@inproceedings{samir2024smartcomp,
title = {A Graph Neural Network Framework for Imbalanced Bus Ridership Forecasting},
author = {Gupta, Samir and Khanna, Agrima and Talusan, Jose Paolo and Said, Anwar and Freudberg, Dan and Mukhopadhyay, Ayan and Dubey, Abhishek},
year = {2024},
month = jun,
booktitle = {2024 IEEE International Conference on Smart Computing (SMARTCOMP)},
volume = {},
number = {}
}
Dr. Jose Paolo Talusan is a Post Doctoral Researcher in the Department of Computer Science and Computer Engineering at Vanderbilt University. He earned his PhD from the Nara Institute of Science and Technology, Japan in 2020. His research interests include middleware and distributed computing systems, with a focus on smart transportation networks.
Dr. Jose Paolo Talusan Publications
- S. Gupta, A. Khanna, J. P. Talusan, A. Said, D. Freudberg, A. Mukhopadhyay, and A. Dubey, A Graph Neural Network Framework for Imbalanced Bus Ridership Forecasting, in 2024 IEEE International Conference on Smart Computing (SMARTCOMP), 2024.
Public transit systems are paramount in lowering carbon emissions and reducing urban congestion for environmental sustainability. However, overcrowding has adverse effects on the quality of service, passenger experience, and overall efficiency of public transit causing a decline in the usage of public transit systems. Therefore, it is crucial to identify and forecast potential windows of overcrowding to improve passenger experience and encourage higher ridership. Predicting ridership is a complex task, due to the inherent noise of collected data and the sparsity of overcrowding events. Existing studies in predicting public transit ridership consider only a static depiction of bus networks. We address these issues by first applying a data processing pipeline that cleans noisy data and engineers several features for training. Then, we address sparsity by converting the network to a dynamic graph and using a graph convolutional network, incorporating temporal, spatial, and auto-regressive features, to learn generalizable patterns for each route. Finally, since conventional loss functions like categorical cross-entropy have limitations in addressing class imbalance inherent in ridership data, our proposed approach uses focal loss to refine the prediction focus on less frequent yet task-critical overcrowding instances. Our experiments, using real-world data from our partner agency, show that the proposed approach outperforms existing state-of-the-art baselines in terms of accuracy and robustness.
- J. P. Talusan, R. Sen, A. K. Ava Pettet, Y. Suzue, L. Pedersen, A. Mukhopadhyay, and A. Dubey, OPTIMUS: Discrete Event Simulator for Vehicle-to-Building Charging Optimization, in 2024 IEEE International Conference on Smart Computing (SMARTCOMP), 2024.
@inproceedings{talusan2024smartcomp, title = {OPTIMUS: Discrete Event Simulator for Vehicle-to-Building Charging Optimization}, author = {Talusan, Jose Paolo and Sen, Rishav and Ava Pettet, Aaron Kandel and Suzue, Yoshinori and Pedersen, Liam and Mukhopadhyay, Ayan and Dubey, Abhishek}, year = {2024}, month = jun, booktitle = {2024 IEEE International Conference on Smart Computing (SMARTCOMP)}, volume = {}, number = {} }The increasing popularity of electronic vehicles has spurred a demand for EV charging infrastructure. In the United States alone, over 160,000 public and private charging ports have been installed. This has stoked fear of potential grid issues in the future. Meanwhile, companies, specifically building owners are also seeing the opportunity to leverage EV batteries as energy stores to serve as buffers against the electric grid. The main idea is to influence and control charging behavior to provide a certain level of energy resiliency and demand responsiveness to the building from grid events while ensuring that they meet the demands of EV users. However, managing and co-optimizing energy requirements of EVs and cost-saving measures of building owners is a difficult task. First, user behavior and grid uncertainty contribute greatly to the potential effectiveness of different policies. Second, different charger configurations can have drastically different effects on the cost. Therefore, we propose a complete end-to-end discrete event simulator for vehicle-to-building charging optimization. This software is aimed at building owners and EV manufacturers such as Nissan, looking to deploy their charging stations with state-of-the-art optimization algorithms. We provide a complete solution that allows the owners to train, evaluate, introduce uncertainty, and benchmark policies on their datasets. Lastly, we discuss the potential for extending our work with other vehicle-to-grid deployments.
