The goal of this research area is to improve emergency response systems using proactive resource management that minimizes time and maximizes the effectiveness of the response. With road accidents accounting for 1.25 million deaths globally and 240 million emergency medical services (EMS) calls in the U.S. each year, there is a critical need for a proactive and effective response to these emergencies. Furthermore, a timely response to these incidents is crucial and life-saving for severe incidents. The process of managing emergencies requires full integration of planning and response data and models and their implementation in a dynamic and uncertain environment to support real-time decisions of dispatching emergency response resources. However, the current state-of-the-art research has mainly focused on advances that target individual aspects of emergency response (e.g., prediction, optimization) when different components of an Emergency Response Management (ERM) system are highly interconnected. Additionally, the current practice of ERM workflow in the U.S. is reactive, resulting in a large variance in response times
Background
Our work in ERM has spanned the last six years. This project was started by a collaboration between the Smart and Resilient Computing for Physical Environments Lab (SCOPE) and the Computational Economics Research Lab (CERL) at Vanderbilt University. We are thankful to the Center of Automotive Research at Stanford (CARS), the National Science Foundation (NSF), and Tennessee Department of Transportation (TDOT) for sponsoring the project. We have had the fortune of collaborating with the Tennessee Department of Transportation (TDOT), the Nashville Fire Department (MNPD) and Chattanooga City during this project. Currently, this open-source repository is a collection of forecasting, planning, and operationalization tools. We also collaborate actively with Hemant Purohit from George Mason University to model the dynamics of crowd sourced incident data and use it in the resource allocation models. A key component of this work is a set of open source forecasting, clustering and visualization tools to aid first responders better understand the dynamics of spatial-temporal incident occurrence.
Our research has been showcased at multiple global smart city summits, won an innovation from the government technology magazine, covered in the Financial Times, and won the best paper award at ICLR’s AI for Social Good Workshop. Our broader approach can be understood from checking the Research page, or through the overview paper. An early prototype of our tool is available at the following link.
We are building ‘StatResp’ – an open-source integrated tool-chain to aid first responders understand where and when incidents occur, and how to allocate responders in anticipation of incidents. The historical analysis module of the toolchain is available as a public data dashboard at nashville.statresp.ai and tn.statresp.ai.
In this work, we use continuous-time generative models to forecast spatiotemporal incidents and the decision-theoretic problem of dispatching responders based on semi-Markovian dynamics. We have also developed efficient and scalable approaches to solve the high-dimensional optimization problem of proactive stationing and dispatch under uncertainty by using Multi-agent Monte Carlo Tree Search (MMCTS). This is important because the problem of stationing and dispatch (response strategies) is complex and requires solutions that can cope with extremely large state spaces resulting from semi-Markovian decision processes (SMDP). One possible approach is to directly solve the SMDP model by estimating the underlying transition function that governs the evolution of the process. Unfortunately, this approach is too slow for dynamically rebalancing the distribution of responders (proactive stationing), which for an average-sized metropolitan area, has a cardinality of 10^25. Therefore, we use the Monte-Carlo Tree Search (MCTS) family of algorithms, which evaluate actions by sampling from a large number of possible scenarios.
Note that a standard MCTS-based approach is not suitable for dynamic allocation due to the sheer size of the state-space in consideration coupled with the low latency that ERM systems can afford. Therefore, we sought a decentralized multi-agent MCTS (MMCTS) approach, originally explored by Claes et. al for multi-robot task allocation during warehouse commissioning. In MMCTS, individual agents build separate trees focused on their own actions, rather than having one monolithic, centralized tree, dramatically reducing their search space. To this end, we use a queue-based rebalancing heuristic, described in our AAMAS 2020 paper [1], to approximate agent behavior. Our innovation lies in adding the concept of action filtering to the standard MCTS approach. This is required because the dispatching domain has several global constraints to adhere to, such as ensuring that an incident is serviced if possible.
The results from the AAMAS 2020 paper indicating that proper choice of parameters reduces response time variance without incurring large rebalancing costs (i.e. the distance agents move during rebalancing). Similar improvements accrue for the dispatching stage as shown in our ICCPS 2019 paper. Using these methods we have also developed analysis procedures to help answer questions such as where should the next fire station be located, how many new emergency dispatch trucks are required and reducing the average response time as shown in our existing research. Recently, we have improved the scalability of the approach using heirarchical abstractions as shown in our ICCPS 2021 paper.
