移动轨迹数据支持下的城市居民通勤活动分析

Citizen Commuting Analysis Using Mobile Trajectory Data

城市居民通勤活动分析是解决城市职住失衡和交通拥堵问题的重要技术支持。移动轨迹大数据增加了城市居民通勤活动观测的数据类型、数据规模、采样频率和信息量,有助于揭示就业居住空间模式和通勤活动的内在规律。概述了多学科领域城市居民通勤活动研究及相关移动轨迹数据分析的进展,明晰了通勤活动分析的概念、指标、模型和方法。从城市居民通勤活动的行为特征、空间格局和受职住空间影响这3个方面,给出了移动轨迹数据支持下的居民通勤活动分析技术架构,指出城市居民通勤活动分析技术正朝着城市规划、交通预测和移动轨迹大数据的多学科交叉理论方向发展。

Objectives: Citizen commuting analysis is useful for solving jobs-housing imbalance and traffic congestion. Mobile trajectory data, strengthening the observation of citizen commuting in data types, data volume, sampling frequency, and information content, is helpful for exploring jobs-housing patterns and citizen commuting distribution.Methods: We have investigated the research progress of citizen commuting and mobile trajectory analysis, and have worked out relevant concepts, indexes, metrics, models and theoretical methods of commuting activity analysis. The representative results of spatial interaction model,transportation planning model, linear programming model, and micro-physical model are compared, and the characteristics of their application in the analysis of citizen commuting activities are discussed from a multidisciplinary perspective. Results: Four types of trajectory data characteristics of citizen commuting activities are summarized. From the perspectives of citizen commuting behaviors pattern and its jobs-housing restrictions, we have proposed the architecture of exploring the relationships between citizen commuting and jobs-housing from mobile trajectory data. Conclusions: The technology of multidisciplinary development of citizen commuting analysis is addressed, and the comprehensive method of analyzing trajectory big data, multi-source data fusion, and commuting activity prediction are the focus of future research.