CoolWalks: Assessing the potential of shaded routing for active mobility in urban street networks
- URL: http://arxiv.org/abs/2405.01225v1
- Date: Thu, 2 May 2024 12:09:08 GMT
- Title: CoolWalks: Assessing the potential of shaded routing for active mobility in urban street networks
- Abstract summary: Shade from buildings can provide cooling and protection for pedestrians, but the extent of this potential benefit is unknown.
We explore the potential for shaded walking, using building footprints and street networks from both synthetic and real cities.
Our research identifies the limitations and potential of shade for cool, active travel, and is a first step towards a rigorous understanding of shade provision for sustainable mobility in cities.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Walking is the most sustainable form of urban mobility, but is compromised by uncomfortable or unhealthy sun exposure, which is an increasing problem due to global warming. Shade from buildings can provide cooling and protection for pedestrians, but the extent of this potential benefit is unknown. Here we explore the potential for shaded walking, using building footprints and street networks from both synthetic and real cities. We introduce a route choice model with a sun avoidance parameter $\alpha$ and define the CoolWalkability metric to measure opportunities for walking in shade. We derive analytically that on a regular grid with constant building heights, CoolWalkability is independent of $\alpha$, and that the grid provides no CoolWalkability benefit for shade-seeking individuals compared to the shortest path. However, variations in street geometry and building heights create such benefits. We further uncover that the potential for shaded routing differs between grid-like and irregular street networks, forms local clusters, and is sensitive to the mapped network geometry. Our research identifies the limitations and potential of shade for cool, active travel, and is a first step towards a rigorous understanding of shade provision for sustainable mobility in cities.
Related papers
- Exploring Sidewalk Sheds in New York City through Chatbot Surveys and Human Computer Interaction [47.311965900698084]
We develop an AI-based survey that collects image-based annotations and route choices from pedestrians.<n>This paper conducts a grid-based analysis of entrance annotations and applies logistic mixed-effects modeling to assess sidewalk choice patterns.<n>By integrating generative AI into urban research, this study demonstrates a novel method for evaluating sidewalk shed designs.
arXiv Detail & Related papers (2026-01-30T15:41:44Z) - A computational framework for quantifying route diversification in road networks [1.9232329432211988]
We examine how road network structure shapes the counterpart to traffic concentration: route diversification.<n>We introduce DiverCity, a measure that quantifies the extent to which traffic can potentially be distributed across multiple, loosely overlapping near-shortest routes.
arXiv Detail & Related papers (2025-10-02T21:41:53Z) - DeepShade: Enable Shade Simulation by Text-conditioned Image Generation [25.13463236406671]
Current routing systems fail to incorporate shade information due to the difficulty of estimating shades directly from satellite imagery.<n>We build an extensive dataset covering diverse longitude-latitude regions, varying levels of building density, and different urban layouts.<n>We propose the DeepShade, a diffusion-based model designed to learn and synthesize shade variations over time.
arXiv Detail & Related papers (2025-07-16T10:19:12Z) - Spatio-Temporal Jump Model for Urban Thermal Comfort Monitoring [0.0]
We introduce a-temporal handles that handle data with persistence across both spatial and temporal dimensions.
We validate our approach through extensive simulations, demonstrating its accuracy in recovering the true underlying partition.
Our proposal identifies meaningful thermal comfort regimes, demonstrating its effectiveness in dynamic urban settings and suitability for real-world monitoring.
arXiv Detail & Related papers (2024-11-14T15:36:19Z) - StreetSurfGS: Scalable Urban Street Surface Reconstruction with Planar-based Gaussian Splatting [85.67616000086232]
StreetSurfGS is first method to employ Gaussian Splatting specifically tailored for scalable urban street scene surface reconstruction.
StreetSurfGS utilizes a planar-based octree representation and segmented training to reduce memory costs, accommodate unique camera characteristics, and ensure scalability.