A key aspect of our solution approach is the development of demand forecasting models that describe the need for resources in a given area at a given future time. For this, we build forecasting models using historical incident data, temporal data (weather and traffic), and static roadway data. The data is processed and fed into an aggregator, which identifies clusters of roadway segments (or any user-specified unit of spatial discretization) that are similar to each other using a visual analytic tool. For each such cluster, a set of online forecasting models are learned and compared automatically using criteria such as test-set likelihood and AIC (Akaike Information Criteria) and are updated as new incidents are reported. Our tool currently supports Poisson regression, negative binomial regression, parametric survival modeling, and zero-inflated Poisson regression.
Y. Senarath, A. Mukhopadhyay, S. Vazirizade, hemant Purohit, S. Nannapaneni, and A. Dubey, Practitioner-Centric Approach for Early Incident Detection Using Crowdsourced Data for Emergency Services, in 21st IEEE International Conference on Data Mining (ICDM 2021), 2021.
@inproceedings{ICDM_2021,
author = {Senarath, Yasas and Mukhopadhyay, Ayan and Vazirizade, Sayyed and hemant Purohit and Nannapaneni, Saideep and Dubey, Abhishek},
booktitle = {21st IEEE International Conference on Data Mining (ICDM 2021)},
tag = {ai4cps,incident},
title = {Practitioner-Centric Approach for Early Incident Detection Using Crowdsourced Data for Emergency Services},
year = {2021}
}
Emergency response is highly dependent on the time of incident reporting. Unfortunately, the traditional approach to receiving incident reports (e.g., calling 911 in the USA) has time delays. Crowdsourcing platforms such as Waze provide an opportunity for early identification of incidents. However, detecting incidents from crowdsourced data streams is difficult due to the challenges of noise and uncertainty associated with such data. Further, simply optimizing over detection accuracy can compromise spatial-temporal localization of the inference, thereby making such approaches infeasible for real-world deployment. This paper presents a novel problem formulation and solution approach for practitioner-centered incident detection using crowdsourced data by using emergency response management as a case-study. The proposed approach CROME (Crowdsourced Multi-objective Event Detection) quantifies the relationship between the performance metrics of incident classification (e.g., F1 score) and the requirements of model practitioners (e.g., 1 km. radius for incident detection). First, we show how crowdsourced reports, ground-truth historical data, and other relevant determinants such as traffic and weather can be used together in a Convolutional Neural Network (CNN) architecture for early detection of emergency incidents. Then, we use a Pareto optimization-based approach to optimize the output of the CNN in tandem with practitioner-centric parameters to balance detection accuracy and spatial-temporal localization. Finally, we demonstrate the applicability of this approach using crowdsourced data from Waze and traffic accident reports from Nashville, TN, USA. Our experiments demonstrate that the proposed approach outperforms existing approaches in incident detection while simultaneously optimizing the needs for realworld deployment and usability.
S. Singla, A. Mukhopadhyay, M. Wilbur, T. Diao, V. Gajjewar, A. Eldawy, M. Kochenderfer, R. Shachter, and A. Dubey, WildfireDB: An Open-Source Dataset ConnectingWildfire Spread with Relevant Determinants, in 35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks, 2021.
@inproceedings{wildfiredb2021,
author = {Singla, Samriddhi and Mukhopadhyay, Ayan and Wilbur, Michael and Diao, Tina and Gajjewar, Vinayak and Eldawy, Ahmed and Kochenderfer, Mykel and Shachter, Ross and Dubey, Abhishek},
booktitle = {35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks},
title = {WildfireDB: An Open-Source Dataset ConnectingWildfire Spread with Relevant Determinants},
tag = {ai4cps,incident},
year = {2021}
}
Modeling fire spread is critical in fire risk management. Creating data-driven models to forecast spread remains challenging due to the lack of comprehensive data sources that relate fires with relevant covariates. We present the first comprehensive and open-source dataset that relates historical fire data with relevant covariates such as weather, vegetation, and topography. Our dataset, named \textitWildfireDB, contains over 17 million data points that capture how fires spread in the continental USA in the last decade. In this paper, we describe the algorithmic approach used to create and integrate the data, describe the dataset, and present benchmark results regarding data-driven models that can be learned to forecast the spread of wildfires.
S. M. Vazirizade, A. Mukhopadhyay, G. Pettet, S. E. Said, H. Baroud, and A. Dubey, Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response Systems, 2021.
