Applying optimization algorithms for spatial estimation of travel demand variables

Rocha, Samille Santos; Pitombo, Cira Souza; Costa, Luis Henrique Magalhães; Marques, Samuel de França

doi:10.1016/j.trip.2021.100369

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…The semi-variogram is one of the most effective methods for analyzing the spatial variation of regionalized variables [55]. In this paper, it is mainly used to reveal and observe the spatial correlation and spatial variation characteristics of regional tourism efficiency.…”

Section: Semi-variogrammentioning

confidence: 99%

Understanding the Evolution of Regional Tourism Efficiency: Through the Lens of Evolutionary Economic Geography

Zhao

Tai

2022

Sustainability

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To further understand the evolution of regional tourism efficiency, a more systematic and theoretical analysis is required. Taking the urban agglomeration in the middle reaches of the Yangtze River as a case, this study applied evolutionary economic geography to analyze the evolutionary process of regional tourism efficiency. Data envelopment analysis (DEA) and the Malmquist index were used to measure the regional tourism efficiency and total factors productivity changes. Moreover, this paper employed the semi-variogram, Kriging interpolation, and Markov chain to explore the spatiotemporal evolution and transition characteristics of regional tourism efficiency. Finally, based on the test results of Geo-detector, the driving mechanism of the spatiotemporal evolution of regional tourism efficiency was constructed. The results show that the overall tourism development was inefficient, and the leading sources of inefficiency were primarily embedded in pure technology inefficiency, while the main contributor to the growth of total factor productivity was the positive technical change. Over time, the spatial spillover effect of regional tourism continued to increase, and the spatial pattern changed from divergence to convergence, resulting in co-evolution. The inertial trajectory of the evolution of regional tourism efficiency reveals a significant path dependence. Factors such as traffic accessibility, tourism resource endowments, tourism specialization, industrial structure, informatization, and openness can reasonably explain the evolution of regional tourism efficiency.

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Section: Semi-variogrammentioning

confidence: 99%

Understanding the Evolution of Regional Tourism Efficiency: Through the Lens of Evolutionary Economic Geography

Zhao

Tai

2022

Sustainability

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“…Meanwhile, socioeconomic factors include population density, distance to the nearest road, distance to the National Capital, distance to residential areas, and the center of business activities [3]. Land cover in a province in Indonesia is divided into eight classes, namely: (1) Forest, (2) Protected/conservation areas, (3) Urban land, (4) Grasslands, (5) Plantations, (6) Wetland agriculture, (7) Dryland farming, and (8) Ponds. Land cover data is obtained from satellite images with a resolution of 30 × 30 meters per pixel with a total of 53,283,768 pixels, so it is classified as location-based big data, called spatial data.…”

Section: Introductionmentioning

confidence: 99%

“…Semivariogram modeling has various applications such as estimating the spatial behavior of agricultural soil fertility by kriging interpolation and parameter estimation using the methods of moments and restricted maximum likelihood [4], comparison of the Matheron, Cressie-Hawkins, and Dowd models for the prediction of theft in the city of Bandung [5], modeling the spread of the insect Bradysia ocellaris on oyster mushroom cultivation using isotropic spherical models [6], and estimating travel demand variables for transportation planning using genetic algorithms and geostatistical methods to predict travel demand variables to optimize the calculation and matching of semivariograms [7].…”

Section: Introductionmentioning

confidence: 99%

Comparison Between Iterative Least Square and Nonparametric Epanechnikov Kernel in Semivariogram Modeling, Case study: Urban Land Cover in East Java Province

Sari,

Budiman,

Pasaribu

et al. 2024

ITM Web Conf.

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Landcover is an example of spatial data that contains location coordinate information along with the variables measured at each location, namely height, slope, and curvature. The spatial relationship between locations can be measured using a semivariogram. Semivariogram is a statistic used to measure the spatial correlation of pairs of locations separated by a certain distance and angle. In estimating semivariogram parameters, namely, nugget, sill, and range, there are two methods, namely parametric via iterative least squares and nonparametric via kernel functions. These two methods will be compared for semivariogram modeling of built-up land in East Java province. The best method is selected based on the smallest SSE value. For each physical factor, the best model with the smallest SSE is the Epanechnikov kernel function, with 3.6 for the elevation SSE, 8.84 ⋅ 10−7 for the slope SSE, and 2.15 ⋅ 10−21 for the curvature SSE. So, it is concluded in this case that the nonparametric kernel Epanechnikov method is much better than the parametric method using Gaussian and exponential models.

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Analysing Urban Transport Using Synthetic Journeys

Luckner,

Wrona,

Grzenda

et al. 2024

Lecture Notes in Computer Science

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Applying optimization algorithms for spatial estimation of travel demand variables

Cited by 4 publications

References 21 publications

Understanding the Evolution of Regional Tourism Efficiency: Through the Lens of Evolutionary Economic Geography

Understanding the Evolution of Regional Tourism Efficiency: Through the Lens of Evolutionary Economic Geography

Comparison Between Iterative Least Square and Nonparametric Epanechnikov Kernel in Semivariogram Modeling, Case study: Urban Land Cover in East Java Province

Analysing Urban Transport Using Synthetic Journeys

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