Travel Choice Behavior Model Based on Mental Accounting of Travel Time and Cost

This paper analyzes the utility calculation principle of travelers from the perspective of mental accounting and proposes a travel choice behavior model that considers travel time and cost (MA-TC model). Then, a questionnaire is designed to analyze the results of the travel choice under different decision-making scenarios. Model parameters are estimated using nonlinear regression, and the utility calculation principles are developed under different hypothetical scenarios. Then, new expressions for the utility function under deterministic and risky conditions are presented. For verification, the nonlinear correlation coefficient and hit rate are used to compare the proposed MA-TC model with the other two models: (1) the classical prospect theory with travel time and cost (PT-TC model) and (2) mental accounting based on the original hedonic editing criterion (MA-HE model). The results show that model parameters under deterministic and risky conditions are pretty different. In the deterministic case, travelers have similar sensitivity to the change in gain and loss of travel time and cost. The prediction accuracy of the MA-TC model is 3% lower than the PT-TC model and 6% higher than the MA-HE model. Under risky conditions, travelers are more sensitive to the change in loss than to the change in gain. Additionally, travelers tend to overestimate small probabilities and underestimate high probabilities when losing more than when gaining. The prediction accuracy of the MA-TC model is 2% higher than the PT-TC model and 6% higher than the MA-HE model.

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An Empirical Study of Travel Time Variability and Travel Choice Behavior

Transportation Science ◽

10.1287/trsc.16.4.460 ◽

1982 ◽

Vol 16 (4) ◽

pp. 460-475 ◽

Cited By ~ 151

Author(s):

W. Burke Jackson ◽

James V. Jucker

Keyword(s):

Empirical Study ◽

Travel Time ◽

Choice Behavior ◽

Time Variability ◽

Travel Choice ◽

Travel Time Variability

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Predicting Corporate Financial Sustainability Using Novel Business Analytics

Sustainability ◽

10.3390/su11010064 ◽

2018 ◽

Vol 11 (1) ◽

pp. 64 ◽

Cited By ~ 5

Author(s):

Kyoung-jae Kim ◽

Kichun Lee ◽

Hyunchul Ahn

Keyword(s):

Financial Distress ◽

Prediction Accuracy ◽

Prediction Models ◽

Support Vector ◽

Model Parameters ◽

Financial Sustainability ◽

Business Analytics ◽

Financial Distress Prediction ◽

Proposed Model ◽

Distress Prediction

Measuring and managing the financial sustainability of the borrowers is crucial to financial institutions for their risk management. As a result, building an effective corporate financial distress prediction model has been an important research topic for a long time. Recently, researchers are exerting themselves to improve the accuracy of financial distress prediction models by applying various business analytics approaches including statistical and artificial intelligence methods. Among them, support vector machines (SVMs) are becoming popular. SVMs require only small training samples and have little possibility of overfitting if model parameters are properly tuned. Nonetheless, SVMs generally show high prediction accuracy since it can deal with complex nonlinear patterns. Despite of these advantages, SVMs are often criticized because their architectural factors are determined by heuristics, such as the parameters of a kernel function and the subsets of appropriate features and instances. In this study, we propose globally optimized SVMs, denoted by GOSVM, a novel hybrid SVM model designed to optimize feature selection, instance selection, and kernel parameters altogether. This study introduces genetic algorithm (GA) in order to simultaneously optimize multiple heterogeneous design factors of SVMs. Our study applies the proposed model to the real-world case for predicting financial distress. Experiments show that the proposed model significantly improves the prediction accuracy of conventional SVMs.

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Prediction of Commuter’s Daily Time Allocation

PROMET - Traffic&Transportation ◽

10.7307/ptt.v25i5.1190 ◽

2013 ◽

Vol 25 (5) ◽

pp. 445-455 ◽

Cited By ~ 7

Author(s):

Fang Zong ◽

Jia Hongfei ◽

Pan Xiang ◽

Wu Yang

Keyword(s):

Support Vector Regression ◽

Travel Time ◽

Time Allocation ◽

Prediction Accuracy ◽

Travel Demand ◽

Model System ◽

Support Vector ◽

Activity Duration ◽

Wide Range ◽

Duration Prediction

This paper presents a model system to predict the time allocation in commuters’ daily activity-travel pattern. The departure time and the arrival time are estimated with Ordered Probit model and Support Vector Regression is introduced for travel time and activity duration prediction. Applied in a real-world time allocation prediction experiment, the model system shows a satisfactory level of prediction accuracy. This study provides useful insights into commuters’ activity-travel time allocation decision by identifying the important influences, and the results are readily applied to a wide range of transportation practice, such as travel information system, by providing reliable forecast for variations in travel demand over time. By introducing the Support Vector Regression, it also makes a methodological contribution in enhancing prediction accuracy of travel time and activity duration prediction.