@inproceedings{vazirizade2021learning,
title = {Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response Systems},
author = {Vazirizade, Sayyed Mohsen and Mukhopadhyay, Ayan and Pettet, Geoffrey and Said, Said El and Baroud, Hiba and Dubey, Abhishek},
year = {2021},
eprint = {2106.08307},
archiveprefix = {arXiv},
tag = {ai4cps,incident},
preprint = {https://arxiv.org/abs/2106.08307},
primaryclass = {cs.LG}
}
Principled decision making in emergency response management necessitates the use of statistical models that predict the spatial-temporal likelihood of incident occurrence. These statistical models are then used for proactive stationing which allocates first responders across the spatial area in order to reduce overall response time. Traditional methods that simply aggregate past incidents over space and time fail to make useful short-term predictions when the spatial region is large and focused on fine-grained spatial entities like interstate highway networks. This is partially due to the sparsity of incidents with respect to the area in consideration. Further, accidents are affected by several covariates, and collecting, cleaning, and managing multiple streams of data from various sources is challenging for large spatial areas. In this paper, we highlight how this problem is being solved for the state of Tennessee, a state in the USA with a total area of over 100,000 sq. km. Our pipeline, based on a combination of synthetic resampling, non-spatial clustering, and learning from data can efficiently forecast the spatial and temporal dynamics of accident occurrence, even under sparse conditions. In the paper, we describe our pipeline that uses data related to roadway geometry, weather, historical accidents, and real-time traffic congestion to aid accident forecasting. To understand how our forecasting model can affect allocation and dispatch, we improve upon a classical resource allocation approach. Experimental results show that our approach can significantly reduce response times in the field in comparison with current approaches followed by first responders.
G. Pettet, A. Mukhopadhyay, M. Kochenderfer, and A. Dubey, Hierarchical Planning for Resource Allocation in Emergency Response Systems, in Proceedings of the 12th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2021, Nashville, TN, USA, 2021.
@inproceedings{iccps2021,
author = {Pettet, Geoffrey and Mukhopadhyay, Ayan and Kochenderfer, Mykel and Dubey, Abhishek},
title = {Hierarchical Planning for Resource Allocation in Emergency Response Systems},
booktitle = {Proceedings of the 12th {ACM/IEEE} International Conference on Cyber-Physical Systems, {ICCPS} 2021, Nashville, TN, USA},
year = {2021},
tag = {ai4cps,decentralization,incident},
keywords = {emergency},
project = {smart-cities,smart-emergency-response}
}
A classical problem in city-scale cyber-physical systems (CPS) is resource allocation under uncertainty. Spatial-temporal allocation of resources is optimized to allocate electric scooters across urban areas, place charging stations for vehicles, and design efficient on-demand transit. Typically, such problems are modeled as Markov (or semi-Markov) decision processes. While online, offline, and decentralized methodologies have been used to tackle such problems, none of the approaches scale well for large-scale decision problems. We create a general approach to hierarchical planning that leverages structure in city-level CPS problems to tackle resource allocation under uncertainty. We use emergency response as a case study and show how a large resource allocation problem can be split into smaller problems. We then create a principled framework for solving the smaller problems and tackling the interaction between them. Finally, we use real-world data from a major metropolitan area in the United States to validate our approach. Our experiments show that the proposed approach outperforms state-of-the-art approaches used in the field of emergency response.
G. Pettet, A. Mukhopadhyay, M. Kochenderfer, Y. Vorobeychik, and A. Dubey, On Algorithmic Decision Procedures in Emergency Response Systems in Smart and Connected Communities, in Proceedings of the 19th Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2020, Auckland, New Zealand, 2020.
@inproceedings{Pettet2020,
author = {Pettet, Geoffrey and Mukhopadhyay, Ayan and Kochenderfer, Mykel and Vorobeychik, Yevgeniy and Dubey, Abhishek},
title = {On Algorithmic Decision Procedures in Emergency Response Systems in Smart and Connected Communities},
booktitle = {Proceedings of the 19th Conference on Autonomous Agents and MultiAgent Systems, {AAMAS} 2020, Auckland, New Zealand},
year = {2020},
tag = {ai4cps, decentralization,incident},
category = {selectiveconference},
keywords = {emergency, performance},
project = {smart-emergency-response,smart-cities},
timestamp = {Wed, 17 Jan 2020 07:24:00 +0200}
}
Emergency Response Management (ERM) is a critical problem faced by communities across the globe. Despite its importance, it is common for ERM systems to follow myopic and straight-forward decision policies in the real world. Principled approaches to aid decision-making under uncertainty have been explored in this context but have failed to be accepted into real systems. We identify a key issue impeding their adoption — algorithmic approaches to emergency response focus on reactive, post-incident dispatching actions, i.e. optimally dispatching a responder after incidents occur. However, the critical nature of emergency response dictates that when an incident occurs, first responders always dispatch the closest available responder to the incident. We argue that the crucial period of planning for ERM systems is not post-incident, but between incidents. However, this is not a trivial planning problem - a major challenge with dynamically balancing the spatial distribution of responders is the complexity of the problem. An orthogonal problem in ERM systems is to plan under limited communication, which is particularly important in disaster scenarios that affect communication networks. We address both the problems by proposing two partially decentralized multi-agent planning algorithms that utilize heuristics and the structure of the dispatch problem. We evaluate our proposed approach using real-world data, and find that in several contexts, dynamic re-balancing the spatial distribution of emergency responders reduces both the average response time as well as its variance.
