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

TravelBot:Utilising Social Media Dialogue to Provide Journey Disruption Alerts

ACKNOWLEDGEMENTS The research described here is supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Research Hub; award reference: EP/G066051/1. We extend our grateful thanks to the participants who have contributed to the studies throughout, and to the industry partner FirstGroup plc for their support.Peer reviewedPublisher PD

BuSCOPE: Fusing individual & aggregated mobility behavior for “Live” smart city services

While analysis of urban commuting data has a long and demonstrated history of providing useful insights into human mobility behavior, such analysis has been performed largely in offline fashion and to aid medium-to-long term urban planning. In this work, we demonstrate the power of applying predictive analytics on real-time mobility data, specifically the smart-card generated trip data of millions of public bus commuters in Singapore, to create two novel and "live" smart city services. The key analytical novelty in our work lies in combining two aspects of urban mobility: (a) conformity: which reflects the predictability in the aggregated flow of commuters along bus routes, and (b) regularity: which captures the repeated trip patterns of each individual commuter. We demonstrate that the fusion of these two measures of behavior can be performed at city-scale using our BuScope platform, and can be used to create two innovative smart city applications. The Last-Mile Demand Generator provides O(mins) lookahead into the number of disembarking passengers at neighborhood bus stops; it achieves over 85% accuracy in predicting such disembarkations by an ingenious combination of individual-level regularity with aggregate-level conformity. By moving driverless vehicles proactively to match this predicted demand, we can reduce wait times for disembarking passengers by over 75%. Independently, the Neighborhood Event Detector uses outlier measures of currently operating buses to detect and spatiotemporally localize dynamic urban events, as much as 1.5 hours in advance, with a localization error of 450 meters.Comment: ACM MobiSys 201

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Analysis and assessment of a knowledge based smart city architecture providing service APIs

Abstract The main technical issues regarding smart city solutions are related to data gathering, aggregation, reasoning, data analytics, access, and service delivering via Smart City APIs (Application Program Interfaces). Different kinds of Smart City APIs enable smart city services and applications, while their effectiveness depends on the architectural solutions to pass from data to services for city users and operators, exploiting data analytics, and presenting services via APIs. Therefore, there is a strong activity on defining smart city architectures to cope with this complexity, putting in place a significant range of different kinds of services and processes. In this paper, the work performed in the context of Sii-Mobility smart city project on defining a smart city architecture addressing a wide range of processes and data is presented. To this end, comparisons of the state of the art solutions of smart city architectures for data aggregation and for Smart City API are presented by putting in evidence the usage semantic ontologies and knowledge base in the data aggregation in the production of smart services. The solution proposed aggregate and re-conciliate data (open and private, static and real time) by using reasoning/smart algorithms for enabling sophisticated service delivering via Smart City API. The work presented has been developed in the context of the Sii-Mobility national smart city project on mobility and transport integrated with smart city services with the aim of reaching a more sustainable mobility and transport systems. Sii-Mobility is grounded on Km4City ontology and tools for smart city data aggregation, analytics support and service production exploiting smart city API. To this end, Sii-Mobility/Km4City APIs have been compared to the state of the art solutions. Moreover, the proposed architecture has been assessed in terms of performance, computational and network costs in terms of measures that can be easily performed on private cloud on premise. The computational costs and workloads of the data ingestion and data analytics processes have been assessed to identify suitable measures to estimate needed resources. Finally, the API consumption related data in the recent period are presented

Privacy-Friendly Mobility Analytics using Aggregate Location Data

Location data can be extremely useful to study commuting patterns and disruptions, as well as to predict real-time traffic volumes. At the same time, however, the fine-grained collection of user locations raises serious privacy concerns, as this can reveal sensitive information about the users, such as, life style, political and religious inclinations, or even identities. In this paper, we study the feasibility of crowd-sourced mobility analytics over aggregate location information: users periodically report their location, using a privacy-preserving aggregation protocol, so that the server can only recover aggregates -- i.e., how many, but not which, users are in a region at a given time. We experiment with real-world mobility datasets obtained from the Transport For London authority and the San Francisco Cabs network, and present a novel methodology based on time series modeling that is geared to forecast traffic volumes in regions of interest and to detect mobility anomalies in them. In the presence of anomalies, we also make enhanced traffic volume predictions by feeding our model with additional information from correlated regions. Finally, we present and evaluate a mobile app prototype, called Mobility Data Donors (MDD), in terms of computation, communication, and energy overhead, demonstrating the real-world deployability of our techniques.Comment: Published at ACM SIGSPATIAL 201

Investigating the possibility of electronic intermodality and interoperability of innovative urban public transport systems in the City of Tshwane

Abstract: Urban areas have the purpose of satisfying citizen’s needs to interact and conduct different activities such as work, study or leisure. Public transport systems are designed to allow the efficiently and reliable movement of people within the city (Amaya et al. 2017). Globally, developed countries always work on different methods in order to have the best formal urban public transportation system. This involves integration of various modes of public transport including technological innovations such as integrated e-smart cards and information dissemination. In South Africa, there has been the development of innovative urban public transport to enhance the public transport network and eliminate negative impacts on the road. Within Gauteng province in the past 10 years, the City of Tshwane has introduced innovative Formal Urban Public Transport (FUPT) systems that will convey commuters efficiently to desired locations with no delays and at more frequent intervals through an effective public transport network. However, the innovative FUPT network is fragmented and departments do not work with one other in any form...M.Tech. (Operations Management

Trends in Smart City Development

This report examines the meanings and practices associated with the term 'smart cities.' Smart city initiatives involve three components: information and communication technologies (ICTs) that generate and aggregate data; analytical tools which convert that data into usable information; and organizational structures that encourage collaboration, innovation, and the application of that information to solve public problems