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Author  |
Marion Lara Tan; Raj Prasanna; Kristin Stock; Emma Hudson-Doyle; Graham Leonard; David Johnston |
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Title |
Enhancing the usability of a disaster app: exploring the perspective of the public as users |
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Conference Article |
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Year |
2019 |
Publication |
Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management |
Abbreviated Journal |
Iscram 2019 |
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Keywords |
usability inquiry, mobile application, disasters, alerts, public perspective |
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Abstract |
Limited research has studied how citizens? perspectives as end-users can contribute to improving the usability of disaster apps. This study addresses this gap by exploring end-user insights with the use of a conceptual disaster app in the New Zealand (NZ) context. NZ has multiple public alerting authorities that have various technological options in delivering information to the population?s mobile devices; including social media platforms, apps, as well as the Emergency Mobile Alert system. However, during critical events, the multiplicity of information may become overwhelming. A disaster app, conceptualised in the NZ context, aims to aggregate, organise, and deliver information from official sources to the public. After the initial conceptual design, a usability inquiry was administered by interviewing members of the public. Partial results of the inquiry show that the public?s perspective has value; in the process of understanding the new user?s viewpoint, usability highlights and issues are identified. |
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Massey University, New Zealand;GNS Science, New Zealand |
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Iscram |
Place of Publication |
Valencia, Spain |
Editor |
Franco, Z.; González, J.J.; Canós, J.H. |
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English |
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English |
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ISSN |
2411-3387 |
ISBN |
978-84-09-10498-7 |
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Track |
T8- Social Media in Crises and Conflicts |
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Conference |
16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019) |
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no |
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Call Number |
ISCRAM @ idladmin @ |
Serial |
1946 |
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Author |
Marion Lara Tan; Raj Prasanna; Kristin Stock; Emma Hudson-Doyle; Graham Leonard; David Johnston |
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Title |
Usability Factors Affecting the Continuance Intention of Disaster Apps |
Type |
Conference Article |
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Year |
2018 |
Publication |
Proceedings of ISCRAM Asia Pacific 2018: Innovating for Resilience – 1st International Conference on Information Systems for Crisis Response and Management Asia Pacific. |
Abbreviated Journal |
Iscram Ap 2018 |
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Pages |
326-338 |
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Keywords |
disaster apps, usability, continuance intention |
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Abstract |
Multiple disaster mobile applications (apps) already exist for public use; however, availability does not automatically translate to continued usage. Limited research has explored whether disaster apps are usable and whether the apps' usability affects users' intent for continued use. The paper presents a work-in-progress study that aims to test a usability-continuance intention model for the specific context of disaster apps. The study theorises seven usability factors that influence continued intention to use. An online usability survey was used to gather user experience data on disaster apps. Initial findings, through structural equational modelling, showed that five of the seven usability factors have a significant relationship to continuance intention. Although the relationships have different weights and directions, key influencers to users' intent to continue usage are app utility, app dependability, interface output, interface input, and interface graphics. The next step of the study will investigate the mediating effects of the factors and the moderating effects of users' experience and technological comfort. |
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Address |
Joint Centre for Disaster Research, Massey University; Joint Centre for Disaster Research, Massey University; Institute of Natural and Mathematical Sciences, Massey University; Joint Centre for Disaster Research, Massey University; GNS Science; Joint Centre for Disaster Research, Massey University; GNS Science |
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Publisher |
Massey Univeristy |
Place of Publication |
Albany, Auckland, New Zealand |
Editor |
Kristin Stock; Deborah Bunker |
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Language |
English |
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Human centred design for collaborative systems supporting 4Rs (Reduction, Readiness, Response and Recovery) |
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no |
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Serial |
1643 |
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Author |
Nilani Algiriyage; Raj Prasanna; Emma E H Doyle; Kristin Stock; David Johnston |
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Title |
Traffic Flow Estimation based on Deep Learning for Emergency Traffic Management using CCTV Images |
Type |
Conference Article |
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Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
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Pages |
100-109 |
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Keywords |
CCTV Big Data, YOLOv3, Traffic Flow Estimation. |
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Abstract |
Emergency Traffic Management (ETM) is one of the main problems in smart urban cities. This paper focuses on selecting an appropriate object detection model for identifying and counting vehicles from closed-circuit television (CCTV) images and then estimating traffic flow as the first step in a broader project. Therefore, a case is selected at one of the busiest roads in Christchurch, New Zealand. Two experiments were conducted in this research; 1) to evaluate the accuracy and speed of three famous object detection models namely faster R-CNN, mask R-CNN and YOLOv3 for the data set, 2) to estimate the traffic flow by counting the number of vehicles in each of the four classes such as car, bus, truck and motorcycle. A simple Region of Interest (ROI) heuristic algorithm is used to classify vehicle movement direction such as \quotes{left-lane} and \quotes{right-lane}. This paper presents the early results and discusses the next steps. |
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Address |
Joint Centre for Disaster Research, Massey University; Joint Centre for Disaster Research, Massey University; Joint Centre for Disaster Research, Massey University; Institute of Natural and Mathematical Sciences, Massey University; Joint Centre for Disaster Research, Massey University; |
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Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
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Language |
English |
Summary Language |
English |
Original Title |
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ISSN |
978-1-949373-27-10 |
ISBN |
2411-3396 |
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Track |
AI Systems for Crisis and Risks |
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Conference |
17th International Conference on Information Systems for Crisis Response and Management |
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Notes |
r.nilani@massey.ac.nz |
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Call Number |
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Serial |
2211 |
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Author |
Nilani Algiriyage; Raj Prasanna; Kristin Stock; Emma Hudson-Doyle; David Johnston; Minura Punchihewa; Santhoopa Jayawardhana |
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Title |
