This project addresses the problem of urban transportation and congestion by building analytical tools that help the customers and the transit agencies reduce uncertainties and optimize the transit operations. We adress this problem at three fronts - Data Analytics, Planning and analysis tool for understanding and projecting the impact of transportation choices, and developing scalable data stores that can enable cities to operate their own data lakes and analytics engines.
Data Analytics
We focus on data analytics to understand bottlenecks and improve the operational reliability. For this, we start by first collecting multimodal data about transit operations, traffic, public events and congestion from cities of Nashville and Chattanooga. Then, we perform data analytics to understand the causes of transit delays and help provide tools that inform the community as well as transit operators deal with both long term planning as well as short term delays.
Some results from this work are listed below.
Deep Learning Based Anomaly Detection
Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. The data we use consists of historical traffic speed, jam factor (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network. The traffic dataset contains over 900 million data records. The network is thereafter used to classify real-time data and identify anomalous operations. Compared with traditional approaches of using statistical or machine learning techniques, our model reaches an accuracy of 98.73 percent when identifying traffic congestion caused by football games. Our approach first encodes the traffic across a region as an image. After that, the image data from different timestamps is fused with event- and time-related data. Then a crossover operator is used as a data augmentation method to generate training datasets with more balanced classes. Finally, we use the receiver operating characteristic (ROC) analysis to tune the sensitivity of the classifier.
Data generated by transit vehicles that are equipped with GPS can be used to provide surrogate sensing of traffic conditions in the city. We propose a multivariate predictive multi-model approach called SpeedPro. It can identify similar clusters of operation from historical data that includes the real-time position of a probe vehicle, weather, and driver identifier, and then employs different models to estimate the traffic speed in real-time as a function of current weather, and transit vehicle speed. The work has been published by the SmartSys 2017 workshop. See the folloing slides: SpeedPro: A Predictive Multi-modal Approach for Urban Traffic Speed Estimation
Understanding Delays and Optimizing Schedule
The on-time arrival performance of buses at stops is a critical metric for both riders and city planners to evaluate the reliability of a transit system. Identifying the bottlenecks in transit networks that often have abnormal delay is the first step for scheduling optimization. We built a prescriptive analytics mechanism to identify historical bus delay patterns and locate the bottlenecks in the transit network by measuring transit performance.
The transit performance is affected by various factors, such as the travel demand, traffic conditions, weather, etc. These stochastic factors make it very difficult to optimize timetables to match the actual transit operation. To better undestand the factors affecting delays we built a system called Delay Radar. It uses multivariate linear regression models and random forest models to analyze the traffic and weather data to make predictions on transit travel time.
Further, we created a robust delay prediction algorithm that uses multiple data sources and combines clustering analysis and Kalman filters. Additionally, a novel route segmentation mechanism that handles the issue of data sparsity was developed. You can read more about it in these slides.
To understand the impact of events we built multi-task deep neural networks that utilize contextual features (e.g., scheduled sports games and forecasted weather conditions) to make context-aware predictions of the expected travel delay, as well as the likelihood of accidents on the bus routes. Compared to existing models that rely solely on static and historical data, utilizing scheduled and predicted contextual information could provide a better estimate of transit system performance. Furthermore, the multi-task deep neural network architecture allows faster training and prediction, and reduces the possibility of overfitting, which improves the prediction accuracy. To learn more read the following papers: SmartComp2018 and the following poster.
Finally, we use the long term delay models to create an optimization problem for helping improve the fixed line transit schedule. See the following slides
Mobilytics Gym
As part of the work to improve the efficiency of public transit and urban transportation in general, we also build solutions that will educate the community on benefits of public transit. To mitigate this problem, we build a simulation framework to evaluate the effect of personal transportation choices and also help the cities evaluate the impact of incentive policies in nudging commuters towards alternate modes of travel, such as bike and car-share options. For this purpose, we leverage MATSim, an agent-based simulation framework, to integrate agent preference models that capture the altruistic behavior of an agent in addition to their disutility proportional to the travel time and cost. These models are learned in a data-driven approach and can be used to evaluate the sensitivity of an agent to system-level disutility and monetary incentives given, e.g., by the transportation authority. This framework provides a standardized environment to evaluate the effectiveness of any particular incentive policy of a city, in nudging its residents towards alternate modes of transportation. We show the effectiveness of the approach and provide analysis using a case study from the Metropolitan Nashville area.
