scholarly journals Revealing Spatial-Temporal Characteristics and Patterns of Urban Travel: A Large-Scale Analysis and Visualization Study with Taxi GPS Data

2019 ◽  
Vol 8 (6) ◽  
pp. 257 ◽  
Author(s):  
Huihui Wang ◽  
Hong Huang ◽  
Xiaoyong Ni ◽  
Weihua Zeng
Keyword(s):  
Large Scale ◽  
Travel Demand ◽  
Chord Diagram ◽  
Future Research ◽  
Trajectory Data ◽  
Temporal Scales ◽  
Data Set ◽  
Ring Road ◽  
Temporal Characteristics ◽  
Taxi Trajectory

Mobility and spatial interaction data have become increasingly available due to the widespread adoption of location-aware technologies. Examples of mobile data include human daily activities, vehicle trajectories, and animal movements. In this study we focus on a special type of mobility data, i.e., origin–destination (OD) pairs, and propose a new adapted chord diagram plot to reveal the urban human travel spatial-temporal characteristics and patterns of a seven-day taxi trajectory data set collected in Beijing; this large scale data set includes approximately 88.5 million trips of anonymous customers. The spatial distribution patterns of the pick-up points (PUPs) and the drop-off points (DOPs) on weekdays and weekends are analyzed first. The maximum of the morning and the evening peaks are at 8:00–10:00 and 17:00–19:00. The morning peaks of taxis are delayed by 0.5–1 h compared with the commuting morning peaks. Second, travel demand, intensity, time, and distance on weekdays and weekends are analyzed to explore human mobility. The travel demand and high-intensity travel of residents in Beijing is mainly concentrated within the 6th Ring Road. The residents who travel long distances (>10 km) and for a long time (>60 min) mainly from outside the 6th Ring Road and the surrounding new towns of Beijing. The circular structure of the travel distance distribution also confirms the single-center urban structure of Beijing. Finally, a new adapted chord diagram plot is proposed to achieve the spatial-temporal scale visualization of taxi trajectory origin–destination (OD) flows. The method can characterize the volume, direction, and properties of OD flows in multiple spatial-temporal scales; it is implemented using a circular visualization package in R (circlize). Through the visualization experiment of taxi GPS trajectory data in Beijing, the results show that the proposed visualization technology is able to characterize the spatial-temporal patterns of trajectory OD flows in multiple spatial-temporal scales. These results are expected to enhance current urban mobility research and suggest some interesting avenues for future research.

Demographic Characterization of Anonymous Trace Travel Data

2015 ◽  
Vol 2526 (1) ◽  
pp. 19-28 ◽  
Author(s):  
Joshua Auld ◽  
Abolfazl (Kouros) Mohammadian ◽  
Marcelo Simas Oliveira ◽  
Jean Wolf ◽  
William Bachman
Keyword(s):  
Test Data ◽  
Large Scale ◽  
Travel Demand ◽  
Null Model ◽  
Demographic Characteristics ◽  
Travel Pattern ◽  
Data Set ◽  
Trace Data ◽  
Demographic Models ◽  
Pattern Information

Research was undertaken to determine whether demographic characteristics of individual travelers could be derived from travel pattern information when no information about the individual was available. This question is relevant in the context of anonymously collected travel information, such as cell phone traces, when used for travel demand modeling. Determining the demographics of a traveler from such data could partially obviate the need for large-scale collection of travel survey data, depending on the purpose for which the data were to be used. This research complements methodologies used to identify activity stops, purposes, and mode types from raw trace data and presumes that such methods exist and are available. The paper documents the development of procedures for taking raw activity streams estimated from GPS trace data and converting these into activity travel pattern characteristics that are then combined with basic land use information and used to estimate various models of demographic characteristics. The work status, education level, age, and license possession of individuals and the presence of children in their households were all estimated successfully with substantial increases in performance versus null model expectations for both training and test data sets. The gender, household size, and number of vehicles proved more difficult to estimate, and performance was lower on the test data set; these aspects indicate overfitting in these models. Overall, the demographic models appear to have potential for characterizing anonymous data streams, which could extend the usability and applicability of such data sources to the travel demand context.

Electroconvulsive therapy versus transcranial magnetic stimulation for major depression: a review with recommendations for future research

2008 ◽  
Vol 20 (6) ◽  
pp. 291-294 ◽  
Author(s):  
Keith G. Rasmussen
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Major Depression ◽  
Electroconvulsive Therapy ◽  
Magnetic Stimulation ◽  
Large Scale ◽  
The Elderly ◽  
Future Research ◽  
Sample Sizes ◽  
Data Set ◽  
Differential Outcomes