Towards Real-time Traffic Flow Estimation using YOLO and SORT from Surveillance Video Footage |
Type |
Conference Article |
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Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
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Pages |
40-48 |
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Keywords |
Computer Vision, Traffic Flow, YOLOv4, CCTV Big Data |
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Abstract |
Traffic emergencies and resulting delays cause a significant impact on the economy and society. Traffic flow estimation is one of the early steps in urban planning and managing traffic infrastructure. Traditionally, traffic flow rates were commonly measured using underground inductive loops, pneumatic road tubes, and temporary manual counts. However, these approaches can not be used in large areas due to high costs, road surface degradation and implementation difficulties. Recent advancement of computer vision techniques in combination with freely available closed-circuit television (CCTV) datasets has provided opportunities for vehicle detection and classification. This study addresses the problem of estimating traffic flow using low-quality video data from a surveillance camera. Therefore, we have trained the novel YOLOv4 algorithm for five object classes (car, truck, van, bike, and bus). Also, we introduce an algorithm to count the vehicles using the SORT tracker based on movement direction such as ``northbound'' and ``southbound'' to obtain the traffic flow rates. The experimental results, for a CCTV footage in Christchurch, New Zealand shows the effectiveness of the proposed approach. In future research, we expect to train on large and more diverse datasets that cover various weather and lighting conditions. |
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Address |
Massey University; Massey University; Massey University; Joint Centre for Disaster Research, Massey University; Joint Center of Disaster Research, Massey University Wellington; University of Kelaniya; Univerity of Kelaniya |
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Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
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Language |
English |
Summary Language |
English |
Original Title |
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ISSN |
978-1-949373-61-5 |
ISBN |
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Track |
AI and Intelligent Systems for Crises and Risks |
Expedition |
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Conference |
18th International Conference on Information Systems for Crisis Response and Management |
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Notes |
rangika.nilani@gmail.com |
Approved |
no |
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Call Number |
ISCRAM @ idladmin @ |
Serial |
2311 |
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Author |
Nilani Algiriyage; Rangana Sampath; Raj Prasanna; Kristin Stock; Emma Hudson-Doyle; David Johnston |
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Title |
Identifying Disaster-related Tweets: A Large-Scale Detection Model Comparison |
Type |
Conference Article |
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Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
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Pages |
731-743 |
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Keywords |
Tweet Classification, Machine Learning, Deep Learning, Disasters |
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Abstract |
Social media applications such as Twitter and Facebook are fast becoming a key instrument in gaining situational awareness (understanding the bigger picture of the situation) during disasters. This has provided multiple opportunities to gather relevant information in a timely manner to improve disaster response. In recent years, identifying crisis-related social media posts is analysed as an automatic task using machine learning (ML) or deep learning (DL) techniques. However, such supervised learning algorithms require labelled training data in the early hours of a crisis. Recently, multiple manually labelled disaster-related open-source twitter datasets have been released. In this work, we create a large dataset with 186,718 tweets by combining a number of such datasets and evaluate the performance of multiple ML and DL algorithms in classifying disaster-related tweets in three settings, namely ``in-disaster'', ``out-disaster'' and ``cross-disaster''. Our results show that the Bidirectional LSTM model with Word2Vec embeddings performs well for the tweet classification task in all three settings. We also make available the preprocessing steps and trained weights for future research. |
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Address |
Massey University; Massey University; Massey University; Massey University; Joint Centre for Disaster Research, Massey University; Joint Center of Disaster Research, Massey University Wellington |
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Corporate Author |
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Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
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Language |
English |
Summary Language |
English |
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ISSN |
978-1-949373-61-5 |
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Track |
Social Media for Disaster Response and Resilience |
Expedition |
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Conference |
18th International Conference on Information Systems for Crisis Response and Management |
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Notes |
rangika.nilani@gmail.com |
Approved |
no |
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Call Number |
ISCRAM @ idladmin @ |
Serial |
2368 |
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Author |
Yasir Imtiaz Syed; Raj Prasanna; S Uma; Kristin Stock; Denise Blake |
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Title |
A Design Science based Simulation Framework for Critical Infrastructure Interdependency |
Type |
Conference Article |
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Year |
2018 |
Publication |
Proceedings of ISCRAM Asia Pacific 2018: Innovating for Resilience – 1st International Conference on Information Systems for Crisis Response and Management Asia Pacific. |
Abbreviated Journal |
Iscram Ap 2018 |
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Pages |
516-524 |
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Keywords |
Infrastructure, interdependency, electricity, road, restoration. |
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Abstract |
Critical Infrastructures (CI) such as electricity, water, fuel, telecommunication and road networks are a crucial factor for secure and reliable operation of a society. In a normal situation, most of the businesses operate on an individual infrastructure. However, after major natural disasters such as earthquakes, the conflicts and complex interdependencies among the different infrastructures can cause significant disturbances because a failure can propagate from one infrastructure to another. This paper discusses the development of an integrated simulation framework that models interdependencies between electricity and road infrastructure networks of Wellington region. The framework uses a damage map of electricity network components and integrates them with road access time to the damaged components for determining electricity outage time of a region. The results can be used for recovery planning, identification of vulnerabilities, and adding or discarding redundancies in an infrastructure network. |
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Address |
Institute of Natural and Mathematical Sciences, Massey University; School of Psychology, Massey University; Joint Centre for Disaster Research, Massey University; GNS Science; Joint Centre for Disaster Research, Massey University |
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Publisher |
Massey Univeristy |
Place of Publication |
Albany, Auckland, New Zealand |
Editor |
Kristin Stock; Deborah Bunker |
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Language |
English |
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Track |
Enhancing Resilience of Natural, Built, and Socio-economic Environment |
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Call Number |
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Serial |
1645 |
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