WO2020186770A1 - Visual method for analyzing taxi pick-up or drop-off features - Google Patents
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- the invention relates to a visualized method for analyzing the characteristics of taxi getting on and off.
- the research on the hot spots and travel purpose of taxis is the key to taxi scheduling and route optimization.
- the traditional visual display of taxi travel mode is mainly heat map or cold hotspot drawing research.
- the drawing of the taxi boarding and boarding heat map mainly depends on the two parameters of the kernel size and the search radius.
- Each pick-up point is regarded as the center of the two-dimensional Gaussian distribution.
- the kernel size determines the discrete type of the two-dimensional Gaussian distribution
- the search radius determines the area
- the number of the inner pick-up and drop-off points can be obtained by summing these two-dimensional Gaussian distributions to obtain the hotspot or heat map, but the optimal solution of this data method comes from two parameters, and the values of the two parameters are determined by experience .
- taxi pick-up and drop-off points are generally along urban roads, while traditional heat map or cold and hot research and drawing uses each pick-and-drop point as the center of a two-dimensional Gaussian distribution.
- the Gaussian distribution has no direction constraint, which means that In the heat map, there are also getting on and off behavior in areas without road coverage.
- this visual display method of heat map and heat map can only display regional hot spots, and cannot display time information. It is necessary to manually filter the data collection statistical time period before visual display.
- the purpose of the present invention is to provide a visualized taxi boarding and boarding feature analysis method, so that users can effectively grasp the taxi boarding and boarding travel feature information in the road network and avoid the traditional
- the heat map display method needs to manually determine the empirical mode of parameter information, and solves the problem of how to display the time and space information of the road and the car together in the existing technology, so that the traffic control user can clearly grasp the information of the taxi alight and the car on each road problem.
- a visualized taxi boarding and boarding feature analysis method by extracting the boarding and boarding information from the taxi GPS trajectory data, clustering analysis of taxi travel features based on the K-medoids algorithm, and then analyzing the average fluctuation trend curve based on the road section.
- the pattern shape of the extracted and clustered daily commuting travel purpose feature analysis, and finally the taxi travel feature information of each road section is presented in the GIS electronic map, including the following steps:
- step S3 Use k-medoids to perform cluster analysis on the frequency domain values transformed in step S2 to determine the type of taxi travel, including daily commuting travel mode, holiday travel mode, mixed travel mode and data missing mode;
- step S5 Based on the travel characteristics determined in step S3 and the commuter travel purpose of each road segment determined in step S4, mark the characteristic information of the taxi boarding and boarding travel purpose of each road segment in the GIS electronic map.
- step S1 is specifically:
- step S11 the taxi getting on and off data is extracted, specifically,
- the taxi status changes from empty to heavy, indicating that there are passengers
- the status of the taxi carrying passengers from heavy to empty means that the passengers get off, and then the points of getting on and off are extracted from the GPS track data and arranged in time series.
- step S12 the average daily getting on and off status of each road section at each time period is obtained, specifically,
- S121 Determine the road section according to the urban road network information, the distance between adjacent intersections is the road section, and then rely on the longitude and latitude (lng i , lat i ) in the GPS trajectory data to divide the GPS trajectory data into each road section; Time sorts and sorts the time information t i in the GPS trajectory data, and then integrates the number of getting on and off vehicles in each time period of each road segment;
- S122 Solve the average value of taxis getting on and off at each time period of each day, and obtain the average number of getting on and off vehicles at each time of each road section each day, and arrange them in time series.
- the specific Fourier formula is:
- ⁇ represents frequency
- t represents time
- e- i ⁇ t is a complex variable function
- f(t) is the amount of data for getting on and off cars
- F( ⁇ ) is the frequency value of getting on and off cars.
- step S3 the taxi travel mode is determined, including daily commuting travel mode, holiday travel mode, mixed travel mode and data missing mode, specifically,
- This kind of visualized taxi boarding and boarding feature analysis method is compared with the traditional heat map display method. This method does not need to manually set the kernel size and search radius parameter values based on experience, and can avoid the traditional heat map from configuring the kernel through manual experience
- the size and search radius result in the visualization of the area of the pick-up and drop-off point.
