Xu Yanyan, González Marta C
Department of Civil and Environmental Engineering, MIT, Cambridge, MA 02139, USA.
Department of Civil and Environmental Engineering, MIT, Cambridge, MA 02139, USA
J R Soc Interface. 2017 Apr;14(129). doi: 10.1098/rsif.2016.1041.
Information technologies today can inform each of us about the route with the shortest time, but they do not contain incentives to manage travellers such that we all get collective benefits in travel times. To that end we need travel demand estimates and target strategies to reduce the traffic volume from the congested roads during peak hours in a feasible way. During large events, the traffic inconveniences in large cities are unusually high, yet temporary, and the entire population may be more willing to adopt collective recommendations for collective benefits in traffic. In this paper, we integrate, for the first time, big data resources to estimate the impact of events on traffic and propose target strategies for collective good at the urban scale. In the context of the Olympic Games in Rio de Janeiro, we first predict the expected increase in traffic. To that end, we integrate data from mobile phones, Airbnb, Waze and transit information, with game schedules and expected attendance in each venue. Next, we evaluate different route choice scenarios for drivers during the peak hours. Finally, we gather information on the trips that contribute the most to the global congestion which could be redirected from vehicles to transit. Interestingly, we show that (i) following new route alternatives during the event with individual shortest times can save more collective travel time than keeping the routine routes used before the event, uncovering the positive value of information technologies during events; (ii) with only a small proportion of people selected from specific areas switching from driving to public transport, the collective travel time can be reduced to a great extent. Results are presented online for evaluation by the public and policymakers (www.flows-rio2016.com (last accessed 3 September 2017)).
如今的信息技术能为我们每个人提供耗时最短的路线信息,但却没有激励措施来引导出行者,以使我们在出行时间上获得集体利益。为此,我们需要出行需求预估和目标策略,以可行的方式减少高峰时段拥堵道路上的交通流量。在大型活动期间,大城市的交通不便会异常严重,但只是临时性的,而且全体民众可能更愿意为了交通方面的集体利益而采纳集体建议。在本文中,我们首次整合大数据资源来评估活动对交通的影响,并提出城市层面实现集体利益的目标策略。以里约热内卢奥运会为例,我们首先预测交通流量的预期增长。为此,我们整合了来自手机、爱彼迎(Airbnb)、瓦兹(Waze)和公交信息的数据,以及比赛日程和每个场馆的预期上座人数。接下来,我们评估了高峰时段司机的不同路线选择方案。最后,我们收集了对全球拥堵贡献最大的出行信息,这些出行可以从机动车转移至公共交通。有趣的是,我们发现:(i)在活动期间选择个人用时最短的新路线方案比沿用活动前的常规路线能节省更多的集体出行时间,这揭示了活动期间信息技术的积极价值;(ii)仅让特定区域的一小部分人从驾车改为乘坐公共交通,就能大幅减少集体出行时间。研究结果已在网上公布,供公众和政策制定者评估(www.flows-rio2016.com(最后访问时间为2017年9月3日))。