Public Transit for Special Events: Ridership Prediction and Train Optimization

• URL: http://arxiv.org/abs/2106.05359v1
• Date: Wed, 9 Jun 2021 19:52:18 GMT
• Title: Public Transit for Special Events: Ridership Prediction and Train Optimization
• Authors: Tejas Santanam, Anthony Trasatti, Pascal Van Hentenryck, and Hanyu Zhang
• Abstract summary: It is important for transit providers to understand their impact on disruptions, delays, and fare revenues. This paper proposes a suite of data-driven techniques for evaluating, anticipating, and managing the performance of transit systems during recurring congestion peaks due to special events.
• Score: 10.531110013870792
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many special events, including sport games and concerts, often cause surges in demand and congestion for transit systems. Therefore, it is important for transit providers to understand their impact on disruptions, delays, and fare revenues. This paper proposes a suite of data-driven techniques that exploit Automated Fare Collection (AFC) data for evaluating, anticipating, and managing the performance of transit systems during recurring congestion peaks due to special events. This includes an extensive analysis of ridership of the two major stadiums in downtown Atlanta using rail data from the Metropolitan Atlanta Rapid Transit Authority (MARTA). The paper first highlights the ridership predictability at the aggregate level for each station on both event and non-event days. It then presents an unsupervised machine-learning model to cluster passengers and identify which train they are boarding. The model makes it possible to evaluate system performance in terms of fundamental metrics such as the passenger load per train and the wait times of riders. The paper also presents linear regression and random forest models for predicting ridership that are used in combination with historical throughput analysis to forecast demand. Finally, simulations are performed that showcase the potential improvements to wait times and demand matching by leveraging proposed techniques to optimize train frequencies based on forecasted demand.

Related papers

• GPT-Augmented Reinforcement Learning with Intelligent Control for Vehicle Dispatching [82.19172267487998]
  GARLIC: a framework of GPT-Augmented Reinforcement Learning with Intelligent Control for vehicle dispatching. This paper introduces GARLIC: a framework of GPT-Augmented Reinforcement Learning with Intelligent Control for vehicle dispatching.
  arXiv  Detail & Related papers  (2024-08-19T08:23:38Z)
• On Designing Day Ahead and Same Day Ridership Level Prediction Models for City-Scale Transit Networks Using Noisy APC Data [0.0]
  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 evaluate our approach on real-world transit data provided by the public transit agency of Nashville, TN.
  arXiv  Detail & Related papers  (2022-10-10T19:50:59Z)
• Meta-Learning over Time for Destination Prediction Tasks [53.12827614887103]
  A need to understand and predict vehicles' behavior underlies both public and private goals in the transportation domain. Recent studies have found, at best, only marginal improvements in predictive performance from incorporating temporal information. We propose an approach based on hypernetworks, in which a neural network learns to change its own weights in response to an input.
  arXiv  Detail & Related papers  (2022-06-29T17:58:12Z)
• Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion [87.77727495366702]
  We introduce the new task of pedestrian stop and go forecasting. Considering the lack of suitable existing datasets for it, we release TRANS, a benchmark for explicitly studying the stop and go behaviors of pedestrians in urban traffic. We build it from several existing datasets annotated with pedestrians' walking motions, in order to have various scenarios and behaviors.
  arXiv  Detail & Related papers  (2022-03-04T18:39:31Z)
• Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data [86.1450118623908]
  We investigate a supervised machine learning approach based on deformable convolutional neural networks (deCNNs) We forecast the North Atlantic-European weather regimes during extended boreal winter for 1 to 15 days into the future. Due to its wider field of view, we also observe deCNN achieving considerably better performance than regular convolutional neural networks at lead times beyond 5-6 days.
  arXiv  Detail & Related papers  (2022-02-10T11:37:00Z)
• Transformers \`a Grande Vitesse [0.09208007322096534]
  We aim at predicting the travel time of trains on rail sections at the scale of an entire rail network in real-time. Our approach yields very positive results on real-world data when compared to currently-used and experimental prediction techniques.
  arXiv  Detail & Related papers  (2021-05-18T13:43:18Z)
• A model for traffic incident prediction using emergency braking data [77.34726150561087]
  We address the fundamental problem of data scarcity in road traffic accident prediction by training our model on emergency braking events instead of accidents. We present a prototype implementing a traffic incident prediction model for Germany based on emergency braking data from Mercedes-Benz vehicles.
  arXiv  Detail & Related papers  (2021-02-12T18:17:12Z)
• Crowding Prediction of In-Situ Metro Passengers Using Smart Card Data [11.781685156308475]
  We propose a statistical model to predict in-situ passenger density inside a closed metro system. Based on the prediction results, we are able to provide accurate prediction of in-situ passenger density for a future time point.
  arXiv  Detail & Related papers  (2020-09-07T04:07:37Z)
• Forecasting of the Montreal Subway Smart Card Entry Logs with Event Data [0.814375035858607]
  We propose generic data shaping for the long-term forecasting of passenger demand with fine-grained temporal resolution. Specifically, this paper investigates the forecasting until one year ahead of the number of passengers entering each station of a transport network with a quarter-hour aggregation. To compare the models and the quality of the prediction, we use a real smart card and event data set from the city of Montr'eal, Canada.
  arXiv  Detail & Related papers  (2020-08-22T14:08:57Z)
• Study on Key Technologies of Transit Passengers Travel Pattern Mining and Applications based on Multiple Sources of Data [1.370633147306388]
  We propose a series of methodologies to mine transit riders travel pattern and behavioral preferences. We use these knowledges to adjust and optimize the transit systems.
  arXiv  Detail & Related papers  (2020-05-26T22:35:28Z)
• Physical-Virtual Collaboration Modeling for Intra-and Inter-Station Metro Ridership Prediction [116.66657468425645]
  We propose a unified Physical-Virtual Collaboration Graph Network (PVCGN), which can effectively learn the complex ridership patterns from the tailor-designed graphs. Specifically, a physical graph is directly built based on the realistic topology of the studied metro system. A similarity graph and a correlation graph are built with virtual topologies under the guidance of the inter-station passenger flow similarity and correlation.
  arXiv  Detail & Related papers  (2020-01-14T16:47:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.