- This kind of visualized taxi boarding and boarding feature analysis method uses Fourier transform to convert the time series boarding and boarding data in the GPS trajectory data into frequency values. Spatio-temporal information will not be lost during the conversion, which is more convenient for data clustering analysis.
- the method of the present invention uses the K-medoids center point method to perform cluster analysis on GPS getting on and off trajectory data, and divides the four situations of daily commuting travel mode, holiday travel mode, mixed travel mode and data missing travel mode, and based on getting on and off.
- the trend graph shape (English letters) of the average fluctuation trend of the data divides the characteristic information of the taxi travel purpose, so that the characteristic information of the taxi travel is displayed by superimposing the English letters on the GIS electronic map.
- this kind of visualized taxi boarding and boarding feature analysis method by extracting and integrating GPS trajectory information, analyzing taxi boarding and boarding data, clustering analysis of taxi travel patterns with K-medoids algorithm, and showing the trend graph of the fluctuations of the data on boarding and alighting
- the shape of the English letters analyzes travel characteristics, thereby forming an intuitive and clear innovative visual display of taxi boarding and boarding characteristics.
- This kind of visualized taxi boarding and boarding feature analysis method is compared with the spatial clustering analysis of traditional heat map, through k-medoids center point clustering analysis of the boarding and boarding data on each road section and the average fluctuation trend graph of the boarding and boarding data Draw, find the information of taxi boarding and boarding travel patterns and travel characteristics, so as to facilitate the traffic planning managers to perform taxi scheduling and route optimization.
- FIG. 1 is a schematic flowchart of a visualized method for analyzing the characteristics of getting on and off a taxi according to an embodiment of the present invention.
- Fig. 2 is a schematic diagram of four types of entertainment travel (U-type), work travel (M-type), dining travel (W-type), and mixed-type (VVV-type) in the situation of getting on a taxi in the embodiment.
- Fig. 3 is a schematic diagram of the distribution of taxi boarding points in a certain city as a specific example in the embodiment.
- Fig. 4 is a histogram from time domain to frequency domain for a certain road section in a certain city on a certain two days in an embodiment.
- Figure 5 shows the four modes of daily commuting travel mode, holiday travel mode, mixed travel mode and data missing mode after clustering analysis on the amount of data on the bus in each day and each time period of a certain road section statistical period (quarter) in the embodiment. Illustration of the situation.
- Fig. 6 is a schematic diagram of a curve drawn in the daily commuting travel mode in the embodiment.
- Fig. 7 is a schematic diagram illustrating an embodiment of the recreational travel (U-type), work travel (M-type), dining travel (W-type), and hybrid (VVV-type) types superimposed on the map in the embodiment.
- a visualized taxi boarding and boarding feature analysis method by extracting the boarding and boarding information from the taxi GPS trajectory data, clustering analysis of taxi travel features based on the K-medoids algorithm, and then analyzing the average fluctuation trend curve based on the road section.
- the pattern shape of analyzes the characteristics of the trip purpose of the daily commuting class after the extraction and clustering, and finally presents the trip feature information of each road section in the GIS electronic map, as shown in Figure 1.
- a heavy vehicle means that there are passengers getting on the bus, and the status of a taxi from heavy to empty means that the passengers get off the bus, and then the points of getting on and off from the GPS track data (arranged in time series) are extracted.
- S121 Determine the road section according to the urban road network information (the distance between adjacent intersections is the road section), and then rely on the longitude and latitude (lng i , lat i ) in the GPS trajectory data to divide the GPS trajectory data into each road section; according to the date Sort and sort the time information t i in the GPS trajectory data with time, and then integrate the number of getting on and off vehicles in each time period of each road segment;
- S122 Solve the average value of taxis getting on and off at each time of the day, and get the average number of getting on and off each road each day, and arrange them in time series. For example, extracting the boarding data in section A, average the data of boarding at the same time on section A to get the average fluctuation trend of boarding in section A in one day.
