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1. DelayRadar: A multivariate predictive model for transit systems

   https://doi.org/10.1109/BigData.2016.7840797  

   Oruganti, Aparna ; Sun, Fangzhou ; Baroud, Hiba ; Dubey, Abhishek ( December 2016 , Big Data (Big Data), 2016 IEEE International Conference on)

   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. 

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2. Human-Centric Urban Transit Evaluation and Planning

   https://doi.org/10.1109/ICDM.2018.00070  

   Wu, Guojun ; Li, Yanhua ; Bao, Jie ; Zheng, Yu ; Ye, Jieping ; Luo, Jun ( November 2018 , 2018 IEEE International Conference on Data Mining (ICDM))

   Public transits, such as buses and subway lines, offer affordable ride-sharing services and reduce the road network traffic, thus have significant impacts in mitigating the urban traffic congestion problem. However, it is non-trivial to evaluate a new transit plan, such as a new bus route or a new subway line, of its future ridership prior to actual deployment, since the travel preferences of passengers along the planned routes may vary. In this paper, we make the first attempt to model passengers' preferences of making various transit choices using a Markov Decision Process (MDP). Moreover, we develop a novel inverse preference learning algorithm to infer the passengers' preferences and predict the future human behavior changes, e.g., ridership, of a new urban transit plan before its deployment. We validate our proposed framework using a unique real-world dataset (from Shenzhen, China) with three subway lines opened during the data time span. With the data collected from both before and after the transit plan deployments, Our evaluation results demonstrated that the proposed framework can predict the ridership with only 19.8% relative error, which is 23%-51% lower than other baseline approaches. 

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3. On Designing Day Ahead and Same Day Ridership Level Prediction Models for City-Scale Transit Networks Using Noisy APC Data

   https://doi.org/10.1109/BigData55660.2022.10020390  

   Talusan, Jose Paolo ; Mukhopadhyay, Ayan ; Freudberg, Dan ; Dubey, Abhishek ( December 2022 , 2022 IEEE International Conference on Big Data (Big Data))

   The ability to accurately predict public transit ridership demand benefits passengers and transit agencies. Agencies will be able to reallocate buses to handle under or over-utilized bus routes, improving resource utilization, and passengers will be able to adjust and plan their schedules to avoid overcrowded buses and maintain a certain level of comfort. However, accurately predicting occupancy is a non-trivial task. Various reasons such as heterogeneity, evolving ridership patterns, exogenous events like weather, and other stochastic variables, make the task much more challenging. With the progress of big data, transit authorities now have access to real-time passenger occupancy information for their vehicles. The amount of data generated is staggering. While there is no shortage in data, it must still be cleaned, processed, augmented, and merged before any useful information can be generated. In this paper, we propose the use and fusion of data from multiple sources, cleaned, processed, and merged together, for use in training machine learning models to predict transit ridership. We use data that spans a 2-year period (2020-2022) incorporating transit, weather, traffic, and calendar data. The resulting data, which equates to 17 million observations, is used to train separate models for the trip and stop level prediction. We evaluate our approach on real-world transit data provided by the public transit agency of Nashville, TN. We demonstrate that the trip level model based on Xgboost and the stop level model based on LSTM outperform the baseline statistical model across the entire transit service day. 

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4. Short-Term Transit Decision Support System Using Multi-task Deep Neural Networks

   https://doi.org/10.1109/SMARTCOMP.2018.00086  

   Sun, Fangzhou ; Dubey, Abhishek ; Samal, Chinmaya ; Baroud, Hiba ; Kulkarni, Chetan ( June 2018 , IEEE Smartcomp)

   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% 

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5. The University of Michigan Implements a Hub-and-Spoke Design to Accommodate Social Distancing in the Campus Bus System Under COVID-19 Restrictions

   https://doi.org/10.1287/inte.2022.1131  

   Chen, Gongyu ; Fei, Xinyu ; Jia, Huiwen ; Yu, Xian ; Shen, Siqian ( November 2022 , INFORMS Journal on Applied Analytics)

   The outbreak of coronavirus disease 2019 (COVID-19) has led to significant challenges for schools and communities during the pandemic, requiring policy makers to ensure both safety and operational feasibility. In this paper, we develop mixed-integer programming models and simulation tools to redesign routes and bus schedules for operating a real university campus bus system during the COVID-19 pandemic. We propose a hub-and-spoke design and utilize real data of student activities to identify hub locations and bus stops to be used in the new routes. To reduce disease transmission via expiratory aerosol, we design new bus routes that are shorter than 15 minutes to travel and operate using at most 50% seat capacity and the same number of buses before the pandemic. We sample a variety of scenarios that cover variations of peak demand, social distancing requirements, and bus breakdowns to demonstrate the system resiliency of the new routes and schedules via simulation. The new bus routes were implemented and used during the academic year 2020–2021 to ensure social distancing and short travel time. Our approach can be generalized to redesign public transit systems with a social distancing requirement to reduce passengers’ infection risk. History: This paper was refereed. This article has been selected for inclusion in the Special Issue on Analytics Remedies to COVID-19. Funding: This work was supported by the National Science Foundation [Grant CMMI-2041745] and the University of Michigan, College of Engineering. 

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