Advances by using Mobile Phone Data in mobility analysis in the Netherlands

被引:0
|
作者
Friso, Klaas [1 ]
Oakil, Abu Toasin [1 ]
机构
[1] DAT Mobil, Deventer, Netherlands
关键词
mobile phone data; transport model; data-driven modeling; big data;
D O I
10.1109/mtits.2019.8883346
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper we will present the progress we made in the past few years by extracting mobility information from Mobile Phone Data (MPD). MPD is collected continuously, 24-hours a day and every day of the year (24/7/365). Using these data (more than 12 billion location-based events monthly in the Netherlands) both regular and irregular traffic patterns can be determined at local, regional and national scales for any time period, and of course the average working day, which is commonly used for transport policy purposes. MPD-data shows reliable information that can be used for monitoring of traffic, improving the quality of origin-destination matrices (OD-matrices) in transport models but also in direct use determining traffic flows. Regarding the improvement of OD-matrices in transport models, we showed in several studies that the distribution, i.e. the structure of the synthetic OD-matrix of transport models can be improved significantly using MPD-data. For example, MPD data perform much better for OD-relations that are difficult to model with the gravity model, where historical patterns and spatial policy differ in a significant way from the general gravity principles between OD pairs. Such an example is the Zoetermeer-The Hague-connection and the AlmereAmsterdam connection in the Netherlands. Recently progress is made in the determination of traffic flows for all roads in the Netherlands directly from MPD-data. Currently, we work on presenting up-to-date traffic flows at national level fully based on MPD in an online platform which can be updated on a regular base (say quarterly or monthly), including daily profiles and per hour of the day. Of course, the traditional transport models still will be necessary to determine growth factors. A transition to data-driven models for the current situation however will become regular practice.
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页数:7
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