45 research outputs found
Adaptive traffic signal control for developing countries using fused parameters derived from crowd-source data
Full text linkMishra, S., Singh, V., Gupta, A., Bhattacharya, D., & Mudgal, A. (2023). Adaptive traffic signal control for developing countries using fused parameters derived from crowd-source data. Transportation Letters, 15(4), 296-307. https://doi.org/10.1080/19427867.2022.2050493 ----The present work in the paper was not funded by any organization. One of the coauthor, Devanjan Bhattacharya has received funding from UKRI ESRC Impact Acceleration Grant (ES/T50189X/1), and European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie COFUND Grant Agreement No. 801215: TRAIN@Ed: ‘Transnational Research And Innovation Network At Edinburgh.’Advancement of mobile technologies has enabled economical collection, storage, processing, and sharing of traffic data. These data are made accessible to intended users through various application program interfaces (API) and can be used to recognize and mitigate congestion in real time. In this paper, quantitative (time of arrival) and qualitative (color-coded congestion levels) data were acquired from the Google traffic APIs. New parameters that reflect heterogeneous traffic conditions were defined and utilized for real-time control of traffic signals while maintaining the green-to-red time ratio. The proposed method utilizes a congestion-avoiding principle commonly used in computer networking. Adaptive congestion levels were observed on three different intersections of Delhi (India), in peak hours. It showed good variation, hence sensitive for the control algorithm to act efficiently. Also, simulation study establishes that proposed control algorithm decreases waiting time and congestion. The proposed method provides an economical alternative to expensive sensing and tracking technologies.authorsversionpublishe
Automated Geo-Spatial System for Generalized Assessment of Socio-Economic Vulnerability due to Landslide in a Region
No full textThe paper explains a system to assess automatically vulnerability due to landslide on socio-economy of a region by categorizing landslide hazard using spatial as well as temporal causative factors. The expert system has input, understanding, expert & output modules & uses digital spatial data of causative factors of landslide. Input accepts thematic images of contributing factors for landslides, Understanding module interprets to extract relevant information as required by expert module consisting of a Knowledge Base & Inference strategy categorizing region into different susceptibilities of landslide. Overlaid on socio-economic parameters in output module for vulnerability maps of landslide on population, forestry, urban, rural, agriculture separately to ascertain the impact of landslide on socio-economy of the Tehri-Garhwal region lower Himalayas, India
Application-Based COVID-19 Micro-Mobility Solution for Safe and Smart Navigation in Pandemics
Full text linkShort distance travel and commute being inevitable, safe route planning in pandemics for micro-mobility, i.e., cycling and walking, is extremely important for the safety of oneself and others. Hence, we propose an application-based solution using COVID-19 occurrence data and a multi-criteria route planning technique for cyclists and pedestrians. This study aims at objectively determining the routes based on various criteria on COVID-19 safety of a given route while keeping the user away from potential COVID-19 transmission spots. The vulnerable spots include places such as a hospital or medical zones, contained residential areas, and roads with a high connectivity and influx of people. The proposed algorithm returns a multi-criteria route modeled on COVID-19-modified parameters of micro-mobility and betweenness centrality considering COVID-19 avoidance as well as the shortest available safe route for user ease and shortened time of outside environment exposure. We verified our routing algorithm in a part of Delhi, India, by visualizing containment zones and medical establishments. The results with COVID-19 data analysis and route planning suggest a safer route in the context of the coronavirus outbreak as compared to normal navigation and on average route extension is within 8%–12%. Moreover, for further advancement and post-COVID-19 era, we discuss the need for adding open data policy and the spatial system architecture for data usage, as a part of a pandemic strategy. The study contributes new micro-mobility parameters adapted for COVID-19 and policy guidelines based on aggregated contact tracing data analysis maintaining privacy, security, and anonymity
Assessing Micro-Mobility Services in Pandemics for Studying the 10-Minutes Cities Concept in India Using Geospatial Data Analysis: an Application
