Navigation services amplify concentration of traffic and emissions in our cities

Giuliano Cornacchia, Mirco Nanni, Dino Pedreschi, Luca Pappalardo
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Abstract

The proliferation of human-AI ecosystems involving human interaction with algorithms, such as assistants and recommenders, raises concerns about large-scale social behaviour. Despite evidence of such phenomena across several contexts, the collective impact of GPS navigation services remains unclear: while beneficial to the user, they can also cause chaos if too many vehicles are driven through the same few roads. Our study employs a simulation framework to assess navigation services' influence on road network usage and CO2 emissions. The results demonstrate a universal pattern of amplified conformity: increasing adoption rates of navigation services cause a reduction of route diversity of mobile travellers and increased concentration of traffic and emissions on fewer roads, thus exacerbating an unequal distribution of negative externalities on selected neighbourhoods. Although navigation services recommendations can help reduce CO2 emissions when their adoption rate is low, these benefits diminish or even disappear when the adoption rate is high and exceeds a certain city- and service-dependent threshold. We summarize these discoveries in a non-linear function that connects the marginal increase of conformity with the marginal reduction in CO2 emissions. Our simulation approach addresses the challenges posed by the complexity of transportation systems and the lack of data and algorithmic transparency.
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导航服务扩大了城市交通和排放的集中度
人机交互生态系统(如助手和推荐器)的激增引起了人们对大规模社会行为的担忧。尽管有证据表明在多种情况下存在这种现象,但 GPS 导航服务的集体影响仍不明确:虽然对用户有利,但如果太多车辆驶过同几条道路,也会造成混乱。我们的研究采用了一个模拟框架来评估导航服务对道路网络使用和二氧化碳排放的影响。研究结果表明了一种普遍的 "一致性放大 "模式:导航服务的采用率不断提高,导致移动旅行者的路线多样性减少,交通量和排放量集中在更少的道路上,从而加剧了选定街区的负外部性的不平等分布。虽然导航服务建议在采用率较低时有助于减少二氧化碳排放,但当采用率较高并超过一定的城市和服务阈值时,这些益处就会减少甚至消失。我们将这些发现总结为一个非线性函数,该函数将一致性的边际增加与二氧化碳排放量的边际减少联系起来。我们的模拟方法解决了交通系统的复杂性以及缺乏数据和算法透明度所带来的挑战。
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