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 Modeling the Distribution of Human Mobility Metrics with Online Car-Hailing Data—An Empirical Study in Xi’an, China

2021 ◽  
Vol 10 (4) ◽  
pp. 268
Author(s):  
Chaoyang Shi ◽  
Qingquan Li ◽  
Shiwei Lu ◽  
Xiping Yang
Keyword(s):  
Travel Time ◽  
Human Mobility ◽  
Information Criterion ◽  
Travel Speed ◽  
Distribution Functions ◽  
Travel Distance ◽  
Model Fit ◽  
Travel Patterns ◽  
Distance Travel

Modeling the distribution of daily and hourly human mobility metrics is beneficial for studying underlying human travel patterns. In previous studies, some probability distribution functions were employed in order to establish a base for human mobility research. However, the selection of the most suitable distribution is still a challenging task. In this paper, we focus on modeling the distributions of travel distance, travel time, and travel speed. The daily and hourly trip data are fitted with several candidate distributions, and the best one is selected based on the Bayesian information criterion. A case study with online car-hailing data in Xi’an, China, is presented to demonstrate and evaluate the model fit. The results indicate that travel distance and travel time of daily and hourly human mobility tend to follow Gamma distribution, and travel speed can be approximated by Burr distribution. These results can contribute to a better understanding of online car-hailing travel patterns and establish a base for human mobility research.

Estimation of Path Travel Time Distributions in Stochastic Time-Varying Networks with Correlations

2021 ◽  
pp. 036119812110184
Author(s):  
Monika Filipovska ◽  
Hani S. Mahmassani ◽  
Archak Mittal
Keyword(s):  
Time Dependence ◽  
Travel Time ◽  
Correlation Structure ◽  
Time Varying ◽  
Travel Times ◽  
Trajectory Data ◽  
Time Varying Networks ◽  
Transportation Research ◽  
Search Approach ◽  
Stochastic Time

Transportation research has increasingly focused on the modeling of travel time uncertainty in transportation networks. From a user’s perspective, the performance of the network is experienced at the level of a path, and, as such, knowledge of variability of travel times along paths contemplated by the user is necessary. This paper focuses on developing approaches for the estimation of path travel time distributions in stochastic time-varying networks so as to capture generalized correlations between link travel times. Specifically, the goal is to develop methods to estimate path travel time distributions for any path in the networks by synthesizing available trajectory data from various portions of the path, and this paper addresses that problem in a two-fold manner. Firstly, a Monte Carlo simulation (MCS)-based approach is presented for the convolution of time-varying random variables with general correlation structures and distribution shapes. Secondly, a combinatorial data-mining approach is developed, which aims to utilize sparse trajectory data for the estimation of path travel time distributions by implicitly capturing the complex correlation structure in the network travel times. Numerical results indicate that the MCS approach allowing for time-dependence and a time-varying correlation structure outperforms other approaches, and that its performance is robust with respect to different path travel time distributions. Additionally, using the path segmentations from the segment search approach with a MCS approach with time-dependence also produces accurate and robust estimates of the path travel time distributions with the added benefit of shorter computation times.

scholarly journals Identifying Foreign Tourists’ Nationality from Mobility Traces via LSTM Neural Network and Location Embeddings

2019 ◽  
Vol 9 (14) ◽  
pp. 2861 ◽  
Author(s):  
Alessandro Crivellari ◽  
Euro Beinat
Keyword(s):  
Neural Network ◽  
Motion Tracking ◽  
Large Scale ◽  
Short Term Memory ◽  
Human Mobility ◽  
User Profiling ◽  
Personalized Recommendation ◽  
Mobility Patterns ◽  
Trajectory Data ◽  
Short Term