- J. P. Talusan, C. Han, A. Mukhopadhyay, A. Laszka, D. Freudberg, and A. Dubey, An Online Approach to Solving Public Transit Stationing and Dispatch Problem, in Proceedings of the ACM/IEEE 15th International Conference on Cyber-Physical Systems (ICCPS), New York, NY, USA, 2024.
@inproceedings{talusan2024ICCPS, title = {An Online Approach to Solving Public Transit Stationing and Dispatch Problem}, author = {Talusan, Jose Paolo and Han, Chaeeun and Mukhopadhyay, Ayan and Laszka, Aron and Freudberg, Dan and Dubey, Abhishek}, year = {2024}, booktitle = {Proceedings of the ACM/IEEE 15th International Conference on Cyber-Physical Systems (ICCPS)}, location = {Hong Kong, China}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, series = {ICCPS '24}, numpages = {10} }Public bus transit systems provide critical transportation services for large sections of modern communities. On-time performance and maintaining the reliable quality of service is therefore very important. Unfortunately, disruptions caused by overcrowding, vehicular failures, and road accidents often lead to service performance degradation. Though transit agencies keep a limited number of vehicles in reserve and dispatch them to relieve the affected routes during disruptions, the procedure is often ad-hoc and has to rely on human experience and intuition to allocate resources (vehicles) to affected trips under uncertainty. In this paper, we describe a principled approach using non-myopic sequential decision procedures to solve the problem and decide (a) if it is advantageous to anticipate problems and proactively station transit buses near areas with high-likelihood of disruptions and (b) decide if and which vehicle to dispatch to a particular problem. Our approach was developed in partnership with the Metropolitan Transportation Authority for a mid-sized city in the USA and models the system as a semi-Markov decision problem (solved as a Monte-Carlo tree search procedure) and shows that it is possible to obtain an answer to these two coupled decision problems in a way that maximizes the overall reward (number of people served). We sample many possible futures from generative models, each is assigned to a tree and processed using root parallelization. We validate our approach using 3 years of data from our partner agency. Our experiments show that the proposed framework serves 2% more passengers while reducing deadhead miles by 40%.
- C. Han, J. P. Talusan, D. Freudberg, A. Mukhopadhyay, A. Dubey, and A. Laszka, Forecasting and Mitigating Disruptions in Public Bus Transit Services, in Proceedings of the 23rd Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2024, Auckland, New Zealand, Richland, SC, 2024.
@inproceedings{talusan2024AAMAS, title = {Forecasting and Mitigating Disruptions in Public Bus Transit Services}, author = {Han, Chaeeun and Talusan, Jose Paolo and Freudberg, Dan and Mukhopadhyay, Ayan and Dubey, Abhishek and Laszka, Aron}, year = {2024}, booktitle = {Proceedings of the 23rd Conference on Autonomous Agents and MultiAgent Systems, {AAMAS} 2024, Auckland, New Zealand}, location = {Auckland, New Zealand}, publisher = {International Foundation for Autonomous Agents and Multiagent Systems}, address = {Richland, SC}, series = {AAMAS '24}, numpages = {9}, keywords = {Public transportation, Data-driven optimization, Disruption forecasting, Simulation, Metaheuristic optimization} }Public transportation systems often suffer from unexpected fluctuations in demand and disruptions, such as mechanical failures and medical emergencies. These fluctuations and disruptions lead to delays and overcrowding, which are detrimental to the passengers’ experience and to the overall performance of the transit service. To proactively mitigate such events, many transit agencies station substitute (reserve) vehicles throughout their service areas, which they can dispatch to augment or replace vehicles on routes that suffer overcrowding or disruption. However, determining the optimal locations where substitute vehicles should be stationed is a challenging problem due to the inherent randomness of disruptions and due to the combinatorial nature of selecting locations across a city. In collaboration with the transit agency of a mid-size U.S. city, we address this problem by introducing data-driven statistical and machine-learning models for forecasting disruptions and an effective randomized local-search algorithm for selecting locations where substitute vehicles are to be stationed. Our research demonstrates promising results in proactive disruption management, offering a practical and easily implementable solution for transit agencies to enhance the reliability of their services. Our results resonate beyond mere operational efficiency—by advancing proactive strategies, our approach fosters more resilient and accessible public transportation, contributing to equitable urban mobility and ultimately benefiting the communities that rely on public transportation the most.
