Prediction of Commuter’s Daily Time Allocation

Zong, Fang; Jia, Hongfei; Pan, Xiang; Wu, Yaming

doi:10.7307/ptt.v25i5.1190

Cited by 10 publications

(3 citation statements)

References 29 publications

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“…Different from a single or combined travel mode of intercity commuting, the ARIS transfer process is more complex, with more influencing factors and more diversified types of travelers. Additionally, the reservation time can reflect travelers' perception of the complexity of the travel process [27][28][29].…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of the Transfer Time and Influencing Factors of Air-Rail Integration Passengers: A Case Study of Shijiazhuang Zhengding International Airport

Jiang

Wang²,

Huang³

2022

Sustainability

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As China’s high-speed rail and civil aviation gradually move from a competitive relationship to a cooperative relationship, air-rail integration services (ARISs) have begun to be promoted, and an increasing number of passengers are choosing the combined travel mode of high-speed rail and aviation. As two long-distance transportation modes are involved, transfer time also affects transfer service satisfaction, and the factors affecting the transfer time of ARIS need to be further clarified. In this study, a stated preference (SP) survey on the transfer reservation time of ARIS passengers was conducted, the perception accuracy of the transfer reservation time was analyzed, and the influencing factors of the transfer reservation time in each link were analyzed by using a multivariate ordered logistic regression model. The results show that the average transfer time of air-rail combined transportation passengers is 102 to 128 min; more than half of passengers’ reservation time is reasonable; and passengers’ genders, educations, occupations, incomes, travel modes, costs, and distances have different degrees of impact on the transfer reservation time. This study holds a certain guiding significance for improving the service quality of air-rail intermodal transportation and improving transfer efficiency and provides a theoretical basis for personalized and customized services for air-rail intermodal passengers.

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Section: Literature Reviewmentioning

confidence: 99%

Analysis of the Transfer Time and Influencing Factors of Air-Rail Integration Passengers: A Case Study of Shijiazhuang Zhengding International Airport

Jiang

Wang²,

Huang³

2022

Sustainability

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“…This restriction leads to limitation of random taste variation and the independence of irrelevant alternatives, which causes the cross-elastics among all pairs of alternatives to be identical [21]. Compared to the MNL model, SVC has been more frequently applied in modeling disaggregate choices in recent years due to its ability to enhance the prediction accuracy and calculation efficiency [22]. Therefore, this paper will introduce SVC in estimating of travel mode with GPS data.…”

Section: Svcmentioning

confidence: 99%

Identifying Travel Mode with GPS Data Using Support Vector Machines and Genetic Algorithm

et al. 2015

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Travel mode identification is one of the essential steps in travel information detection with Global Positioning System (GPS) survey data. This paper presents a Support Vector Classification (SVC) model for travel mode identification with GPS data. Genetic algorithm (GA) is employed for optimizing the parameters in the model. The travel modes of walking, bicycle, subway, bus, and car are recognized in this model. The results indicate that the developed model shows a high level of accuracy for mode identification. The estimation results also present GA's contribution to the optimization of the model. The findings can be used to identify travel mode based on GPS survey data, which will significantly enhance the efficiency and accuracy of travel survey and data processing. By providing crucial trip information, the results also contribute to the modeling and analyzing of travel behavior and are readily applicable to a wide range of transportation practices.

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“…Moreover, they expanded the notion of a travel time frontier developed by Banerjee et al (16) and concluded that the frontier values, as well as the ratio of the travel time expenditure to these frontier values differ considerably across socio-demographic groups. With respect to the factors explaining differences in daily travel time expenditure a variety of socio-demographic variables, residential characteristics, transport options, modal choice habits and journey characteristics play a significant role (17)(18)(19)(20)(21)(22)(23)(24)(25).…”

Section: Introductionmentioning

confidence: 99%

Effect of Traveler’s Nationality on Daily Travel Time Expenditure Using Zero-Inflated Negative Binomial Regression Models: Results from Belgian National Household Travel Survey

Eftekhar

Creemers

Cools

2016

Transportation Research Record: Journal of the Transportation R

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In this study, data stemming from the 2010 Belgian National Household Travel Survey were used to assess the effect of a traveler’s nationality on daily travel time expenditure. Negative binomial (zero-inflated) models were estimated to isolate the effect of nationality after other contributing factors such as sociodemographics, residential characteristics, transport options, and temporal characteristics were controlled for. The results indicate that even if one controls for a series of other influencing factors, nationality plays a significant role in differences in travel time expenditure. This finding is especially relevant in the development of policy packages that are targeted at social inequalities. From a methodological perspective, methodological options—two weighting schemes and two bootstrap solutions—were presented to provide sufficient support for the conclusions. To generalize the results in further studies, an oversampling of travelers of different nationalities is strongly recommended. Future research should focus more on the underlying psychological constructs of why ethnic and cultural differences persist even if one accounts for other determinants.

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Prediction of Commuter’s Daily Time Allocation

Abstract: ABSTRACT

Cited by 10 publications

References 29 publications

Analysis of the Transfer Time and Influencing Factors of Air-Rail Integration Passengers: A Case Study of Shijiazhuang Zhengding International Airport

Analysis of the Transfer Time and Influencing Factors of Air-Rail Integration Passengers: A Case Study of Shijiazhuang Zhengding International Airport

Identifying Travel Mode with GPS Data Using Support Vector Machines and Genetic Algorithm

Effect of Traveler’s Nationality on Daily Travel Time Expenditure Using Zero-Inflated Negative Binomial Regression Models: Results from Belgian National Household Travel Survey

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