Objective:To review the literature comparing electroconvulsive therapy (ECT) and transcranial magnetic stimulation (TMS) for major depression.Methods:Data from the six randomised, prospective studies were agglutinated into one data set. Special attention was given to the methods of both TMS and ECT as well as data pertaining to differential outcomes in subgroups such as psychotic depressives and the elderly.Results:There is a highly significant advantage for ECT in the prospective, randomised trials. The two non-randomised, retrospective comparative trials found the treatments to be equal in one study and superior for ECT in another. However, sample sizes are small in these studies, and both TMS and ECT may have been used suboptimally. Furthermore, the possibilities of differential efficacy of ECT or TMS for psychotic depressives or as a function of age have yet to be fully explored.Conclusions:The data to date do not support the contention that TMS is equivalent in efficacy to ECT. It is recommended that a large-scale trial be undertaken using aggressive forms of both TMS and ECT with sample sizes sufficiently large to detect effects of moderating variables such as age and psychosis status.

scholarly journals PARALLEL SPATIOTEMPORAL SPECTRAL CLUSTERING WITH MASSIVE TRAJECTORY DATA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1173-1180 ◽  
Author(s):  
Y. Z. Gu ◽  
K. Qin ◽  
Y. X. Chen ◽  
M. X. Yue ◽  
T. Guo
Keyword(s):  
Data Mining ◽  
Computational Complexity ◽  
Spectral Clustering ◽  
Large Scale ◽  
Trajectory Data ◽  
Wuhan City ◽  
Large Scale Problems ◽  
Trajectory Data Mining ◽  
Taxi Trajectory ◽  
High Computational Complexity

Massive trajectory data contains wealth useful information and knowledge. Spectral clustering, which has been shown to be effective in finding clusters, becomes an important clustering approaches in the trajectory data mining. However, the traditional spectral clustering lacks the temporal expansion on the algorithm and limited in its applicability to large-scale problems due to its high computational complexity. This paper presents a parallel spatiotemporal spectral clustering based on multiple acceleration solutions to make the algorithm more effective and efficient, the performance is proved due to the experiment carried out on the massive taxi trajectory dataset in Wuhan city, China.

Mother nature’s son?

2016 ◽  
Vol 8 (2) ◽  
pp. 137-172 ◽  
Author(s):  
Diana M. Hechavarría
Keyword(s):  
Cultural Values ◽  
Large Scale ◽  
Covariance Structure ◽  
Gender Socialization ◽  
Entrepreneurial Activity ◽  
Future Research ◽  
Female Entrepreneurs ◽  
Data Set ◽  
Content Type ◽  
Country Level

Purpose Drawing on the multiplicity of context approach, this study investigates whether female entrepreneurs are more likely than male entrepreneurs to create environmentally oriented organizations. This study aims to examine how context, measured by gender socialization stereotypes and post-materialism, differentially affects the kinds of organizations entrepreneurs choose to create. Design/methodology/approach To test the hypotheses, this study utilizes Global Entrepreneurship Monitor data from 2009 (n = 17,364) for nascent entrepreneurs, baby businesses owners and established business owners in 47 counties. This study also utilizes the World Values Surveys to measure gender ideologies and post-materialist cultural values at the country level. To test the hypotheses, a logistic multi-level model is estimated to identify the drivers of environmental venturing. Data are nested by countries, and this allows random intercepts by countries with a variance components covariance structure. Findings Findings indicate that female entrepreneurs are more likely to engage in ecological venturing. Societies with high levels of post-materialist national values are significantly more likely to affect female entrepreneurs to engage in environmental ventures when compared to male entrepreneurs. Moreover, traditional gender socialization stereotypes decrease the probability of engaging in environmental entrepreneurship. Likewise, female entrepreneurs in societies with strong stereotypes regarding gender socialization will more likely engage in environmental entrepreneurship than male entrepreneurs. Research limitations/implications The present study uses a gender analysis approach to investigate empirical differences in environmental entrepreneurial activity based on biological sex. However, this research assumes that gender is the driver behind variations in ecopreneurship emphasis between the engagement of males and females in venturing activity. The findings suggest that female entrepreneurs pursuing ecological ventures are more strongly influenced by contextual factors, when compared to male entrepreneurs. Future research can build upon these findings by applying a more nuanced view of gender via constructivist approaches. Originality/value This study is one of the few to investigate ecologically oriented ventures with large-scale empirical data by utilizing a 47-country data set. As a result, it begins to open the black box of environmental entrepreneurship by investigating the role of gender, seeking to understand if men and women entrepreneurs equally engage in environmental venturing. And it responds to calls that request more research at the intersection of gender and context in terms of environmental entrepreneurship.

Impact of Major Road Supply on Individual Travel Time Expenditure: An Exploration with a 30-Year Variation of Infrastructure and Travel

2018 ◽  
Vol 2672 (3) ◽  
pp. 56-68
Author(s):  
Ryosuke Abe ◽  
Kay W. Axhausen
Keyword(s):  
Travel Time ◽  
Large Scale ◽  
Travel Demand ◽  
Road Traffic ◽  
Land Development ◽  
Transportation Infrastructure ◽  
Major Road ◽  
Birth Cohorts ◽  
Data Set ◽  
The Impact