Y. Senarath, S. Nannapaneni, H. Purohit, and A. Dubey, Emergency Incident Detection from Crowdsourced Waze Data using Bayesian Information Fusion, in The 2020 IEEE/WIC/ACM International Joint Conference On Web Intelligence And Intelligent Agent Technology, 2020.
@inproceedings{senarath_emergency_2020,
title = {Emergency {Incident} {Detection} from {Crowdsourced} {Waze} {Data} using {Bayesian} {Information} {Fusion}},
copyright = {All rights reserved},
url = {http://arxiv.org/abs/2011.05440},
urldate = {2021-01-31},
booktitle = {The 2020 {IEEE}/{WIC}/{ACM} {International} {Joint} {Conference} {On} {Web} {Intelligence} {And} {Intelligent} {Agent} {Technology}},
publisher = {IEEE},
author = {Senarath, Yasas and Nannapaneni, Saideep and Purohit, Hemant and Dubey, Abhishek},
month = nov,
tag = {incident},
year = {2020},
note = {arXiv: 2011.05440},
keywords = {Computer Science - Artificial Intelligence, Computer Science - Social and Information Networks},
annote = {Comment: 8 pages, The 2020 IEEE/WIC/ACM International Joint Conference On Web Intelligence And Intelligent Agent Technology (WI-IAT '20)},
file = {arXiv Fulltext PDF:/Users/abhishek/Zotero/storage/B8WHQRUX/Senarath et al. - 2020 - Emergency Incident Detection from Crowdsourced Waz.pdf:application/pdf;arXiv.org Snapshot:/Users/abhishek/Zotero/storage/98PX572Y/2011.html:text/html}
}
The number of emergencies have increased over the years with the growth in urbanization. This pattern has overwhelmed the emergency services with limited resources and demands the optimization of response processes. It is partly due to traditional ‘reactive’ approach of emergency services to collect data about incidents, where a source initiates a call to the emergency number (e.g., 911 in U.S.), delaying and limiting the potentially optimal response. Crowdsourcing platforms such as Waze provides an opportunity to develop a rapid, ‘proactive’ approach to collect data about incidents through crowd-generated observational reports. However, the reliability of reporting sources and spatio-temporal uncertainty of the reported incidents challenge the design of such a proactive approach. Thus, this paper presents a novel method for emergency incident detection using noisy crowdsourced Waze data. We propose a principled computational framework based on Bayesian theory to model the uncertainty in the reliability of crowd-generated reports and their integration across space and time to detect incidents. Extensive experiments using data collected from Waze and the official reported incidents in Nashville, Tenessee in the U.S. show our method can outperform strong baselines for both F1-score and AUC. The application of this work provides an extensible framework to incorporate different noisy data sources for proactive incident detection to improve and optimize emergency response operations in our communities.
S. Basak, A. Dubey, and B. P. Leao, Analyzing the Cascading Effect of Traffic Congestion Using LSTM Networks, in IEEE Big Data, Los Angeles, Ca, 2019.
@inproceedings{basak2019bigdata,
author = {Basak, Sanchita and Dubey, Abhishek and Leao, Bruno P.},
title = {Analyzing the Cascading Effect of Traffic Congestion Using LSTM Networks},
booktitle = {IEEE Big Data},
year = {2019},
tag = {ai4cps,incident,transit},
address = {Los Angeles, Ca},
category = {selectiveconference},
keywords = {reliability, transit}
}
This paper presents a data-driven approach for predicting the propagation of traffic congestion at road seg-ments as a function of the congestion in their neighboring segments. In the past, this problem has mostly been addressed by modelling the traffic congestion over some standard physical phenomenon through which it is difficult to capture all the modalities of such a dynamic and complex system. While other recent works have focused on applying a generalized data-driven technique on the whole network at once, they often ignore intersection characteristics. On the contrary, we propose a city-wide ensemble of intersection level connected LSTM models and propose mechanisms for identifying congestion events using the predictions from the networks. To reduce the search space of likely congestion sinks we use the likelihood of congestion propagation in neighboring road segments of a congestion source that we learn from the past historical data. We validated our congestion forecasting framework on the real world traffic data of Nashville, USA and identified the onset of congestion in each of the neighboring segments of any congestion source with an average precision of 0.9269 and an average recall of 0.9118 tested over ten congestion events.
