Travel Mode Choice Research Articles

Does the information provider choose to promote public transport use or protect public health? A case study in China

BackgroundHealth risks from air pollution require cutting motorized private vehicle use. Providing new public travel patterns, along with appropriate smartphone-based multimodal travel information services (SMTIS) is considered an effective way to alleviate this stress. However, air pollution is not likely to be solved overnight. There is a growing demand for more detailed air pollution information accessible through mobile apps to avoid exposure to pollution, which may increase the use of private cars. So far, little is known about this contradictory behavior. MethodsBased on 1926 observations obtained from a stated preference experiment conducted in Shanghai, a mixed logit model was developed to investigate the effect of SMTIS on travelers’ mode choice behavior. Specifically, this study aims to reveal the impact of progressively providing typical multimodal information, social influence information, and air pollution information expressed in cigarette equivalents on commuters’ choice of travel modes, including private car, park-and-ride (P&R), and customized bus. ResultsInformation offered by SMTIS, including travel time, travel cost, available parking spaces, comfort level, and air pollution information, exerts significant influences on travel mode choice. Also, there is observed taste heterogeneity for such information across the population. Respondents placed less emphasis on air pollution information during commute trips than on travel time. Providing information on modal pollution exposure prevents people from using public transport. Even when air quality is good, we still observe an increase in the proportion of people choosing cars. With regard to social influence information, people were more likely to choose P&R when online word-of-mouth about P&R displayed a positive propensity, while the percentage of other travelers who had selected P&R did not exhibit a significant effect. ImplicationsThe findings provide valuable implications for the design of low-carbon traffic management policies and the deployment of multimodal information services.

Is environmental behavior related to economic risk preferences? An exploratory case by case analysis.

Do risk preferences play a role when deciding whether to act pro-environmentally? Looking at 28 different behaviors case by case - including recycling, waste reduction, energy and water conservation, consumer behavior, and environmental policy support - our data suggest no relation between most of the behaviors and economic risk preferences. However, economic risk preferences appear to have some relevance for travel mode choice and for specific consumer preferences (eco-friendly detergents, organic food, and single-use plastics), perhaps because people are better able to appreciate aspects of these behaviors related to risk (e.g., possibility of traffic accidents, health risks).

Investigating the Impacts of Urban–Rural Bus Service Quality on Rural Residents’ Travel Choices Using an SEM–MNL Integration Model

The high-quality development of urban–rural buses is an important way to narrow the gap between urban and rural areas. However, studies on the quality of bus services and its impact on bus travel-mode choice have mostly focused on urban buses, with less attention paid to urban–rural buses. This paper aims to explore how urban–rural bus service quality affects rural residents’ travel-mode-choice behavior based on survey data from rural residents in Henan Province, China. A structural equation model (SEM) is applied to examine the critical factors affecting rural residents’ satisfaction with urban–rural bus service quality and the heterogeneity of satisfaction among rural residents with different attributes. Furthermore, an integrated approach of SEM and the multinomial logit (MNL) model is proposed to identify the key factors that affect rural residents’ bus travel-mode choice. The results indicate that reliability and convenience are the key factors affecting satisfaction with urban–rural bus service quality. There are significant differences in rural residents’ satisfaction by gender, age, income, education level, occupation, and travel-mode attributes. Urban–rural bus service quality has a significant positive impact on rural residents’ bus travel-choice behavior, and its degree of influence is greater than all explicit variables except private car ownership. The findings of this study can help operators and governments formulate policies to improve the service quality of urban–rural buses and ultimately promote the use of buses.

Understanding Travel Mode Choice Behavior: Influencing Factors Analysis and Prediction with Machine Learning Method

Building a multimode transportation system could effectively reduce traffic congestion and improve travel quality. In many cities, use of public transport and green travel modes is encouraged in order to reduce the emission of greenhouse gas. With the development of the economy and society, travelers’ behaviors become complex. Analyzing the travel mode choices of urban residents is conducive to constructing an effective multimode transportation system. In this paper, we propose a statistical analysis framework to study travelers’ behavior with a large amount of survey data. Then, a stacking machine learning method considering travelers’ behavior is introduced. The results show that electric bikes play a dominant role in Jinan city and age is an important factor impacting travel mode choice. Travelers’ income could impact travel mode choice and rich people prefer to use private cars. Private cars and electric bikes are two main travel modes for commuting, accounting for 30% and 35%, respectively. Moreover, the proposed stacking method achieved 0.83 accuracy, outperforming the traditional multinomial logit (MNL) mode and nine other machine learning methods.