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Determinants of Travel Choice Behaviour with Travel-Time Variability in the Presence of Real-Time Information

International Journal of Intelligent Transportation Systems Research ◽

10.1007/s13177-018-0157-7 ◽

2018 ◽

Vol 17 (1) ◽

pp. 61-73 ◽

Cited By ~ 1

Author(s):

Fumitaka Kurauchi ◽

Amr M. Wahaballa ◽

Ayman M. Othman ◽

Nobuhiro Uno ◽

Akiyoshi Takagi

Keyword(s):

Travel Time ◽

Real Time ◽

Time Variability ◽

Choice Behaviour ◽

Travel Choice ◽

Real Time Information ◽

Time Information ◽

Travel Time Variability

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Correction of Mathematical Model Parameters of Polymeric Textile Material Deformation Processes with the Purpose of Increasing the Prediction Accuracy

Proceedings of Higher Education Institutions. Textile Industry Technology ◽

10.47367/0021-3497_2021_5_205 ◽

2021 ◽

pp. 205-212

Author(s):

A.V. Demidov ◽

A.G. Makarov ◽

N.V. Pereborova ◽

A.M. Litvinov

Keyword(s):

Mathematical Model ◽

Prediction Accuracy ◽

Textile Material ◽

Model Parameters ◽

Material Deformation ◽

Deformation Processes

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GeneMark-EP+: eukaryotic gene prediction with self-training in the space of genes and proteins

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqaa026 ◽

2020 ◽

Vol 2 (2) ◽

Cited By ~ 3

Author(s):

Tomáš Brůna ◽

Alexandre Lomsadze ◽

Mark Borodovsky

Keyword(s):

Prediction Accuracy ◽

Gene Prediction ◽

External Information ◽

Model Parameters ◽

Protein Database ◽

Sequencing Project ◽

Automated Method ◽

Eukaryotic Gene ◽

Estimation Of Model Parameters ◽

Eukaryotic Genomes

Abstract We have made several steps toward creating a fast and accurate algorithm for gene prediction in eukaryotic genomes. First, we introduced an automated method for efficient ab initio gene finding, GeneMark-ES, with parameters trained in iterative unsupervised mode. Next, in GeneMark-ET we proposed a method of integration of unsupervised training with information on intron positions revealed by mapping short RNA reads. Now we describe GeneMark-EP, a tool that utilizes another source of external information, a protein database, readily available prior to the start of a sequencing project. A new specialized pipeline, ProtHint, initiates massive protein mapping to genome and extracts hints to splice sites and translation start and stop sites of potential genes. GeneMark-EP uses the hints to improve estimation of model parameters as well as to adjust coordinates of predicted genes if they disagree with the most reliable hints (the -EP+ mode). Tests of GeneMark-EP and -EP+ demonstrated improvements in gene prediction accuracy in comparison with GeneMark-ES, while the GeneMark-EP+ showed higher accuracy than GeneMark-ET. We have observed that the most pronounced improvements in gene prediction accuracy happened in large eukaryotic genomes.

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Exploring Passengers’ Travel Behaviors Based on Elaboration Likelihood Model under the Impact of Intelligent Bus Information

Journal of Advanced Transportation ◽

10.1155/2019/9095279 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Fan Wu ◽

Xiaowei Hu ◽

Shi An ◽

Duo Zhang

Keyword(s):

Travel Time ◽

Significant Influence ◽

Choice Behavior ◽

Elaboration Likelihood Model ◽

Elaboration Likelihood ◽

Time Of Arrival ◽

Road Congestion ◽

Likelihood Model ◽

Types Of Information ◽

The Impact

The ubiquitous intelligent transportation infrastructure in metropolitan cities has enabled bus passengers to access comprehensive (even real-time) bus information. However, the impact of different types of information on passenger behavior is still insufficiently understood. Combining with the theory of information processing path, this study partially fills this gap by adopting an elaboration likelihood model (ELM) suitable for explaining how the various types of intelligent bus information influence passengers’ choice behavior. Six types of intelligent bus information (information of bus lines, estimated travel time, estimated time of arrival, congestion inside bus, road congestion, and bus fare) are used as six independent variables, and passengers’ departure time, travel routes, and travel modes as dependent variables. Valid questionnaire assessments were collected from 285 participants at 4 bus stops equipped with intelligent bus system in Harbin, providing quantitative data to verify each hypothesis. The results show that six types of intelligent bus information to different degrees (significant influence, slight influence, and no significant influence) affect three types of passengers’ choice behaviors; the information of estimated travel time and that of road congestion are both significantly effective in all three types of choice behavior while bus fare has no significant influence. Meanwhile, other types of information have a significant or slight effect on certain behavior. The results of this study can be used to design more reasonable intelligent bus information provision strategies to meet passengers’ requirements.

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