Classification and regression tree, principal components analysis and multiple linear regression to summarize data and understand travel behavior

Pitombo, Cira Souza; Sousa, Antonio Jorge Gonçalves de; Filipe, Leandro

doi:10.3328/tl.2009.01.04.295-308

Cited by 12 publications

(7 citation statements)

References 16 publications

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“…This could be due to knowledge retained from previously successful route choices or having route-seeking skills to find optimal routes. Consistent with previous findings in the field of travel behavior (Pitombo et al, 2009), drivers earning between Can$80 000 and $99 999 place more importance on their travel time. As a result, it appears that their time is used more wisely when commuting to work.…”

Section: Resultssupporting

confidence: 91%

Route Choice Efficiency: An Investigation of Home-To-Work Trips Using GPS Data

Papinski

Scott

2013

Environ Plan A

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This paper proposes a new way to study commuting efficiency based on the degree to which actual routes between places deviate from shortest paths. Factors influencing 'route choice efficiency' are investigated using a sample of home-based work trips obtained from the Space-Time Activity Research (STAR) survey, which was conducted in Halifax, Canada, between April 2007 and May 2008. The findings indicate that many drivers pursue suboptimal routes. Furthermore, the results from two ordered probit models reveal that sex, household size, work schedule, actual distance to work, distance to the central business district, travel time, and exposure to public transit are among the factors found to influence route choice efficiency.

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Section: Resultssupporting

confidence: 91%

Route Choice Efficiency: An Investigation of Home-To-Work Trips Using GPS Data

Papinski

Scott

2013

Environ Plan A

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“…Although some of these limitations are absent in other models of discrete choice models, such as Mixed models and Nested Logit models, various studies have been published about applications of Arti8icial Intelligence (AI) tools for forecasting travel demand and their spatial interactions (Faghri and Sandeep, 1998;Tillema et al, 2006;Pitombo et al, 2009;Rasouli and Nikraz, 2013;Pitombo et al, 2017). This research concluded that AI tools can forecast spatial trip distribution accurately.…”

Section: Introductionmentioning

confidence: 89%

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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“…In general, the built environment, socio-demographics and attitudes are found to be the key factors influencing the car ownership and use. In particular, Wang and Cao [8] found that the impacts of built environment on the individuals' activity-travel behaviour are different among different socio-demographic groups, and that neighbourhood planning can affect the influence. Jiang et al [9] pointed out that a strategic urban planning favouring bus rapid transit development, mixed land uses, human friendly streets and restrictive parking are vital to reduce car dependency in rapidly motorizing Chinese cities.…”

Section: Related Studiesmentioning

confidence: 99%

“…Early studies of land use and travel behaviours show that land use characteristics, such as land use intensity and mixed use, are found to have a significant impact on travel decisions [17][18][19][20]. Pitombo et al [21] and João et al [22] found that areas with high degree of mixed land use increase the number of trips using sustainable travel modes like public transportation and non-motorized modes, and travellers there prefer to accomplish short travel distances. Especially, a high degree of mixed land use around a workplace might be associated with a more complex trip chain pattern including both vehicle and walking trips, and residents may have more non-work activities before and after their work time [20,22,23].…”

Section: Travel Pattern Classificationmentioning

confidence: 99%

Spatial Distribution of Travel Activities and its Relationship with Points of Interest

Cao

Yin

et al. 2021

PROMET

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This study explores the spatial distribution characteristics of travel activities and their relationship with land use, using data from the resident travel survey in 2015 of Xiaoshan District of Hangzhou City, China. A new classification method is proposed to classify the travel activity patterns into three groups: single-activity travel, multi-activity intermittent travel, and multi-activity continuous travel. The main findings are: (a) the length of activity chain and the proportion of multi-activity travels increase with the distance between residence and activity centre; (b) the non-home destinations of single-activity travel, multi-activity intermittent travel and multi-activity continuous travel agglomerate towards the activity centre, and the degree of agglomeration increases in this order; (c) the distribution density of Point Of Interest (POI) and activity destinations have strong positive correlations in space; (d) some attributes of POIs and demographics have significant influence on multi-activity continuous travels. These findings are useful in inducing the activities through reasonable combinations and spatial interconnections of POIs in urban planning.

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Classification and regression tree, principal components analysis and multiple linear regression to summarize data and understand travel behavior

Cited by 12 publications

References 16 publications

Route Choice Efficiency: An Investigation of Home-To-Work Trips Using GPS Data

Route Choice Efficiency: An Investigation of Home-To-Work Trips Using GPS Data

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

Spatial Distribution of Travel Activities and its Relationship with Points of Interest

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