Statistical characteristics and transportation mode identification of individual trajectories

2020 ◽  
Vol 34 (10) ◽  
pp. 2050092
Author(s):  
Zhiren Huang ◽  
Pu Wang ◽  
Yang Liu
Keyword(s):  
Human Mobility ◽  
Location Based Services ◽  
Statistical Characteristics ◽  
Trajectory Data ◽  
Single Class ◽  
Transportation Mode ◽  
Mode Identification ◽  
Transportation Modes ◽  
Gps Trajectory Data ◽  
Gps Trajectories

Entering big data era, individual GPS trajectory data have created great opportunities for human mobility and collective behavior studies. Individual GPS trajectories can be collected by location-based services on mobile phones. However, GPS data often do not record transportation modes (e.g., walking, riding a bus, or driving a car). In this study, we analyzed the statistical characteristics of individual trajectories and present a collaborative isolation forest (Co-IF) model to identify the transportation modes of mobile phone GPS trajectories. Unlike previous models that identify multiple transportation modes simultaneously, the proposed Co-IF model builds a single-class classifier for each transportation mode and then combines their results. Compared to the existing models, the Co-IF model offers competitive performance and shows improved reliability with noisy trajectories.

scholarly journals Transportation mode identification with GPS trajectory data and GIS information

2021 ◽  
Vol 26 (4) ◽  
pp. 403-416
Author(s):  
Ji Li ◽  
Xin Pei ◽  
Xuejiao Wang ◽  
Danya Yao ◽  
Yi Zhang ◽  
...  
Keyword(s):  
Trajectory Data ◽  
Transportation Mode ◽  
Mode Identification ◽  
Gps Trajectory Data ◽  
Gps Trajectory

scholarly journals Extracting Stops from Spatio-Temporal Trajectories within Dynamic Contextual Features

2021 ◽  
Vol 13 (2) ◽  
pp. 690
Author(s):  
Tao Wu ◽  
Huiqing Shen ◽  
Jianxin Qin ◽  
Longgang Xiang
Keyword(s):  
Moving Objects ◽  
Major Effect ◽  
Location Based Services ◽  
Trajectory Data ◽  
The Road ◽  
Contextual Features ◽  
Novel Approach ◽  
Gps Trajectories ◽  
Time Distance ◽  
Spatio Temporal

Identifying stops from GPS trajectories is one of the main concerns in the study of moving objects and has a major effect on a wide variety of location-based services and applications. Although the spatial and non-spatial characteristics of trajectories have been widely investigated for the identification of stops, few studies have concentrated on the impacts of the contextual features, which are also connected to the road network and nearby Points of Interest (POIs). In order to obtain more precise stop information from moving objects, this paper proposes and implements a novel approach that represents a spatio-temproal dynamics relationship between stopping behaviors and geospatial elements to detect stops. The relationship between the candidate stops based on the standard time–distance threshold approach and the surrounding environmental elements are integrated in a complex way (the mobility context cube) to extract stop features and precisely derive stops using the classifier classification. The methodology presented is designed to reduce the error rate of detection of stops in the work of trajectory data mining. It turns out that 26 features can contribute to recognizing stop behaviors from trajectory data. Additionally, experiments on a real-world trajectory dataset further demonstrate the effectiveness of the proposed approach in improving the accuracy of identifying stops from trajectories.

scholarly journals Extended Classification Course Improves Road Intersection Detection from Low-Frequency GPS Trajectory Data

2020 ◽  
Vol 9 (3) ◽  
pp. 181
Author(s):  
Banqiao Chen ◽  
Chibiao Ding ◽  
Wenjuan Ren ◽  
Guangluan Xu
Keyword(s):  
Clustering Algorithm ◽  
Recall Rate ◽  
Location Based Services ◽  
Trajectory Data ◽  
Intersection Detection ◽  
Map Generation ◽  
Gps Trajectory Data ◽  
Road Intersections ◽  
Road Maps ◽  
Gps Trajectory

The requirements of location-based services have generated an increasing need for up-to-date digital road maps. However, traditional methods are expensive and time-consuming, requiring many skilled operators. The feasibility of using massive GPS trajectory data provides a cheap and quick means for generating and updating road maps. The detection of road intersections, being the critical component of a road map, is a key problem in map generation. Unfortunately, low sampling rates and high disparities are ubiquitous among floating car data (FCD), making road intersection detection from such GPS trajectories very challenging. In this paper, we extend a point clustering-based road intersection detection framework to include a post-classification course, which utilizes the geometric features of road intersections. First, we propose a novel turn-point position compensation algorithm, in order to improve the concentration of selected turn-points under low sampling rates. The initial detection results given by the clustering algorithm are recall-focused. Then, we rule out false detections in an extended classification course based on an image thinning algorithm. The detection results of the proposed method are quantitatively evaluated by matching with intersections from OpenStreetMap using a variety of distance thresholds. Compared with other methods, our approach can achieve a much higher recall rate and better overall performance, thereby better supporting map generation and other similar applications.

scholarly journals Exploring Temporal Intra-Urban Travel Patterns: An Online Car-Hailing Trajectory Data Perspective

2021 ◽  
Vol 13 (9) ◽  
pp. 1825
Author(s):  
Chaoyang Shi ◽  
Qingquan Li ◽  
Shiwei Lu ◽  
Xiping Yang
Keyword(s):  
Travel Time ◽  
Human Mobility ◽  
Travel Distance ◽  
Statistical Characteristics ◽  
Future Research ◽  
Travel Times ◽  
Travel Patterns ◽  
Mobility Patterns ◽  
Trajectory Data ◽  
Urban Travel