The interest in human mobility analysis has increased with the rapid growth of positioning technology and motion tracking, leading to a variety of studies based on trajectory recordings. Mapping the routes that people commonly perform was revealed to be very useful for location-based service applications, where individual mobility behaviors can potentially disclose meaningful information about each customer and be fruitfully used for personalized recommendation systems. This paper tackles a novel trajectory labeling problem related to the context of user profiling in “smart” tourism, inferring the nationality of individual users on the basis of their motion trajectories. In particular, we use large-scale motion traces of short-term foreign visitors as a way of detecting the nationality of individuals. This task is not trivial, relying on the hypothesis that foreign tourists of different nationalities may not only visit different locations, but also move in a different way between the same locations. The problem is defined as a multinomial classification with a few tens of classes (nationalities) and sparse location-based trajectory data. We hereby propose a machine learning-based methodology, consisting of a long short-term memory (LSTM) neural network trained on vector representations of locations, in order to capture the underlying semantics of user mobility patterns. Experiments conducted on a real-world big dataset demonstrate that our method achieves considerably higher performances than baseline and traditional approaches.

scholarly journals Identifying Human Mobility via Trajectory Embeddings

2017 ◽  
Author(s):  
Qiang Gao ◽  
Fan Zhou ◽  
Kunpeng Zhang ◽  
Goce Trajcevski ◽  
Xucheng Luo ◽  
...  
Keyword(s):  
Human Mobility ◽  
Route Planning ◽  
Classification Problem ◽  
Personalized Recommendation ◽  
Mobility Patterns ◽  
Classification Problems ◽  
Trajectory Data ◽  
Trajectory Classification ◽  
Spatio Temporal ◽  
Real World Datasets

Understanding human trajectory patterns is an important task in many location based social networks (LBSNs) applications, such as personalized recommendation and preference-based route planning. Most of the existing methods classify a trajectory (or its segments) based on spatio-temporal values and activities, into some predefined categories, e.g., walking or jogging. We tackle a novel trajectory classification problem: we identify and link trajectories to users who generate them in the LBSNs, a problem called Trajectory-User Linking (TUL). Solving the TUL problem is not a trivial task because: (1) the number of the classes (i.e., users) is much larger than the number of motion patterns in the common trajectory classification problems; and (2) the location based trajectory data, especially the check-ins, are often extremely sparse. To address these challenges, a Recurrent Neural Networks (RNN) based semi-supervised learning model, called TULER (TUL via Embedding and RNN) is proposed, which exploits the spatio-temporal data to capture the underlying semantics of user mobility patterns. Experiments conducted on real-world datasets demonstrate that TULER achieves better accuracy than the existing methods.

Comparison of Door-to-Door Transit Travel Time Estimation Using Schedules, Real-Time Vehicle Arrivals, and Smartcard Inference Methods

2021 ◽  
pp. 036119812110237
Author(s):  
Ting Li ◽  
Patrick Meredith-Karam ◽  
Hui Kong ◽  
Anson Stewart ◽  
John P. Attanucci ◽  
...  
Keyword(s):  
Travel Time ◽  
Optimal Path ◽  
Time Estimation ◽  
Future Research ◽  
Travel Time Estimation ◽  
Travel Times ◽  
Transit Systems ◽  
Time Estimates ◽  
Bus Service ◽  
Transportation Research

Estimating passengers’ door-to-door travel time, for journeys that combine walking and public transit, can be complex for large networks with many available path alternatives. Additional complications arise in tap-on only transit systems, where passengers alightings are not recorded. For one such system, the Chicago Transit Authority, this study compares three methods for estimating door-to-door travel time: assuming optimal path choice given scheduled service, as represented in the General Transit Feed Specification (GTFS); assuming optimal path choice given actually operated bus service, as recorded by automatic vehicle location systems; and using inferred path choices based on automated fare collection smartcard records, as processed with an origin-destination-interchange (ODX) inference algorithm. As expected, ODX-derived travel times are found to be longer than those derived from GTFS, indicating that purely schedule-based travel times underestimate the travel times that are actually available and experienced, which can be attributed primarily to suboptimal passenger route choice. These discrepancies additionally manifest in significant spatial variations, raising concerns about potential biases in travel time estimates that do not account for reliability. The findings bring about a more comprehensive understanding of the interactions between transit reliability and passenger behavior in transportation research. Furthermore, these discrepancies suggest areas of future research into the implications of systematic and behavioral assumptions implied by using conventional schedule-based travel time estimates.