- F. Tiausas, K. Yasumoto, J. P. Talusan, H. Yamana, H. Yamaguchi, S. Bhattacharjee, A. Dubey, and S. K. Das, HPRoP: Hierarchical Privacy-Preserving Route Planning for Smart Cities, ACM Trans. Cyber-Phys. Syst., Jun. 2023.
@article{talusan2023tcps2, title = {HPRoP: Hierarchical Privacy-Preserving Route Planning for Smart Cities}, author = {Tiausas, Francis and Yasumoto, Keiichi and Talusan, Jose Paolo and Yamana, Hayato and Yamaguchi, Hirozumi and Bhattacharjee, Shameek and Dubey, Abhishek and Das, Sajal K.}, year = {2023}, month = jun, journal = {ACM Trans. Cyber-Phys. Syst.}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, doi = {10.1145/3603381}, issn = {2378-962X}, url = {}, note = {Just Accepted}, keywords = {Security and privacy, Privacy-preserving protocols, Domain-specific security and privacy architectures} }Route Planning Systems (RPS) are a core component of autonomous personal transport systems essential for safe and efficient navigation of dynamic urban environments with the support of edge-based smart city infrastructure, but they also raise concerns about user route privacy in the context of both privately-owned and commercial vehicles. Numerous high profile data breaches in recent years have fortunately motivated research on privacy-preserving RPS, but most of them are rendered impractical by greatly increased communication and processing overhead. We address this by proposing an approach called Hierarchical Privacy-Preserving Route Planning (HPRoP) which divides and distributes the route planning task across multiple levels, and protects locations along the entire route. This is done by combining Inertial Flow partitioning, Private Information Retrieval (PIR), and Edge Computing techniques with our novel route planning heuristic algorithm. Normalized metrics were also formulated to quantify the privacy of the source/destination points (endpoint location privacy) and the route itself (route privacy). Evaluation on a simulated road network showed that HPRoP reliably produces routes differing only by <=20% in length from optimal shortest paths, with completion times within 25 seconds which is reasonable for a PIR-based approach. On top of this, more than half of the produced routes achieved near-optimal endpoint location privacy ( 1.0) and good route privacy (>= 0.8).
- M. J. Islam, J. P. Talusan, S. Bhattacharjee, F. Tiausas, A. Dubey, K. Yasumoto, and S. K. Das, Scalable Pythagorean Mean Based Incident Detection in Smart Transportation Systems, ACM Trans. Cyber-Phys. Syst., Jun. 2023.
@article{talusan2023tcps1, title = {Scalable Pythagorean Mean Based Incident Detection in Smart Transportation Systems}, author = {Islam, Md. Jaminur and Talusan, Jose Paolo and Bhattacharjee, Shameek and Tiausas, Francis and Dubey, Abhishek and Yasumoto, Keiichi and Das, Sajal K.}, year = {2023}, month = jun, journal = {ACM Trans. Cyber-Phys. Syst.}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, doi = {10.1145/3603381}, issn = {2378-962X}, url = {https://doi.org/10.1145/3603381}, note = {Just Accepted}, keywords = {Incident Detection, Weakly Unsupervised Learning, Graph Algorithms, Approximation Algorithm., Regression, Smart Transportation, Cluster Analysis, Anomaly Detection} }Modern smart cities need smart transportation solutions to quickly detect various traffic emergencies and incidents in the city to avoid cascading traffic disruptions. To materialize this, roadside units and ambient transportation sensors are being deployed to collect speed data that enables the monitoring of traffic conditions on each road segment. In this paper, we first propose a scalable data-driven anomaly-based traffic incident detection framework for a city-scale smart transportation system. Specifically, we propose an incremental region growing approximation algorithm for optimal Spatio-temporal clustering of road segments and their data; such that road segments are strategically divided into highly correlated clusters. The highly correlated clusters enable identifying a Pythagorean Mean-based invariant as an anomaly detection metric that is highly stable under no incidents but shows a deviation in the presence of incidents. We learn the bounds of the invariants in a robust manner such that anomaly detection can generalize to unseen events, even when learning from real noisy data. Second, using cluster-level detection, we propose a folded Gaussian classifier to pinpoint the particular segment in a cluster where the incident happened in an automated manner. We perform extensive experimental validation using mobility data collected from four cities in Tennessee, compare with the state-of-the-art ML methods, to prove that our method can detect incidents within each cluster in real-time and outperforms known ML methods.