- the specific Fourier formula is:
- step S3 Use k-medoids to perform cluster analysis on the frequency domain values transformed in step S2 to determine the type of taxi trips, including daily commuting trips, holiday trips, mixed trips and missing data.
- S31 Respectively integrate the frequency data of getting on and off the vehicle in each time period of each road section of the sample number of the statistical time period, and determine the number of clusters k of the sample clustering analysis;
- GPS trajectory data in the frequency cluster analysis of GPS trajectory data, it can be divided into four categories, including daily commuting travel mode, holiday travel mode, mixed travel mode and data missing mode. Among them, the difference between weekdays and weekends is less and the default is Daily commuting travel patterns, while the changes between days of the week are small.
- the characteristic information of the taxi boarding and boarding travel purpose of each road segment is marked in the GIS electronic map.
- This kind of visualized taxi boarding and boarding feature analysis method extracts the GPS trajectory information of taxi vehicles, sorts the data of each road section in time, and clusters the taxi trips through Fourier transform and k-medoids algorithm
- the purpose of taxi trips on the road section is displayed in a visual way in the shape of English letters, so as to display and process in the GIS electronic map, so that users can effectively grasp the characteristics of taxis getting on and off the road network and avoid the traditional heat map display method Need to manually determine the empirical model of parameter information.
- the innovative visual display method of taxi boarding and boarding features of the embodiment is compared with the spatial clustering analysis of the traditional heat map, and the k-medoids center point clustering analysis of the boarding and boarding data on each road section and the average fluctuation trend graph of the boarding and boarding data are performed Draw, find the information of taxi boarding and boarding travel patterns and travel characteristics, so as to facilitate traffic planning managers in taxi scheduling and route optimization.
- Step S1 Select a certain city as the research object, obtain the operating data of taxis in a certain quarter of the city, including GPS trajectory data, and extract the boarding data for research according to the status of the heavy/empty vehicle.
- the distribution is shown in Figure 3.
- Step S2 collect statistics on the data of each time of the day, and convert it to frequency value through Fourier transform, draw a histogram from time domain to frequency domain, and obtain the frequency index after conversion.
- the histogram of a certain road section is shown in Figure 4.
- Step S3 Perform cluster analysis on the amount of vehicle boarding data in each day and each time period of a certain road section statistical period (quarter), and divide four modes, as shown in FIG. 5.
- Step S4 Further draw the fluctuation trend of the number of taxis on the day in each mode. Taking daily commuting as an example, the drawn curve is shown in FIG. 6.
- the peak time period for each type of travel mode is determined as follows:
- Step S5 Further superimpose each type of data on the map, and the specific information is shown in FIG. 7.
