A complex network perspective for characterizing urban travel demand patterns: graph theoretical analysis of large-scale origin–destination demand networks

2016 ◽  
Vol 44 (6) ◽  
pp. 1383-1402 ◽  
Author(s):  
Meead Saberi ◽  
Hani S. Mahmassani ◽  
Dirk Brockmann ◽  
Amir Hosseini
Keyword(s):  
Theoretical Analysis ◽  
Complex Network ◽  
Large Scale ◽  
Travel Demand ◽  
Urban Travel ◽  
Graph Theoretical Analysis ◽  
Demand Patterns

All-Links-Based E-Hailing Pricing and Surcharge Mechanism for Transportation System Performance Improvement

2019 ◽  
Vol 2673 (12) ◽  
pp. 103-114 ◽  
Author(s):  
Qixing Wang ◽  
Nicholas E. Lownes
Keyword(s):  
System Performance ◽  
Large Scale ◽  
Travel Demand ◽  
Inverse Function ◽  
Optimal Level ◽  
Transportation System ◽  
Difficult Problem ◽  
Service Demand ◽  
Demand Changes ◽  
Demand Patterns

E-hailing services, in which riders request rides from their mobile devices, have rapidly developed into a viable transportation alternative for many travelers. This technology has changed the set of choices for travelers and has shifted travel patterns, most significantly away from traditional taxi services. However, several issues have arisen during this expansion. In this paper, an economical approach is proposed which considers both the effects of the travelers’ route choices and travel demand patterns. In this approach, we assume that all links can be surcharged for those using e-hailing services, and a heuristic process is applied to address this computationally difficult problem. A cost inverse function is introduced to update the demand changes along paths with different rates of e-hailing surcharges. The method is demonstrated on the mid-size network of Sioux Falls, South Dakota, and on the large-scale city network of Anaheim, California. Results indicate that an optimal price could efficiently reduce e-hailing service demand during congestion hours and improve the transportation system performance to system optimal level.

scholarly journals GAT-FD: An Integrated MATLAB Toolbox for Graph Theoretical Analysis of Task-Related Functional Dynamics

2021 ◽  
Author(s):  
Meng Cao ◽  
Ziyan Wu ◽  
Xiaobo Li
Keyword(s):  
Theoretical Analysis ◽  
Large Scale ◽  
Human Subjects ◽  
Functional Network ◽  
Normal Brain ◽  
Matlab Toolbox ◽  
Graph Theoretical Analysis ◽  
Link Type ◽  
Functional Dynamics ◽  
Network Properties

ABSTRACTFunctional connectivity (FC) has been demonstrated to be varying over time during sensory and cognitive processes. Quantitative examinations of such variations can significantly advance our understanding on large-scale functional organizations and their topological dynamics that support normal brain functional connectome and can be altered in individuals with brain disorders. However, toolboxes that integrate the complete functions for analyzing task-related brain FC, functional network topological properties, and their dynamics, are still lacking. The current study has developed a MATLAB toolbox, the Graph Theoretical Analysis of Task-Related Functional Dynamics (GAT-FD), which consists of four modules for sliding-window analyses, temporal mask generation, estimations of network properties and dynamics, and result display, respectively. All the involved functions have been tested and validated using fMRI data collected from human subjects when performing a block-designed task. The results demonstrated that the GAT-FD allows for effective and quantitative evaluations of the functional network properties and their dynamics during the task period. As an open-source and user-friendly package, the GAT-FD and its detailed user manual are freely available at https://www.nitrc.org/projects/gat_fd and https://centers.njit.edu/cnnl/gat_fd/.

Nonresponse and Urban Travel Models

1996 ◽  
Vol 1551 (1) ◽  
pp. 82-87
Author(s):  
Piyushimita (Vonu) Thakuriah ◽  
Ashish Sen ◽  
Siim Sööt ◽  
Ed J. Christopher
Keyword(s):  
Large Scale ◽  
Model Building ◽  
Travel Demand ◽  
Gravity Models ◽  
Parameter Estimates ◽  
Trip Generation ◽  
Urban Travel ◽  
Mode Split ◽  
Demand Models ◽  
Simulation Results