A. Mukhopadhyay, G. Pettet, C. Samal, A. Dubey, and Y. Vorobeychik, An online decision-theoretic pipeline for responder dispatch, in Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2019, Montreal, QC, Canada, 2019, pp. 185–196.
@inproceedings{Mukhopadhyay2019,
author = {Mukhopadhyay, Ayan and Pettet, Geoffrey and Samal, Chinmaya and Dubey, Abhishek and Vorobeychik, Yevgeniy},
title = {An online decision-theoretic pipeline for responder dispatch},
booktitle = {Proceedings of the 10th {ACM/IEEE} International Conference on Cyber-Physical Systems, {ICCPS} 2019, Montreal, QC, Canada},
year = {2019},
tag = {ai4cps,incident},
pages = {185--196},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/conf/iccps/MukhopadhyayPSD19},
category = {selectiveconference},
doi = {10.1145/3302509.3311055},
file = {:Mukhopadhyay2019-An_Online_Decision_Theoretic_Pipeline_for_Responder_Dispatch.pdf:PDF},
keywords = {emergency},
project = {smart-cities,smart-emergency-response},
timestamp = {Sun, 07 Apr 2019 16:25:36 +0200},
url = {https://doi.org/10.1145/3302509.3311055}
}
The problem of dispatching emergency responders to service traffic accidents, fire, distress calls and crimes plagues urban areas across the globe. While such problems have been extensively looked at, most approaches are offline. Such methodologies fail to capture the dynamically changing environments under which critical emergency response occurs, and therefore, fail to be implemented in practice. Any holistic approach towards creating a pipeline for effective emergency response must also look at other challenges that it subsumes - predicting when and where incidents happen and understanding the changing environmental dynamics. We describe a system that collectively deals with all these problems in an online manner, meaning that the models get updated with streaming data sources. We highlight why such an approach is crucial to the effectiveness of emergency response, and present an algorithmic framework that can compute promising actions for a given decision-theoretic model for responder dispatch. We argue that carefully crafted heuristic measures can balance the trade-off between computational time and the quality of solutions achieved and highlight why such an approach is more scalable and tractable than traditional approaches. We also present an online mechanism for incident prediction, as well as an approach based on recurrent neural networks for learning and predicting environmental features that affect responder dispatch. We compare our methodology with prior state-of-the-art and existing dispatch strategies in the field, which show that our approach results in a reduction in response time with a drastic reduction in computational time.
G. Pettet, A. Mukhopadhyay, C. Samal, A. Dubey, and Y. Vorobeychik, Incident management and analysis dashboard for fire departments: ICCPS demo, in Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2019, Montreal, QC, Canada, 2019, pp. 336–337.
@inproceedings{Pettet2019,
author = {Pettet, Geoffrey and Mukhopadhyay, Ayan and Samal, Chinmaya and Dubey, Abhishek and Vorobeychik, Yevgeniy},
title = {Incident management and analysis dashboard for fire departments: {ICCPS} demo},
booktitle = {Proceedings of the 10th {ACM/IEEE} International Conference on Cyber-Physical Systems, {ICCPS} 2019, Montreal, QC, Canada},
year = {2019},
pages = {336--337},
tag = {incident},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/conf/iccps/PettetMSDV19},
category = {poster},
doi = {10.1145/3302509.3313329},
file = {:Pettet2019-Incident_management_and_analysis_dashboard_for_fire_departments_ICCPS_demo.pdf:PDF},
keywords = {emergency},
project = {smart-cities,smart-emergency-response},
timestamp = {Sun, 07 Apr 2019 16:25:36 +0200},
url = {https://doi.org/10.1145/3302509.3313329}
}
This work presents a dashboard tool that helps emergency responders analyze and manage spatial-temporal incidents like crime and traffic accidents. It uses state-of-the-art statistical models to learn incident probabilities based on factors such as prior incidents, time and weather. The dashboard can then present historic and predicted incident distributions. It also allows responders to analyze how moving or adding depots (stations for emergency responders) affects average response times, and can make dispatching recommendations based on heuristics. Broadly, it is a one-stop tool that helps responders visualize historical data as well as plan for and respond to incidents.
G. Pettet, S. Sahoo, and A. Dubey, Towards an Adaptive Multi-Modal Traffic Analytics Framework at the Edge, in IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019, Kyoto, Japan, March 11-15, 2019, 2019, pp. 511–516.
