Data-Driven Approach for Urban Micromobility Enhancement through Safety Mapping and Intelligent Route Planning

IF 7 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

Smart Cities Pub Date : 2023-08-17 DOI:10.3390/smartcities6040094

Tiago Tamagusko, Matheus Gomes Correia, Luís Rita, Tudor-Codrin Bostan, Miguel Peliteiro, Rodrigo Martins, L. Santos, Adelino Ferreira

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引用次数: 2

Abstract

Micromobility responds to urban transport challenges by reducing emissions, mitigating traffic, and improving accessibility. Nevertheless, the safety of micromobility users, particularly cyclists, remains a concern in urban environments. This study aims to construct a safety map and a risk-averse routing system for micromobility users in diverse urban environments, as exemplified by a case study in Lisbon. A data-driven methodology uses object detection algorithms and image segmentation techniques to identify potential risk factors on cycling routes from Google Street View images. The ‘Bikeable’ Multilayer Perceptron neural network measures these risks, assigning safety scores to each image. The method analyzed 5321 points across 24 parishes in Lisbon, with an average safety score of 4.5, indicating a generally safe environment for cyclists. Carnide emerged as the safest area, while Alcântara exhibited a higher level of potential risks. Additionally, an equation is proposed to compute route efficiency, enabling comparisons between different routes for identical origin-destination pairs. Preliminary findings suggest that the presented routing solution exhibits higher efficiency than the commercial routing benchmark. Risk-averse routes did not result in a substantial rise in travel distance or time, with increments of 7% on average. The study also contributed to increasing the existing amount of cycle path data in Lisbon by 12%, correcting inaccuracies, and updating the network in OpenStreetMap, providing access to more precise information and, consequently, more routes. The key contributions of this study, such as the safety map and risk-averse router, underscore the potential of data-driven tools for boosting urban micromobility. The solutions proposed demonstrate modularity and adaptability, making them fit for a range of urban scenarios and highlighting their value for cities prioritizing safe, sustainable urban mobility.

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基于安全测绘和智能路径规划的城市微交通增强数据驱动方法

微交通通过减少排放、缓解交通拥堵和改善可达性来应对城市交通挑战。然而，在城市环境中，微型交通工具使用者，特别是骑自行车者的安全仍然是一个令人关切的问题。本研究旨在为不同城市环境下的微出行用户构建安全地图和风险规避路径系统，并以里斯本的案例研究为例。数据驱动的方法使用目标检测算法和图像分割技术，从谷歌街景图像中识别自行车路线上的潜在风险因素。“Bikeable”多层感知器神经网络测量这些风险，为每张图像分配安全分数。该方法分析了里斯本24个教区的5321分，平均安全得分为4.5分，表明骑车者的环境总体安全。Carnide是最安全的地区，而alc ntara则显示出更高的潜在风险。此外，还提出了一种计算路由效率的公式，以便对相同的始发目的地对进行不同路由之间的比较。初步结果表明，所提出的路由解决方案比商用路由基准具有更高的效率。规避风险的路线没有导致旅行距离或时间的大幅增加，平均增加7%。这项研究还使里斯本现有的自行车路径数据量增加了12%，修正了不准确的数据，并更新了OpenStreetMap的网络，提供了更精确的信息，从而提供了更多的路线。本研究的主要贡献，如安全地图和风险规避路由器，强调了数据驱动工具在促进城市微交通方面的潜力。提出的解决方案展示了模块化和适应性，使其适合一系列城市场景，并突出了其对优先考虑安全、可持续城市交通的城市的价值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Smart Cities Multiple-

CiteScore

11.20

自引率

6.20%

发文量

审稿时长

11 weeks

期刊介绍： Smart Cities (ISSN 2624-6511) provides an advanced forum for the dissemination of information on the science and technology of smart cities, publishing reviews, regular research papers (articles) and communications in all areas of research concerning smart cities. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible, with no restriction on the maximum length of the papers published so that all experimental results can be reproduced.