Discovering Cyclists' Visual Preferences Through Shared Bike Trajectories and Street View Images Using Inverse Reinforcement Learning

• URL: http://arxiv.org/abs/2409.03148v2
• Date: Tue, 8 Oct 2024 12:03:46 GMT
• Title: Discovering Cyclists' Visual Preferences Through Shared Bike Trajectories and Street View Images Using Inverse Reinforcement Learning
• Authors: Kezhou Ren, Meihan Jin, Huiming Liu, Yongxi Gong, Yu Liu,
• Abstract summary: We propose a novel framework aimed to quantify and interpret cyclists' complicated visual preferences. We adapt MEDIRL model for efficient estimation of cycling reward function. We find that cyclists focus on specific street visual elements when making route decisions.
• Score: 2.678595263943329
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cycling has gained global popularity for its health benefits and positive urban impacts. To effectively promote cycling, early studies have extensively investigated the relationship between cycling behaviors and environmental factors, especially cyclists' preferences when making route decisions. However, these studies often struggle to comprehensively describe detailed cycling procedures at a large scale due to data limitations, and they tend to overlook the complex nature of cyclists' preferences. To address these issues, we propose a novel framework aimed to quantify and interpret cyclists' complicated visual preferences by leveraging maximum entropy deep inverse reinforcement learning(MEDIRL)and explainable artificial intelligence(XAI). Implemented in Bantian Sub-district, Shenzhen, we adapt MEDIRL model for efficient estimation of cycling reward function by integrating dockless-bike-sharing(DBS) trajectory and street view images(SVIs), which serves as a representation of cyclists' preferences for street visual environments during routing. In addition, we demonstrate the feasibility and reliability of MEDIRL in discovering cyclists' visual preferences. We find that cyclists focus on specific street visual elements when making route decisions, which can be summarized as their attention to safety, street enclosure, and cycling comfort. Further analysis reveals the complex nonlinear effects of street visual elements on cyclists' preferences, offering a cost-effective perspective on streetscapes design. Our proposed framework advances the understanding of individual cycling behaviors and provides actionable insights for urban planners to design bicycle-friendly streetscapes that prioritize cyclists' preferences.

Related papers

• Evaluating the effects of Data Sparsity on the Link-level Bicycling Volume Estimation: A Graph Convolutional Neural Network Approach [54.84957282120537]
  We present the first study to utilize a Graph Convolutional Network architecture to model link-level bicycling volumes. We estimate the Annual Average Daily Bicycle (AADB) counts across the City of Melbourne, Australia using Strava Metro bicycling count data. Our results show that the GCN model performs better than these traditional models in predicting AADB counts.
  arXiv  Detail & Related papers  (2024-10-11T04:53:18Z)
• RoadBEV: Road Surface Reconstruction in Bird's Eye View [55.0558717607946]
  Road surface conditions, especially geometry profiles, enormously affect driving performance of autonomous vehicles. Vision-based online road reconstruction promisingly captures road information in advance. Bird's-Eye-View (BEV) perception provides immense potential to more reliable and accurate reconstruction. This paper uniformly proposes two simple yet effective models for road elevation reconstruction in BEV named RoadBEV-mono and RoadBEV-stereo.
  arXiv  Detail & Related papers  (2024-04-09T20:24:29Z)
• RSRD: A Road Surface Reconstruction Dataset and Benchmark for Safe and Comfortable Autonomous Driving [67.09546127265034]
  Road surface reconstruction helps to enhance the analysis and prediction of vehicle responses for motion planning and control systems. We introduce the Road Surface Reconstruction dataset, a real-world, high-resolution, and high-precision dataset collected with a specialized platform in diverse driving conditions. It covers common road types containing approximately 16,000 pairs of stereo images, original point clouds, and ground-truth depth/disparity maps.
  arXiv  Detail & Related papers  (2023-10-03T17:59:32Z)
• AutoLTS: Automating Cycling Stress Assessment via Contrastive Learning and Spatial Post-processing [4.599618895656792]
  We propose a deep learning framework to support accurate, fast, and large-scale cycling stress assessments. Our framework features i) a contrastive learning approach that leverages the ordinal relationship among cycling stress labels, and ii) a post-processing technique that enforces spatial smoothness into our predictions.
  arXiv  Detail & Related papers  (2023-08-15T05:51:25Z)
• Scoring Cycling Environments Perceived Safety using Pairwise Image Comparisons [0.9299655616863538]
  This study presents a novel approach to identifying how the perception of cycling safety can be analyzed and understood. We repeatedly show respondents two road environments and ask them to select the one they perceive as safer for cycling. We compare several methods capable of rating cycling environments from pairwise comparisons and classify cycling environments perceived as safe or unsafe.
  arXiv  Detail & Related papers  (2023-07-25T10:31:45Z)
• Predicting Citi Bike Demand Evolution Using Dynamic Graphs [81.12174591442479]
  We apply a graph neural network model to predict bike demand in the New York City, Citi Bike dataset. In this paper, we attempt to apply a graph neural network model to predict bike demand in the New York City, Citi Bike dataset.
  arXiv  Detail & Related papers  (2022-12-18T21:43:27Z)
• 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)
• Classifying Cycling Hazards in Egocentric Data [3.1925030748447747]
  This proposal is for the creation and annotation of an egocentric video data set of hazardous cycling situations. The resulting data set will facilitate projects to improve the safety and experience of cyclists.
  arXiv  Detail & Related papers  (2021-03-15T02:37:04Z)
• CyclingNet: Detecting cycling near misses from video streams in complex urban scenes with deep learning [1.462434043267217]
  CyclingNet is a deep computer vision model based on convolutional structure embedded with self-attention bidirectional long-short term memory (LSTM) blocks. After 42 hours of training on a single GPU, the model shows high accuracy on the training, testing and validation sets. The model is intended to be used for generating information that can draw significant conclusions regarding cycling behaviour in cities.
  arXiv  Detail & Related papers  (2021-01-31T23:59:28Z)
• Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems using Multi-objective Reinforcement Learning [79.61517670541863]
  How to use AI to provide efficient bicycle dispatching solutions based on dynamic bicycle rental demand is an essential issue for dockless PBS (DL-PBS) We propose a dynamic bicycle dispatching algorithm based on multi-objective reinforcement learning (MORL-BD) to provide the optimal bicycle dispatching solution for DL-PBS.
  arXiv  Detail & Related papers  (2021-01-19T03:09:51Z)
• Exploring the weather impact on bike sharing usage through a clustering analysis [7.541020519717183]
  This study aims to explore how and in what magnitude weather impacts bike usage in Washington D.C. Bike usage data and weather data are gathered for the city of Washington D.C. and are analyzed using k-means clustering algorithm.
  arXiv  Detail & Related papers  (2020-08-17T12:24:37Z)

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.