topological configuration of a road-based fecal sludge transportation network on the basis of travel time. Using crowd-sourced spatial data for the Kibera settlement and the surrounding city, Nairobi, a proof-of-concept network model was created simulating the transport of waste from the 158 public toilets within Kibera. The toilets are serviced by vacuum pump trucks which move fecal sludge to a transfer station from where a tanker transports waste to a treatment plant. The model was used to evaluate the efficiency of different network configurations, based on transportation time. The results show that the location of the transfer station is a critical factor in network optimisation, demonstrating the utility of network analysis as part of the sanitation planning process

Investigating the performance of transport infrastructure using real-time data and a scalable multi-modal agent based model

The idea that including more information in more dynamic and iterative ways is central to the promise of the big data paradigm. The hope is that via new data sources, such as remote sensors and mobile phones, the reliance on heavily simplified generalised functions for model inputs will be erased. This trade between idealised and actual empirical data will be matched with dynamic models which consider complexity at a fundamental level, inherently mirroring the systems they are attempting to replicate. Cloud computing brings the possibility of doing all of this, in less time than the simplified macro models of the past, thus enabling better answers and at the time of critical decision making junctures. This research was task driven - the question of high speed rail versus aviation led to an investigation into the simplifications and assumptions that back up many of the commonly held beliefs on the sustainability of different modes of transport. The literature ultimately highlighted the need for context specific information; actual load factors, actual journey times considering traffic/engineering works and so on. Thus, rather than being explicitly an exercise in answering a specific question, a specific question was used to drive the development of a tool which may hold promise for answering a range of transportation related questions. The original contributions of this work are, firstly the use of real-time data sources to quantify temporally and spatially dynamic network performance metrics (eg. journey times on different transport models) and secondly to organise these data sources in a framework which can handle the volume and type of the data and organise the data in a way so that it is useful for the dynamic agent based modelling of future scenarios.EPSRC I Case Studentship with Ove Arup & Partner

Network modelling for road-based fecal sludge management

Improvements in the collection and treatment of sewage are critical to reduce health and environmental hazards in rapidly urbanising informal settlements. Where sewerage infrastructure is not available, road-based faecal sludge management options are often the only alternative. However, the costs of faecal sludge transportation are often a barrier to its implementation and operation and thus it is desirable to optimise travel time from source to treatment to reduce costs. This paper presents a novel technique, employing spatial network analysis, to optimise the spatiotopological configuration of a road-based faecal sludge transportation network on the basis of travel time. Using crowd-sourced spatial data for the Kibera settlement and the surrounding city, Nairobi, a proof-of-concept network model was created simulating the transport of waste from the 158 public toilets within Kibera. The toilets are serviced by vacuum pump trucks which move faecal sludge to a transfer station, and from there a tanker transports waste to a treatment plant. The model was used to evaluate the efficiency of different network configurations, based on transportation time. The results show that the location of the transfer station is a critical factor in network optimisation, demonstrating the utility of network analysis as part of the sanitation planning process

Calibration of a spatial simulation model with volunteered geographical information

For many scientific disciplines, the continued progression of information technology has increased the availability of data, computation and analytical methodologies including simulation and visualisation. Geographical information science is no exception. In this article, we investigate the possibilities for deployment of e-infrastructures to inform spatial planning, analysis and policy-making. We describe an existing architecture that feeds both static and dynamic simulation models from a variety of sources, including not only administrative datasets but also attitudes and behaviours which are harvested online from crowds. This infrastructure also supports visualisation and computationally intensive processing. The main aim of this article is to illustrate how spatial simulation models can be calibrated with crowd-sourced data. We introduce an example in which popular attitudes to congestion charging in a major UK city (Manchester) were collected, with promotional support from a high-profile media organisation (the BBC). These data are used to estimate the parameters of a transport simulation model, using a hungry estimation procedure which is deployed within a high-performance computational grid. We indicate how the resulting model might be used to evaluate the impact of alternative policy options for regulating the traffic in Manchester. Whilst the procedure is novel in itself, we argue that greater credibility could be added by the incorporation of open-source simulation models and by the use of social networking mechanisms to share policy evaluations much more widely

Using crowdsourced data (Twitter & Facebook) to delineate the origin and destination of commuters of the Gautrain public transit system in South Africa

Abstract: The study of commuters’ origins and destinations (O_D) promises to assist transportation planners with prediction models to inform decision making. Conventionally O_D surveys are undertaken through travel surveys and traffic counts, however data collection for these surveys has historically proven to be time consuming and having a strain on human resources, thus a need for an alternative data source arises. This study combines the use social media data and geographic information systems in the creation of a model for origin and destination surveys. The model tests the potential of using big data from Echo echo software which contains Twitter and Facebook data obtained from social media users in Gauteng. This data contains geolocation and it is used to determine origin and destination as well as concentration levels of Gautrain commuters. A krigging analysis was performed on the data to determine the O-D and concentration levels of Gautrain users. The results reveal the concentration of Gautrain commuters at various points of interest that is where they work, live or socialise. The results from the study highlight which nodes attract the most commuters and also possible locations for the expansion for Gautrain. Lastly, the study also highlights some weakness of crowdsourced data for informing transportation planning. (208

Web 2.0 Broker: A standards-based service for spatio-temporal search of crowd-sourced information

Recent trends in information technology show that citizens are increasingly willing to share information using tools provided by Web 2.0 and crowdsourcing platforms to describe events that may have social impact. This is fuelled by the proliferation of location-aware devices such as smartphones and tablets; users are able to share information in these crowdsourcing platforms directly from the field at real time, augmenting this information with its location. Afterwards, to retrieve this information, users must deal with the different search mechanisms provided by the each Web 2.0 services. This paper explores how to improve on the interoperability of Web 2.0 services by providing a single service as a unique entry to search over several Web 2.0 services in a single step. This paper demonstrates the usefulness of the Open Geospatial Consortium's OpenSearch Geospatial and Time specification as an interface for a service that searches and retrieves information available in crowdsourcing services. We present how this information is valuable in complementing other authoritative information by providing an alternative, contemporary source. We demonstrate the intrinsic interoperability of the system showing the integration of crowd-sourced data in different scenarios