To address sparse views and multi-scale challenges, we use a dual-step matching strategy that leverages adjacent and long-term information.
arXiv Detail & Related papers (2024-10-06T04:21:59Z) - Streetscapes: Large-scale Consistent Street View Generation Using Autoregressive Video Diffusion [61.929653153389964]
We present a method for generating Streetscapes-long sequences of views through an on-the-fly synthesized city-scale scene.
Our method can scale to much longer-range camera trajectories, spanning several city blocks, while maintaining visual quality and consistency.
arXiv Detail & Related papers (2024-07-18T17:56:30Z) - Shaded Route Planning Using Active Segmentation and Identification of Satellite Images [2.2162879952427343]
Vulnerable groups, especially pedestrians and cyclists on sun-exposed sidewalks, motivate the development of a route planning method.
This paper is the first to introduce a pipeline that utilizes segmentation foundation models to extract shaded areas from satellite images.
These areas are then integrated into a multi-layered road map, enabling users to customize routes based on a balance between distance and shade exposure.
This system is already implemented online, with a video demonstration, and will be specifically adapted to assist travelers during the 2024 Olympic Games in Paris.
arXiv Detail & Related papers (2024-07-18T16:57:11Z) - Reconsidering utility: unveiling the limitations of synthetic mobility data generation algorithms in real-life scenarios [49.1574468325115]
We evaluate the utility of five state-of-the-art synthesis approaches in terms of real-world applicability.
We focus on so-called trip data that encode fine granular urban movements such as GPS-tracked taxi rides.
One model fails to produce data within reasonable time and another generates too many jumps to meet the requirements for map matching.
arXiv Detail & Related papers (2024-07-03T16:08:05Z) - Enhancing Roadway Safety: LiDAR-based Tree Clearance Analysis [0.2877502288155167]
Trees or other vegetation is growing above roadways, blocking the sight of traffic signs and lights and posing danger to traffic participants.
This is where LiDAR technology comes into play, a laser scanning sensor that reveals a three-dimensional perspective.
We present a new point cloud algorithm that can automatically detect those parts of the trees that grow over the street and need to be trimmed.
arXiv Detail & Related papers (2024-02-28T13:08:46Z) - Climate-sensitive Urban Planning through Optimization of Tree Placements [55.11389516857789]
Climate change is increasing the intensity and frequency of many extreme weather events, including heatwaves.
Among the most promising strategies is harnessing the benefits of urban trees in shading and cooling pedestrian-level environments.
Physical simulations can estimate the radiative and thermal impact of trees on human thermal comfort but induce high computational costs.
We employ neural networks to simulate the point-wise mean radiant temperatures--a driving factor of outdoor human thermal comfort--across various time scales.
arXiv Detail & Related papers (2023-10-09T13:07:23Z) - Dfferentiable Raycasting for Self-supervised Occupancy Forecasting [52.61762537741392]
Motion planning for autonomous driving requires learning how the environment around an ego-vehicle evolves with time.
In this paper, we use geometric occupancy as a natural alternative to view-dependent representations such as freespace.
Our key insight is to use differentiable raycasting to "render" future occupancy predictions into future LiDAR sweep predictions.
arXiv Detail & Related papers (2022-10-04T21:35:21Z) - 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) - The ubiquitous efficiency of going further: how street networks affect
travel speed [0.0]
We conducted a study of over 200 cities around the world to understand the impact that the patterns of deceleration points in streets due to traffic signs has in trajectories done from motorized vehicles.
We demonstrate that there is a ubiquitous nonlinear relationship between time and distance in the optimal trajectories within each city.
arXiv Detail & Related papers (2021-11-15T14:38:06Z) - Roofpedia: Automatic mapping of green and solar roofs for an open
roofscape registry and evaluation of urban sustainability [0.0]
Roofpedia is a set of three contributions: (i) automatic mapping of relevant urban roof typology from satellite imagery; (ii) an open roof registry mapping the spatial distribution and area of solar and green roofs of more than one million buildings across 17 cities; and (iii) the Roofpedia Index, a derivative of the registry, to benchmark the cities by the extent of sustainable roofscape in term of solar and green roof penetration.
arXiv Detail & Related papers (2020-12-22T13:34:50Z)
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.