Considerable attention has been paid to the presence of nonresponse in large-scale travel surveys on the basis of which urban travel demand models are developed. It has been shown that the effect of nonresponse can be reduced by careful model building, with categorical trip generation models as an example. The same philosophy is extended to logit mode split models and exponential gravity models to show that the usual levels of nonresponse that one encounters in urban travel surveys have virtually no adverse effects on the parameter estimates of these models if the model has been specified correctly. Some simulation results are also presented to show the behavior of logit and exponential gravity model parameter estimates under conditions on nonresponse.

scholarly journals Cortical Thinning and Abnormal Structural Covariance Network After Three Hours Sleep Restriction

2021 ◽  
Vol 12 ◽  
Author(s):  
Chaoyan Wang ◽  
Peng Zhang ◽  
Caihong Wang ◽  
Lu Yang ◽  
Xinzhong Zhang
Keyword(s):  
Theoretical Analysis ◽  
Cortical Thickness ◽  
Emotional Processing ◽  
Large Scale ◽  
Structural Connectivity ◽  
Clustering Coefficient ◽  
Sleep Restriction ◽  
Structural Covariance ◽  
Neurobehavioral Performance ◽  
Graph Theoretical Analysis

Sleep loss leads to serious health problems, impaired attention, and emotional processing. It has been suggested that the abnormal neurobehavioral performance after sleep deprivation was involved in dysfunction of specific functional connectivity between brain areas. However, to the best of our knowledge, there was no study investigating the structural connectivity mechanisms underlying the dysfunction at network level. Surface morphological analysis and graph theoretical analysis were employed to investigate changes in cortical thickness following 3 h sleep restriction, and test whether the topological properties of structural covariance network was affected by sleep restriction. We found that sleep restriction significantly decreased cortical thickness in the right parieto-occipital cortex (Brodmann area 19). In addition, graph theoretical analysis revealed significantly enhanced global properties of structural covariance network including clustering coefficient and local efficiency, and increased nodal properties of the left insula cortex including nodal efficiency and betweenness, after 3 h sleep restriction. These results provided insights into understanding structural mechanisms of dysfunction of large-scale functional networks after sleep restriction.

scholarly journals Aberrant Frontal and Temporal Complex Network Structure in Schizophrenia: A Graph Theoretical Analysis

2010 ◽  
Vol 30 (47) ◽  
pp. 15915-15926 ◽  
Author(s):  
M. P. van den Heuvel ◽  
R. C. W. Mandl ◽  
C. J. Stam ◽  
R. S. Kahn ◽  
H. E. Hulshoff Pol
Keyword(s):  
Theoretical Analysis ◽  
Complex Network ◽  
Network Structure ◽  
Graph Theoretical Analysis

scholarly journals Comparative Analysis of Travel Patterns from Cellular Network Data and an Urban Travel Demand Model

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Nils Breyer ◽  
Clas Rydergren ◽  
David Gundlegård
Keyword(s):  
Cellular Network ◽  
Large Scale ◽  
Travel Demand ◽  
Public Transit ◽  
Small Scale ◽  
Network Data ◽  
Demand Model ◽  
Travel Patterns ◽  
Urban Travel ◽  
Travel Demand Model

Data on travel patterns and travel demand are an important input to today’s traffic models used for traffic planning. Traditionally, travel demand is modelled using census data, travel surveys, and traffic counts. Problems arise from the fact that the sample sizes are rather limited and that they are expensive to collect and update the data. Cellular network data are a promising large-scale data source to obtain a better understanding of human mobility. To infer travel demand, we propose a method that starts by extracting trips from cellular network data. To find out which types of trips can be extracted, we use a small-scale cellular network dataset collected from 20 mobile phones together with GPS tracks collected on the same device. Using a large-scale dataset of cellular network data from a Swedish operator for the municipality of Norrköping, we compare the travel demand inferred from cellular network data to the municipality’s existing urban travel demand model as well as public transit tap-ins. The results for the small-scale dataset show that, with the proposed trip extraction methods, the recall (trip detection rate) is about 50% for short trips of 1-2 km, while it is 75–80% for trips of more than 5 km. Similarly, the recall also differs by a travel mode with more than 80% for public transit, 74% for car, but only 53% for bicycle and walking. After aggregating trips into an origin-destination matrix, the correlation is weak (R2<0.2) using the original zoning used in the travel demand model with 189 zones, while it is significant with R2=0.82 when aggregating to 24 zones. We find that the choice of the trip extraction method is crucial for the travel demand estimation as we find systematic differences in the resulting travel demand matrices using two different methods.

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