Strategy to reduce the number of parameters to be estimated in discrete choice models: An approach to large choice sets

Caldas, Marina Urano de Carvalho; Pitombo, Cira Souza; Assirati, Lucas

doi:10.1016/j.tbs.2021.05.001

Cited by 3 publications

(4 citation statements)

References 15 publications

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“…Given that the database has a high number of explanatory variables compared to the number of observations, the variables that would best represent each utility function had to be chosen. Thus, the method developed by Caldas, Pitombo, and Assirati [39] was applied. This strategy uses the classification and regression tree (CART) algorithm to reduce the number of parameters to be estimated without compromising the quality of the model.…”

Section: Methodological Proceduresmentioning

confidence: 99%

“…Therefore, some independent variables could be included for only some of the 5 utility functions. For a detailed methodological explanation, the reader is referred to Caldas, Pitombo, and Assirati [39] and Gomes, Caldas, and Pitombo [48]. With this reduced set of variables, the Variance Inflation Factor (VIF) was also calculated to evaluate any remaining multicollinearity in the data.…”

Section: Methodological Proceduresmentioning

confidence: 99%

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Relationships between Teleworking and Travel Behavior in the Brazilian COVID-19 Crisis

Dias

2023

Future Transportation

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The COVID-19 pandemic caused a significant shift towards teleworking, resulting in changes in travel patterns. The relationship between teleworking and individual travel behavior is crucial for transportation planning and policymaking. Thereby, this article investigates the relationships between teleworking, COVID-19, and mobility patterns in Brazil during two different periods of the COVID-19 pandemic. Given the exceptional nature of recent events, it is crucial to conduct studies related to teleworking during the pandemic crisis to contribute relevant information and evidence to the literature on this phenomenon. Such research has the potential to provide new insights and perspectives that enhance our understanding of the challenges and opportunities associated with teleworking. Two different approaches were used in the methodological procedure. The first approach used exploratory analysis to study public aggregated data related to the beginning of the pandemic, followed by a cluster analysis and a multinomial logit model. The second approach, relative to a longer relaxation period of sanitary/health measures, collected disaggregated self-reported data using an online survey, which were analyzed through non-parametric tests. The results show a strong relationship between the teleworking regime and the frequency of trips, especially for work purposes. It also highlights the influence of economic development and the region of Brazil in the kind of work regime. The continuation of studies, such as this one, in current periods is important for analyzing possible impacts, such as the reduction in congestion, vehicle emissions, and to enable the use of teleworking as a demand management policy.

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Section: Methodological Proceduresmentioning

confidence: 99%

Section: Methodological Proceduresmentioning

confidence: 99%

Relationships between Teleworking and Travel Behavior in the Brazilian COVID-19 Crisis

Dias

2023

Future Transportation

Self Cite

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“…Figure 3 illustrates the layout of the 20 resulting TAZs and Table 1 identi8ies the neighborhoods that make up each TAZ at the end of the clustering. More details regarding the unit areas clustering can be found in the study carried out by Caldas et al (2021). In addition, the household survey database included information about the TAZs of origin and possible destinations and travel distances, which correspond to the Euclidean distances between the TAZs' centroids.…”

Section: Data Processingmentioning

confidence: 99%

Disaggregated approach to urban trip distribution: a comparative analysis between artificial neural networks and discrete choice models

et al. 2022

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Discrete choice models have been used over the years in disaggregated approaches to forecast destination choices. However, there are important constraints in some of these models that pose obstacles to using them, such as the Independence of Irrelevant Alternatives (IIA) property in the Multinomial Logit model, the need to assume specific structures and high calibration times, depending on the complexity of the case being evaluated. However, some of these mentioned constraints could be mitigated using Mixed Models or Nested Logit. Therefore, this paper proposes a comparative analysis between the Artificial Neural Network (ANNs), the Multinomial and Nested Logit models for disaggregated forecasting of urban trip distribution. A case study was conducted in a medium-sized Brazilian city, Santa Maria (RS), Brazil. The data used come from a household survey, prepared for the Urban Mobility Master Plan. For the sake of comparison, hit rates and frequency of trip distribution distances were analyzed, showing that ANNs can be as efficient as the Discrete Choice models for disaggregated forecasting of urban trip destination without, however, assuming some constraints. Finally, based on the results obtained, the efficiency of ANNs is observed for predicting alternatives with a low number of observations. They are important tools for obtaining Origin-Destination matrices from incomplete sample matrices or with a low number of observations. However, it is important to mention that discrete choice models can provide important information for the analyst, such as statistical significance of parameters, elasticities, subjective value of attributes, etc.

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“…Discrete choice model applications provide a good degree of predictability of user behavior and, therefore, have been widely used in the area of transport demand for decades [4,5,6,7,8,9,10,11]. These models make use of data obtained from Revealed Preference (RP) and Stated Preference (SP) techniques, in which the RP data represent choices effectively made by individuals and the SP data refer to choices considering a set of options in which hypothetical scenarios are presented to the consumer for him/her to indicate his/her option [12,13].…”

Section: Discrete Choice Models and Revealed And Stated Preference Su...mentioning

confidence: 99%

In general terms, discrete choice models are calibrated using data obtained from Revealed Preference (RP) and Stated Preference (SP) surveys. In transportation planning, one of the main sources of data is the Origin/Destination (O/D) Survey, which is an R

Gomes

Pitombo

Assirati

2022

ECUM

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In general terms, discrete choice models are calibrated using data obtained from Revealed Preference (RP) and Stated Preference (SP) surveys. In transportation planning, one of the main sources of data is the Origin/Destination (O/D) Survey, which is an RP survey and describes the actual choices and behaviors of individuals. However, it is not possible, through this source, to characterize the alternatives not chosen. This study has two related aims: (1) to propose a criterion to characterize the travel mode alternatives using RP data, and (2) to test the improvement of travel mode choice estimates based on including characteristics of alternatives. First, the CART (Classification and Regression Tree) algorithm was used to characterize the travel times of the travel modes available in the study area (city of São Paulo, Brazil). The trips were classified according to independent variables selected by the algorithm, and average travel time values were obtained for five travel mode alternatives – information not previously available in the RP survey. Finally, the improvement of discrete choice modeling, based on including average travel times, was tested using a validation sample and performance metrics, such as Hit rates and LogLikelihood values. An increase in estimates was observed from including travel duration, and the proposed method is an academic contribution to the modeling based on RP data.

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Strategy to reduce the number of parameters to be estimated in discrete choice models: An approach to large choice sets

Cited by 3 publications

References 15 publications

Relationships between Teleworking and Travel Behavior in the Brazilian COVID-19 Crisis

Relationships between Teleworking and Travel Behavior in the Brazilian COVID-19 Crisis

Disaggregated approach to urban trip distribution: a comparative analysis between artificial neural networks and discrete choice models

In general terms, discrete choice models are calibrated using data obtained from Revealed Preference (RP) and Stated Preference (SP) surveys. In transportation planning, one of the main sources of data is the Origin/Destination (O/D) Survey, which is an R

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