Study on Key Technologies of Transit Passengers Travel Pattern Mining and Applications based on Multiple Sources of Data

• URL: http://arxiv.org/abs/2006.02526v1
• Date: Tue, 26 May 2020 22:35:28 GMT
• Title: Study on Key Technologies of Transit Passengers Travel Pattern Mining and Applications based on Multiple Sources of Data
• Authors: Yongxin Liu
• Abstract summary: 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.
• Score: 1.370633147306388
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this research, we propose a series of methodologies to mine transit riders travel pattern and behavioral preferences, and then we use these knowledges to adjust and optimize the transit systems. Contributions are: 1) To increase the data validity: a) we propose a novel approach to rectify the time discrepancy of data between the AFC (Automated Fare Collection) systems and AVL (Automated Vehicle Location) system, our approach transforms data events into signals and applies time domain correlation the detect and rectify their relative discrepancies. b) By combining historical data and passengers ticketing time stamps, we induct and compensate missing information in AVL datasets. 2) To infer passengers alighting point, we introduce a maximum probabilistic model incorporating passengers home place to recover their complete transit trajectory from semi-complete boarding records.Then we propose an enhance activity identification algorithm which is capable of specifying passengers short-term activity from ordinary transfers. Finally, we analyze the temporal-spatial characteristic of transit ridership. 3) To discover passengers travel demands. We integrate each passengers trajectory data in multiple days and construct a Hybrid Trip Graph (HTG). We then use a depth search algorithm to derive the spatially closed transit trip chains; Finally, we use closed transit trip chains of passengers to study their travel pattern from various perspectives. Finally, we analyze urban transit corridors by aggregating the passengers critical transit chains.4) We derive eight influential factors, and then construct passengers choice models under various scenarios. Next, we validate our model using ridership re-distribute simulations. Finally, we conduct a comprehensive analysis on passengers temporal choice preference and use this information to optimize urban transit systems.

Related papers

• DelayPTC-LLM: Metro Passenger Travel Choice Prediction under Train Delays with Large Language Models [31.509436717815102]
  This paper proposes a novel Passenger Travel Choice prediction framework under metro delays with the Large Language Model (DelayPTC-LLM) A comparative analysis of DelayPTC-LLM with traditional prediction models demonstrates the superior capability of LLMs in handling complex, sparse datasets commonly encountered under disruption of transportation systems.
  arXiv  Detail & Related papers  (2024-09-28T13:09:15Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• A Bibliometric Analysis and Review on Reinforcement Learning for Transportation Applications [43.356096302298056]
  Transportation is the backbone of the economy and urban development. Reinforcement Learning (RL) that enables autonomous decision-makers to interact with the complex environment. This paper conducts a bibliometric analysis to identify the development of RL-based methods for transportation applications.
  arXiv  Detail & Related papers  (2022-10-26T07:34:51Z)
• 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)
• 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)
• Exploring Human Mobility for Multi-Pattern Passenger Prediction: A Graph Learning Framework [10.75153377806738]
  We propose a multi-pattern passenger flow prediction framework, MPGCN, based on Graph Convolutional Network (GCN) We employ GCN to extract features from the graph by learning useful topology information and introduce a deep clustering method to recognize mobility patterns hidden in bus passengers. To the best of our knowledge, this paper is the first work to adopt a multipattern approach to predict the bus passenger flow from graph learning.
  arXiv  Detail & Related papers  (2022-02-17T06:17:23Z)
• A Deep-Learning Based Optimization Approach to Address Stop-Skipping Strategy in Urban Rail Transit Lines [0.0]
  We introduce an advanced data-driven optimization approach to determine the optimal stop-skip pattern in urban rail transit lines. We employ a Long Short-Term Memory (LSTM) deep learning model to predict the station-level demand rates for the peak hour. Considering the exponential nature of the problem, we propose an Ant Colony Optimization technique to solve the problem in a desirable amount of time.
  arXiv  Detail & Related papers  (2021-09-17T23:52:19Z)
• Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers [126.81938540470847]
  We propose Euro-PVI, a dataset of pedestrian and bicyclist trajectories. In this work, we develop a joint inference model that learns an expressive multi-modal shared latent space across agents in the urban scene. We achieve state of the art results on the nuScenes and Euro-PVI datasets demonstrating the importance of capturing interactions between ego-vehicle and pedestrians (bicyclists) for accurate predictions.
  arXiv  Detail & Related papers  (2021-06-22T15:40:21Z)
• Public Transit for Special Events: Ridership Prediction and Train Optimization [10.531110013870792]
  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.
  arXiv  Detail & Related papers  (2021-06-09T19:52:18Z)
• A Distributed Model-Free Ride-Sharing Approach for Joint Matching, Pricing, and Dispatching using Deep Reinforcement Learning [32.0512015286512]
  We present a dynamic, demand aware, and pricing-based vehicle-passenger matching and route planning framework. Our framework is validated using the New York City Taxi dataset. Experimental results show the effectiveness of our approach in real-time and large scale settings.
  arXiv  Detail & Related papers  (2020-10-05T03:13:47Z)
• 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)

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.