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Abstract
Description
Claims (6)
- 一种可视化的出租车上下车特征分析方法,其特征在于:通过提取出租车GPS轨迹数据中的上下车信息,基于K-medoids算法的出租车出行特征聚类分析,进而对基于路段上下车平均波动趋势曲线图的图案形状对提取聚类后的日常通勤类的出行目的特征分析,最终在GIS电子地图中呈现出各路段出租车出行特征信息,包括以下步骤,A visualized taxi boarding and boarding feature analysis method, which is characterized by: extracting the boarding and boarding information in the taxi GPS trajectory data, clustering analysis of taxi travel features based on the K-medoids algorithm, and then averaging The pattern shape of the fluctuation trend graph analyzes the characteristics of the daily commuting travel purpose after the extraction and clustering, and finally presents the taxi travel characteristic information of each road section in the GIS electronic map, including the following steps:S1、提取出租车的GPS轨迹数据并整合汇总,得到每条路段平均每天各时段的上下车情况;S1. Extract the GPS trajectory data of taxis and integrate them to obtain the average daily getting on and off status of each road section;S2、基于傅里叶变换将上下车数据的时间序列从时域转换为频域数值;S2, based on the Fourier transform, convert the time series of the vehicle getting on and off data from the time domain to the frequency domain value;S3、利用k-medoids将步骤S2中变换后频域数值进行聚类分析,确定出租车出行类型,包括日常通勤出行模式、节假日出行模式、混合出行模式和数据缺失模式;S3. Use k-medoids to perform cluster analysis on the frequency domain values transformed in step S2 to determine the type of taxi travel, including daily commuting travel mode, holiday travel mode, mixed travel mode and data missing mode;S4、提取日常通勤出行模式下的各时段出租车上下车数量,绘制出当日各时段上下车数波动趋势图,基于波动趋势图的图案形状确定时段下出租车出行的出行目的;S4. Extract the number of taxis getting on and off at each time period in the daily commuting travel mode, draw a fluctuation trend graph of the number of cars on and off at each time period of the day, and determine the travel purpose of the taxi trip during the time period based on the pattern shape of the fluctuation trend graph;S5、基于步骤S3确定的出行特征以及步骤S4确定的各路段通勤出行目的,在GIS电子地图中标注出各路段的出租车上下车出行目的特征信息。S5. Based on the travel characteristics determined in step S3 and the commuter travel purpose of each road segment determined in step S4, mark the characteristic information of the taxi boarding and boarding travel purpose of each road segment in the GIS electronic map.
- 如权利要求1所述的可视化的出租车上下车特征分析方法,其特征在于:步骤S1具体为,The visualized taxi getting on and off characteristic analysis method according to claim 1, wherein the step S1 is specifically:S11、接入出租车车载终端,获取出租车GPS轨迹数据,提取出租车上下车数据;S11. Connect to the taxi on-board terminal, obtain the taxi GPS trajectory data, and extract the taxi getting on and off data;S12、整合每条路段每天的出租车GPS轨迹数据,得到每条路段平均每天各时段的上下车情况。S12. Integrate the daily GPS trajectory data of each road section to obtain the average daily getting on and off status of each road section.
- 如权利要求2所述的可视化的出租车上下车特征分析方法,其特征在于:步骤S11中,提取出租车上下车数据,具体为,The visualized taxi getting on and off characteristic analysis method according to claim 2, characterized in that: in step S11, extracting taxi getting on and off data, specifically,首先对出租车轨迹信息进行数据清洗工作再进行数据分析;出租车车辆的GPS轨迹数据是按其时间进行排序所行成的时间序列traj={p 1,p 2,…,p i,…,p n},其中,p i=(lng i,lat i,t i),lng i表示车辆在第i个位置时的经度,lat i表示车辆在第i个位置时的纬度,t i表示车辆在第i个位置时的时刻;出租车上下车情况根 据出租车GPS轨迹数据中的载客状态包括重车和空车确定,即出租车载客状态由空车变为重车则说明有乘客上车,出租车载客状态由重车变为空车则说明乘客下车,进而从GPS轨迹数据中提取出上下车点并按时间序列排列。 First, perform data cleaning work on taxi trajectory information and then perform data analysis; the GPS trajectory data of taxi vehicles is a time sequence traj = {p 1 , p 2 ,..., p i ,..., sorted by time. p n}, where, p i = (lng i, lat i, t i), lng i represents the vehicle longitude at the i-th position, lat i represents the latitude of the vehicle at the time of the i-th position, t i represents the vehicle At the time of the i-th position; the taxi boarding and disembarking conditions are determined according to the passenger loading status in the taxi GPS trajectory data, including heavy and empty vehicles. That is, the taxi status changes from empty to heavy, indicating that there are passengers When getting on the bus, the status of the taxi carrying passengers from heavy to empty means that the passengers get off, and then the points of getting on and off are extracted from the GPS track data and arranged in time series.
