Travel behaviour and society 

About: Travel behaviour and society is an academic journal published by Elsevier BV. The journal publishes majorly in the area(s): Computer science & Travel behavior. It has an ISSN identifier of 2214-367X. Over the lifetime, 604 publications have been published receiving 11238 citations. The journal is also known as: Travel behaviour and society.

What influences travelers to use Uber? Exploring the factors affecting the adoption of on-demand ride services in California

Abstract: On-demand ride services, such as those offered by Uber and Lyft, are transforming transportation supply and demand in many ways As the popularity and visibility of Uber/Lyft grow, an understanding of the factors affecting the use of these services becomes more important In this paper, we investigate the factors affecting the adoption of on-demand ride services among millennials (ie young adults born between 1981 and 1997), and members of the preceding Generation X (ie middle-aged adults born between 1965 and 1980) in California We estimate binary logit models of the adoption of Uber/Lyft with and without the inclusion of attitudinal variables, using the California Millennials Dataset (N = 1975) The results are consistent across models: we find that highly educated, older millennials are more likely to use on-demand ride services than other groups We also find that greater land-use mix and regional accessibility by car are associated with greater likelihood of adopting on-demand ride services Respondents who report higher numbers of long-distance business trips and have a higher share of long-distance trips made by plane are also more likely to have used these services, as are frequent users of smartphone transportation-related apps, and those who have previously used taxi and carsharing services Among various attitudinal factors that were investigated, individuals with stronger pro-environmental, technology-embracing, and variety-seeking attitudes are more inclined to use ridehailing These findings provide a starting point for efforts to forecast the adoption of on-demand services and their impacts on overall travel patterns across various regions and sociodemographics

Applying mobile phone data to travel behaviour research: A literature review

Abstract: Travel behaviour has been studied for decades to guide transportation development and management, with the support of traditional data collected by travel surveys. Recently, with the development of information and communication technologies (ICT), we have entered an era of big data, and many sources of novel data, including mobile phone data, have emerged and been applied to travel behaviour research. Compared with traditional travel data, mobile phone data have many unique features and advantages, which attract scholars in various fields to apply them to travel behaviour research, and a certain amount of progress has been made to date. However, this is only the beginning, and mobile phone data still have great potential that needs to be exploited to further advance human mobility studies. This paper provides a review of existing travel behaviour studies that have applied mobile phone data, and presents the progress that has been achieved to date, and then discusses the potential of mobile phone data in advancing travel behaviour research and raises some challenges that need to be dealt with in this process.

Applying a random forest method approach to model travel mode choice behavior

Abstract: The analysis of travel mode choice is important in transportation planning and policy-making in order to understand and forecast travel demands Research in the field of machine learning has been exploring the use of random forest as a framework within which many traffic and transport problems can be investigated The random forest (RF) is a powerful method for constructing an ensemble of random decision trees It de-correlates the decision trees in the ensemble via randomization that leads to an improvement of forecasting and reduces the variance when averaged over the trees However, the usefulness of RF for travel mode choice behavior remains largely unexplored This paper proposes a robust random forest method to analyze travel mode choices for examining the prediction capability and model interpretability Using the travel diary data from Nanjing, China in 2013, enriched with variables on the built environment, the effects of different model parameters on the prediction performance are investigated The comparison results show that the random forest method performs significantly better in travel mode choice prediction for higher accuracy and less computation cost In addition, the proposed method estimates the relative importance of explanatory variables and how they relate to mode choices This is fundamental for a better understanding and effective modeling of people’s travel behavior

Zooming into individuals to understand the collective: A review of trajectory-based travel behaviour studies

Abstract: Understanding travel behaviour is significant in travel demand management as well as in urban and transport planning. Over the past decade, with the advancement of data collection techniques, such as GPS, transit smart cards, and mobile phones, various types of travel trajectory data are increasingly complementing or replacing conventional travel diaries and stated preference data. Other location-aware data are used in studying human movement patterns, such as social network check-in data and banknote dispersal data. Abundance of the emerging trajectory data has driven a new wave of travel behaviour research, and introduced new research problems. This paper provides a state-of-the-art review of the travel behaviour studies categorised by trajectory data types. Based on the literature review, research challenges are discussed and promising research topics in this field are proposed for future studies.

Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models

Abstract: Some recent studies have shown that machine learning can achieve higher predictive accuracy than logit models. However, existing studies rarely examine behavioral outputs (e.g., marginal effects and elasticities) that can be derived from machine-learning models and compare the results with those obtained from logit models. In other words, there has not been a comprehensive comparison between logit models and machine learning that covers both prediction and behavioral analysis, two equally important subjects in travel-behavior study. This paper addresses this gap by examining the key differences in model development, evaluation, and behavioral interpretation between logit and machine-learning models for mode-choice modeling. We empirically evaluate the two approaches using stated-preference survey data. Consistent with the literature, this paper finds that the best-performing machine-learning model, random forest, has significantly higher predictive accuracy than multinomial logit and mixed logit models. The random forest model and the two logit models largely agree on several aspects of the behavioral outputs, including variable importance and the direction of association between independent variables and mode choice. However, we find that the random forest model produces behaviorally unreasonable arc elasticities and marginal effects when these behavioral outputs are computed from a standard approach. After the introduction of some modifications that overcome the limitations of tree-based models, the results are improved to some extent. There appears to be a tradeoff between predictive accuracy and behavioral soundness when choosing between machine learning and logit models in mode-choice modeling.