Research On Commuter Passenger Flow Identification Technology Based On Subway Passenger Flow Card Data

With the acceleration of urbanization in China,the demand for urban transportation has increased dramatically.Urban rail transit has been favored by many large and medium-sized cities because of its characteristics of speed,safety,convenience,environmental protection and large volume of transportation.Urban rail transit has also entered rapid development.During the period,many cities realized the network operation of the subway.The scale effect of the network has greatly increased the attraction capacity of the subway to the passenger flow,resulting in a large passenger flow effect,which brings great challenges to the operation and management of the subway station,among which the commuter passenger flow has the greatest impact.Commuter passenger flow is an important part of subway passenger flow.Accurate identification of commuter passenger flow,mastering the current distribution characteristics and changing rules of passenger flow are of great significance to rail transit planning,daily driving organization and operation management.The automatic fare collection system(AFC)not only improves the convenience of passengers,but also provides a large amount of basic data for urban rail transit planning and operation management.Metro card data has good advantages in terms of statistical granularity,accuracy and coverage,and can be used as an auxiliary data source for rail transit operation management[1].The subway card data includes IC card,mobile payment and other types of data.The emergence of big data analysis technology makes it possible to analyze and utilize the subway card data.Commuter passenger flow is an important part of subway passenger flow,and its influence on subway operation is increasing.It accurately identifies commuter passenger flow,grasps the current distribution characteristics and changing rules of passenger flow,and makes future passenger flow forecast,rail transit planning and daily driving organization.And operations management work,etc.are of great significance.In order to effectively identify the commuter passenger flow,this paper proposes a method for screening and identifying the big data of subway swipe card based on the screening set.The subway card data includes IC card,mobile payment and other types of data.The emergence of big data analysis technology makes it possible to analyze and utilize the subway card data.This paper analyzes the data of each type of credit card,summarizes the characteristics of commuter passenger flow,and proposes a simple and reliable screening rule to screen and identify commuter passenger flow data.Finally,the paper studies the commuter passenger flow based on the credit card data of Hangzhou Metro Line 2,and the results show that the proposed method meets the actual requirements.This research expands the application range of subway card data,and provides a low-cost and high-efficiency analysis method for the study of refined passenger flow characteristics.Take the Hangzhou Metro as an example.According to the data analysis,it is found that:(1)commuting travels account for 36%of the total daily trips on weekdays,and commuting trips account for about 10%of the total sunrise trips on weekends,which is significantly lower than the working days.It is because there are fewer commuters such as going to work or going to school on weekends,which is consistent with experience.(2)On weekdays,commuting trips accounted for more than 80%of total trips during peak hours,and dropped by half on weekends.This indicates that commuting trips occupy a dominant position during peak hours during the working day,and the impact of commuter trips on weekends is greatly reduced.More concentrated on the weekend,the challenge of subway operation management is even greater.