@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},
organization = {IEEE},
category = {selectiveconference},
keywords = {transit, middleware}
}
2020
- 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.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.</p>
- 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}, 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.</p> - A. Sivagnanam, A. Ayman, M. Wilbur, P. Pugliese, A. Dubey, and A. Laszka, Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service, in Preprint at Arxiv, 2020.
@inproceedings{sivagnanam2020minimizing, author = {Sivagnanam, Amutheezan and Ayman, Afiya and Wilbur, Michael and Pugliese, Philip and Dubey, Abhishek and Laszka, Aron}, title = {Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service}, booktitle = {Preprint at Arxiv}, year = {2020}, archiveprefix = {arXiv}, eprint = {2004.05146}, preprint = {https://arxiv.org/abs/2004.05146}, primaryclass = {cs.AI} }Public transit can have significantly lower environmental impact than personal vehicles; however, it still uses a substantial amount of energy, causing air pollution and greenhouse gas emission. While electric vehicles (EVs) can reduce energy use, most public transit agencies have to employ them in combination with conventional, internal-combustion engine vehicles due to the high upfront costs of EVs. To make the best use of such a mixed fleet of vehicles, transit agencies need to optimize route assignments and charging schedules, which presents a challenging problem for large public transit networks. We introduce a novel problem formulation to minimize fuel and electricity use by assigning vehicles to transit trips and scheduling them for charging while serving an existing fixed-route transit schedule. We present an integer program for optimal discrete-time scheduling, and we propose polynomial-time heuristic algorithms and a genetic algorithm for finding solutions for larger networks. We evaluate our algorithms on the transit service of a mid-size U.S. city using operational data collected from public transit vehicles. Our results show that the proposed algorithms are scalable and achieve near-minimum energy use.</p> - Y. Chen, G. Wu, R. Sun, A. Dubey, A. Laszka, and P. Pugliese, A Review and Outlook of Energy Consumption Estimation Models for Electric Vehicles, in Preprint at Arxiv, 2020.
@inproceedings{chen2020review, author = {Chen, Yuche and Wu, Guoyuan and Sun, Ruixiao and Dubey, Abhishek and Laszka, Aron and Pugliese, Philip}, title = {A Review and Outlook of Energy Consumption Estimation Models for Electric Vehicles}, booktitle = {Preprint at Arxiv}, year = {2020}, archiveprefix = {arXiv}, eprint = {2003.12873}, preprint = {https://arxiv.org/abs/2003.12873}, primaryclass = {eess.SY} }Electric vehicles (EVs) are critical to the transition to a low-carbon transportation system. The successful adoption of EVs heavily depends on energy consumption models that can accurately and reliably estimate electricity consumption. This paper reviews the state-of-the-art of EV energy consumption models, aiming to provide guidance for future development of EV applications. We summarize influential variables of EV energy consumption into four categories: vehicle component, vehicle dynamics, traffic and environment related factors. We classify and discuss EV energy consumption models in terms of modeling scale (microscopic vs. macroscopic) and methodology (data-driven vs. rule-based). Our review shows trends of increasing macroscopic models that can be used to estimate trip-level EV energy consumption and increasing data-driven models that utilized machine learning technologies to estimate EV energy consumption based on large volume real-world data. We identify research gaps for EV energy consumption models, including the development of energy estimation models for modes other than personal vehicles (e.g., electric buses, electric trucks, and electric non-road vehicles); the development of energy estimation models that are suitable for applications related to vehicle-to-grid integration; and the development of multi-scale energy estimation models as a holistic modeling approach.</p> - M. Wilbur et al., Impact of COVID-19 on Public Transit Accessibility and Ridership, in Preprint at Arxiv, 2020.
