A GeoSocial Intelligence Framework for Studying & Promoting Resilience to Seasonal Flooding in Jakarta, Indonesia

PetaJakarta.org is a web-based platform developed to harness the power of social media to gather, sort, and display information about flooding for Jakarta residents in real time. The platform runs on the open source software CogniCity—an OSS platform developed by the SMART Infrastructure Facility, University of Wollongong—which allows data to be collected and disseminated by community members through their location-enabled mobile devices. The project uses a GeoSocial Intelligence Framework to approach the complexity of Jakarta’s entangled hydraulic, hydrological and meteorological systems and thereby converts the noise of social media into knowledge about urban infrastructure and situational conditions related to flooding and inundation. In this paper, PetaJakarta.org co-directors Dr Tomas Holderness, Geomatics Research Fellow at the SMART Infrastructure Facility, Dr Etienne Turpin, Vice-Chancellor’s Postdoctoral Research Fellow at the SMART Infrastructure Facility, and Dr Rohan Wickramasuriyam, GIS Research Fellow at the SMART Infrastructure Facility, will discuss their GeoSocial Intelligence Framework as it applies to their current research in Jakarta. They will also present their preliminary findings from their 2014 Twitter #DataGrant, which has allowed them to develop a correlative analysis between historic social media information, the Jakarta government’s flood maps, and the infrastructure used to manage critical flood emergencies. Finally, they will speculate on several future applications of the CogniCity OSS and suggest how it might be developed to further promote an integrated civic co-management platform with the support of business, industry, government and community organizations

Tour-based Travel Mode Choice Estimation based on Data Mining and Fuzzy Techniques

This paper extends tour-based mode choice model, which mainly includes individual trip level interactions, to include linked travel modes of consecutive trips of an individual. Travel modes of consecutive trip made by an individual in a household have strong dependency or co-relation because individuals try to maintain their travel modes or use a few combinations of modes for current and subsequent trips. Traditionally, tour based mode choice models involved nested logit models derived from expert knowledge. There are limitations associated with this approach. Logit models assumes i) specific model structure (linear utility model) in advance; and, ii) it holds across an entire historical observations. These assumptions about the predefined model may be representative of reality, however these rules or heuristics for tour based mode choice should ideally be derived from the survey data rather than based on expert knowledge/ judgment. Therefore, in this paper, we propose a novel data-driven methodology to address the issues identified in tour based mode choice. The proposed methodology is tested using the Household Travel Survey (HTS) data of Sydney metropolitan area and its performances are compared with the state-of-the-art approaches in this area

An Agent Based Model for the Simulation of Transport Demand and Land Use

Agent based modelling has emerged as a promising tool to provide planners with insights on social behaviour and the interdependencies characterising urban system, particularly with respect to transport and infrastructure planning. This paper presents an agent based model for the simulation of land use and transport demand of an urban area of Sydney, Australia. Each individual in the model has a travel diary which comprises a sequence of trips the person makes in a representative day as well as trip attributes such as travel mode, trip purpose, and departure time. Individuals are associated with each other by their household relationship, which helps define the interdependencies of their travel diary and constrains their mode choice. This allows the model to not only realistically reproduce how the current population uses existing transport infrastructure but more importantly provide comprehensive insight into future transport demands. The router of the traffic micro-simulator TRANSIMS is incorporated in the model to inform the actual travel time of each trip and changes of traffic density on the road network. Simulation results show very good agreement with survey data in terms of the distribution of trips done by transport modes and by trip purposes, as well as the traffic density along the main road in the study area