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>
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>
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. Nannapaneni and A. Dubey, Towards demand-oriented flexible rerouting of public transit under uncertainty, in Proceedings of the Fourth Workshop on International Science of Smart City Operations and Platforms Engineering, SCOPE@CPSIoTWeek 2019, Montreal, QC, Canada, 2019, pp. 35–40.
@inproceedings{Nannapaneni2019,
author = {Nannapaneni, Saideep and Dubey, Abhishek},
title = {Towards demand-oriented flexible rerouting of public transit under uncertainty},
booktitle = {Proceedings of the Fourth Workshop on International Science of Smart City Operations and Platforms Engineering, SCOPE@CPSIoTWeek 2019, Montreal, QC, Canada},
year = {2019},
pages = {35--40},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/conf/cpsweek/NannapaneniD19},
category = {workshop},
doi = {10.1145/3313237.3313302},
file = {:Nannapaneni2019-Towards_demand-oriented_flexible_rerouting_of_public_transit_under_uncertainty.pdf:PDF},
keywords = {transit},
project = {smart-transit,smart-cities},
timestamp = {Tue, 10 Sep 2019 13:47:28 +0200},
url = {https://doi.org/10.1145/3313237.3313302}
}
This paper proposes a flexible rerouting strategy for the public transit to accommodate the spatio-temporal variation in the travel demand. Transit routes are typically static in nature, i.e., the buses serve well-defined routes; this results in people living in away from the bus routes choose alternate transit modes such as private automotive vehicles resulting in ever-increasing traffic congestion. In the flex-transit mode, we reroute the buses to accommodate high travel demand areas away from the static routes considering its spatio-temporal variation. We perform clustering to identify several flex stops; these are stops not on the static routes, but with high travel demand around them. We divide the bus stops on the static routes into critical and non-critical bus stops; critical bus stops refer to transfer points, where people change bus routes to reach their destinations. In the existing static scheduling process, some slack time is provided at the end of each trip to account for any travel delays. Thus, the additional travel time incurred due to taking flexible routes is constrained to be less than the available slack time. We use the percent increase in travel demand to analyze the effectiveness of the rerouting process. The proposed methodology is demonstrated using real-world travel data for Route 7 operated by the Nashville Metropolitan Transit Authority (MTA).</p>
C. Samal, A. Dubey, and L. J. Ratliff, Mobilytics-Gym: A Simulation Framework for Analyzing Urban Mobility Decision Strategies, in IEEE International Conference on Smart Computing, SMARTCOMP 2019, Washington, DC, USA, 2019, pp. 283–291.
@inproceedings{Samal2019,
author = {Samal, Chinmaya and Dubey, Abhishek and Ratliff, Lillian J.},
title = {Mobilytics-Gym: {A} Simulation Framework for Analyzing Urban Mobility Decision Strategies},
booktitle = {{IEEE} International Conference on Smart Computing, {SMARTCOMP} 2019, Washington, DC, USA},
year = {2019},
pages = {283--291},
month = jun,
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/conf/smartcomp/SamalDR19},
category = {selectiveconference},
doi = {10.1109/SMARTCOMP.2019.00064},
file = {:Samal2019-Mobilytics-Gym_A_Simulation_Framework_for_Analyzing_Urban_Mobility_Decision_Strategies.pdf:PDF},
keywords = {transit},
project = {smart-transit,smart-cities},
timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
url = {https://doi.org/10.1109/SMARTCOMP.2019.00064}
}
The rise in deep learning models in recent years has led to various innovative solutions for intelligent transportation technologies. Use of personal and on-demand mobility services puts a strain on the existing road network in a city. To mitigate this problem, city planners need a simulation framework to evaluate the effect of any incentive policy in nudging commuters towards alternate modes of travel, such as bike and car-share options. In this paper, we leverage MATSim, an agent-based simulation framework, to integrate agent preference models that capture the altruistic behavior of an agent in addition to their disutility proportional to the travel time and cost. These models are learned in a data-driven approach and can be used to evaluate the sensitivity of an agent to system-level disutility and monetary incentives given, e.g., by the transportation authority. This framework provides a standardized environment to evaluate the effectiveness of any particular incentive policy of a city, in nudging its residents towards alternate modes of transportation. We show the effectiveness of the approach and provide analysis using a case study from the Metropolitan Nashville area.</p>
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.</p>
F. Sun, A. Dubey, C. Samal, H. Baroud, and C. Kulkarni, Short-Term Transit Decision Support System Using Multi-task Deep Neural Networks, in 2018 IEEE International Conference on Smart Computing, SMARTCOMP 2018, Taormina, Sicily, Italy, June 18-20, 2018, 2018, pp. 155–162.
