| Urban transportation faces issues related to resource waste,poor public transit planning,and inefficiency during the urbanization process,which causes an imbalance between inhabitants’ rising travel needs and the city’s current transportation infrastructure.For urban dwellers,the quality of travel is diminished by the mismatch between supply and demand.The daily work and existence of urban people are affected by this issue.By examining the features of residents’ travel,identifying hotspots,and examining residents’ mobility in central Chongqing,it is possible to increase the efficiency of urban systems and the quality of life of urban residents.This paper uses the Python big data processing method,GIS technology,and spectral clustering to analyze the spatial and temporal semantic characteristics of residents’ travel,identify the hot spots of residents’ travel,and explore the mobility of residents’ travel based on the taxi trajectory data in the central urban area of Chongqing in January 2019.The following are the primary conclusions:(1)Analyze the spatiotemporal semantic features of resident travel by processing taxi trajectory data in Chongqing’s central metropolitan region.(1)The average daily travel volume of residents in Chongqing’s central urban region is 503600,515700 during holidays,512700 during downtime,and 482340 during working hours;There is a gradual increase trend of variations on weekdays from Monday through Friday,with larger traffic volume on rest days,with the highest on Saturday,and a marked decline in traffic volume on weekdays after holidays;From 22:00 to 4am the next day,resident travel volume progressively declines and reaches its lowest point,then from 4:00 to 9:00,demand for travel gradually rises.The resident travel volume has a cyclical pattern,peaking between 22:00 and 23:00,and troughing at 4:00 to 15:00.(2)Between Banan District,Yubei District,and Beibei District,there isn’t much of a difference in general changes.Banan District and Beibei District primarily rely on internal travel on rest days,while other areas exhibit the highest proportion of external travel.During the three periods,Yubei District residents’ internal travel volume was the highest and was far ahead of that of residents in other regions.Residents of Jiangbei District,Yubei District,Jiulongpo District,and Nan’an District have close travel connections and complementary effects.Jiangbei District experiences the most inter-regional flow during vacations,with residents moving in and out of Yubei District,Nan’an District,and Yuzhong District.(3)In the central urban region of Chongqing,the overall performance of resident travel is outflow,inflow,and balance.Among the 10 semantic groups,Yubei District is where most citizens commute on workdays,rest days,and holidays.(2)Based on spectral clustering,cluster analysis is carried out on the hot travel areas of residents to identify the hot travel areas of residents on working days,rest days and holidays.(1)In the center metropolitan region of Chongqing,there are four forms of aggregation: multipolar nuclear clusters,radiative diffusion types,banded aggregation types,and unipolar aggregation types.(2)With population gathering areas and commercial centers like large-scale transportation hubs,public transportation transfer hubs,residential communities,and medical institutions as the hot travel areas for residents,Yubei District,Yuzhong District,Shapingba District,Jiangbei District,and Nan’an District are the main distributions for the hot travel areas for residents during the three periods of holidays,working days,and rest days.(3)On holidays,people typically prefer the categories of leisure and entertainment,residential communities,catering,hotels,and hotels for boarding the bus while the categories of leisure and entertainment,residential communities,catering,hotels,and tourist attractions are typically preferred for getting off the bus;On weekdays,the categories of company enterprises,residential communities,life services,and catering tend to be the hotspots for residents to board buses,while the categories of company enterprises,medical institutions,residential communities,and catering tend to be the hotspots for residents to disembark;Residents typically prefer leisure and entertainment,business and enterprise,healthcare facilities,residential communities,tourist attractions,life services,and catering categories when riding the bus on rest days,while business and enterprise,catering,shopping,and hotels are typically preferred when getting off the bus.(3)Based on the two perspectives of the district level administrative division scale and the "source sink" micro scale in landscape ecology,as well as the taxi track data of residents on weekdays,rest days,and holidays,the interregional connectivity,flow intensity,flow trend index,and traffic attractiveness index were calculated.(1)Banan District and Dadukou District have a significant flow trend,primarily flowing towards Jiangbei District and Yubei District,while residents in other areas have a highly balanced flow trend.1The connection and flow intensity between different regions remain largely stable during working days,rest days,and holidays,with the most frequent communication occurring during rest days.(2)The areas that generate traffic attractiveness reach their peak during the 21-23 o’clock time period on rest days,and the areas that generate traffic attractiveness are primarily concentrated around the core area of the two rivers and four banks on weekdays,rest days,and holidays,forming a balanced traffic area;The number of high attractiveness areas is at its highest during the morning rush hour on weekdays,and the areas that generate traffic attractiveness reach its peak during this time period on rest days,with scattered spatial distribution.Large transit hubs,tourist destinations,and regions where a significant portion of the population congregates are three examples of areas with concentrated distributions of high attractiveness areas during holidays.In comparison to weekdays and rest days,there is a substantial difference in the spatial distribution of traffic attraction regions. |
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