- A. Zulqarnain, S. Gupta, J. P. Talusan, P. Pugliese, A. Mukhopadhyay, and A. Dubey, Addressing APC Data Sparsity in Predicting Occupancy and Delay of Transit Buses: A Multitask Learning Approach, in 2023 IEEE International Conference on Smart Computing (SMARTCOMP), 2023.
@inproceedings{Zulqarnain2023, title = {Addressing APC Data Sparsity in Predicting Occupancy and Delay of Transit Buses: A Multitask Learning Approach}, author = {Zulqarnain, Ammar and Gupta, Samir and Talusan, Jose Paolo and Pugliese, Philip and Mukhopadhyay, Ayan and Dubey, Abhishek}, year = {2023}, booktitle = {2023 IEEE International Conference on Smart Computing (SMARTCOMP)}, volume = {}, number = {} }Public transit is a vital mode of transportation in urban areas, and its efficiency is crucial for the daily commute of millions of people. To improve the reliability and predictability of transit systems, researchers have developed separate single-task learning models to predict the occupancy and delay of buses at the stop or route level. However, these models provide a narrow view of delay and occupancy at each stop and do not account for the correlation between the two. We propose a novel approach that leverages broader generalizable patterns governing delay and occupancy for improved prediction. We introduce a multitask learning toolchain that takes into account General Transit Feed Specification feeds, Automatic Passenger Counter data, and contextual information temporal and spatial information. The toolchain predicts transit delay and occupancy at the stop level, improving the accuracy of the predictions of these two features of a trip given sparse and noisy data. We also show that our toolchain can adapt to fewer samples of new transit data once it has been trained on previous routes/trips as compared to state-of-the-art methods. Finally, we use actual data from Chattanooga, Tennessee, to validate our approach. We compare our approach against the state-of-the-art methods and we show that treating occupancy and delay as related problems improves the accuracy of the predictions. We show that our approach improves delay prediction significantly by as much as 6% in F1 scores while producing equivalent or better results for occupancy.
- J. P. Talusan, A. Mukhopadhyay, D. Freudberg, and A. Dubey, On Designing Day Ahead and Same Day Ridership Level Prediction Models for City-Scale Transit Networks Using Noisy APC Data, in 2022 IEEE International Conference on Big Data (Big Data), Los Alamitos, CA, USA, 2022, pp. 5598–5606.
@inproceedings{talusan2022apc, author = {Talusan, Jose Paolo and Mukhopadhyay, Ayan and Freudberg, Dan and Dubey, Abhishek}, booktitle = {2022 IEEE International Conference on Big Data (Big Data)}, title = {On Designing Day Ahead and Same Day Ridership Level Prediction Models for City-Scale Transit Networks Using Noisy APC Data}, year = {2022}, volume = {}, issn = {}, pages = {5598-5606}, keywords = {training;schedules;statistical analysis;stochastic processes;predictive models;big data;data models}, doi = {10.1109/BigData55660.2022.10020390}, url = {https://doi.ieeecomputersociety.org/10.1109/BigData55660.2022.10020390}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, month = dec }The ability to accurately predict public transit ridership demand benefits passengers and transit agencies. Agencies will be able to reallocate buses to handle under or over-utilized bus routes, improving resource utilization, and passengers will be able to adjust and plan their schedules to avoid overcrowded buses and maintain a certain level of comfort. However, accurately predicting occupancy is a non-trivial task. Various reasons such as heterogeneity, evolving ridership patterns, exogenous events like weather, and other stochastic variables, make the task much more challenging. With the progress of big data, transit authorities now have access to real-time passenger occupancy information for their vehicles. The amount of data generated is staggering. While there is no shortage in data, it must still be cleaned, processed, augmented, and merged before any useful information can be generated. In this paper, we propose the use and fusion of data from multiple sources, cleaned, processed, and merged together, for use in training machine learning models to predict transit ridership. We use data that spans a 2-year period (2020-2022) incorporating transit, weather, traffic, and calendar data. The resulting data, which equates to 17 million observations, is used to train separate models for the trip and stop level prediction. We evaluate our approach on real-world transit data provided by the public transit agency of Nashville, TN. We demonstrate that the trip level model based on Xgboost and the stop level model based on LSTM outperform the baseline statistical model across the entire transit service day.
