Building climate change resilience and adaptive capacity in Australia’s community sector using social media and technology innovation

Public and private sector organisations worldwide are putting strategies in place to manage the commercial and operational risks of climate change. However, community organisations are lagging behind in their understanding and preparedness, despite them being among the most exposed to the effects of climate change impacts and regulation. This poster presents a proposal for a multidisciplinary study that addresses this issue by developing, testing and applying a novel climate risk assessment methodology that is tailored to the needs of Australia’s community sector and its clients. Strategies to mitigate risks and build resilience and adaptive capacity will be identified including new opportunities afforded by urban informatics, social media, and technologies of scale making.

Understanding the impact of strategic alignment on the operational performance of post implemented technological innovations

Purpose – The purpose of this paper is to examine the role of three strategies - organisational, business and information system – in post implementation of technological innovations. The findings reported in the paper are that improvements in operational performance can only be achieved by aligning technological innovation effectiveness with operational effectiveness. Design/methodology/approach – A combination of qualitative and quantitative methods was used to apply a two-stage methodological approach. Unstructured and semi structured interviews, based on the findings of the literature, were used to identify key factors used in the survey instrument design. Confirmatory factor analysis (CFA) was used to examine structural relationships between the set of observed variables and the set of continuous latent variables. Findings – Initial findings suggest that organisations looking for improvements in operational performance through adoption of technological innovations need to align with operational strategies of the firm. Impact of operational effectiveness and technological innovation effectiveness are related directly and significantly to improved operational performance. Perception of increase of operational effectiveness is positively and significantly correlated with improved operational performance. The findings suggest that technological innovation effectiveness is also positively correlated with improved operational performance. However, the study found that there is no direct influence of strategiesorganisational, business and information systems (IS) - on improvement of operational performance. Improved operational performance is the result of interactions between the implementation of strategies and related outcomes of both technological innovation and operational effectiveness. Practical implications – Some organisations are using technological innovations such as enterprise information systems to innovate through improvements in operational performance. However, they often focus strategically only on effectiveness of technological innovation or on operational effectiveness. Such a focus will be detrimental in the long-term of the enterprise. This research demonstrated that it is not possible to achieve maximum returns through technological innovations as dimensions of operational effectiveness need to be aligned with technological innovations to improve their operational performance. Originality/value – No single technological innovation implementation can deliver a sustained competitive advantage; rather, an advantage is obtained through the capacity of an organisation to exploit technological innovations’ functionality on a continuous basis. To achieve sustainable results, technology strategy must be aligned with organisational and operational strategies. This research proposes the key performance objectives and dimensions that organisations should focus to achieve a strategic alignment. Research limitations/implications – The principal limitation of this study is that the findings are based on investigation of small sample size. There is a need to explore the appropriateness of influence of scale prior to generalizing the results of this study.

Busway platform bus capacity analysis

Bus Rapid Transit (BRT), because of its operational flexibility and simplicity, is rapidly gaining popularity with urban designers and transit planners. Earlier BRTs were bus shared lane or bus only lane, which share the roadway with general and other forms of traffic. In recent time, more sophisticated designs of BRT have emerged, such as busway, which has separate carriageway for buses and provides very high physical separation of buses from general traffic. Line capacities of a busway are predominately dependent on bus capacity of its stations. Despite new developments in BRT designs, the methodology of capacity analysis is still based on traditional principles of kerbside bus stop on bus only lane operations. Consequently, the tradition methodology lacks accounting for various dimensions of busway station operation, such as passenger crowd, passenger walking and bus lost time along the long busway station platform. This research has developed a purpose made bus capacity analysis methodology for busway station analysis. Extensive observations of kerbside bus stops and busway stations in Brisbane, Australia were made and differences in their operation were studied. A large scale data collection was conducted using the video recording technique at the Mater Hill Busway Station on the South East Busway in Brisbane. This research identified new parameters concerning busway station operation, and through intricate analysis identified the elements and processes which influence the bus dwell time at a busway station platform. A new variable, Bus lost time, was defined and its quantitative descriptions were established. Based on these finding and analysis, a busway station platform bus capacity methodology was developed, comprising of new models for busway station lost time, busway station dwell time, busway station loading area bus capacity, and busway station platform bus capacity. The new methodology not only accounts for passenger boarding and alighting, but also covers platform crowd and bus lost time in station platform bus capacity estimation. The applicability of this methodology was shown through demonstrative examples. Additionally, these examples illustrated the significance of the bus lost time variable in determining station capacities.