@inproceedings{Sun2018,
author = {Sun, Fangzhou and Dubey, Abhishek and Samal, Chinmaya and Baroud, Hiba and Kulkarni, Chetan},
title = {Short-Term Transit Decision Support System Using Multi-task Deep Neural Networks},
booktitle = {2018 {IEEE} International Conference on Smart Computing, {SMARTCOMP} 2018, Taormina, Sicily, Italy, June 18-20, 2018},
year = {2018},
pages = {155--162},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/conf/smartcomp/SunDSBK18},
category = {selectiveconference},
doi = {10.1109/SMARTCOMP.2018.00086},
file = {:Sun2018-Short-Term_Transit_Decision_Support_System_Using_Multi-task_Deep_Neural_Networks.pdf:PDF},
keywords = {transit},
project = {smart-transit,cps-reliability,smart-cities},
timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
url = {https://doi.org/10.1109/SMARTCOMP.2018.00086}
}
Unpredictability is one of the top reasons that prevent people from using public transportation. To improve the on-time performance of transit systems, prior work focuses on updating schedule periodically in the long-term and providing arrival delay prediction in real-time. But when no real-time transit and traffic feed is available (e.g., one day ahead), there is a lack of effective contextual prediction mechanism that can give alerts of possible delay to commuters. In this paper, we propose a generic tool-chain that takes standard General Transit Feed Specification (GTFS) transit feeds and contextual information (recurring delay patterns before and after big events in the city and the contextual information such as scheduled events and forecasted weather conditions) as inputs and provides service alerts as output. Particularly, we utilize shared route segment networks and multi-task deep neural networks to solve the data sparsity and generalization issues. Experimental evaluation shows that the proposed toolchain is effective at predicting severe delay with a relatively high recall of 76% and F1 score of 55%.</p>
C. Samal, A. Dubey, and L. J. Ratliff, Mobilytics- An Extensible, Modular and Resilient Mobility Platform, in 2018 IEEE International Conference on Smart Computing, SMARTCOMP 2018, Taormina, Sicily, Italy, June 18-20, 2018, 2018, pp. 356–361.
@inproceedings{Samal2018,
author = {Samal, Chinmaya and Dubey, Abhishek and Ratliff, Lillian J.},
title = {Mobilytics- An Extensible, Modular and Resilient Mobility Platform},
booktitle = {2018 {IEEE} International Conference on Smart Computing, {SMARTCOMP} 2018, Taormina, Sicily, Italy, June 18-20, 2018},
year = {2018},
pages = {356--361},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/conf/smartcomp/SamalDR18},
category = {selectiveconference},
doi = {10.1109/SMARTCOMP.2018.00029},
file = {:Samal2018-Mobilytics-An_Extensible_Modular_and_Resilient_Mobility_Platform.pdf:PDF},
keywords = {transit},
project = {smart-transit,smart-cities},
timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
url = {https://doi.org/10.1109/SMARTCOMP.2018.00029}
}
Transportation management platforms provide communities the ability to integrate the available mobility options and localized transportation demand management policies. A central component of a transportation management platform is the mobility planning application. Given the societal relevance of these platforms, it is necessary to ensure that they operate resiliently. Modularity and extensibility are also critical properties that are required for manageability. Modularity allows to isolate faults easily. Extensibility enables update of policies and integration of new mobility modes or new routing algorithms. However, state of the art mobility planning applications like open trip planner, are monolithic applications, which makes it difficult to scale and modify them dynamically. This paper describes a microservices based modular multi-modal mobility platform Mobilytics, that integrates mobility providers, commuters, and community stakeholders. We describe our requirements, architecture, and discuss the resilience challenges, and how our platform functions properly in presence of failure. Conceivably, the patterns and principles manifested in our system can serve as guidelines for current and future practitioners in this field.</p>
C. Samal, L. Zheng, F. Sun, L. J. Ratliff, and A. Dubey, Towards a Socially Optimal Multi-Modal Routing Platform, CoRR, vol. abs/1802.10140, 2018.