@inproceedings{Pettet2019a,
author = {Pettet, Geoffrey and Sahoo, Saroj and Dubey, Abhishek},
title = {Towards an Adaptive Multi-Modal Traffic Analytics Framework at the Edge},
booktitle = {{IEEE} International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019, Kyoto, Japan, March 11-15, 2019},
year = {2019},
pages = {511--516},
tag = {platform,incident,transit},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/conf/percom/PettetSD19},
category = {workshop},
doi = {10.1109/PERCOMW.2019.8730577},
file = {:Pettet2019a-Towards_an_Adaptive_Multi-Modal_Traffic_Analytics_Framework_at_the_Edge.pdf:PDF},
keywords = {middleware, transit},
project = {cps-middleware,smart-transit,smart-cities},
timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
url = {https://doi.org/10.1109/PERCOMW.2019.8730577}
}
The Internet of Things (IoT) requires distributed, large scale data collection via geographically distributed devices. While IoT devices typically send data to the cloud for processing, this is problematic for bandwidth constrained applications. Fog and edge computing (processing data near where it is gathered, and sending only results to the cloud) has become more popular, as it lowers network overhead and latency. Edge computing often uses devices with low computational capacity, therefore service frameworks and middleware are needed to efficiently compose services. While many frameworks use a top-down perspective, quality of service is an emergent property of the entire system and often requires a bottom up approach. We define services as multi-modal, allowing resource and performance tradeoffs. Different modes can be composed to meet an application’s high level goal, which is modeled as a function. We examine a case study for counting vehicle traffic through intersections in Nashville. We apply object detection and tracking to video of the intersection, which must be performed at the edge due to privacy and bandwidth constraints. We explore the hardware and software architectures, and identify the various modes. This paper lays the foundation to formulate the online optimization problem presented by the system which makes tradeoffs between the quantity of services and their quality constrained by available resources.
J. P. Talusan, F. Tiausas, K. Yasumoto, M. Wilbur, G. Pettet, A. Dubey, and S. Bhattacharjee, Smart Transportation Delay and Resiliency Testbed Based on Information Flow of Things Middleware, in IEEE International Conference on Smart Computing, SMARTCOMP 2019, Washington, DC, USA, June 12-15, 2019, 2019, pp. 13–18.
@inproceedings{Talusan2019,
author = {Talusan, Jose Paolo and Tiausas, Francis and Yasumoto, Keiichi and Wilbur, Michael and Pettet, Geoffrey and Dubey, Abhishek and Bhattacharjee, Shameek},
title = {Smart Transportation Delay and Resiliency Testbed Based on Information Flow of Things Middleware},
booktitle = {{IEEE} International Conference on Smart Computing, {SMARTCOMP} 2019, Washington, DC, USA, June 12-15, 2019},
year = {2019},
pages = {13--18},
tag = {platform,incident,transit},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/conf/smartcomp/TalusanTYWPDB19},
category = {workshop},
doi = {10.1109/SMARTCOMP.2019.00022},
file = {:Talusan2019-Smart_Transportation_Delay_and_Resiliency_Testbed_Based_on_Information_Flow_of_Things_Middleware.pdf:PDF},
keywords = {middleware, transit},
project = {cps-middleware,smart-transit},
timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
url = {https://doi.org/10.1109/SMARTCOMP.2019.00022}
}
Edge and Fog computing paradigms are used to process big data generated by the increasing number of IoT devices. These paradigms have enabled cities to become smarter in various aspects via real-time data-driven applications. While these have addressed some flaws of cloud computing some challenges remain particularly in terms of privacy and security. We create a testbed based on a distributed processing platform called the Information flow of Things (IFoT) middleware. We briefly describe a decentralized traffic speed query and routing service implemented on this framework testbed. We configure the testbed to test countermeasure systems that aim to address the security challenges faced by prior paradigms. Using this testbed, we investigate a novel decentralized anomaly detection approach for time-sensitive distributed smart transportation systems.
M. Wilbur, A. Dubey, B. Leão, and S. Bhattacharjee, A Decentralized Approach for Real Time Anomaly Detection in Transportation Networks, in IEEE International Conference on Smart Computing, SMARTCOMP 2019, Washington, DC, USA, 2019, pp. 274–282.
