Gender-based differences in school travel mode choice behaviour: Examining the relationship between the neighbourhood environment and perceived traffic safety

2015 ◽  
Vol 2 (4) ◽  
pp. 502-511 ◽  
Author(s):  
Amarpreet Guliani ◽  
Raktim Mitra ◽  
Ron N. Buliung ◽  
Kristian Larsen ◽  
Guy E.J. Faulkner
Keyword(s):  
Traffic Safety ◽  
Mode Choice ◽  
School Travel ◽  
Choice Behaviour ◽  
Neighbourhood Environment ◽  
Travel Mode ◽  
Travel Mode Choice ◽  
Gender Based ◽  
The Relationship

scholarly journals Model for Children's School Travel Mode Choice

2011 ◽  
Vol 2213 (1) ◽  
pp. 78-86 ◽  
Author(s):  
Raghuprasad Sidharthan ◽  
Chandra R. Bhat ◽  
Ram M. Pendyala ◽  
Konstadinos G. Goulias
Keyword(s):  
Mode Choice ◽  
School Travel ◽  
Travel Mode ◽  
Travel Mode Choice

scholarly journals Modelling the effects of COVID-19 on travel mode choice behaviour in India

2020 ◽  
Vol 8 ◽  
pp. 100273
Author(s):  
Eeshan Bhaduri ◽  
B.S. Manoj ◽  
Zia Wadud ◽  
Arkopal K. Goswami ◽  
Charisma F. Choudhury
Keyword(s):  
Mode Choice ◽  
Choice Behaviour ◽  
Travel Mode ◽  
Travel Mode Choice

School travel mode choice in Beijing, China

2017 ◽  
Vol 62 ◽  
pp. 98-110 ◽  
Author(s):  
Rui Zhang ◽  
Enjian Yao ◽  
Zhili Liu
Keyword(s):  
Mode Choice ◽  
School Travel ◽  
Travel Mode ◽  
Travel Mode Choice ◽  
Beijing China

Travel Mode Choice: Effects of Previous Experience on Choice Behaviour and Valuation

2003 ◽  
Vol 9 (1) ◽  
pp. 5-30 ◽  
Author(s):  
Lena Nerhagen
Keyword(s):  
Mode Choice ◽  
Past Experience ◽  
Choice Situation ◽  
Stated Choice ◽  
Choice Behaviour ◽  
Travel Mode ◽  
Travel Mode Choice ◽  
The Individual ◽  
Car Travel ◽  
Analysis Models

This paper investigates how past experience influences choice behaviour and valuation in a hypothetical travel mode choice situation. Using a stated choice question asked of visitors to a major ski resort in Sweden, the author explores whether an individual's choice behaviour, when he or she is offered a comfort improvement to train travel, can be explained with reference to the individual and to the circumstances of his or her previous journey. The analysis models and compares the response behaviour of travellers who used a car and travellers who used the train on their original trip. It is found that past experience influences travellers' choice behaviour. Twenty per cent of former car users choose the train, while most train users again choose the train. As reasons for choosing car travel once again, car users mention a preference for shorter travel time and/or a preference for flexibility, while environmental concerns and long travel distance favour the use of the train. Concerning comfort improvement, as expected, willingness-to-pay estimates for the former train users are lower and more precise than those for the former car users.

scholarly journals Modeling and Predicting Commuters' Travel Mode Choice in Lahore, Pakistan

2021 ◽  
Vol VI (III) ◽  
pp. 106-118
Author(s):  
Fariha Tariq ◽  
Nabeel Shakeel
Keyword(s):  
Nearest Neighbor ◽  
Mode Choice ◽  
Multinomial Logit Model ◽  
Vehicle Ownership ◽  
K Nearest Neighbor ◽  
Choice Behaviour ◽  
Travel Mode ◽  
Travel Mode Choice ◽  
Different Types ◽  
Wide Scale

The travel mode preference exists in both culture and theenvironment. The wide scale of people's mobility makesour cities more polluted and congested, eventually affecting urban assets.Understanding people’s mode choice is important to develop urbantransportation planning policies effectively. This study aims to model andpredict the commuter’s mode choice behaviour in Lahore, Pakistan. A surveywas conducted, and the data was used for model validation. Thecomparative study was further done among multinomial logit model (MNL),Random Forest (RF), and K-Nearest Neighbor (KNN) classificationapproaches. It’s common in existing studies that vehicle ownership is rankedas the most important among all features impacting commuters’ travel modechoice. Since many commuters in Lahore own no vehicle, it’s unclear whatthe rank of factors impacting non-vehicle owners is. Other than thecomparison of predicting the performance of the methods, our contributionis to do more analysis of the rank of factors impacting the different types ofcommuters. It was observed that occupation is ranked as the most importantamong all features for non-vehicle owners.

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