@article{Samal2018a,
author = {Samal, Chinmaya and Zheng, Liyuan and Sun, Fangzhou and Ratliff, Lillian J. and Dubey, Abhishek},
title = {Towards a Socially Optimal Multi-Modal Routing Platform},
journal = {CoRR},
year = {2018},
volume = {abs/1802.10140},
archiveprefix = {arXiv},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1802-10140},
eprint = {1802.10140},
file = {:Samal2018a-Towards_a_Socially_Optimal_Multi-Modal_Routing_Platform.pdf:PDF},
keywords = {transit},
project = {smart-transit,smart-cities},
timestamp = {Mon, 13 Aug 2018 01:00:00 +0200},
url = {http://arxiv.org/abs/1802.10140}
}
The increasing rate of urbanization has added pressure on the already constrained transportation networks in our communities. Ride-sharing platforms such as Uber and Lyft are becoming a more commonplace, particularly in urban environments. While such services may be deemed more convenient than riding public transit due to their on-demand nature, reports show that they do not necessarily decrease the congestion in major cities. One of the key problems is that typically mobility decision support systems focus on individual utility and react only after congestion appears. In this paper, we propose socially considerate multi-modal routing algorithms that are proactive and consider, via predictions, the shared effect of riders on the overall efficacy of mobility services. We have adapted the MATSim simulator framework to incorporate the proposed algorithms present a simulation analysis of a case study in Nashville, Tennessee that assesses the effects of our routing models on the traffic congestion for different levels of penetration and adoption of socially considerate routes. Our results indicate that even at a low penetration (social ratio), we are able to achieve an improvement in system-level performance.</p>
C. Samal, F. Sun, and A. Dubey, SpeedPro: A Predictive Multi-Model Approach for Urban Traffic Speed Estimation, in 2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017, Hong Kong, China, May 29-31, 2017, 2017, pp. 1–6.
@inproceedings{Samal2017,
author = {Samal, Chinmaya and Sun, Fangzhou and Dubey, Abhishek},
title = {SpeedPro: {A} Predictive Multi-Model Approach for Urban Traffic Speed Estimation},
booktitle = {2017 {IEEE} International Conference on Smart Computing, {SMARTCOMP} 2017, Hong Kong, China, May 29-31, 2017},
year = {2017},
pages = {1--6},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/conf/smartcomp/SamalSD17},
category = {workshop},
doi = {10.1109/SMARTCOMP.2017.7947048},
file = {:Samal2017-SpeedPro_A_Predictive_Multi-Model_Approach_for_Urban_Traffic_Speed_Estimation.pdf:PDF},
keywords = {transit},
project = {smart-transit,smart-cities},
timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
url = {https://doi.org/10.1109/SMARTCOMP.2017.7947048}
}
Data generated by GPS-equipped probe vehicles, especially public transit vehicles can be a reliable source for traffic speed estimation. Traditionally, this estimation is done by learning the parameters of a model that describes the relationship between the speed of the probe vehicle and the actual traffic speed. However, such approaches typically suffer from data sparsity issues. Furthermore, most state of the art approaches does not consider the effect of weather and the driver of the probe vehicle on the parameters of the learned model. In this paper, we describe a multivariate predictive multi-model approach called SpeedPro that (a) first identifies similar clusters of operation from the historic data that includes the real-time position of the probe vehicle, the weather data, and anonymized driver identifier, and then (b) uses these different models to estimate the traffic speed in real-time as a function of current weather, driver and probe vehicle speed. When the real-time information is not available our approach uses a different model that uses the historical weather and traffic information for estimation. Our results show that the purely historical data is less accurate than the model that uses the real-time information.</p>
F. Sun, C. Samal, J. White, and A. Dubey, Unsupervised Mechanisms for Optimizing On-Time Performance of Fixed Schedule Transit Vehicles, in 2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017, Hong Kong, China, May 29-31, 2017, 2017, pp. 1–8.