@inproceedings{Wilbur2019,
author = {Wilbur, Michael and Dubey, Abhishek and Le{\~{a}}o, Bruno and Bhattacharjee, Shameek},
title = {A Decentralized Approach for Real Time Anomaly Detection in Transportation Networks},
booktitle = {{IEEE} International Conference on Smart Computing, {SMARTCOMP} 2019, Washington, DC, USA},
year = {2019},
pages = {274--282},
month = jun,
tag = {ai4cps,platform,decentralization,incident,transit},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/conf/smartcomp/WilburDLB19},
category = {selectiveconference},
doi = {10.1109/SMARTCOMP.2019.00063},
file = {:Wilbur2019-A_Decentralized_Approach_for_Real_Time_Anomaly_Detection_in_Transportation_Networks.pdf:PDF},
keywords = {transit, reliability},
project = {cps-reliability,smart-transit,smart-cities},
timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
url = {https://doi.org/10.1109/SMARTCOMP.2019.00063}
}
H. Purohit, S. Nannapaneni, A. Dubey, P. Karuna, and G. Biswas, Structured Summarization of Social Web for Smart Emergency Services by Uncertain Concept Graph, in 2018 IEEE International Science of Smart City Operations and Platforms Engineering in Partnership with Global City Teams Challenge (SCOPE-GCTC), 2018, pp. 30–35.
@inproceedings{Purohit2018,
author = {{Purohit}, H. and {Nannapaneni}, S. and Dubey, Abhishek and {Karuna}, P. and {Biswas}, G.},
title = {Structured Summarization of Social Web for Smart Emergency Services by Uncertain Concept Graph},
booktitle = {2018 IEEE International Science of Smart City Operations and Platforms Engineering in Partnership with Global City Teams Challenge (SCOPE-GCTC)},
year = {2018},
pages = {30-35},
month = apr,
tag = {decentralization,incident},
category = {workshop},
doi = {10.1109/SCOPE-GCTC.2018.00012},
file = {:Purohit2018-Structured_Summarization_of_Social_Web_for_Smart_Emergency_Services_by_Uncertain_Concept_Graph.pdf:PDF},
issn = {null},
keywords = {emergency}
}
The Web has empowered emergency services to enhance operations by collecting real-time information about incidents from diverse data sources such as social media. However, the high volume of unstructured data from the heterogeneous sources with varying degrees of veracity challenges the timely extraction and integration of relevant information to summarize the current situation. Existing work on event detection and summarization on social media relates to this challenge of timely extraction of information during an evolving event. However, it is limited in both integrating incomplete information from diverse sources and using the integrated information to dynamically infer knowledge representation of the situation that captures optimal actions (e.g., allocate available finite ambulances to incident regions). In this paper, we present a novel concept of an Uncertain Concept Graph (UCG) that is capable of representing dynamic knowledge of a disaster event from heterogeneous data sources, particularly for the regions of interest, and resources/services required. The information sources, incident regions, and resources (e.g., ambulances) are represented as nodes in UCG, while the edges represent the weighted relationships between these nodes. We then propose a solution for probabilistic edge inference between nodes in UCG. We model a novel optimization problem for the edge assignment between a service resource to a region node over time trajectory. The output of such structured summarization over time can be valuable for modeling event dynamics in the real world beyond emergency management, across different smart city operations such as transportation.
A. Mukhopadhyay, Y. Vorobeychik, A. Dubey, and G. Biswas, Prioritized Allocation of Emergency Responders based on a Continuous-Time Incident Prediction Model, in Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2017, São Paulo, Brazil, May 8-12, 2017, 2017, pp. 168–177.
@inproceedings{Mukhopadhyay2017,
author = {Mukhopadhyay, Ayan and Vorobeychik, Yevgeniy and Dubey, Abhishek and Biswas, Gautam},
title = {Prioritized Allocation of Emergency Responders based on a Continuous-Time Incident Prediction Model},
booktitle = {Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, {AAMAS} 2017, S{\~{a}}o Paulo, Brazil, May 8-12, 2017},
year = {2017},
pages = {168--177},
tag = {ai4cps,incident},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/conf/atal/MukhopadhyayVDB17},
category = {selectiveconference},
file = {:Mukhopadhyay2017-Prioritized_Allocation_of_Emergency_Responders_based_on_a_Continuous-Time_Incident_Prediction_Model.pdf:PDF},
keywords = {emergency},
project = {smart-emergency-response,smart-cities},
timestamp = {Wed, 27 Sep 2017 07:24:00 +0200},
url = {http://dl.acm.org/citation.cfm?id=3091154}
}
Efficient emergency response is a major concern in densely populated urban areas. Numerous techniques have been proposed to allocate emergency responders to optimize response times, coverage, and incident prevention. Effective response depends, in turn, on effective prediction of incidents occurring in space and time, a problem which has also received considerable prior attention. We formulate a non-linear mathematical program maximizing expected incident coverage, and propose a novel algorithmic framework for solving this problem. In order to aid the optimization problem, we propose a novel incident prediction mechanism. Prior art in incident prediction does not generally consider incident priorities which are crucial in optimal dispatch, and spatial modeling either considers each discretized area independently, or learns a homogeneous model. We bridge these gaps by learning a joint distribution of both incident arrival time and severity, with spatial heterogeneity captured using a hierarchical clustering approach. Moreover, our decomposition of the joint arrival and severity distributions allows us to independently learn the continuous-time arrival model, and subsequently use a multinomial logistic regression to capture severity, conditional on incident time. We use real traffic accident and response data from the urban
area around Nashville, USA, to evaluate the proposed approach, showing that it significantly outperforms prior art as well as the real dispatch method currently in use.
