Transportation melting pot Dhaka: road-link based traffic volume estimation from sparse CDR data
Transportation melting pot Dhaka: road-link based traffic volume estimation from sparse CDR data
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DOI：
10.4108/icst.urb-iot.2014.257272
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发表时间：
2014-10
期刊：
影响因子：
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通讯作者：
Y. Hasegawa;Y. Sekimoto;Takehiro Kashiyama;H. Kanasugi
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文献类型：
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作者：
Y. Hasegawa;Y. Sekimoto;Takehiro Kashiyama;H. Kanasugi
了解城市地区的交通状况是一个重要的研究方向，特别是在快速发展的城市，仍然与拥堵和低效的交通控制策略作斗争。本研究的目的是估计道路连接规模的交通条件的大都市地区的达卡，各种交通需求得到混合起来，如果它是一个“熔炉”，稀疏的移动的电话数据和道路网络的基础上。此处使用的移动的电话数据是呼叫详细信息记录（CDR）。我们的方法提取的起源和目的地（OD）从CDR两种方式。一个是一个简单的提取连续记录与基站的差异，另一个是通过CDR聚类和行程分割的重要位置之间的行程提取。全日路段交通量的估计，然后分配每小时的行程与每个OD的实际道路网络上的路线。该方法使用来自达卡的685万用户的1个月CDR来证明，平均每天只有5.8个日志。我们的估计结果显示了一个相对较强的相关性（r=0.75）与实际的交通计数在道路连接规模。此外，估计结果与基于Person Trip调查数据的估计具有接近的准确性的事实表明，基于长期移动的电话数据的交通状况理解是大规模交通调查的有效方法。
Understanding traffic conditions in urban areas is an important research direction, especially in rapidly growing cities that still struggle with congestion and inefficient traffic control strategies. The purpose of this study is to estimate the road-link scale traffic conditions of the metropolitan area of Dhaka, where a variety of transportation demands get mixed up as if it were a 'melting pot', based on sparse mobile phone data and road networks. The mobile phone data used here is the Call Detail Records (CDR). Our method extracted Origin and Destination (OD) from CDR in two ways. One is a simple extraction of continuous records with base station differences, and another is an extraction of trips between significant locations through CDR clustering and trip segmentation. Full-day link traffic volume is then estimated by assigning hourly trips with each OD to routes on actual road network. The methodology is demonstrated using 1 month CDR from 6.85 million users of Dhaka, with only 5.8 logs per day in average. Our estimation results show a relatively strong correlation (r=0.75) with the actual traffic count in a road-link scale. Moreover, the fact that the estimation results have close accuracy with the Person Trip survey data based estimation suggests that traffic conditions understanding based on long-term mobile phone data is a valid method for large-scale traffic survey.