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.

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

scholarly journals A review of Machine Learning (ML) algorithms used for modeling travel mode choice

DYNA ◽  
2019 ◽  
Vol 86 (211) ◽  
pp. 32-41 ◽  
Author(s):  
Juan D. Pineda-Jaramillo
Keyword(s):  
Machine Learning ◽  
Choice Model ◽  
Mode Choice ◽  
Multinomial Logit Model ◽  
Support Vector ◽  
Travel Mode ◽  
Wide Range ◽  
Travel Mode Choice ◽  
And Cluster Analysis ◽  
Transportation Research

In recent decades, transportation planning researchers have used diverse types of machine learning (ML) algorithms to research a wide range of topics. This review paper starts with a brief explanation of some ML algorithms commonly used for transportation research, specifically Artificial Neural Networks (ANN), Decision Trees (DT), Support Vector Machines (SVM) and Cluster Analysis (CA). Then, these different methodologies used by researchers for modeling travel mode choice are collected and compared with the Multinomial Logit Model (MNL) which is the most commonly-used discrete choice model. Finally, the characterization of ML algorithms is discussed and Random Forest (RF), a variant of Decision Tree algorithms, is presented as the best methodology for modeling travel mode choice.

scholarly journals Quantifying Travel Physical Energy Expenditure for Urban Travellers – A Case Study of Beijing

2020 ◽  
Vol 32 (2) ◽  
pp. 219-228
Author(s):  
Xin Hong ◽  
Lingyun Meng ◽  
Jian An
Keyword(s):  
Energy Expenditure ◽  
Public Transportation ◽  
Choice Model ◽  
Mode Choice ◽  
Economic Cost ◽  
Multinomial Logit Model ◽  
Travel Mode ◽  
Physical Energy ◽  
Travel Mode Choice

Travel physical energy expenditure for travellers has impact on travel mode choice behaviour. However, quantitative study on travel physical energy expenditure is rare. In this paper, the concept of travel physical energy expenditure coefficient has been presented. A case study has been carried out of young travellers in Beijing to get the value of physical energy expenditure per unit time under three transport modes, walking, car and public transportation. A series of experiments have been designed and conducted, which consider influence factors including age, gender, travel mode, riding posture, luggage level and crowded level. By analysing the travel data of money, travel time and physical energy expenditure, we determined that the value of travel physical energy expenditure coefficient δ is 0.058 RMB/KJ, which means that travellers can pay 0.058 RMB to reduce 1 KJ physical energy expenditure. Next, a travel mode choice model has been proposed using a multinomial logit model (MNL), considering economic cost, time cost and physical energy cost. Finally, the case study based on OD from Xizhimen to Tiantongyuan in Beijing was conducted. It is verified that it will be in better agreement with the actual travel behaviour when we take the physical energy expenditure for different types of travellers into account.

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 Exploring Influencing Factors of Intercity Mode Choice from View of Entire Travel Chain

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xiaowei Li ◽  
Siyu Zhang ◽  
Yao Wu ◽  
Yuting Wang ◽  
Wenbo Wang
Keyword(s):  
Logit Model ◽  
Unobserved Heterogeneity ◽  
Mode Choice ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Travel Mode ◽  
Intercity Travel ◽  
Travel Mode Choice ◽  
Mixed Multinomial Logit Model ◽  
Significant Factors

Exploring the influencing factors of intercity travel mode choice can reveal passengers’ travel decision mechanisms and help traffic departments to develop an effective demand management policy. To investigate these factors, a survey was conducted in Xi’an, China, to collect data about passengers’ travel chains, including airplane, high-speed railway (HSR), train, and express bus. A Bayesian mixed multinomial logit model is developed to identify significant factors and explicate unobserved heterogeneity across observations. The effect of significant factors on intercity travel mode choice is quantitatively assessed by the odds ratio (OR) technique. The results show that the Bayesian mixed multinomial logit model outperforms the traditional Bayesian multinomial logit model, indicating that accommodating the unobserved heterogeneity across observations can improve the model fit. The model estimation results show that ticket purchasing method, comfort, punctuality, and access time are random parameters that have heterogeneous effects on intercity travel mode choice.

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