scholarly journals An Improved Fuzzy Trajectory Clustering Method for Exploring Urban Travel Patterns

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Fang Liu ◽  
Wei Bi ◽  
Wei Hao ◽  
Fan Gao ◽  
Jinjun Tang
Keyword(s):  
Large Scale ◽  
Spatial Clustering ◽  
Urban Traffic ◽  
Clustering Methods ◽  
Travel Patterns ◽  
Trajectory Clustering ◽  
Trajectory Data ◽  
Clustering Method ◽  
Urban Travel ◽  
Fuzzy Clustering Method

Exploring urban travel patterns can analyze the mobility regularity of residents to provide guidance for urban traffic planning and emergency decision. Clustering methods have been widely applied to explore the hidden information from large-scale trajectory data on travel patterns exploring. How to implement soft constraints in the clustering method and evaluate the effectiveness quantitatively is still a challenge. In this study, we propose an improved trajectory clustering method based on fuzzy density-based spatial clustering of applications with noise (TC-FDBSCAN) to conduct classification on trajectory data. Firstly, we define the trajectory distance which considers the influence of different attributes and determines the corresponding weight coefficients to measure the similarity among trajectories. Secondly, membership degrees and membership functions are designed in the fuzzy clustering method as the extension of the classical DBSCAN method. Finally, trajectory data collected in Shenzhen city, China, are divided into two types (workdays and weekends) and then implemented in the experiment to explore different travel patterns. Moreover, three indices including Silhouette Coefficient, Davies–Bouldin index, and Calinski–Harabasz index are used to evaluate the effectiveness among the proposed method and other traditional clustering methods. The results also demonstrate the advantage of the proposed method.

Understanding Urban Dynamics from Taxi GPS Traces

2014 ◽  
pp. 299-317
Author(s):  
Lin Sun ◽  
Chao Chen ◽  
Daqing Zhang
Keyword(s):  
Travel Time ◽  
Large Scale ◽  
Human Mobility ◽  
Real Life ◽  
Urban Traffic ◽  
Mobility Patterns ◽  
Urban Dynamics ◽  
The Public ◽  
Taxi Service ◽  
Gps Traces

The GPS traces collected from a large taxi fleet provide researchers novel opportunities to inspect the urban dynamics in a city and lead to applications that can bring great benefits to the public. In this chapter, based on a real life large-scale taxi GPS dataset, the authors reveal the unique characteristics in the four different trace stages according to the passenger status, study the urban dynamics revealed in each stage, and explain the possible applications. Specifically, from passenger vacant traces, they study the taxi service dynamics, introduce how to use them to help taxis and passengers find each other, and reveal the work shifting dynamics in a city. From passenger occupied traces, they introduce their capabilities in monitoring and predicting urban traffic and estimating travel time. From the pick-up and drop-off events, the authors show the passenger hotspots and human mobility patterns in a city. They also consider taxis as mobile GPS sensors, which probe the urban road infrastructure dynamics.

Exploring Human Mobility Patterns in Urban Scenarios: A Trajectory Data Perspective

2018 ◽  
Vol 56 (3) ◽  
pp. 142-149 ◽  
Author(s):  
Feng Xia ◽  
Jinzhong Wang ◽  
Xiangjie Kong ◽  
Zhibo Wang ◽  
Jianxin Li ◽  
...  
Keyword(s):  
Human Mobility ◽  
Mobility Patterns ◽  
Trajectory Data

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.

Sign in / Sign up

Export Citation Format

Share Document