scholarly journals Kriging Geostatistical Methods for Travel Mode Choice: A Spatial Data Analysis to Travel Demand Forecasting

2016 ◽  
Vol 06 (03) ◽  
pp. 514-527 ◽  
Author(s):  
Viviani Antunes Gomes ◽  
Cira Souza Pitombo ◽  
Samille Santos Rocha ◽  
Ana Rita Salgueiro
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Travel Demand ◽  
Mode Choice ◽  
Demand Forecasting ◽  
Spatial Data Analysis ◽  
Travel Mode ◽  
Travel Demand Forecasting ◽  
Geostatistical Methods ◽  
Travel Mode Choice

scholarly journals Exploring the Factors Influencing Parental Choices on School Trips

2021 ◽  
Author(s):  
Kornilia Maria Kotoula ◽  
George Botzoris ◽  
Georgia Aifantopoulou ◽  
Vassilios Profillidis
Keyword(s):  
Decision Process ◽  
Travel Demand ◽  
Mode Choice ◽  
Travel Mode ◽  
Greek City ◽  
Factors Affecting ◽  
Travel Mode Choice ◽  
Choice Decision ◽  
Transport Modes ◽  
Behavioural Aspects

Within the last decades, the examination and definition of factors affecting the mode choice decision on school trips has gained much of attention, as the completion of such trips represent a vast percentage of total travel demand. Key players of the decision process are students' parents, deciding how their children will complete everyday trips from their residence to the school unit and vice versa. The current study examines the factors affecting parents' travel mode choice for school trips of both primary and high school students in Thessaloniki city, Greece. Data collected is based on a questionnaire survey in which, 512 parents participated, stating their perception regarding the use of several transport modes for school trips and the motives behind specific adopted travel behavioural aspects. Three main topics are examined and analysed related to the parents' attitudes and their travel habits in the choice of motorized and non-motorized transport modes, the parents' perception regarding the built environment safety, and the parents' perception regarding specific parameters which appear to motivate them in the mode choice decision process. For the research analysis, a number of statistical methods and techniques are deployed, starting with descriptive statistical and Pearson's correlation analysis and proceeding with the exploratory and confirmatory factor analysis. The results verify initial thoughts for critical factors which appear to affect parents' choices regarding their children’s school trips while they also gives an initial picture of parents' experiences regarding the school travel mode choice, in an urban environment of a typical Greek city.

scholarly journals Analysis of Travel Mode Choice in Seoul Using an Interpretable Machine Learning Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Eui-Jin Kim
Keyword(s):  
Machine Learning ◽  
Choice Behavior ◽  
Travel Demand ◽  
Mode Choice ◽  
Variable Importance ◽  
Choice Modeling ◽  
Travel Mode ◽  
Extreme Gradient Boosting ◽  
Travel Mode Choice ◽  
Mode Choice Modeling

Understanding choice behavior regarding travel mode is essential in forecasting travel demand. Machine learning (ML) approaches have been proposed to model mode choice behavior, and their usefulness for predicting performance has been reported. However, due to the black-box nature of ML, it is difficult to determine a suitable explanation for the relationship between the input and output variables. This paper proposes an interpretable ML approach to improve the interpretability (i.e., the degree of understanding the cause of decisions) of ML concerning travel mode choice modeling. This approach applied to national household travel survey data in Seoul. First, extreme gradient boosting (XGB) was applied to travel mode choice modeling, and the XGB outperformed the other ML models. Variable importance, variable interaction, and accumulated local effects (ALE) were measured to interpret the prediction of the best-performing XGB. The results of variable importance and interaction indicated that the correlated trip- and tour-related variables significantly influence predicting travel mode choice by the main and cross effects between them. Age and number of trips on tour were also shown to be an important variable in choosing travel mode. ALE measured the main effect of variables that have a nonlinear relation to choice probability, which cannot be observed in the conventional multinomial logit model. This information can provide interesting behavioral insights on urban mobility.

scholarly journals Spatial prediction of a scalar variable based on data of a functional random field

2017 ◽  
Vol 10 (2) ◽  
pp. 315-344 ◽  
Author(s):  
Ramón Giraldo ◽  
Pedro Delicado ◽  
Jorge Mateu
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Auxiliary Information ◽  
Real Data ◽  
Spatial Prediction ◽  
Spatial Data Analysis ◽  
Sampling Location ◽  
Linear Model Of Coregionalization ◽  
Geostatistical Methods ◽  
Functional Context

Kriging and cokriging and their several related versions are techniques widely known and used in spatial data analysis. However, when the spatial data are functions a bridge between functional data analysis and geostatistics has to be built. I give an overview to cokriging analysis and multivariable spatial prediction to the case where the observations at each sampling location consist of samples of random functions. I extend multivariable geostatistical methods to the functional context. Our cokriging method predicts one variable at a time as in a classical multivariable sense, but considering as auxiliary information curves instead of vectors. I also give an extension of multivariable kriging to the functional context where is defined a predictor of a whole curve based on samples of curves located at a neighborhood of the prediction site. In both cases a non-parametric approach based on basis function expansion is used to estimate the parameters, and I prove that both proposals coincide when using such an approach. A linear model of coregionalization is used to define the spatial dependence among the coefficients of the basis functions, and therefore for estimating the functional parameters. As an illustration the methodological proposals are applied to analyze two real data sets corresponding to average daily temperatures measured at 35 weather stations located in the Canadian Maritime Provinces, and penetration resistance data collected at 32 sampling sites of an experimental plot.

scholarly journals Exploratory Spatial Data Analysis of Traffic Forecasting: A Case Study

2022 ◽  
Vol 14 (2) ◽  
pp. 964
Author(s):  
Derek Hungness ◽  
Raj Bridgelall
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Travel Demand ◽  
The United States ◽  
Sustainable Transportation ◽  
Spatial Data Analysis ◽  
Federal State ◽  
Exploratory Spatial Data Analysis ◽  
Demand Model ◽  
State And Local

Transportation planning has historically relied on statistical models to analyze travel patterns across space and time. Recently, an urgency has developed in the United States to address outdated policies and approaches to infrastructure planning, design, and construction. Policymakers at the federal, state, and local levels are expressing greater interest in promoting and funding sustainable transportation infrastructure systems to reduce the damaging effects of pollutive emissions. Consequently, there is a growing trend of local agencies transitioning away from the traditional level-of-service measures to vehicle miles of travel (VMT) measures. However, planners are finding it difficult to leverage their investments in their regional travel demand network models and datasets in the transition. This paper evaluates the applicability of VMT forecasting and impact assessment using the current travel demand model for Dane County, Wisconsin. The main finding is that exploratory spatial data analysis of the derived data uncovered statistically significant spatial relationships and interactions that planners cannot sufficiently visualize using other methods. Planners can apply these techniques to identify places where focused VMT remediation measures for sustainable networks and environments can be most cost-effective.

scholarly journals From attitude to satisfaction: introducing the travel mode choice cycle

2021 ◽  
pp. 1-18
Author(s):  
Jonas De Vos ◽  
Patrick A. Singleton ◽  
Tommy Gärling
Keyword(s):  
Mode Choice ◽  
Travel Mode ◽  
Travel Mode Choice
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