The Role of Urban Form in the Performance of Shared Automated Vehicles

• URL: http://arxiv.org/abs/2012.01384v1
• Date: Wed, 2 Dec 2020 18:28:44 GMT
• Title: The Role of Urban Form in the Performance of Shared Automated Vehicles
• Authors: Kaidi Wang, Wenwen Zhang
• Abstract summary: It remains unclear what key urban form measurements may influence SAV system's sustainability. This study identifies critical urban form measurements correlated with the simulated SAV performance using fixed effects regression models. The results suggest that SAVs are more efficient and generate less VMT in denser cities with more connected networks and diversified land use development patterns.
• Score: 8.627374199434097
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The technology of Shared Automated Vehicles (SAVs) has advanced significantly in recent years. However, existing SAV studies primarily focus on the system design while limited studies have examined the impacts of exogenous variables, especially urban form, on SAV performance. Therefore, it remains unclear what key urban form measurements may influence SAV system's sustainability. This study fills the research gap by conducting simulation experiments using data collected from 286 cities. This study identifies critical urban form measurements correlated with the simulated SAV performance using fixed effects regression models. The results suggest that SAVs are more efficient and generate less VMT in denser cities with more connected networks and diversified land use development patterns. The model results can help provide insights on land use and transportation policies to curb the adverse effects of SAVs in the future and generalize existing SAV simulation results to the rest of U.S. cities.

Related papers

• Generalization of Urban Wind Environment Using Fourier Neural Operator Across Different Wind Directions and Cities [3.161712948860127]
  This study investigates the effectiveness of the Fourier Neural Operator (FNO) model in predicting flow fields under different wind directions and urban layouts. By training the model on velocity data from large eddy simulation data, we evaluate the performance of the model under different urban configurations and wind conditions. The results show that the FNO model can provide accurate predictions while significantly reducing the computational time by 99%.
  arXiv  Detail & Related papers  (2025-01-09T18:02:12Z)
• Extrapolated Urban View Synthesis Benchmark [53.657271730352214]
  Photorealistic simulators are essential for the training and evaluation of vision-centric autonomous vehicles (AVs) At their core is Novel View Synthesis (NVS), a crucial capability that generates diverse unseen viewpoints to accommodate the broad and continuous pose distribution of AVs. Recent advances in radiance fields, such as 3D Gaussian Splatting, achieve photorealistic rendering at real-time speeds and have been widely used in modeling large-scale driving scenes. We have released our data to help advance self-driving and urban robotics simulation technology.
  arXiv  Detail & Related papers  (2024-12-06T18:41:39Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• Benchmarking and Improving Bird's Eye View Perception Robustness in Autonomous Driving [55.93813178692077]
  We present RoboBEV, an extensive benchmark suite designed to evaluate the resilience of BEV algorithms. We assess 33 state-of-the-art BEV-based perception models spanning tasks like detection, map segmentation, depth estimation, and occupancy prediction. Our experimental results also underline the efficacy of strategies like pre-training and depth-free BEV transformations in enhancing robustness against out-of-distribution data.
  arXiv  Detail & Related papers  (2024-05-27T17:59:39Z)
• Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Network [12.349403667141559]
  We propose deep hybrid models (DHM) which directly combine road networks and sociodemographic features as inputs for travel mode share analysis. In experiments of mode share prediction in Chicago, results demonstrate that DHM can provide valuable spatial insights into the sociodemographic structure.
  arXiv  Detail & Related papers  (2024-05-23T00:59:00Z)
• UAV-Sim: NeRF-based Synthetic Data Generation for UAV-based Perception [62.71374902455154]
  We leverage recent advancements in neural rendering to improve static and dynamic novelview UAV-based image rendering. We demonstrate a considerable performance boost when a state-of-the-art detection model is optimized primarily on hybrid sets of real and synthetic data.
  arXiv  Detail & Related papers  (2023-10-25T00:20:37Z)
• Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark] [78.05103666987655]
  This work addresses challenges in accessing and utilizing diverse urban spatial-temporal datasets. We introduceatomic files, a unified storage format designed for urban spatial-temporal big data, and validate its effectiveness on 40 diverse datasets. We conduct extensive experiments using diverse models and datasets, establishing a performance leaderboard and identifying promising research directions.
  arXiv  Detail & Related papers  (2023-08-24T16:20:00Z)
• A comparison of machine learning surrogate models of street-scale flooding in Norfolk, Virginia [0.0]
  Low-lying coastal cities, exemplified by Norfolk, Virginia, face the challenge of street flooding caused by rainfall and tides. High-fidelity, physics-based simulations provide accurate predictions of urban pluvial flooding, but their computational complexity renders them unsuitable for real-time applications. This study compares the performance of a surrogate model based on a random forest algorithm with two deep learning models: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)
  arXiv  Detail & Related papers  (2023-07-26T13:24:01Z)
• 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)
• UrbanVCA: a vector-based cellular automata framework to simulate the urban land-use change at the land-parcel level [4.12627107272774]
  The UrbanVCA is a brand-new vector CA-based urban development simulation framework. Using Shunde, Guangdong as the study area, the UrbanVCA simulates multiple types of urban land-use changes at the land-parcel level. The simulation results in 2030 show that the eco-protection scenario can promote urban agglomeration and reduce ecological aggression and loss of arable land by at least 60%.
  arXiv  Detail & Related papers  (2021-03-15T17:03:22Z)
• Network Impacts of Automated Mobility-on-Demand: A Macroscopic Fundamental Diagram Perspective [0.0]
  Automated Mobility on Demand (AMOD) is a promising solution that may improve future urban mobility. This paper investigates the network impacts of AMOD through high-fidelity activity- and agent-based traffic simulation.
  arXiv  Detail & Related papers  (2020-11-10T13:39:35Z)

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.