No full textActive micro-mobility decreases traffic, bolsters personal health, and helps communities thrive by protecting the environment Moreover, sustainable micro-mobility demand is expected to get boosted in the present and post-COVID society. In this work we highlight the micro-mobility modes of walkability and bicycling to city administrators controlling urban city-space, by adapting the mobility parameters and their use cases through a map-based interface. Software tools and web-based applications are introduced for easy policy decisions by city managers. Present study scope is circumscribed by exploration of different parameters in traditional and state of art data science models, for resource planning like cycle usage prediction and planning. These parameters show hazard safe-distance pedestrian flow, optimal resource planning, amenity reach (10 min cycling and walking distance) and mobility using walking and cycling modes. Parameters of the traditional Social Force Model for Pedestrian Dynamics are also inspected, according to COVID social norms, to capture safe pedestrian flow density. Finally, the analysis of two case studies, of Bhubaneshwar city and New Delhi, in India, are discussed for policy suggestions to enhance mobility via sustainable micro-mobility modes. The developed system assists managers in decisions based on urban data intelligence, and at user end eases commute related mental tension, anxiety and dependencies. The developed application is running live on our server maintained at Edinburgh University
A landslide hazard warning system
No full textHazard warning is necessary for effective mitigation due to disaster. People moving into a hazard prone area need to be made aware of the level of threat. In this study, a system to warn against hazard is being proposed. It aims to be independent, fast and pervasive. It is designed to be a generalized system that could be deployed across any region. The system is modular in structure consisting of four functional units and gets activated once it is fed with threat level with geo-location. The existing cellular network is being utilized for disseminating hazard information as short messages. Currently the system is built for warning hazard due to landslides and classification accuracy was previously tested in this domain. Now the message permeability is shown to be virtually instantaneous with a maximum time lag recorded as 50 seconds, minimum of 10 seconds. On an average, the perceived threat message whether high, moderate or low threat, reaches a mobile user within 30 seconds. Such a handy system could be very useful in a densely populated country like India where existing governmental policies to reach effected people is time consuming leaving the people unaware of the impending hazard. The system is light on resources and expenditure and thus, offers a trade-off between accuracy and usability. The system can be accommodated with any kind of hazard simply by replacing the kernel domain knowledg
Automated Geo-Spatial Hazard Warning System GEOWARNS: Italian Case Study
No full textHazard warning is an area of research that requires both hazard evaluation and warning dissemination. At present, no such system carrying out both hazard evaluation and warning communication directly to the user community exists. Thus, there has been a need to develop an automated integrated system to categorize hazard and issue warning that reaches users directly. The objective of this paper is to develop an integrated, independent, generalized, and automated geo-hazard warning system, making use of geo-spatial data under popular usage platform. Thus, in this paper, development of GEOWARNS, an automated geo-spatial hazard warning system, has been elaborated. Testing and validation of the developed system has been carried out for landslide hazard evaluation and its warning dissemination pertaining to a comprehensive case study in Italy. The functionality of GEOWARNS is modular in architecture, having input, understanding, rainfall prediction, expert, output, and warning modules. The categories of hazard zones that have been evaluated by GEOWARNS show discrepancy of 5.9% in high hazard zones, nearly approximately 1.1 and 3.8% in moderate- and low-hazard zones, respectively, in comparison with the in situ expert evaluation. Further, the message dissemination through local cellular network has been found to be immediate with a maximum time lag recorded of 50 s, a minimum of 5 s, and an average of 15 s within the acceptable limits as indicated by the authorities in the United Nations (UN). Thus, it can be concluded that an automated hazard warning system has been developed. However, other scopes are needed to develop it furthe
Design for Geospatially Enabled Climate Modeling and Alert System (CLIMSYS): A Position Paper (Short Paper)
