SCOPE-LAB@Vanderbilt

High-dimensional Data-driven Energy optimization for Multi-Modal transit Agencies (HD-EMMA)

This is a project funded by the Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE), under Award Number DE-EE0008467. The research approach is to use continuous monitoring sensors on the complete mix of CARTA transit buses and to develop predictors and optimization mechanisms using the data. Specific activities are:

Acquire high-resolution (updated every minute) Spatio-temporal telemetry data from CARTA vehicles and exogenous data sources, such as traffic and weather
Develop an efficient framework to store and process the operational data and external data, including street and elevation maps
Create macro-level energy predictor using route information and general fleet parameters
Create a higher-resolution micro model that is tuned to specific vehicle parameters
Create an optimization framework to select the optimal assignment of vehicles to trips with the goal of reducing overall energy consumption
Develop a visualization framework to analyze the data

The following slide deck provides a brief overview of the project.

For details please see smarttransit.ai.

Publications

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.
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},
 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.
S. Shekhar et al., URMILA: Dynamically Trading-off Fog and Edge Resources for Performance and Mobility-Aware IoT Services, Journal of Systems Architecture, 2020.
```
@article{SHEKHAR2020101710,
 author = {Shekhar, Shashank and Chhokra, Ajay and Sun, Hongyang and Gokhale, Aniruddha and Dubey, Abhishek and Koutsoukos, Xenofon and Karsai, Gabor},
 title = {URMILA: Dynamically Trading-off Fog and Edge Resources for Performance and Mobility-Aware IoT Services},
 journal = {Journal of Systems Architecture},
 year = {2020},
 issn = {1383-7621},
 doi = {https://doi.org/10.1016/j.sysarc.2020.101710},
 keywords = {Fog/Edge Computing, User Mobility, Latency-sensitive IoT Services, Resource Management, middleware, performance},
 project = {cps-middleware},
 url = {http://www.sciencedirect.com/science/article/pii/S1383762120300047}
}
```
The fog/edge computing paradigm is increasingly being adopted to support a range of latency-sensitive IoT services due to its ability to assure the latency requirements of these services while supporting the elastic properties of cloud computing. IoT services that cater to user mobility, however, face a number of challenges in this context. First, since user mobility can incur wireless connectivity issues, executing these services entirely on edge resources, such as smartphones, will result in a rapid drain in the battery charge. In contrast, executing these services entirely on fog resources, such as cloudlets or micro data centers, will incur higher communication costs and increased latencies in the face of fluctuating wireless connectivity and signal strength. Second, a high degree of multi-tenancy on fog resources involving different IoT services can lead to performance interference issues due to resource contention. In order to address these challenges, this paper describes URMILA, which makes dynamic resource management decisions to achieve effective trade-offs between using the fog and edge resources yet ensuring that the latency requirements of the IoT services are met. We evaluate URMILA’s capabilities in the context of a real-world use case on an emulated but realistic IoT testbed.
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.
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.
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.
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.
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.
S. Basak, A. Aman, A. Laszka, A. Dubey, and B. Leao, Data-Driven Detection of Anomalies and Cascading Failures in Traffic Networks, in Proceedings of the 11th Annual Conference of the Prognostics and Health Management Society (PHM), 2019.
```
@inproceedings{Basak2019b,
 author = {Basak, Sanchita and Aman, Afiya and Laszka, Aron and Dubey, Abhishek and Leao, Bruno},
 title = {Data-Driven Detection of Anomalies and Cascading Failures in Traffic Networks},
 booktitle = {Proceedings of the 11th Annual Conference of the Prognostics and Health Management Society (PHM)},
 year = {2019},
 month = oct,
 attachments = {https://www.isis.vanderbilt.edu/sites/default/files/PHM_traffic_cascades_paper.pdf},
 category = {conference},
 doi = {https://doi.org/10.36001/phmconf.2019.v11i1.861},
 file = {:Basak2019b-Data_Driven_Detection_of_Anomalies_and_Cascading_Failures_in_Traffic_Networks.pdf:PDF},
 keywords = {transit, reliability},
