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Mobile spatial dynamics generated from operational data of mobile networks indicate population flows nationwide on a 24/7 basis. The data have stable statistical reliability and are highly consistent based on existing surveys such as person trip surveys. However, there is no established method for estimating the purpose of a trip and the means of transportation. This study proposes a method for estimating the purpose of a trip based on the purpose obtained from person trip surveys. The proposed method estimates the purpose using a large amount of operational data from mobile networks and thus reduces the cost of collecting questionnaires for urban transportation surveys.

Diverse and massive road traffic data, such as about traffic congestion, disasters, traffic restrictions, travel history and traffic volume, are expressed by different location referencing methods including latitude & longitude, DRM links, milepost, sight frontage names, and addresses. Each of these location referencing methods is suitable for the respective use. In treating some different types of road traffic data under the same environment, however, e.g. in superimposing road traffic data created by different location referencing methods on a map or spatial processing requires conversion of their location referencing methods, which is being an obstructive factor to smooth distribution of road traffic data. In this research, we developed a geocoding algorithm that enables interconversion of location referencing methods including latitude & longitude, digital road map (DRM), VICS, the Road Section Identification Data set (RSIDs), Reference Road Sections, and milepost. And we verified the usefulness by mutually converting actual road traffic data using the system that implements the algorithm.

The possibility of traffic demand management using car probe data has led the authors to develop a method for understanding total traffic flow. If the total traffic flow can be monitored, dynamic fee measures, such as adjusting expressway fees, are possible, which will greatly contribute to the smart use of roads. Prior research has resulted in the development of the wide-area road traffic monitoring method by understanding the total amount of road traffic flow using car probe data and mobile spatial dynamics generated based on the operational data of the mobile network. In this research, we attempted to generate car origin-destination by mobile spatial dynamics to understand the total amount of traffic flow utilizing car probe data. From the trial results, it was found that an origin-destination distribution with a certain degree of validity was obtained.

This paper presents a simple method for using the separation distance (offset) between a smartphone GPS and the center line on a digital road map to determine the lane position of a car. The method was verified.