Urban Travel Time Estimation in Greater Maputo Using Mobile Phone Big Data
Urban Travel Time Estimation in Greater Maputo Using Mobile Phone Big Data
复制标题
使用手机大数据估算大马普托的城市旅行时间
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
R. Shibasaki
中科院分区：
文献类型：
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作者：
M. Batran;Ayumi Arai;H. Kanasugi;S. Cumbane;Cecilio Grachane;Y. Sekimoto;R. Shibasaki
城市人口的快速增长可能会超过所需城市基础设施（如与交通相关的基础设施）的发展速度，从而导致公共交通服务不足和交通拥堵。出行时间是描述交通效率的关键因素，一直是研究和控制的重要要素。在本文中，我们利用数百万条此类记录来估算莫桑比克马普托大区的城市出行时间。为了验证我们的结果，我们将估算的出行时间与600名用户的GPS轨迹计算出的出行时间进行了比较。此外，我们展示了大量的手机记录如何使我们能够区分三个不同时间类别（工作日高峰时段、工作日非高峰时段和周末）的出行时间。与移动数据不同，GPS点数量较少，这使我们只能估算平均出行时间，而无法区分不同的时间类别。然而，我们发现从通话详单记录估算的平均出行时间与GPS数据集之间存在87%的线性相关性。这在一定程度上支持了移动数据在监测交通时间以及评估新的交通基础设施对城市出行时间的影响方面的潜力。
The rapid growth of urban populations may outpace the development of needed urban infrastructure, such as related to transportation, therefore, resulting to inadequacy of public transportation services and traffic congestion. Travel time is a key component to describe traffic efficiency and has always been an important element to study and control. In this paper, we leverage millions of these records in order to estimate urban travel time in Greater Maputo, Mozambique. To validate our results, we compared the estimated travel time with that computed from GPS trajectories for 600 users. Furthermore, we show how the large number of phone records allow us to segregate travel times of three different time categories, weekday rush hour, weekday non-rush hour, and weekends. Unlike mobile data, the low number of GPS points enabled us to only estimate average travel time without distinguishing different time categories. However, we found 87% linear correlation between average travel time estimated from call details records and GPS datasets. This, to some extent, support the potential of mobile data to monitor traffic time and assess the impact of new transportation infrastructure on urban travel time.