Sequential Gaussian Simulation as a Promising Tool in Travel Demand Modeling

Lindner, Anabele; Pitombo, Cira Souza

doi:10.1007/s41651-019-0038-x

Cited by 10 publications

(5 citation statements)

References 58 publications

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“…The suggestion of a simple procedure to propose a simulation of a spatially correlated database is the last research contribution, taking into account that, despite the observed advances in population synthesizers for travel data microsimulation, none of the approaches recognizes the spatial correlation of data as an extraordinary input to reproduce travel behavior. Although there is a recent application of Sequential Gaussian Simulation to simulate travel demand data (Lindner and Pitombo, 2019), the procedure proposed in this article requires less conceptual effort.…”

Section: Conclusion and Research Contributionsmentioning

confidence: 99%

“…Additionally, despite the observed advances in population synthesizers for travel data microsimulation, none of the approaches recognize the spatial correlation of data as a relevant input to reproduce travel behavior. Recently, Sequential Gaussian Simulation was presented as a promising simulation tool in Travel Demand Modeling (Lindner and Pitombo, 2019). Then, the specific objective of this paper is to propose a simulation of a spatially correlated database, using the intrinsic spatial dependence on travel demand data and mathematical principles of bilinear interpolation.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Spatial Effect on Travel Mode Choice Using a Synthetic Spatially Correlated Data Set

Assirati

Pitombo

2021

Bol. Ciênc. Geod.

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Urban dynamics can be characterized more effectively by considering spatial aspects in studies. This paper, using a synthetic spatially correlated data set, aims to model the spatial effect on travel mode choice based on geostatistics precepts. A method was proposed based on three main steps. The first step consists of building synthetic spatially correlated data, using the intrinsic spatial dependence on travel demand data and mathematical principles of bilinear interpolation. The following two steps correspond to the modeling approach. The Exploratory Spatial Data Analysis stage aimed to attest the existence of spatial autocorrelation of the data set using two indicators: Moran and G-SIVAR (Global Spatial Indicator Based on Variogram). The Confirmatory Spatial Data Analysis stage proposed the calibration of two Binomial Logit models. The first model includes only the original database variables (nonspatial model). The second one is analogous to the original but added to spatial covariates obtained by geostatistical concepts (spatial model). A 15% increase in cross-validation hit rates is achieved when spatial variables are included. This paper presents three significant research contributions: (1) The methodological procedure to model spatial effect on travel mode choice; (2) The proposal of spatial covariates based on geostatistical assumptions; and (3) The suggestion of a simple procedure to propose a simulation of a spatially correlated database.

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Section: Conclusion and Research Contributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling Spatial Effect on Travel Mode Choice Using a Synthetic Spatially Correlated Data Set

Assirati

Pitombo

2021

Bol. Ciênc. Geod.

Self Cite

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“…In view of this, this paper, on the basis of Weibo check-in data, selecting 50 typical Chinese cities as the research objects, uses social network analysis method to study Chinese tourism flow from the perspective of urban network structure, so as to play a guiding role in urban tourism planning, passenger flow prediction and regulation [41]. In addition, it studies the structure of domestic urban tourism flow space, trying to establish a spatial structure system of tourism flows between urban tourism destinations.…”

Section: Literature Reviewmentioning

confidence: 99%

Tourism Flow Between Major Cities During China’s National Day Holiday: A Social Network Analysis Using Weibo Check-in Data

et al. 2020

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“…The lack of data on travel demand variables, which are usually spatially discrete, has led to an increasing number of geostatistical applications to travel demand modeling, with results that represent an important contribution to the planning and operation of transport systems (Gomes et al, 2018;Lindner and Pitombo, 2019;Marques and Pitombo, 2021a;Yang et al, 2018;Zhang and Wang, 2014). Several studies using Geostatistics for spatially estimate travel demand variables can be found in the bibliographic review by Marques and Pitombo (2020).…”

Section: Introductionmentioning

confidence: 99%

Should We Account for Network Distances or Anisotropy in the Spatial Estimation of Missing Traffic Data?

Marques¹,

Favero

Pitombo

2023

TRANSPORTES

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In light of the unavailability of traffic volume data for all road segments, the scientific literature proposes estimating this variable using spatial interpolators. However, most of the methods found use the Euclidean distance between the database points as a proximity measure, in addition to ignoring the anisotropy of the phenomenon. Thus, the objective of the present study was to apply Ordinary Kriging (OK) with network distances and anisotropic OK in traffic volume data on highways in the state of São Paulo (Brazil), comparing its results to the traditional isotropic approach with Euclidean distances. Goodness-of-fit measures confirmed the good performance and better suitability of OK with network distances over the analyses that use Euclidean distances. Addressing the anisotropy of the traffic volume data also helped to improve the results. The proposed method can effectively support estimating traffic volume in segments without flow data.

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Sequential Gaussian Simulation as a Promising Tool in Travel Demand Modeling

Cited by 10 publications

References 58 publications

Modeling Spatial Effect on Travel Mode Choice Using a Synthetic Spatially Correlated Data Set

Modeling Spatial Effect on Travel Mode Choice Using a Synthetic Spatially Correlated Data Set

Tourism Flow Between Major Cities During China’s National Day Holiday: A Social Network Analysis Using Weibo Check-in Data

Should We Account for Network Distances or Anisotropy in the Spatial Estimation of Missing Traffic Data?

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