@inproceedings{Sun2017a,
author = {Sun, Fangzhou and Samal, Chinmaya and White, Jules and Dubey, Abhishek},
title = {Unsupervised Mechanisms for Optimizing On-Time Performance of Fixed Schedule Transit Vehicles},
booktitle = {2017 {IEEE} International Conference on Smart Computing, {SMARTCOMP} 2017, Hong Kong, China, May 29-31, 2017},
year = {2017},
pages = {1--8},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/conf/smartcomp/SunSWD17},
category = {selectiveconference},
doi = {10.1109/SMARTCOMP.2017.7947057},
file = {:Sun2017a-Unsupervised_Mechanisms_for_Optimizing_On-Time_Performance_of_Fixed_Schedule_Transit_Vehicles.pdf:PDF},
keywords = {transit},
project = {smart-transit,smart-cities},
timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
url = {https://doi.org/10.1109/SMARTCOMP.2017.7947057}
}
The on-time arrival performance of vehicles at stops is a critical metric for both riders and city planners to evaluate the reliability of a transit system. However, it is a non-trivial task for transit agencies to adjust the existing bus schedule to optimize the on-time performance for the future. For example, severe weather conditions and special events in the city could slow down traffic and cause bus delay. Furthermore, the delay of previous trips may affect the initial departure time of consecutive trips and generate accumulated delay. In this paper, we formulate the problem as a single-objective optimization task with constraints and propose a greedy algorithm and a genetic algorithm to generate bus schedules at timepoints that improve the bus on-time performance at timepoints which is indicated by whether the arrival delay is within the desired range. We use the Nashville bus system as a case study and simulate the optimization performance using historical data. The comparative analysis of the results identifies that delay patterns change over time and reveals the efficiency of the greedy and genetic algorithms.</p>
A. Oruganti, F. Sun, H. Baroud, and A. Dubey, DelayRadar: A multivariate predictive model for transit systems, in 2016 IEEE International Conference on Big Data, BigData 2016, Washington DC, USA, December 5-8, 2016, 2016, pp. 1799–1806.
@inproceedings{Oruganti2016,
author = {Oruganti, Aparna and Sun, Fangzhou and Baroud, Hiba and Dubey, Abhishek},
title = {DelayRadar: {A} multivariate predictive model for transit systems},
booktitle = {2016 {IEEE} International Conference on Big Data, BigData 2016, Washington DC, USA, December 5-8, 2016},
year = {2016},
pages = {1799--1806},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/conf/bigdataconf/OrugantiSBD16},
category = {selectiveconference},
doi = {10.1109/BigData.2016.7840797},
file = {:Oruganti2016-DelayRadar_A_multivariate_predictive_model_for_transit_systems.pdf:PDF},
keywords = {transit},
project = {smart-transit,smart-cities},
timestamp = {Wed, 16 Oct 2019 14:14:51 +0200},
url = {https://doi.org/10.1109/BigData.2016.7840797}
}
Effective public transit operations are one of the fundamental requirements for a modern community. Recently, a number of transit agencies have started integrating automated vehicle locators in their fleet, which provides a real-time estimate of the time of arrival. In this paper, we use the data collected over several months from one such transit system and show how this data can be potentially used to learn long term patterns of travel time. More specifically, we study the effect of weather and other factors such as traffic on the transit system delay. These models can later be used to understand the seasonal variations and to design adaptive and transient transit schedules. Towards this goal, we also propose an online architecture called DelayRadar. The novelty of DelayRadar lies in three aspects: (1) a data store that collects and integrates real-time and static data from multiple data sources, (2) a predictive statistical model that analyzes the data to make predictions on transit travel time, and (3) a decision making framework to develop an optimal transit schedule based on variable forecasts related to traffic, weather, and other impactful factors. This paper focuses on identifying the model with the best predictive accuracy to be used in DelayRadar. According to the preliminary study results, we are able to explain more than 70% of the variance in the bus travel time and we can make future travel predictions with an out-of-sample error of 4.8 minutes with information on the bus schedule, traffic, and weather.</p>
S. Shekhar et al., A Smart Decision Support System for Public Transit Operations, in Internet of Things and Data Analytics Handbook, 2016.