@inproceedings{wilbur2020impact, author = {Wilbur, Michael and Ayman, Afiya and Ouyang, Anna and Poon, Vincent and Kabir, Riyan and Vadali, Abhiram and Pugliese, Philip and Freudberg, Daniel and Laszka, Aron and Dubey, Abhishek}, title = {Impact of COVID-19 on Public Transit Accessibility and Ridership}, booktitle = {Preprint at Arxiv}, year = {2020}, archiveprefix = {arXiv}, eprint = {2008.02413}, preprint = {https://arxiv.org/abs/2008.02413}, primaryclass = {physics.soc-ph} }Public transit is central to cultivating equitable communities. Meanwhile, the novel coronavirus disease COVID-19 and associated social restrictions has radically transformed ridership behavior in urban areas. Perhaps the most concerning aspect of the COVID-19 pandemic is that low-income and historically marginalized groups are not only the most susceptible to economic shifts but are also most reliant on public transportation. As revenue decreases, transit agencies are tasked with providing adequate public transportation services in an increasingly hostile economic environment. Transit agencies therefore have two primary concerns. First, how has COVID-19 impacted ridership and what is the new post-COVID normal? Second, how has ridership varied spatio-temporally and between socio-economic groups? In this work we provide a data-driven analysis of COVID-19’s affect on public transit operations and identify temporal variation in ridership change. We then combine spatial distributions of ridership decline with local economic data to identify variation between socio-economic groups. We find that in Nashville and Chattanooga, TN, fixed-line bus ridership dropped by 66.9% and 65.1% from 2019 baselines before stabilizing at 48.4% and 42.8% declines respectively. The largest declines were during morning and evening commute time. Additionally, there was a significant difference in ridership decline between the highest-income areas and lowest-income areas (77% vs 58%) in Nashville.</p> - W. Barbour et al., Data Driven Methods for Effective Micromobility Parking, in Proceedings of the Transportation Research Board Annual Meeting, 2020.
@inproceedings{micromobility2020, author = {Barbour, William and Wilbur, Michael and Sandoval, Ricardo and Geffen, Caleb Van and Hall, Brandon and Dubey, Abhishek and Work, Dan}, title = {Data Driven Methods for Effective Micromobility Parking}, booktitle = {Proceedings of the Transportation Research Board Annual Meeting}, year = {2020}, category = {selectiveconference}, keywords = {transit} }Proliferation of shared urban mobility devices (SUMDs), particularly dockless e-scooters, has created opportunities for users with efficient, short trips, but raised management challenges for cities and regulators in terms of safety, infrastructure, and parking. There is a need in some high-demand areas for dedicated parking locations for dockless e-scooters and other devices. We propose the use of data generated by SUMD trips for establishing locations of parking facilities and assessing their required capacity and anticipated utilization. The problem objective is: find locations for a given number of parking facilities that maximize the number of trips that could reasonably be ended and parked at these facilities. Posed another way, what is the minimum number and best locations of parking facilities needed to cover a desired portion of trips at these facilities? In order to determine parking locations, areas of high-density trip destination points are found using unsupervised machine learning algorithms. The dwell time of each device is used to estimate the number of devices parked in a location over time and the necessary capacity of the parking facility. The methodology is tested on a dataset of approximately 100,000 e-scooter trips at Vanderbilt University in Nashville, Tennessee, USA. We find DBSCAN to be the most effective algorithm at determining high-performing parking locations. A selection of 19 parking locations, is enough to capture roughly 25 percent of all trips in the dataset. The vast majority of parking facilities found require a mean capacity of 6 scooters when sized for the 98th percentile observed demand.</p> - A. Ayman, M. Wilbur, A. Sivagnanam, P. Pugliese, A. Dubey, and A. Laszka, Data-Driven Prediction of Route-Level Energy Use for Mixed-Vehicle Transit Fleets, in 2020 IEEE International Conference on Smart Computing (SMARTCOMP) (SMARTCOMP 2020), Bologna, Italy, 2020.
@inproceedings{Lasz2006Data, author = {Ayman, Afiya and Wilbur, Michael and Sivagnanam, Amutheezan and Pugliese, Philip and Dubey, Abhishek and Laszka, Aron}, title = {{Data-Driven} Prediction of {Route-Level} Energy Use for {Mixed-Vehicle} Transit Fleets}, booktitle = {2020 IEEE International Conference on Smart Computing (SMARTCOMP) (SMARTCOMP 2020)}, address = {Bologna, Italy}, days = {21}, month = jun, year = {2020}, keywords = {data-driven prediction; electric vehicle; public transit; on-board diagnostics data; deep learning; traffic data} }Due to increasing concerns about environmental impact, operating costs, and energy security, public transit agencies are seeking to reduce their fuel use by employing electric vehicles (EVs). However, because of the high upfront cost of EVs, most agencies can afford only mixed fleets of internal-combustion and electric vehicles. Making the best use of these mixed fleets presents a challenge for agencies since optimizing the assignment of vehicles to transit routes, scheduling charging, etc. require accurate predictions of electricity and fuel use. Recent advances in sensor-based technologies, data analytics, and machine learning enable remedying this situation; however, to the best of our knowledge, there exists no framework that would integrate all relevant data into a route-level prediction model for public transit. In this paper, we present a novel framework for the data-driven prediction of route-level energy use for mixed-vehicle transit fleets, which we evaluate using data collected from the bus fleet of CARTA, the public transit authority of Chattanooga, TN. We present a data collection and storage framework, which we use to capture system-level data, including traffic and weather conditions, and high-frequency vehicle-level data, including location traces, fuel or electricity use, etc. We present domain-specific methods and algorithms for integrating and cleansing data from various sources, including street and elevation maps. Finally, we train and evaluate machine learning models, including deep neural networks, decision trees, and linear regression, on our integrated dataset. Our results show that neural networks provide accurate estimates, while other models can help us discover relations between energy use and factors such as road and weather conditions.</p>
2019
- F. Sun, A. Dubey, J. White, and A. Gokhale, Transit-hub: a smart public transportation decision support system with multi-timescale analytical services, Cluster Computing, vol. 22, no. Suppl 1, pp. 2239–2254, Jan. 2019.