 project = {smart-transit,smart-cities,cps-reliability}
}
```
Traffic networks are one of the most critical infrastructures for any community. The increasing integration of smart and connected sensors in traffic networks provides researchers with unique opportunities to study the dynamics of this critical community infrastructure. Our focus in this paper is on the failure dynamics of traffic networks. By failure, we mean in this domain the hindrance of the normal operation of a traffic network due to cyber anomalies or physical incidents that cause cascaded congestion throughout the network. We are specifically interested in analyzing the cascade effects of traffic congestion caused by physical incidents, focusing on developing mechanisms to isolate and identify the source of a congestion. To analyze failure propagation, it is crucial to develop (a) monitors that can identify an anomaly and (b) a model to capture the dynamics of anomaly propagation. In this paper, we use real traffic data from Nashville, TN to demonstrate a novel anomaly detector and a Timed Failure Propagation Graph based diagnostics mechanism. Our novelty lies in the ability to capture the the spatial information and the interconnections of the traffic network as well as the use of recurrent neural network architectures to learn and predict the operation of a graph edge as a function of its immediate peers, including both incoming and outgoing branches. Our results show that our LSTM-based traffic-speed predictors attain an average mean squared error of 6.55\times10^-4 on predicting normalized traffic speed, while Gaussian Process Regression based predictors attain a much higher average mean squared error of 1.78\times10^-2. We are also able to detect anomalies with high precision and recall, resulting in an AUC (Area Under Curve) of 0.8507 for the precision-recall curve. To study physical traffic incidents, we augment the real data with simulated data generated using SUMO, a traffic simulator. Finally, we analyzed the cascading effect of the congestion propagation by formulating the problem as a Timed Failure Propagation Graph, which led us in identifying the source of a failure/congestion accurately.
A. Oruganti, S. Basak, F. Sun, H. Baroud, and A. Dubey, Modeling and Predicting the Cascading Effects of Delay in Transit Systems, in Transportation Research Board Annual Meeting, 2019.
```
@inproceedings{Oruganti2019,
 author = {Oruganti, Aparna and Basak, Sanchita and Sun, Fangzhou and Baroud, Hiba and Dubey, Abhishek},
 title = {Modeling and Predicting the Cascading Effects of Delay in Transit Systems},
 booktitle = {Transportation Research Board Annual Meeting},
 year = {2019},
 attachments = {https://www.isis.vanderbilt.edu/sites/default/files/final poster.pdf},
 category = {selectiveconference},
 file = {:Oruganti2019-Modeling_and_Predicting_the_Cascading_Effects_of_Delay_in_Transit_Systems.pdf:PDF},
 keywords = {transit},
 project = {smart-transit,smart-cities}
}
```
An effective real-time estimation of the travel time for vehicles, using AVL(Automatic Vehicle Locators) has added a new dimension to the smart city planning. In this paper, we used data collected over several months from a transit agency and show how this data can be potentially used to learn patterns of travel time during specially planned events like NFL (National Football League) games and music award ceremonies. The impact of NFL games along with consideration of other factors like weather, traffic condition, distance is discussed with their relative importance to the prediction of travel time. Statistical learning models are used to predict travel time and subsequently assess the cascading effects of delay. The model performance is determined based on its predictive accuracy according to the out-of-sample error. In addition, the models help identify the most significant variables that influence the delay in the transit system. In order to compare the actual and predicted travel time for days having special events, heat maps are generated showing the delay impacts in different time windows between two timepoint-segments in comparison to a non-game day. This work focuses on the prediction and visualization of the delay in the public transit system and the analysis of its cascading effects on the entire transportation network. According to the study results, we are able to explain more than 80% of the variance in the bus travel time at each segment and can make future travel predictions during planned events with an out-of-sample error of 2.0 minutes using information on the bus schedule, traffic, weather, and scheduled events. According to the variable importance analysis, traffic information is most significant in predicting the delay in the transit system.