- J. Islam, J. P. Talusan, S. Bhattacharjee, F. Tiausas, S. M. Vazirizade, A. Dubey, K. Yasumoto, and S. Das, Anomaly based Incident Detection in Large Scale Smart Transportation Systems, in ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS), 2022.
@inproceedings{jp2022, title = {Anomaly based Incident Detection in Large Scale Smart Transportation Systems}, booktitle = {ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)}, publisher = {IEEE}, author = {Islam, Jaminur and Talusan, Jose Paolo and Bhattacharjee, Shameek and Tiausas, Francis and Vazirizade, Sayyed Mohsen and Dubey, Abhishek and Yasumoto, Keiichi and Das, Sajal}, year = {2022}, month = apr }Modern smart cities are focusing on smart transportation solutions to detect and mitigate the effects of various traffic incidents in the city. To materialize this, roadside units and ambient transportation sensors are being deployed to collect vehicular data that provides real-time traffic monitoring. In this paper, we first propose a real-time data-driven anomaly-based traffic incident detection framework for a city-scale smart transportation system. Specifically, we propose an incremental region growing approximation algorithm for optimal Spatio-temporal clustering of road segments and their data; such that road segments are strategically divided into highly correlated clusters. The highly correlated clusters enable identifying a Pythagorean Mean-based invariant as an anomaly detection metric that is highly stable under no incidents but shows a deviation in the presence of incidents. We learn the bounds of the invariants in a robust manner such that anomaly detection can generalize to unseen events, even when learning from real noisy data. We perform extensive experimental validation using mobility data collected from the City of Nashville, Tennessee, and prove that the method can detect incidents within each cluster in real-time.
- F. Tiausas, J. P. Talusan, Y. Ishimaki, H. Yamana, H. Yamaguchi, S. Bhattacharjee, A. Dubey, K. Yasumoto, and S. K. Das, User-centric Distributed Route Planning in Smart Cities based on Multi-objective Optimization, in 2021 IEEE International Conference on Smart Computing (SMARTCOMP), 2021, pp. 77–82.
@inproceedings{jp21, author = {Tiausas, Francis and Talusan, Jose Paolo and Ishimaki, Yu and Yamana, Hayato and Yamaguchi, Hirozumi and Bhattacharjee, Shameek and Dubey, Abhishek and Yasumoto, Keiichi and Das, Sajal K.}, booktitle = {2021 IEEE International Conference on Smart Computing (SMARTCOMP)}, title = {User-centric Distributed Route Planning in Smart Cities based on Multi-objective Optimization}, year = {2021}, tag = {transit}, volume = {}, number = {}, pages = {77-82}, doi = {10.1109/SMARTCOMP52413.2021.00031} }The realization of edge-based cyber-physical systems (CPS) poses important challenges in terms of performance, robustness, security, etc. This paper examines a novel approach to providing a user-centric adaptive route planning service over a network of Road Side Units (RSUs) in smart cities. The key idea is to adaptively select routing task parameters such as privacy-cloaked area sizes and number of retained intersections to balance processing time, privacy protection level, and route accuracy for privacy-augmented distributed route search while also handling per-query user preferences. This is formulated as an optimization problem with a set of parameters giving the best result for a set of queries given system constraints. Processing Throughput, Privacy Protection, and Travel Time Accuracy were developed as the objective functions to be balanced. A Multi-Objective Genetic Algorithm based technique (NSGA-II) is applied to recover a feasible solution. The performance of this approach was then evaluated using traffic data from Osaka, Japan. Results show good performance of the approach in balancing the aforementioned objectives based on user preferences.
- M. Wilbur, C. Samal, J. P. Talusan, K. Yasumoto, and A. Dubey, Time-dependent Decentralized Routing using Federated Learning, in 2020 IEEE 23nd International Symposium on Real-Time Distributed Computing (ISORC), 2020.
@inproceedings{wilbur2020decentralized, title = {Time-dependent Decentralized Routing using Federated Learning}, author = {Wilbur, Michael and Samal, Chinmaya and Talusan, Jose Paolo and Yasumoto, Keiichi and Dubey, Abhishek}, booktitle = {2020 IEEE 23nd International Symposium on Real-Time Distributed Computing (ISORC)}, year = {2020}, tag = {decentralization,transit}, organization = {IEEE} }Recent advancements in cloud computing have driven rapid development in data-intensive smart city applications by providing near real time processing and storage scalability. This has resulted in efficient centralized route planning services such as Google Maps, upon which millions of users rely. Route planning algorithms have progressed in line with the cloud environments in which they run. Current state of the art solutions assume a shared memory model, hence deployment is limited to multiprocessing environments in data centers. By centralizing these services, latency has become the limiting parameter in the technologies of the future, such as autonomous cars. Additionally, these services require access to outside networks, raising availability concerns in disaster scenarios. Therefore, this paper provides a decentralized route planning approach for private fog networks. We leverage recent advances in federated learning to collaboratively learn shared prediction models online and investigate our approach with a simulated case study from a mid-size U.S. city.