Understanding intra-urban travel patterns is beneficial for urban planning and transportation management, among other fields. As an emerging travel mode, online car-hailing platforms provide massive and high-precision trajectory data, thus offering new opportunities for gaining insights into human mobility. This paper aims to explore temporal intra-urban travel patterns by fitting the distributions of mobility metrics and leveraging the boxplot. The statistical characteristics of daily and hourly travel distance are relatively stable, while those of travel time and speed have some fluctuations. More specifically, most residents travel between 2 and 10 km, with travel times ranging from 6.6 to 30 min, which is fairly consistent with our daily experience. Mainly attributed to travel cost, individuals seldom use online car-hailing for too short or long trips. It is worth mentioning that a weekly pattern can be found in all mobility metrics, in which the patterns of travel time and speed are more obvious than that of travel distance. In addition, since October has more rainy days than November, travel distances and travel times in October are higher than that in November, while the opposite is true for travel speed. This paper can provide a beneficial reference for understanding temporal human mobility patterns, and lays a solid foundation for future research.

scholarly journals DeepDualMapper: A Gated Fusion Network for Automatic Map Extraction Using Aerial Images and Trajectories

2020 ◽  
Vol 34 (01) ◽  
pp. 1037-1045 ◽  
Author(s):  
Hao Wu ◽  
Hanyuan Zhang ◽  
Xinyu Zhang ◽  
Weiwei Sun ◽  
Baihua Zheng ◽  
...  
Keyword(s):  
Location Based Services ◽  
Aerial Images ◽  
Aerial Image ◽  
Trajectory Data ◽  
Urban Computing ◽  
Digital Map ◽  
Different Types ◽  
Gps Trajectory Data ◽  
Types Of Information ◽  
Coarse To Fine

Automatic map extraction is of great importance to urban computing and location-based services. Aerial image and GPS trajectory data refer to two different data sources that could be leveraged to generate the map, although they carry different types of information. Most previous works on data fusion between aerial images and data from auxiliary sensors do not fully utilize the information of both modalities and hence suffer from the issue of information loss. We propose a deep convolutional neural network called DeepDualMapper which fuses the aerial image and trajectory data in a more seamless manner to extract the digital map. We design a gated fusion module to explicitly control the information flows from both modalities in a complementary-aware manner. Moreover, we propose a novel densely supervised refinement decoder to generate the prediction in a coarse-to-fine way. Our comprehensive experiments demonstrate that DeepDualMapper can fuse the information of images and trajectories much more effectively than existing approaches, and is able to generate maps with higher accuracy.

scholarly journals Estimating Urban Road GPS Environment Friendliness with Bus Trajectories: A City-Scale Approach

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1580
Author(s):  
Liantao Ma ◽  
Chaohe Zhang ◽  
Yasha Wang ◽  
Guangju Peng ◽  
Chao Chen ◽  
...  
Keyword(s):  
Urban Areas ◽  
Negative Impact ◽  
Field Tests ◽  
Location Based Services ◽  
Trajectory Data ◽  
Gps Positioning ◽  
Urban Road ◽  
Gps Trajectory Data ◽  
Gps Trajectory ◽  
Road Segments

GPS is taken as the most prevalent positioning system in practice. However, in urban areas, as the GPS satellite signal could be blocked by buildings, the GPS positioning is not accurate due to multi-path errors. Estimating the negative impact of urban environments on GPS accuracy, that is the GPS environment friendliness (GEF) in this paper, will help to predict the GPS errors in different road segments. It enhances user experiences of location-based services and helps to determine where to deploy auxiliary assistant positioning devices. In this paper, we propose a method of processing and analysing massive historical bus GPS trajectory data to estimate the urban road GEF integrated with the contextual information of roads. First, our approach takes full advantage of the particular feature that bus routes are fixed to improve the performance of map matching. In order to estimate the GEF of all roads fairly and reasonably, the method estimates the GPS positioning error of each bus on the roads that are not covered by its route, by taking POIinformation, tag information of roads, and building layout information into account. Finally, we utilize a weighted estimation strategy to calculate the GEF of each road based on the GPS positioning performance of all buses. Based on one month of GPS trajectory data of 4835 buses within the second ring road in Chengdu, China, we estimate the GEF of 8831 different road segments and verify the rationality of the results by satellite maps, street views, and field tests.

scholarly journals Transportation Mode Detection Based on Permutation Entropy and Extreme Learning Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Lei Zhang ◽  
LeiJun Liu ◽  
SuNing Bao ◽  
MengTing Qiang ◽  
XiaoMei Zou
Keyword(s):  
Extreme Learning Machine ◽  
Mobile User ◽  
Permutation Entropy ◽  
Svm Classifier ◽  
Trajectory Data ◽  
Transportation Mode ◽  
Mode Detection ◽  
Gps Trajectory Data ◽  
Gps Trajectory ◽  
Learning Machine

With the increasing prevalence of GPS devices and mobile phones, transportation mode detection based on GPS data has been a hot topic in GPS trajectory data analysis. Transportation modes such as walking, driving, bus, and taxi denote an important characteristic of the mobile user. Longitude, latitude, speed, acceleration, and direction are usually used as features in transportation mode detection. In this paper, first, we explore the possibility of using Permutation Entropy (PE) of speed, a measure of complexity and uncertainty of GPS trajectory segment, as a feature for transportation mode detection. Second, we employ Extreme Learning Machine (ELM) to distinguish GPS trajectory segments of different transportation. Finally, to evaluate the performance of the proposed method, we make experiments on GeoLife dataset. Experiments results show that we can get more than 50% accuracy when only using PE as a feature to characterize trajectory sequence. PE can indeed be effectively used to detect transportation mode from GPS trajectory. The proposed method has much better accuracy and faster running time than the methods based on the other features and SVM classifier.

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