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Data Brief
Data Brief
Data in Brief
2352-3409
Elsevier

S2352-3409(24)00828-X
10.1016/j.dib.2024.110864
110864
Data Article
High-precision vehicle trajectory data from an intersection in Shanghai: A unique dataset for microscopic traffic flow studies collected by drone and GNSS receiver
Zhao Jing a
Ma Ruoming a
Wang Meng meng.wang@tu-dresden.de
b⁎
a Department of Traffic Engineering, University of Shanghai for Science and Technology, Shanghai, China
b Chair of Traffic Process Automation, “Friedrich List” Faculty of Transport and Traffic Sciences, Technische Universität Dresden, Dresden, Germany
⁎ Corresponding author. meng.wang@tu-dresden.de
29 8 2024
12 2024
29 8 2024
57 11086430 3 2024
14 7 2024
16 8 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Vehicle trajectory data are invaluable for driving behaviour and traffic flow modelling studies, especially at the microscopic level. However, existing public vehicle trajectory datasets only provide data with inherent errors and lack the corresponding ground truth. This study presents a comprehensive vehicle trajectory dataset obtained using both drone and high-precision Global Navigation Satellite System (GNSS) receiver technologies with an error of less than 5 cm. The dataset contains 70 complete trajectories with a total of 10,840 data points and an average length of 48.4 m. This includes 27 left-turn trajectories, 27 through trajectories and 16 right-turn trajectories. The trajectories collected by the centimetre-level precision GNSS receiver can be regarded as the ground truth of the trajectories extracted by the drone video. Researchers can use these two trajectory datasets to analyse driving behaviour at interactive scenarios, validate and calibrate microscopic traffic flow models, and validate trajectory reconstruction methods.

Keywords

High-resolution trajectory
Microscopic traffic flow
Driving behaviour
Trajectory reconstruction
High-precision
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pmcSpecifications TableSubject	Transportation Management, Management Science and Operations Research	
Specific subject area	Driving behaviour, traffic flow modelling & simulation	
Type of data	Excel files (.csv, .xlsx, xls), Image (.jpg)
Raw, Processed	
Data collection	The dataset was collected by a GNSS receiver with centimeter-level accuracy and a DJI Mavic-2 drone, simultaneously capturing vehicle trajectories within the intersection at a frequency of 24 Hz. The vehicle trajectories collected by the GNSS receiver are regarded as the ground-truth, while the trajectories extracted from the drone videos are considered to contain errors. After coordinate system conversion, the two different kinds of trajectories can be used for comparison.
In order to convert the two different kinds of trajectories to the same coordinate system, we used the GNSS receiver to collect the latitude and longitude coordinates of 10 reference points at the intersection, and recorded the corresponding pixel coordinates of these points based on the base map captured by the drone.	
Data source location	• Institution: University of Shanghai for Science and Technology

• City/Town/Region: Shanghai

• Country: China

• Latitude and longitude of intersection: 31.3249°N, 121.4968°E

	
Data accessibility	Repository name: Mendeley Data
Data identification number: 10.17632/yx7sy7frnh.1
Direct URL to data: https://doi.org/10.17632/yx7sy7frnh.1	
Related research article	Jing Zhao, Ruoming Ma, Meng Wang*, A behaviourally underpinned approach for two-dimensional vehicular trajectory reconstruction with constrained optimal control, Transportation Research Part C, 2024, 159: 104,489. https://doi.org/10.1016/j.trc.2024.104489	

1 Value of the Data

• This dataset contains natrulistic driving data captured at an intersection. In contrast to typically utilized trajectory datasets, this dataset is collected simultaneously by drone and GNSS receivers with centimeter-level precision.

• The trajectories extracted from the drone videos in this dataset are considered as raw trajectories with inherent errors. Researchers can employ trajectory reconstruction methods to mitigate these inherent errors and objectively assess the performance of their reconstruction methods by comparing the results with ground-truth data obtained from centimeter-level accuracy GNSS receivers.

• The high-precision trajectories obtained from the GNSS receiver in this dataset can be utilized for microscopic traffic research, such as validating vehicle dynamics models and microscopic traffic flow models.

2 Background

Vehicle trajectory data is crucial for driving behaviour and traffic flow modelling & simulation. Due to measurement errors and inaccuracies, trajectory reconstruction methods are often employed to eliminate inherent errors in trajectory data, thereby providing data support for traffic modelling studies. However, existing high-resolution trajectory datasets often only provide trajectories with inherent errors without ground truth values, which makes it difficult for researchers to validate the performance of their proposed reconstruction methods. Therefore, in addition to extracting experimental vehicle trajectories based on drone videos, this dataset utilizes a centimetre-level precision GNSS receiver to collect the trajectories of an experimental vehicle. This provides a high-resolution trajectory dataset with ground truth to fill the gap in validating trajectory reconstruction methods.