- J. P. V. Talusan, M. Wilbur, A. Dubey, and K. Yasumoto, Route Planning Through Distributed Computing by Road Side Units, IEEE Access, vol. 8, pp. 176134–176148, 2020.
@article{wilburaccess2020, author = {{Talusan}, J. P. V. and {Wilbur}, M. and {Dubey}, A. and {Yasumoto}, K.}, journal = {IEEE Access}, title = {Route Planning Through Distributed Computing by Road Side Units}, year = {2020}, tag = {decentralization,transit}, volume = {8}, number = {}, pages = {176134-176148} }Cities are embracing data-intensive applications to maximize their constrained transportation networks. Platforms such as Google offer route planning services to mitigate the effect of traffic congestion. These use remote servers that require an Internet connection, which exposes data to increased risk of network failures and latency issues. Edge computing, an alternative to centralized architectures, offers computational power at the edge that could be used for similar services. Road side units (RSU), Internet of Things (IoT) devices within a city, offer an opportunity to offload computation to the edge. To provide an environment for processing on RSUs, we introduce RSU-Edge, a distributed edge computing system for RSUs. We design and develop a decentralized route planning service over RSU-Edge. In the service, the city is divided into grids and assigned an RSU. Users send trip queries to the service and obtain routes. For maximum accuracy, tasks must be allocated to optimal RSUs. However, this overloads RSUs, increasing delay. To reduce delays, tasks may be reallocated from overloaded RSUs to its neighbors. The distance between the optimal and actual allocation causes accuracy loss due to stale data. The problem is identifying the most efficient allocation of tasks such that response constraints are met while maintaining acceptable accuracy. We created the system and present an analysis of a case study in Nashville, Tennessee that shows the effect of our algorithm on route accuracy and query response, given varying neighbor levels. We find that our system can respond to 1000 queries up to 57.17% faster, with only a model accuracy loss of 5.57% to 7.25% compared to using only optimal grid allocation.
- J. P. Talusan, M. Wilbur, A. Dubey, and K. Yasumoto, On Decentralized Route Planning Using the Road Side Units as Computing Resources, in 2020 IEEE International Conference on Fog Computing (ICFC), 2020.
@inproceedings{rsuicfc2020, author = {Talusan, Jose Paolo and Wilbur, Michael and Dubey, Abhishek and Yasumoto, Keiichi}, title = {On Decentralized Route Planning Using the Road Side Units as Computing Resources}, booktitle = {2020 IEEE International Conference on Fog Computing (ICFC)}, year = {2020}, tag = {decentralization,transit}, organization = {IEEE}, category = {selectiveconference}, keywords = {transit, middleware} }Residents in cities typically use third-party platforms such as Google Maps for route planning services. While providing near real-time processing, these state of the art centralized deployments are limited to multiprocessing environments in data centers. This raises privacy concerns, increases risk for critical data and causes vulnerability to network failure. In this paper, we propose to use decentralized road side units (RSU) (owned by the city) to perform route planning. We divide the city road network into grids, each assigned an RSU where traffic data is kept locally, increasing security and resiliency such that the system can perform even if some RSUs fail. Route generation is done in two steps. First, an optimal grid sequence is generated, prioritizing shortest path calculation accuracy but not RSU load. Second, we assign route planning tasks to the grids in the sequence. Keeping in mind RSU load and constraints, tasks can be allocated and executed in any non-optimal grid but with lower accuracy. We evaluate this system using Metropolitan Nashville road traffic data. We divided the area into 500 grids, configuring load and neighborhood sizes to meet delay constraints while maximizing model accuracy. The results show that there is a 30 percent decrease in processing time with a decrease in model accuracy of 99 percent to 92.3 percent, by simply increasing the search area to the optimal grid’s immediate neighborhood.
- 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.