@article{Sun2019, author = {Sun, Fangzhou and Dubey, Abhishek and White, Jules and Gokhale, Aniruddha}, title = {Transit-hub: a smart public transportation decision support system with multi-timescale analytical services}, journal = {Cluster Computing}, year = {2019}, volume = {22}, number = {Suppl 1}, pages = {2239--2254}, month = jan, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/journals/cluster/SunDWG19}, doi = {10.1007/s10586-018-1708-z}, file = {:Sun2019-Transit-hub_a_smart_public_transportation_decision_support_system_with_multi-timescale_analytical_services.pdf:PDF}, keywords = {transit}, project = {smart-cities,smart-transit}, timestamp = {Wed, 21 Aug 2019 01:00:00 +0200}, url = {https://doi.org/10.1007/s10586-018-1708-z} }Public transit is a critical component of a smart and connected community. As such, citizens expect and require accurate information about real-time arrival/departures of transportation assets. As transit agencies enable large-scale integration of real-time sensors and support back-end data-driven decision support systems, the dynamic data-driven applications systems (DDDAS) paradigm becomes a promising approach to make the system smarter by providing online model learning and multi-time scale analytics as part of the decision support system that is used in the DDDAS feedback loop. In this paper, we describe a system in use in Nashville and illustrate the analytic methods developed by our team. These methods use both historical as well as real-time streaming data for online bus arrival prediction. The historical data is used to build classifiers that enable us to create expected performance models as well as identify anomalies. These classifiers can be used to provide schedule adjustment feedback to the metro transit authority. We also show how these analytics services can be packaged into modular, distributed and resilient micro-services that can be deployed on both cloud back ends as well as edge computing resources.</p> - S. Basak, F. Sun, S. Sengupta, and A. Dubey, Data-Driven Optimization of Public Transit Schedule, in Big Data Analytics - 7th International Conference, BDA 2019, Ahmedabad, India, 2019, pp. 265–284.
@inproceedings{Basak2019, author = {Basak, Sanchita and Sun, Fangzhou and Sengupta, Saptarshi and Dubey, Abhishek}, title = {Data-Driven Optimization of Public Transit Schedule}, booktitle = {Big Data Analytics - 7th International Conference, {BDA} 2019, Ahmedabad, India}, year = {2019}, pages = {265--284}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/conf/bigda/BasakSSD19}, category = {selectiveconference}, doi = {10.1007/978-3-030-37188-3\_16}, file = {:Basak2019-Data_Driven_Optimization_of_Public_Transit_Schedule.pdf:PDF}, keywords = {transit}, project = {smart-cities,smart-transit}, timestamp = {Fri, 13 Dec 2019 12:44:00 +0100}, url = {https://doi.org/10.1007/978-3-030-37188-3\_16} }Bus transit systems are the backbone of public transportation in the United States. An important indicator of the quality of service in such infrastructures is on-time performance at stops, with published transit schedules playing an integral role governing the level of success of the service. However there are relatively few optimization architectures leveraging stochastic search that focus on optimizing bus timetables with the objective of maximizing probability of bus arrivals at timepoints with delays within desired on-time ranges. In addition to this, there is a lack of substantial research considering monthly and seasonal variations of delay patterns integrated with such optimization strategies. To address these, this paper makes the following contributions to the corpus of studies on transit on-time performance optimization: (a) an unsupervised clustering mechanism is presented which groups months with similar seasonal delay patterns, (b) the problem is formulated as a single-objective optimization task and a greedy algorithm, a genetic algorithm (GA) as well as a particle swarm optimization (PSO) algorithm are employed to solve it, (c) a detailed discussion on empirical results comparing the algorithms are provided and sensitivity analysis on hyper-parameters of the heuristics are presented along with execution times, which will help practitioners looking at similar problems. The analyses conducted are insightful in the local context of improving public transit scheduling in the Nashville metro region as well as informative from a global perspective as an elaborate case study which builds upon the growing corpus of empirical studies using nature-inspired approaches to transit schedule optimization.</p> - 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}, 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.</p>