Unlike the original research article [1] proposing an optimal control-based trajectory reconstruction method, which utilizes the dataset to validate the performance of the proposed trajectory reconstruction method. This paper provides a detailed description of how the dataset was collected and also explains it comprehensively. Therefore, this paper serves as supplementary information to the original article.

3 Data Description

This article describes a dataset containing measurement trajectory data from a drone similar to pNEUMA [2], HighD [3] and INTERACTION [4], as well as high-precision trajectory data with an error less than 5 cm collected by centimetre-level progressing GNSS receivers. The sample size (number of trajectories of the experimental vehicle inside the intersection) of each measure method is 70, including 27 left-turn trajectories, 27 through trajectories and 16 right-turn trajectories, as shown in Fig. 1.Fig. 1 Experimental regions and collected trajectories.

Fig 1

According to the different methods of trajectory collection, all data are classified into two main folders: “GNSS receiver data” and “Video data”. Due to the limitation of the drone battery capacity, the data collection process was divided into four experiments, and the folder or file with the suffix “_experiment1” in the dataset indicates that this data is related to the first experiment in the collection process.

The “GNSS receiver data” folder contains the raw trajectories collected by the centimetre-level precision GNSS receiver and the processed data. In each of the four experiments, the GNSS receiver continuously recorded the vehicle position information at a frequency of 24HZ, so the raw data collected in the four experiments were saved in four CSV files. The columns “Time”, “N_Lat”, and “E_Lon” in these files respectively represent the Coordinate Universal Time (UTC), the vehicleʼs north latitude, and east longitude of the location. The “Coordinates of original point (Longitude-Latitude).xlsx” and “Intersection boundary (Longitude–Latitude).xls” files in the “Data processing file” represent the WGS84 coordinates of the origin and the vertex of the intersection range, respectively. The setting of the origin and the intersection range is shown in Fig. 1. The “Processed GNSS data” folder stores the processed trajectories, which are all within the intersection boundary, and all convert the WGS84 coordinates to the actual distance (m) coordinates by using the origin. The “Time” in the processed trajectory CSV file represents the time interval from the beginning of an experiment, and “x” and “y” respectively represent the actual distance of the vehicle from the origin at that time.

The “Video data” folder contains the raw trajectories extracted from the drone videos and the processed trajectories. Different from the way of data collection by the GNSS receiver, trajectory extraction software records the pixel position of each frame in the video from the appearance to the end of each experimental vehicle. These unprocessed trajectories are stored in the “Raw Trajectories Extracted from Videos” folder as CSV files. The “Processed video data” folder in the “Data processing file” stores the trajectories that have been processed and are located in the same coordinate system as the trajectories in the “Processed GNSS data” folder. The trajectories in these two folders have the same data structure and can be directly used by researchers. In addition to the processed video trajectories, the “Data processing file” also contains the necessary files for trajectory processing. The “Base maps” folder contains the video base maps used to obtain the coordinate origin and reference points. The “Reference points” folder contains the coordinates of reference points obtained through GNSS receivers and video base maps, which are utilized to standardize the different coordinate systems adopted by different methods. Fig. 1 illustrates the setting reference points. The “Scales (Pixel to Meter).xlsx” file represents the conversion ratio calculated from reference points.“ The file “Coordinates of original point (Pixel).xlsx” contains the origin coordinates in the pixel coordinate system, while the file “Intersection boundary (Meter).xlsx” records the vertex coordinates of intersection boundaries obtained from a GNSS receiver and converted to actual distance (m) coordinates.

4 Experimental Design, Materials and Methods

4.1 Data collection

We selected an intersection (Guohong Road - Zhengfang Road) in Shanghai, China, as our experimental site for data collection on December 12, 2022. In order to ensure the quality of collection results, the DJI Mavic-2 UAV and a GNSS receiver with centimeter-level accuracy are employed. The drone features 4k video recording capability, a 29-min hover time in calm conditions, and resistance to level 5 winds. The GNSS receiver incorporates an RTK positioning function delivering flat accuracy of 10 mm+1 ppm, time accuracy of 20 ns, and speed accuracy of 0.03 m s-1. The GNSS receiver was installed on the experimental vehicle, as depicted in Fig. 2. To ensure synchronized recording of the experimental vehicle trajectory by both the drone and GNSS receiver, operators of these data collection devices maintained constant communication during the experiment.Fig. 2 Experimental vehicle equipped with high-precision GNSS receiver.