A. Ghafouri, A. Laszka, A. Dubey, and X. D. Koutsoukos, Optimal detection of faulty traffic sensors used in route planning, in Proceedings of the 2nd International Workshop on Science of Smart City Operations and Platforms Engineering, SCOPE@CPSWeek 2017, Pittsburgh, PA, USA, April 21, 2017, 2017, pp. 1–6.
@inproceedings{Ghafouri2017,
author = {Ghafouri, Amin and Laszka, Aron and Dubey, Abhishek and Koutsoukos, Xenofon D.},
title = {Optimal detection of faulty traffic sensors used in route planning},
booktitle = {Proceedings of the 2nd International Workshop on Science of Smart City Operations and Platforms Engineering, SCOPE@CPSWeek 2017, Pittsburgh, PA, USA, April 21, 2017},
year = {2017},
pages = {1--6},
tag = {ai4cps,platform,incident,transit},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/conf/cpsweek/GhafouriLDK17},
category = {workshop},
doi = {10.1145/3063386.3063767},
file = {:Ghafouri2017-Optimal_detection_of_faulty_traffic_sensors_used_in_route_planning.pdf:PDF},
keywords = {transit},
project = {cps-reliability,smart-transit,smart-cities},
timestamp = {Tue, 06 Nov 2018 16:59:05 +0100},
url = {https://doi.org/10.1145/3063386.3063767}
}
In a smart city, real-time traffic sensors may be deployed for various applications, such as route planning. Unfortunately, sensors are prone to failures, which result in erroneous traffic data. Erroneous data can adversely affect applications such as route planning, and can cause increased travel time. To minimize the impact of sensor failures, we must detect them promptly and accurately. However, typical detection algorithms may lead to a large number of false positives (i.e., false alarms) and false negatives (i.e., missed detections), which can result in suboptimal route planning. In this paper, we devise an effective detector for identifying faulty traffic sensors using a prediction model based on Gaussian Processes. Further, we present an approach for computing the optimal parameters of the detector which minimize losses due to false-positive and false-negative errors. We also characterize critical sensors, whose failure can have high impact on the route planning application. Finally, we implement our method and evaluate it numerically using a real- world dataset and the route planning platform OpenTripPlanner.
G. Pettet, S. Nannapaneni, B. Stadnick, A. Dubey, and G. Biswas, Incident analysis and prediction using clustering and Bayesian network, in 2017 IEEE SmartWorld, 2017, pp. 1–8.
@inproceedings{Pettet2017,
author = {Pettet, Geoffrey and Nannapaneni, Saideep and Stadnick, Benjamin and Dubey, Abhishek and Biswas, Gautam},
title = {Incident analysis and prediction using clustering and Bayesian network},
booktitle = {2017 {IEEE} SmartWorld},
year = {2017},
tag = {ai4cps,incident},
pages = {1--8},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/conf/uic/PettetNSDB17},
category = {selectiveconference},
doi = {10.1109/UIC-ATC.2017.8397587},
file = {:Pettet2017-Incident_analysis_and_prediction_using_clustering_and_Bayesian_network.pdf:PDF},
keywords = {emergency},
project = {smart-emergency-response,smart-cities},
timestamp = {Wed, 16 Oct 2019 14:14:50 +0200},
url = {https://doi.org/10.1109/UIC-ATC.2017.8397587}
}
Advances in data collection and storage infrastructure offer an unprecedented opportunity to integrate both data and emergency resources in a city into a dynamic learning system that can anticipate and rapidly respond to heterogeneous incidents. In this paper, we describe integration methods for spatio-temporal incident forecasting using previously collected vehicular accident data provided to us by the Nashville Fire Department. The literature provides several techniques that focus on analyzing features and predicting accidents for specific situations (specific intersections in a city, or certain segments of a freeway, for example), but these models break down when applied to a large, general area consisting of many road and intersection types and other factors like weather conditions. We use Similarity Based Agglomerative Clustering (SBAC) analysis to categorize incidents to account for these variables. Thereafter, we use survival analysis to learn the likelihood of incidents per cluster. The mapping of the clusters to the spatial locations is achieved using a Bayesian network. The prediction methods we have developed lay the foundation for future work on an optimal emergency vehicle allocation and dispatch system in Nashville.