@inbook{Shekhar2016,
title = {A Smart Decision Support System for Public Transit Operations},
year = {2016},
author = {Shekhar, Shashank and Sun, Fangzhou and Dubey, Abhishek and Gokhale, Aniruddha and Neema, Himanshu and Lehofer, Martin and Freudberg, Dan},
booktitle = {Internet of Things and Data Analytics Handbook},
file = {:Shekhar2016-Transit_Hub_A_Smart_Decision_Support_System_for_Public_Transit_Operations.pdf:PDF},
keywords = {transit}
}
F. Sun, Y. Pan, J. White, and A. Dubey, Real-Time and Predictive Analytics for Smart Public Transportation Decision Support System, in 2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016, St Louis, MO, USA, May 18-20, 2016, 2016, pp. 1–8.
@inproceedings{Sun2016,
author = {Sun, Fangzhou and Pan, Yao and White, Jules and Dubey, Abhishek},
title = {Real-Time and Predictive Analytics for Smart Public Transportation Decision Support System},
booktitle = {2016 {IEEE} International Conference on Smart Computing, {SMARTCOMP} 2016, St Louis, MO, USA, May 18-20, 2016},
year = {2016},
pages = {1--8},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/conf/smartcomp/SunPWD16},
category = {selectiveconference},
doi = {10.1109/SMARTCOMP.2016.7501714},
file = {:Sun2016-Real-Time_and_Predictive_Analytics_for_Smart_Public_Transportation_Decision_Support_System.pdf:PDF},
keywords = {transit},
project = {smart-transit,smart-cities},
timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
url = {https://doi.org/10.1109/SMARTCOMP.2016.7501714}
}
Public bus transit plays an important role in city transportation infrastructure. However, public bus transit is often difficult to use because of lack of real- time information about bus locations and delay time, which in the presence of operational delays and service alerts makes it difficult for riders to predict when buses will arrive and plan trips. Precisely tracking vehicle and informing riders of estimated times of arrival is challenging due to a number of factors, such as traffic congestion, operational delays, varying times taken to load passengers at each stop. In this paper, we introduce a public transportation decision support system for both short-term as well as long-term prediction of arrival bus times. The system uses streaming real-time bus position data, which is updated once every minute, and historical arrival and departure data - available for select stops to predict bus arrival times. Our approach combines clustering analysis and Kalman filters with a shared route segment model in order to produce more accurate arrival time predictions. Experiments show that compared to the basic arrival time prediction model that is currently being used by the city, our system reduces arrival time prediction errors by 25 percent on average when predicting the arrival delay an hour ahead and 47 percent when predicting within a 15 minute future time window.</p>
A. Dubey, M. Sturm, M. Lehofer, and J. Sztipanovits, Smart City Hubs: Opportunities for Integrating and Studying Human CPS at Scale, in Workshop on Big Data Analytics in CPS: Enabling the Move from IoT to Real-Time Control, 2015.
@inproceedings{Dubey2015,
author = {Dubey, Abhishek and Sturm, Monika and Lehofer, Martin and Sztipanovits, Janos},
title = {Smart City Hubs: Opportunities for Integrating and Studying Human CPS at Scale},
booktitle = {Workshop on Big Data Analytics in CPS: Enabling the Move from IoT to Real-Time Control},
year = {2015},
category = {workshop},
file = {:Dubey2015-Smart_city_hubs_Opportunities_for_integrating_and_studying_human_cps_at_scale.pdf:PDF},
keywords = {transit},
url = {http://www.isis.vanderbilt.edu/sites/default/files/extendedAbstract.pdf}
}