解决按需移动的“最低停车”问题
Addressing the "minimum parking" problem for on-demand mobility
摘要：
停车基础设施无处不在，占据了城市的大片土地。然而，按需（OD）移动性-例如商业服务Uber，Grab或Didi -已经开始减少全球城市地区的停车需求。随着自动驾驶的出现，这一趋势预计将显著增长，这可能使按需移动性占主导地位。最近的研究已经开始关注按需机动性的预期停车减少，但仍然缺乏系统的框架。在本文中，我们应用基于共享网络的数据驱动的方法来解决我们所谓的“最小停车”问题：在给定的按需移动需求的城市中，需要的最小停车基础设施是什么？在解决这个问题的同时，我们还确定了两个公共政策目标之间的关键权衡：减少停车意味着增加车辆行驶从空驶之间的行程。通过将我们的方法应用于新加坡市，我们发现停车基础设施减少高达86%是可能的，但以车辆行驶公里数（VKT）为代价的交通量增加了24%。然而，更温和的57%的停车减少是可以实现的，只有1.3%的VKT增加。我们发现，停车和交通之间的权衡服从一个逆指数规律，这是不变的车队的规模，导致一个简单的方法来估计总的停车需求在一个城市。最后，我们分析了停车场的要求，由于乘客接送，并表明，增加便利性产生了大量增加停车场的乘客接送。上述数学发现可以告知政策制定者，移动运营商和整个社会在向普遍按需移动性过渡时所需的权衡。
英文摘要：
Parking infrastructure is pervasive and occupies large swaths of land in cities. However, on-demand (OD) mobility -- such as commercial services Uber, Grab or Didi -- has started reducing parking needs in urban areas around the world. This trend is expected to grow significantly with the advent of autonomous driving, which might render on-demand mobility predominant. Recent studies have started looking at expected parking reductions with on-demand mobility, but a systematic framework is still lacking. In this paper, we apply a data-driven methodology based on shareability networks to address what we call the "minimum parking" problem: what is the minimum parking infrastructure needed in a city for given on-demand mobility needs? While solving the problem, we also identify a critical tradeoff between two public policy goals: less parking means increased vehicle travel from deadheading between trips. By applying our methodology to the city of Singapore we discover that parking infrastructure reduction of up to 86% is possible, but at the expense of a 24% increase in traffic measured as vehicle kilometers travelled (VKT). However, a more modest 57% reduction in parking is achievable with only a 1.3% increase in VKT. We find that the tradeoff between parking and traffic obeys an inverse exponential law which is invariant with the size of the vehicle fleet, leading to a simple methodology to estimate aggregate parking demand in a city. Finally, we analyze parking requirements due to passenger pick-ups and show that increasing convenience produces a substantial increase in parking for passenger pickup/dropoff. The above mathematical findings can inform policy-makers, mobility operators, and society at large on the tradeoffs required in the transition towards pervasive on-demand mobility.
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