- 如权利要求2所述的可视化的出租车上下车特征分析方法,其特征在于:步骤S12中,得到每条路段平均每天各时段的上下车情况,具体为,The visualized taxi getting on and off characteristic analysis method according to claim 2, characterized in that: in step S12, the getting on and off conditions of each road section on average every day are obtained, specifically:S121、根据城市路网信息确定路段,相邻交叉口之间的距离即为路段,进而依托GPS轨迹数据中的经纬度(lng i,lat i)将GPS轨迹数据分至各路段上;根据日期和时间将GPS轨迹数据中的时刻信息t i进行分类排序,进而整合每条路段每天各时间段内上下车数量; S121. Determine the road section according to the urban road network information, the distance between adjacent intersections is the road section, and then rely on the longitude and latitude (lng i , lat i ) in the GPS trajectory data to divide the GPS trajectory data into each road section; Time sorts and sorts the time information t i in the GPS trajectory data, and then integrates the number of getting on and off vehicles in each time period of each road segment;S122、求解出每天各时段出租车上下车均值,得到每条路段平均每天各时段的上下车数量,并按时间序列排列。S122. Solve the average value of taxis getting on and off at each time period of each day, and obtain the average number of getting on and off vehicles at each time of each road section each day, and arrange them in time series.
- 如权利要求1-4任一项所述的可视化的出租车上下车特征分析方法,其特征在于:步骤S2中,基于傅里叶变换将上下车数据的时间序列从时域转换为频域数值,具体为,基于时空数据变换特征,通过傅里叶变换公示将上下车数据情况值N t={p 1,p 2,…,p j}转换为频率数值ω(t)={ω 1,ω 2,…,ω j},即针对步骤S1求得各时段内上下车数量转换到各时段频率数值,具体傅里叶公式为: The visualized taxi boarding and boarding feature analysis method according to any one of claims 1 to 4, characterized in that: in step S2, the time series of the boarding and boarding data is converted from the time domain to the frequency domain value based on Fourier transform Specifically, based on the characteristics of spatio-temporal data transformation, through Fourier transform publicity, the vehicle getting on and off data condition value N t ={p 1 , p 2 ,..., p j } is converted into a frequency value ω(t)={ω 1 , ω 2 ,..., ω j }, that is, the number of vehicles getting on and off in each period is converted to the frequency value of each period for step S1. The specific Fourier formula is:式中,ω代表频率,t代表时间,e -iωt为复变函数,f(t)为上下车数据量,F(ω)为上下车数量频率数值。 In the formula, ω represents frequency, t represents time, e- iωt is a complex variable function, f(t) is the amount of data for getting on and off cars, and F(ω) is the frequency value of getting on and off cars.
- 如权利要求1-4任一项所述的可视化的出租车上下车特征分析方法,其特征在于:步骤S3中,确定出租车出行模式,包括日常通勤出行模式、节假日出行模式、混合出行模式和数据缺失模式,具体为,The visualized taxi getting on and off characteristic analysis method according to any one of claims 1-4, characterized in that: in step S3, the taxi travel mode is determined, including daily commuting travel mode, holiday travel mode, mixed travel mode and Data missing pattern, specifically,S31、分别对统计时间段样本数量各路段各时间段内的上下车频率数据进行整合,确定样聚类分析的簇个数k;S31. Integrate the frequency data of getting on and off the vehicle in each time period of each road section of the sample number in the statistical time period respectively, and determine the number of clusters k of the sample clustering analysis;S32、在统计时间范围内样本点中随机选择k个点作为初始中心点,分别计算剩下点距离k个中心点的欧氏距离从而划分出簇,进一步根据划分的簇再求解 簇内剩下的点与其其他样本点的曼哈顿距离之和,并将曼哈顿距离之和最小的点作为簇的中心点;不断重复求解簇直到中心点不再改变,得到聚类结果,即出租车GPS上下车频率数据的划分情况。S32. Randomly select k points from the sample points in the statistical time range as the initial center points, calculate the Euclidean distances between the remaining points and the k center points respectively to divide the clusters, and then solve the remaining clusters according to the divided clusters. The sum of the Manhattan distance between the point and other sample points, and the point with the smallest sum of Manhattan distance as the center point of the cluster; the cluster is solved repeatedly until the center point no longer changes, and the clustering result is obtained, that is, the frequency of taxi GPS getting on and off The division of data.
