Design of intelligent acquisition system for moving object trajectory data under cloud computing

3.1.1 Data layer

Taxi location information is automatically collected by the GPS system equipped with the taxi. The original taxi location data are filtered, classified, and integrated and stored in the data center. Basic road traffic data provide all road information, land use information, traffic district information, and traffic facilities information [22]. Through time-space analysis of basic road traffic data, it is helpful to model the taxi movement pattern. Other data sources include passenger survey results, taxi survey results, etc. Based on this information, mining algorithms can be evaluated and related parameters can be adjusted.

3.1.2 Excavation layer

The mining layer mainly includes parallel data mining algorithms based on Map Reduce. This layer uses spatiotemporal data mining algorithms to mine the trajectory data and transform the original data into useful knowledge. Different types of analysis models can be provided according to different needs to extract, process, and analyze data. First, the raw data collected from mobile sensors are preprocessed and used in the mining stage. Some data received from taxis contain noise and data loss due to GPS unit or transmission problems. Data preprocessing must identify and remove these data to ensure the quality of the data set [23]. On the other hand, traffic forecasting models in different regions are different, and working days and rest days may have different patterns [24,25,26,27,28]. In order to divide the data into different data sets, it is also necessary to classify the data. Next, data mining, association analysis, cluster analysis, and data classification are needed to create different analysis modes to accurately describe the trajectory pattern of taxis.

3.1.3 Application layer

The application layer mainly uses the knowledge obtained by data mining to provide services, including direct access to data interfaces and end-user-oriented application services. Taxi trajectory data are widely used. Passenger waiting time model and taxi empty rate model are important indicators to evaluate taxi service quality, service capability, and passenger satisfaction. From the perspective of a taxi driver, how much he hopes to be able to provide him with a suggestion that his taxi will never be empty [29]. From the perspective of a taxi passenger, he hopes that when he wants to take a taxi, the system can efficiently and timely provide him with an empty taxi to his location. Considering the efficiency and profitability of taxi operations, taxi operators need to learn the relationship between taxi demand and supply and balance the benefits of these two stakeholder groups. The trajectory mining system can provide them with this help. Efficient taxi management is an important means to provide passengers with quality services and achieve corporate profit growth. The mining system can provide analysis results of various types of taxi business, which helps managers make correct decisions [29].

3.2.1 Algorithm preprocessing

When processing massive taxi trajectory data, the entire data area is the map of Shenzhen. This area is linear and can be divided into equal areas. However, after statistics on part of the original data, it is found that a large number of taxi pick-up points are concentrated in some areas, and most of the areas are sparsely distributed or even nonexistent. Therefore, this article uses a density-based partitioning method to ensure that too much The multi-passenger point data are distributed in a region, which prevents the data from consuming too much performance or even node operation errors when they are distributed to a data node for processing, which affects the results of the entire clustering process and the stability of system operation. In order to facilitate data processing, when the data are specifically partitioned, the data are partitioned into a rectangular area, as shown in Figure 2. Through the analysis of the characteristics of the taxi trajectory data, the daily distribution law is similar, and there will not be much change. The specific results refer to the experimental chapter. Therefore, when dealing with massive taxi trajectory data, first analyze and process one day’s data to get a rough partition, and then partition all data. After the data are partitioned, it can provide a basis for the parallel processing of the entire algorithm, and the task of processing massive data can be assigned to a large-scale computer cluster for separate processing. After the data are partitioned, they provide a basis for the task allocation of the cloud computing platform Hadoop, which can allocate small data areas to each data node for processing.

Figure 2

Schematic diagram of data partition.

3.2.2 Parallel implementation of the algorithm

The realization of parallel algorithm based on Map Reduce is mainly based on the algorithm idea of parallelization to write Map function and reduce function. The Map function takes the form of key-value pairs as input parameters. This article uses the partition number of the passenger point data as the key value, and all data objects in the partition as the data value. The following is the pseudo-code implementation of the Map function (Table 1).