Fig 2

In the process of data collection, the drone was positioned directly above the intersection to capture driving videos of the experimental vehicle within the intersection. Since the battery of the DJI Mavic-2 drone can only support about 25 min of flight, excluding the time for the drone to fly to the shooting position and fall, each shooting time is 15–20 min. Four batteries are used in the data collection process, so the whole data collection process is divided into four experiments. When the drone starts shooting, the GNSS receiver will record the driving trajectory of the experimental vehicle with a frequency of 24HZ and an error within 5 cm.

In order to ensure a balanced distribution of left-turn, right-turn and through trajectories in the dataset, the experimental vehicle alternates between through and left-turn movements during the first three experiments. When one of these three experiments ends, the vehicle adjusts its position during the drone's battery replacement period to initiate the next experiment with a through movement. In the last experiment, the right-turn movement was repeated to collect the right-turn trajectory. At the end of the last experiment, the GNSS receiver was used to collect the coordinates of the reference points, the origin of the coordinate system, and the vertices of the intersection boundary for subsequent data processing.

4.2 Data processing

The raw experimental vehicle trajectories obtained from the GNSS receiver and video extraction software cannot be directly applied to relevant research due to the differences in coordinate systems. Therefore, it is imperative to convert both trajectories into a unified coordinate system for further analysis. An overview of the data collection and data processing is shown in Fig. 3.Fig. 3 Summary diagram of data collection and data process.

Fig. 3

The GNSS receiver utilizes the WGS84 coordinate system to record the position of the experimental vehicle. Therefore, the data processing process involves two steps: firstly, filtering out the trajectory within the intersection based on its boundary; secondly, converting the trajectory from WGS84 coordinates to our established Cartesian coordinate system with meter units. This conversion is performed by considering the relationship between WGS84 longitude and latitude and their corresponding actual distances in meters, as shown in the following equations.(1) xG=|Lat−Lato|Clat360

(2) yG=|Lon−Lono|Clon360

where, xG and yG are the Cartesian coordinates of the GNSS receiver trajectory point, in m; Lat and Lon are the latitude and longitude of the trajectory point; Lato and Lono are the latitude and longitude of the Cartesian coordinate system origin; Clat and Clon are the latitude circumference and longitude circumference of Earth at the current location, in m. Considering the Earth is a sphere, the circumferences of different regions are different. The formulas for calculating these circumferences are presented in the following equations.(3) Clat=2πRcosLat

(4) Clon=2πR

where, R is the radius of the Earth, which is set to be 6,378,137 m.

When processing the video extraction trajectory recorded by pixel coordinates, we initially transform the reference points collected by the GNSS receiver into our established Cartesian coordinate system and extract the corresponding pixel coordinates on the video base map. Additionally, we determine the conversion ratios between the pixel coordinate system and our established Cartesian coordinate system. Ultimately, the pixel trajectories within the intersection are converted to obtain the processed trajectories. The calculation formulas are shown in (5), (6). The trajectories converted to the same coordinate system are shown in Fig. 4.(5) xp=|px−pxo|rx

(6) yp=|py−pyo|ry

where, xp and yp are the Cartesian coordinates of the video extraction trajectory point, in m; px and py are the pixel coordinates of the trajectory point; pxo and pyo are the pixel coordinates of the Cartesian coordinate system origin; rx and ry are the conversion ratios between the pixel coordinate system and the established Cartesian coordinate system, which is calculated by the following equations.(7) rx=1n∑i=1n(xripxri)

(8) ry=1n∑i=1n(yripyri)

where, n is the number of the reference points; xri and yri are the Cartesian coordinates of reference point i, in m; pxri and pyri are the pixel coordinates of reference point i.Fig. 4 Comparison of processed trajectories.

Fig. 4

Limitations

Due to the limited number of devices, the dataset in this study exhibits a relatively small collection scale, resulting in a restricted sample size. We will increase the collection scale and expand the dataset in future experiments.

Ethics Statement

This dataset does not include human subjects, nor was it collected through animal experiments or social media platforms, and has not been published elsewhere.

CRediT Author Statement

Jing Zhao: Conceptualization, Methodology, Software, Writing. Ruoming Ma: Data curation, Writing, Original draft preparation. Wang Meng: Methodology, Supervision, Validation, Writing – review & editing.

Data Availability

A high-precision vehicle trajectory dataset in Shanghai (Original data) (Mendeley Data).

Acknowledgements

This work was supported by the 10.13039/501100001809 National Natural Science Foundation of China [grant numbers 52122215 and 52372304 ] and Shanghai Shuguang Program [grant number 22SG45 ].

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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