Full text linkThe paper brings the focus on to multi-disciplinary approach of presenting climate analysis studies, taking help of interdisciplinary fields to structure the information. The system CLIMSYS provides the crucial element of spatially enabling climate data processing. Even though climate change is a matter of great scientific relevance and of broad general interest, there are some problems related to its communication. Its a fact that finding practical, workable and cost-efficient solutions to the problems posed by climate change is now a world priority and one which links government and non-government organizations in a way not seen before. An approach that should suffice is to create an accessible intelligent system that houses prior knowledge and curates the incoming data to deliver meaningful results. The objective of the proposed research is to develop a generalized system for climate data analysis that facilitates open sharing, central implementation, integrated components, knowledge creation, data format understanding, inferencing and ultimately optimal solution delivery, by the way of geospatial enablement
Congestion Adaptive Traffic Light Control and Notification Architecture Using Google Maps APIs
Full text linkMishra, S., Bhattacharya, D., & Gupta, A. (2018). Congestion Adaptive Traffic Light Control and Notification Architecture Using Google Maps APIs. Data, 3(4), [67]. DOI: 10.3390/data3040067Traffic jams can be avoided by controlling traffic signals according to quickly building congestion with steep gradients on short temporal and small spatial scales. With the rising standards of computational technology, single-board computers, software packages, platforms, and APIs (Application Program Interfaces), it has become relatively easy for developers to create systems for controlling signals and informative systems. Hence, for enhancing the power of Intelligent Transport Systems in automotive telematics, in this study, we used crowdsourced traffic congestion data from Google to adjust traffic light cycle times with a system that is adaptable to congestion. One aim of the system proposed here is to inform drivers about the status of the upcoming traffic light on their route. Since crowdsourced data are used, the system does not entail the high infrastructure cost associated with sensing networks. A full system module-level analysis is presented for implementation. The system proposed is fail-safe against temporal communication failure. Along with a case study for examining congestion levels, generic information processing for the cycle time decision and status delivery system was tested and confirmed to be viable and quick for a restricted prototype model. The information required was delivered correctly over sustained trials, with an average time delay of 1.5 s and a maximum of 3 s.publishersversionpublishe
Location intelligence for augmented smart cities integrating sensor web and spatial data infrastructure (SmaCiSENS)
No full textBhattacharya, D., & Painho, M. (2018). Location intelligence for augmented smart cities integrating sensor web and spatial data infrastructure (SmaCiSENS). In GISTAM 2018 - Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management (Vol. 2018-March, pp. 282-289). SciTePress.Spatio-temporal aspects of data lead to critical information. Sensors capture data at all scales continually so it is imperative that useful information be extracted ubiquitously and regularly. Location plays a vital part by helping understand relations between datasets. It is crucial to link developmental works with spatial attributes and current challenge is to create an open platform that manages real-time sensor data and provides critical spatial analytics atop expert domain knowledge provided in the system. That is a two-faced problem where the solution tackles not only data from multiple sources but also runs data management platform, a spatial data infrastructure(SDI) as backbone framework able to harness sensor web(SW). The paper proposes development of such a globally shared open spatial expert system(ES), SmaCiSENS, a first of a kind geo-enabled knowledge based(KB) ES for multiple fields, smarter cities to climate modeling. SmaCiSENS is integration of SW and SDI with domain KB on data and problems, ready to infer solutions. The paper describes an architecture for semantic enablement for SW, SDI; connect interfaces, functions of SDI and SW, and sensor data application program interfaces (APIs) to better manage climate modeling, geohazard, global changes, and other vital areas of attention and action.publishersversionpublishe
a brief assessment
No full textDegbelo, A., Bhattacharya, D., Granell, C., & Trilles, S. (2016). Toolkits for smarter cities: a brief assessment. In C. R. García, P. Caballero-Gil, M. Burmester, & A. Quesada-Arencibia (Eds.), Ubiquitous Computing and Ambient Intelligence (pp. 431-436). ( Lecture Notes in Computer Science). Springer International Publishing.The literature has offered a number of surveys regarding the concept of smart city, but few assessments of toolkits. This paper presents a short analysis of existing smart city toolkits. The analysis yields some general observations about existing toolkits. The article closes with a brief introduction of the Open City Toolkit, a toolkit currently under development which aims at addressing some of the gaps of existing toolkits.preprintauthorsversionpublishe