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---|---|---|---|---|
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KR20210042537A (en) * | 2019-10-10 | 2021-04-20 | 엘지전자 주식회사 | Method of estimating position in local area in large sapce and robot and cloud server implementing thereof |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106529754A (en) * | 2016-06-27 | 2017-03-22 | 江苏智通交通科技有限公司 | Taxi operation condition assessment method based on big data analysis |
CN107577725A (en) * | 2017-08-22 | 2018-01-12 | 长安大学 | A kind of urban taxi passenger trip characteristicses visual analysis method |
CN108959466A (en) * | 2018-06-20 | 2018-12-07 | 淮阴工学院 | Taxi hot spot method for visualizing and system based on BCS-DBSCAN |
CN109254861A (en) * | 2018-09-17 | 2019-01-22 | 江苏智通交通科技有限公司 | OD requirement extract and its analysis method for reliability based on track data |
Family Cites Families (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2010250673A (en) * | 2009-04-17 | 2010-11-04 | Ntt Docomo Inc | Taxi user extraction device and taxi user extraction method |
CN106384509A (en) * | 2016-10-08 | 2017-02-08 | 大连理工大学 | Urban road driving time distribution estimation method considering taxi operation states |
CN108242149B (en) * | 2018-03-16 | 2020-06-30 | 成都智达万应科技有限公司 | Big data analysis method based on traffic data |
-
2019
- 2019-03-21 CN CN201910219988.3A patent/CN109885639B/en active Active
- 2019-11-01 WO PCT/CN2019/115056 patent/WO2020186770A1/en active Application Filing
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106529754A (en) * | 2016-06-27 | 2017-03-22 | 江苏智通交通科技有限公司 | Taxi operation condition assessment method based on big data analysis |
CN107577725A (en) * | 2017-08-22 | 2018-01-12 | 长安大学 | A kind of urban taxi passenger trip characteristicses visual analysis method |
CN108959466A (en) * | 2018-06-20 | 2018-12-07 | 淮阴工学院 | Taxi hot spot method for visualizing and system based on BCS-DBSCAN |
CN109254861A (en) * | 2018-09-17 | 2019-01-22 | 江苏智通交通科技有限公司 | OD requirement extract and its analysis method for reliability based on track data |
Cited By (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112380906A (en) * | 2020-10-19 | 2021-02-19 | 上汽通用五菱汽车股份有限公司 | Method for determining user address based on driving data |
CN112380906B (en) * | 2020-10-19 | 2024-05-31 | 上汽通用五菱汽车股份有限公司 | Method for determining user address based on driving data |
CN112734532A (en) * | 2021-01-15 | 2021-04-30 | 东南大学 | Commuting behavior identification method based on shared electric bicycle borrowing and returning point data |
CN112905729A (en) * | 2021-03-05 | 2021-06-04 | 亿海蓝(北京)数据技术股份公司 | Thermodynamic diagram generation method and device for track data, electronic equipment and storage medium |
CN112905729B (en) * | 2021-03-05 | 2024-01-30 | 亿海蓝(北京)数据技术股份公司 | Thermodynamic diagram generation method and device for track data, electronic equipment and storage medium |
CN114579884A (en) * | 2022-02-16 | 2022-06-03 | 中冶南方城市建设工程技术有限公司 | Shared bicycle trip display method and system in urban built-up area |
CN114579884B (en) * | 2022-02-16 | 2024-05-31 | 中冶南方城市建设工程技术有限公司 | Method and system for displaying travel of shared bicycle in urban built-up area |
CN116029624A (en) * | 2022-07-21 | 2023-04-28 | 大连海事大学 | Goods source place identification method integrating truck track and POI data |
CN116029624B (en) * | 2022-07-21 | 2024-02-06 | 大连海事大学 | Goods source place identification method integrating truck track and POI data |
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