Building a validation measure for activity-based transportation models based on mobile phone data
Building a validation measure for activity-based transportation models based on mobile phone data
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基于手机数据构建基于活动的交通模型的验证措施
DOI：
10.1016/j.eswa.2014.03.054
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发表时间：
2014-10
影响因子：
8.5
通讯作者：
Feng Liu, Davy Janssens, JianXun Cui, YunPeng Wan
中科院分区：
文献类型：
--
作者：
Feng Liu, Davy Janssens, JianXun Cui, YunPeng Wan
基于活动的微观模拟交通模型通常预测24小时的活动，旅行序列为每个人在研究区域。这些序列是该区域旅行需求分析和预测的关键投入。然而，尽管它们的重要性，缺乏一个可靠的基准来评估所产生的序列，阻碍了进一步的发展和应用的模型。随着移动的手机设备的广泛部署，今天，我们探索使用来自移动的手机数据的旅行行为信息来建立这样的验证措施的可能性。首先，从移动的电话记录构建用户执行活动的位置的日常轨迹。为了解决呼叫数据所揭示的停靠点与用户所做的真实的位置轨迹之间的差异，然后将日常轨迹转换为实际的行程序列。最后，所有的衍生序列被归类为典型的活动旅行模式，结合它们的相对频率，定义一个活动旅行配置文件。所建立的配置文件的特点，目前在研究区域的活动出行行为，因此可以作为一个基准的评估活动为基础的交通models.By比较的活动出行配置文件来自呼叫数据与统计，从传统的活动出行调查，验证潜力被证明。此外，还进行了敏感性分析，以评估分析过程中定义的不同参数设置对结果的影响。
Activity-based micro-simulation transportation models typically predict 24-h activity-travel sequences for each individual in a study area. These sequences serve as a key input for travel demand analysis and forecasting in the region. However, despite their importance, the lack of a reliable benchmark to evaluate the generated sequences has hampered further development and application of the models. With the wide deployment of mobile phone devices today, we explore the possibility of using the travel behavioral information derived from mobile phone data to build such a validation measure.Our investigation consists of three steps. First, the daily trajectory of locations, where a user performed activities, is constructed from the mobile phone records. To account for the discrepancy between the stops revealed by the call data and the real location traces that the user has made, the daily trajectories are then transformed into actual travel sequences. Finally, all the derived sequences are classified into typical activity-travel patterns which, in combination with their relative frequencies, define an activity-travel profile. The established profile characterizes the current activity-travel behavior in the study area, and can thus be used as a benchmark for the assessment of the activity-based transportation models.By comparing the activity-travel profiles derived from the call data with statistics that stem from traditional activity-travel surveys, the validation potential is demonstrated. In addition, a sensitivity analysis is carried out to assess how the results are affected by the different parameter settings defined in the profiling process.
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DOI：
10.1007/s13218-012-0183-6
发表时间：
2012-03
期刊：
KI - Künstliche Intelligenz
影响因子：
--
作者：
D. Janssens;F. Giannotti;M. Nanni;D. Pedreschi;S. Rinzivillo
通讯作者：
D. Janssens;F. Giannotti;M. Nanni;D. Pedreschi;S. Rinzivillo
影响因子：
1.7
作者：
M. Cools;E. Moons;G. Wets
通讯作者：
M. Cools;E. Moons;G. Wets
影响因子：
1.7
作者：
C. Joh;T. Arentze;H. Timmermans
通讯作者：
C. Joh;T. Arentze;H. Timmermans
影响因子：
9.8
作者：
G. Rose
通讯作者：
G. Rose
影响因子：
6.4
作者：
W. Davidson;R. Donnelly;P. Vovsha;Joel Freedman;Steve Ruegg;J. Hicks;Joe Castiglione;R. Picado
通讯作者：
W. Davidson;R. Donnelly;P. Vovsha;Joel Freedman;Steve Ruegg;J. Hicks;Joe Castiglione;R. Picado