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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 

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, the authors 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, the authors 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. 

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% 

Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. We use data related to real-time traffic speed, jam factors (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 the 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 a scaled 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. We present the analysis of the training time and the inference time separately. 

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. 

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. 

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. 

This article presents an overview of the collaborative Transit Hub project between Vanderbilt University, the Nashville Metropolitan Transit Authority (MTA) and Siemens, Corporate Technology. This project commenced as part of the NIST Global Cities Team Challenge (GCTC). The goal of this project is to leverage technology effectively to improve public engagement with transit operations and increase the overall efficiency of the system. In the process we want to identify key technical challenges that will require new research to advance the state of the art.