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基于手机信令数据的小城市居民出行时空特征研究 ——以铜仁市为例
赵鹏军1, 刘文洲2, 余 玲3, 王 浩4, 赵东一5, 张浩然6, 李 玲6
1.( 通讯作者):北京大学深圳研究生院城市 规划与设计学院,北京大学城市与环境学 院,教授,博士生导师,pengjun.zhao@ pku.edu.cn;2.北京大学深圳研究生院城市规划与设计学 院, 硕士研究生;3.北京大学深圳研究生院城市规划与设计 学院,副研究员;4.北京大学深圳研究生院城市规划与设计学 院,博士研究生;5.北京大学深圳研究生院城市规划与设计学 院, 硕士研究生,助理研究员;6.北京大学深圳研究生院城市规划与设计学 院,助理教授,博士生导师
摘要:
居民出行特征与交通设施建设、居民 生活质量、疫情防控等密切相关,是城市规划领 域研究的重要主题之一。当前居民出行特征研究 主要集中于特大城市,对中小城市的研究相对较 少。本文以贵州省铜仁市为例,基于2019年5月、 2019年11月、2020年5月三个时段,61 543 902条铜仁市常住人口出行OD 手机信令数据,对小城市居民出行的规模、时耗、距离、分布空间与网 络特征进行研究。结果发现:一、小城市出行规模在一天中有3-4个高峰,大城市则仅有2个高峰, 小城市高峰出行量占比在冬季更高;2020年初新冠疫情后,铜仁市人口密度较大的区县内部出行 比例上升。二、铜仁市的区县内部每次出行平均时耗与人口规模之间有较强的正相关性。三、铜 仁市每次出行距离分布在群体层面上服从幂律分布;且冬季的区县内长距离出行的次数衰减较 快。四、小城市居民出行空间分布呈现明显的向心性,大城市向心性相对较弱。五、铜仁市城镇化 率较高地区在出行网络中的重要程度在冬季往往上升。研究结论进一步认识了小城市居民出行特 征,为交通规划和交通政策制定提供了参考。
关键词:  出行行为  小城市  手机信令数据  复杂网络  铜仁市
DOI:10.13791/j.cnki.hsfwest.20240322
分类号:
基金项目:国家自然科学基金项目(419250 03);深圳市 科技计划资助项目(JCY20220818100810024、 KQTD20221101093604016)
Spatiotemporal characteristics of residents’ travel behaviors in small cities based on mobilesignaling data: A case study of Tongren City, China
ZHAO Pengjun,LIU Wenzhou,YU Ling,WANG Hao,ZHAO Dongyi,ZHANG Haoran,LI Ling
Abstract:
The characteristics of residents’ travel behaviors, one of important issues in urban planning, are closely related to the construction of transport infrastructure, the quality of residents’ life, and the prevention and control of pandemic. Previous studies on residents’ travel behaviors mainly focused on megacities, with few studies having been conducted on small and mediumsized cities. This paper studies the travel scale, travel time, travel distance, and spatial network characteristics of residents’ travel behaviors on the basis of 61 543 902 mobile phone signaling data of the resident population for three time periods in May 2019, November 2019, and May 2020 in Tongren City, China. The findings regarding scale characteristics are as follows: Firstly, unlike big cities that typically experience morning and evening travel peaks, Tongren City exhibits three peaks in travel volume throughout the day. Comparing the data from May 2019 and November 2019, it was observed that the proportion of peak travel volume was higher in winter. Secondly, there is a strong positive correlation between the number of trips and the population size of the districts and counties. Thirdly, by comparing the data from May 2019 and May 2020, it was found that after the outbreak of the COVID-19 pandemic in early 2020, the proportion of trips within districts and counties with higher population densities increased. Fourthly, most districts and counties exhibit a higher number of trips in 1-2 towns/subdistricts, while the rest show a sharp decline in travel volume. The findings regarding on travel time are as follows: (1) The travel time exhibits three peaks throughout the day, but these peaks do not coincide with the peaks in travel volume during the same time periods. (2) There is a strong positive correlation between the average travel time per trip within districts and counties and the population size. The findings regarding on distance characteristics are as follows: (1) The distribution of travel distances in Tongren City and within each district and county follows a power-law distribution at the group level. (2) The number of long-distance trips decays faster within districts and counties in winter. The findings regarding the spatial distribution and network characteristics of travel are as follows:(1) The highest travel volume is observed between the main urban area and other districts and counties. (2) The spatial distribution of trips within the city is primarily influenced by natural geographic factors. (3) The significance of the more urbanized areas of Tongren in the travel network tends to increase in winter. By integrating previous studies on travel characteristics in small cities with the findings of this study, the distinct travel characteristics of small cities compared to big cities can be summarized as follows: (1) In small cities, there are 3-4 peaks in travel volume throughout the day, whereas big cities typically experience only 2 peaks. This discrepancy can be attributed to the shorter commuting distances in small cities, which often prompt individuals to return home for lunch. Conversely, in big cities, commuting distances tend to be longer, resulting in the majority of commuters traveling during the morning and evening. (2) The spatial distribution of travel by residents in small cities exhibits a clear centripetal pattern, whereas the centripetal pattern in big cities is relatively weaker. This difference can be explained by the fact that small cities typically have a single employment and consumption center, while big cities feature a more dispersed distribution of multiple employment and consumption centers. Utilizing big data technology to comprehend residents’ travel characteristics can significantly enhance the scientific optimization of transportation network systems in spatial planning. Tailored planning strategies should be devised considering diverse factors such as population sizes, population densities, and levels of urbanization. Firstly, prioritizing the enhancement of transportation infrastructure construction in regions with higher urbanization levels can effectively cater to the travel needs of residents. Secondly, adopting dynamic and flexible transportation policies that account for seasonal variations in travel patterns, such as augmenting public transportation frequency during peak hour traffic in winter, can mitigate the risk of traffic congestion. Moreover, it is imperative to acknowledge the alterations in travel behavior caused by the pandemic and fortify the resilience of regional transportation systems. This study does have certain limitations. Firstly, the travel distance derived from mobile signaling data represents the Euclidean distance between the origin and destination points. To minimize errors when aggregating travel distances, it adopted a 1 km threshold as the basic unit. For future research, it is recommended to explore fitting the origin and destination points to the road network and obtaining the shortest path as the travel distance. Secondly, mobile signaling data relies on base station signals for positioning, which inevitably introduces spatial errors. To address this in future studies, it is advisable to consider data with higher location accuracy, such as GPS data, for research purposes.
Key words:  travel behavior  small cities  mobile signaling data  complex network  Tongren City