The Reduce function merges the obtained local clustering results. The input data are also key-value pairs. The content in Table 2 is the pseudo-code implementation of the Reduce function.

4.1.1 Data introduction

The data source used in this article is the official data from the D problem in the 2020 National College Student Mathematical Modeling Summer Camp. The data collected the trajectories of 15,000 taxis in Shenzhen from April 18, 2020 to April 26, 2011.

4.1.2 Data preprocessing

Although the data set used in this article is relatively standard, the amount of original data is still relatively large and needs to be filtered accordingly. Attribute filtering is to filter out some attributes that are not used in the data mining process. The taxi pick-up point is the moment when the taxi status changes from 0 to 1, so attributes that are not related to this can be filtered out, such as vehicle speed and vehicle driving direction [31]. In addition to some data that are useless for data mining, there are still some error messages in the original data set. Because taxis shuttle in the city, GPS signals are more susceptible to the influence of surrounding buildings, which will produce some incorrect data. In addition, when passing through some areas such as tunnels, there will be a short interruption of signals, taxis are always in motion, and it is normal for GPS equipment to have accuracy differences, which may lead to different forms of noise data in the data set [32].

4.3.1 Analysis of passenger loading points

The taxi’s driving is restricted by time and space conditions, showing the regularity of changing with time. After preprocessing the data and extracting the passenger load points, the load points from the 18th to the 25th are counted. As shown in Figure 4. In Figure 4, the 18th to the 22nd are Monday to Friday, which are normal working days, and the 23rd and 24th are Saturdays and Sundays, which are the rest days. Figure 5 selects the 19th and 20th, 23rd and 24th to represent working days and rest days, respectively, to make statistics on the passenger load in each time period.

Figure 4

Passenger statistics from 18th to 25th.

Figure 5

Passenger load statistics for each time period in the 4 days.

It can be seen from the passenger load statistics for each time period in the four days in Figure 4 that the areas with the smallest number of passengers in the four days are distributed between 0:00 and 6:00. During this period, most residents are resting at night. From the trend of the four curves, it can be seen that the three peak hours are 7:00–12:00, 12:00–16:00, and 18:00–22:00, which are in the three periods with the largest crowd flow, such as commuting to get off work, school, and so on. It can also be seen from the figure that the 19th and 20th working days are almost the same as the 23rd and 24th rest days. The number of passengers on working days is generally higher than that on rest days of the low valley and peak area.

Through the analysis of the taxi passenger situation, according to the travel pattern of the crowd, the time of day is divided into different characteristic time periods, specifically 0:00–7:00, 7:00–12:00, and 12:00–16. In the five time periods of 00, 16:00–20:00, and 20:00–24:00, the next hotspot clustering of taxi passengers will be carried out according to the above characteristic time periods.

4.3.2 Clustering parameters

For DBSCAN algorithm, the difference of Eps and Min Pts parameter settings will cause the difference of clustering results. The judgment of the core object is based on Eps and Min Pts. Two parameters determine whether the current object is a core object or a noise object and determine the final clustering result. Offline processing mainly explores the hotspot area of taxi passengers, that is, within the radius of Eps, the number of taxi passengers exceeds Min Pts Area. After experimenting with one day’s data, this paper defines the taxi passenger hotspot area in a circular area with a radius of 400M, and the area where the number of taxi passenger points exceeds 12.

4.3.3 Clustering results

Based on Hadoop’s DBSCAN algorithm, with parameters Eps = 400, Min Pts = 12, cluster analysis of daily data is performed. Figure 5 shows the number of passenger hotspots from the 18th to the 25th. It can be seen from the Figure 6 that the daily passenger load points are between 200 and 250. Therefore, in the absence of holidays, the number of passenger hotspots tends to be stable. It is reliable to perform cluster analysis.

Figure 6

Statistics of passenger hot spots from 18th to 25th.