This study estimates the impact of major road supply on individual travel time expenditures (TTEs) using data that cover 30-year variations in transportation infrastructure and travel behavior. The impacts of the supply of road and rail infrastructure are estimated with a data set that combines records of large-scale household travel surveys in the Tokyo metropolitan area conducted in 1978, 1988, 1998, and 2008. Linear and Tobit models of individual TTEs are estimated by following the behavior of birth cohorts over the 30-year period. The models incorporate the changes in transportation infrastructure, measured as lane kilometers of two levels of major road stock and vehicle kilometers of urban rail service. The results show significant negative effects of lane kilometers for higher-level and lower-level major roads on the TTEs for all travel purposes and for commuting, after controlling for socioeconomic backgrounds and generations of individuals. This study discusses that, in Tokyo, the estimated effect is more likely to reflect the effect of a major road network per se on individual TTEs than the (indirect) effect of major road supply on individual TTEs working through land development activities (i.e., induced car travel demand). For example, the caveat is that actual road investment decisions still need to consider the induced component of road traffic in addition to the (direct) effect that is estimated in this study.

Identifying Decision Structures Underlying Activity Patterns: An Exploration of Data Mining Algorithms

2000 ◽  
Vol 1718 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Geert Wets ◽  
Koen Vanhoof ◽  
Theo Arentze ◽  
Harry Timmermans
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Logit Model ◽  
Goodness Of Fit ◽  
Travel Demand ◽  
Activity Patterns ◽  
Future Research ◽  
Data Set ◽  
Data Mining Algorithms ◽  
Mining Algorithms

The utility-maximizing framework—in particular, the logit model—is the dominantly used framework in transportation demand modeling. Computational process modeling has been introduced as an alternative approach to deal with the complexity of activity-based models of travel demand. Current rule-based systems, however, lack a methodology to derive rules from data. The relevance and performance of data-mining algorithms that potentially can provide the required methodology are explored. In particular, the C4 algorithm is applied to derive a decision tree for transport mode choice in the context of activity scheduling from a large activity diary data set. The algorithm is compared with both an alternative method of inducing decision trees (CHAID) and a logit model on the basis of goodness-of-fit on the same data set. The ratio of correctly predicted cases of a holdout sample is almost identical for the three methods. This suggests that for data sets of comparable complexity, the accuracy of predictions does not provide grounds for either rejecting or choosing the C4 method. However, the method may have advantages related to robustness. Future research is required to determine the ability of decision tree-based models in predicting behavioral change.

scholarly journals Trajectory Segmentation Map-Matching Approach for Large-Scale, High-Resolution GPS Data

2017 ◽  
Vol 2645 (1) ◽  
pp. 67-75 ◽  
Author(s):  
Lei Zhu ◽  
Jacob R. Holden ◽  
Jeffrey D. Gonder
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Longest Common Subsequence ◽  
Gps Data ◽  
Map Matching ◽  
Trajectory Data ◽  
Data Set ◽  
Matching Algorithm ◽  
Gps Trajectory ◽  
Trajectory Segmentation

With the development of smartphones and portable GPS devices, large-scale, high-resolution GPS data can be collected. Map matching is a critical step in studying vehicle driving activity and recognizing network traffic conditions from the data. A new trajectory segmentation map-matching algorithm is proposed to deal accurately and efficiently with large-scale, high-resolution GPS trajectory data. The new algorithm separated the GPS trajectory into segments. It found the shortest path for each segment in a scientific manner and ultimately generated a best-matched path for the entire trajectory. The similarity of a trajectory segment and its matched path is described by a similarity score system based on the longest common subsequence. The numerical experiment indicated that the proposed map-matching algorithm was very promising in relation to accuracy and computational efficiency. Large-scale data set applications verified that the proposed method is robust and capable of dealing with real-world, large-scale GPS data in a computationally efficient and accurate manner.

scholarly journals Visualizing Sustainability Research in Business and Management (1990–2019) and Emerging Topics: A Large-Scale Bibliometric Analysis

2019 ◽  
Vol 11 (20) ◽  
pp. 5596 ◽  
Author(s):  
Qiong Jia ◽  
Liyuan Wei ◽  
Xiaotong Li
Keyword(s):  
Economic Growth ◽  
Large Scale ◽  
Future Research ◽  
Data Set ◽  
Holistic View ◽  
Sustainability Research ◽  
Depth Analysis ◽  
Development Goals ◽  
Clear Shift ◽  
Business And Management

While researchers from many disciplines are increasingly interested in studying issues related to sustainability, few studies have presented a holistic view of sustainability from the perspectives of business and management. This bibliometric study quantitatively analyzed a big data set of 30 years of sustainability research (1990–2019), consisting of 37,322 publications and 1,199,398 cited references, visualizing major topics, dynamic evolution, and emerging development. The decade-by-decade in-depth analysis shows a clear shift from a nearly exclusive focus on economic growth and consumption to all three pillars of sustainability, i.e., economic growth, social development, and environmental protection. Highlighting the differences between United Nations’ Sustainable Development Goals and the popular research topics from academia, our analysis uncovers research gaps and suggests future research directions for sustainability researchers and practitioners.

Sign in / Sign up

Export Citation Format

Share Document