Modeling bus rapid transit station bus queuing for operational analysis

This paper presents mathematical models for BRT station operation, calibrated using microscopic simulation modelling. Models are presented for station capacity and bus queue length. No reliable model presently exists to estimate bus queue length. The proposed bus queue model is analogous to an unsignalized intersection queuing model.

Stochastic analysis of transit route segments' passenger load variation for capacity & quality of service assessment

This study uses weekday Automatic Fare Collection (AFC) data on a premium bus line in Brisbane, Australia •Stochastic analysis is compared to peak hour factor (PHF) analysis for insight into passenger loading variability •Hourly design load factor (e.g. 88th percentile) is found to be a useful method of modeling a segment’s passenger demand time-history across a study weekday, for capacity and QoS assessment •Hourly coefficient of variation of load factor is found to be a useful QoS and operational assessment measure, particularly through its relationship with hourly average load factor, and with design load factor •An assessment table based on hourly coefficient of variation of load factor is developed from the case study

Multi-objective models and techniques for analysing the absolute capacity of railway networks

Railway capacity determination and expansion are very important topics. In prior research, the competition between different entities such as train services and train types, on different network corridors however have been ignored, poorly modelled, or else assumed to be static. In response, a comprehensive set of multi-objective models have been formulated in this article to perform a trade-off analysis. These models determine the total absolute capacity of railway networks as the most equitable solution according to a clearly defined set of competing objectives. The models also perform a sensitivity analysis of capacity with respect to those competing objectives. The models have been extensively tested on a case study and their significant worth is shown. The models were solved using a variety of techniques however an adaptive E constraint method was shown to be most superior. In order to identify only the best solution, a Simulated Annealing meta-heuristic was implemented and tested. However a linearization technique based upon separable programming was also developed and shown to be superior in terms of solution quality but far less in terms of computational time.

Optimization models for expanding a railway’s theoretical capacity

Changing the topology of a railway network can greatly affect its capacity. Railway networks however can be altered in a multitude of different ways. As each way has significant immediate and long term financial ramifications, it is a difficult task to decide how and where to expand the network. In response some railway capacity expansion models (RCEM) have been developed to help capacity planning activities, and to remove physical bottlenecks in the current railway system. The exact purpose of these models is to decide given a fixed budget, where track duplications and track sub divisions should be made, in order to increase theoretical capacity most. These models are high level and strategic, and this is why increases to the theoretical capacity is concentrated upon. The optimization models have been applied to a case study to demonstrate their application and their worth. The case study evidently shows how automated approaches of this nature could be a formidable alternative to current manual planning techniques and simulation. If the exact effect of track duplications and sub-divisions can be sufficiently approximated, this approach will be very applicable.

A multi-criteria approach for hospital capacity analysis

Hospitals are critical elements of health care systems and analysing their capacity to do work is a very important topic. To perform a system wide analysis of public hospital resources and capacity, a multi-objective optimization (MOO) approach has been proposed. This approach identifies the theoretical capacity of the entire hospital and facilitates a sensitivity analysis, for example of the patient case mix. It is necessary because the competition for hospital resources, for example between different entities, is highly influential on what work can be done. The MOO approach has been extensively tested on a real life case study and significant worth is shown. In this MOO approach, the epsilon constraint method has been utilized. However, for solving real life applications, with a large number of competing objectives, it was necessary to devise new and improved algorithms. In addition, to identify the best solution, a separable programming approach was developed. Multiple optimal solutions are also obtained via the iterative refinement and re-solution of the model.

Characterization of commercial double-walled carbon nanotube material : composition, structure, and heat capacity

A purified commercial double-walled carbon nanotube (DWCNT) sample was investigated by transmission electron microscopy (TEM), thermogravimetry (TG), and Raman spectroscopy. Moreover, the heat capacity of the DWCNT sample was determined by temperature-modulated differential scanning calorimetry in the range of temperature between -50 and 290 °C. The main thermo-oxidation characterized by TG occurred at 474 °C with the loss of 90 wt% of the sample. Thermo-oxidation of the sample was also investigated by high-resolution TG, which indicated that a fraction rich in carbon nanotube represents more than 80 wt% of the material. Other carbonaceous fractions rich in amorphous coating and graphitic particles were identified by the deconvolution procedure applied to the derivative of TG curve. Complementary structural data were provided by TEM and Raman studies. The information obtained allows the optimization of composites based on this nanomaterial with reliable characteristics.