Exploring the spatial differences in travel mode choice of rail transit in Chongqing

ABSTRACT People’s use of the rail transit system varies over space. Previous literature suggests there is a social equity dimension in the distribution of benefits from new urban transit systems, but how this varies spatially is poorly evidenced. The research aims to examine the spatial differences in the use of rail transit and associated transport equity. Using a case study of Chongqing, a geographically weighted regression (GWR) model is used to reveal the spatial variation in parameter estimates, complementing the traditional multivariate global model. The analysis demonstrates the effect of rail transit in facilitating people’s travel and mitigating transport-related social inequity, including for those with mobility disadvantages and living in areas with poor access to public transport. The results emphasise the necessity of associated development strategies and transport policies in favour of socially disadvantaged groups, such as low fare levels. Implications for planning interventions are proposed based on the model results.

Linking residential mobility with daily mobility: A three-wave cross-lagged panel analysis of travel mode choices and preferences pre–post residential relocation in the Netherlands

The causal impact of the built environment on travel behaviours is a subject of debate. This debate especially concerns the independent effect of the built environment on the observed travel patterns after taking into account residential self-selection arising from pre-existing travel-related attitude. This study argues that travel attitude varies over time, and thus, is also reshaped by residential built environment and interrelated with residents’ travel behaviours. Focusing on the event of residential relocation in the Netherlands, this study longitudinally investigated the interrelations between travel mode choices and preferences before, immediately after and a year after the relocation. Results from the random-intercept cross-lagged panel models substantiated the residential self-selection based on the pre-relocation preferences for motorised means of transport, including cars and public transport. Moreover, travel mode preferences varied to a greater extent than travel mode use pre–post relocation, and especially, frequent use of public transport or bicycles stimulated by the new place of residence had a one-year lagged effect on developing the mode preference. Therefore, the structural role of residential built environment manifests as (re)shaping travel mode choices as well as mode-specific preferences in the process of residential relocation.

Impact of corridor highway system on communities: Built environment and travel mode choices

Conventional corridor management has focused on travel speed and efficiency while ignoring interactions between highway corridors and surrounding areas. This study investigates the interaction between the corridor highway system and the built environment around highways by estimating sustainable mode choices with respect to the land use characteristics of surrounding corridor highways throughout 29 regions in the U.S. The results indicate that the D variables at the corridor level matter. D variables at the corridor level, such as activity density, job-population balance, percentage of accessible jobs within 10 min by car, intersection density, percentage of 4-way intersections, and transit stop density, were highly associated with internal capture, walking trips, and transit use. The neighborhoods with higher activity density, job-population balance, intersection density, and better employment accessibility by transit experienced reduced VMT. The likelihood of internal trips, walking trips, and transit use decreased with household size and income; the opposite signs were found with VMT. The regional gasoline price had significant effects on all trips. In conclusion, this study provides planning and transportation researchers and practitioners with new evidence on the importance of built environmental characteristics (D variables) to encourage sustainable mode choices in the proximate neighborhoods of corridor highways.

Joint prediction of travel mode choice and purpose from travel surveys: A multitask deep learning approach

The prediction and behavioural analysis of travel mode choice and purpose are critical for transport planning and have attracted increasing interest in research. Traditionally, the prediction of travel mode choice and trip purpose has been tackled separately, which fail to fully leverage the shared information between travel mode and purpose. This study addresses this gap by proposing a multitask learning deep neural network framework (MTLDNN) to jointly predict mode choice and purpose. We empirically evaluate and validate this framework using the household travel survey data in Greater London, UK. The results show that this framework has significantly lower cross-entropy loss than multinomial logit models (MNL) and single-task-learning deep neural network models (STLDNN). On the other hand, the predictive accuracy of MTLDNN is similar to STLDNN and is significantly higher than MNL. Moreover, in terms of behaviour analysis, the substitution pattern and choice probability of MTLDNN regarding input variables largely agree with MNL and STLDNN. This work demonstrates that MTLDNN is efficient in utilising the information shared by travel mode choice and purpose, and is capable of producing behaviourally reasonable substitution patterns across travel modes. Future research would develop more advanced MTLDNN frameworks for travel behaviour analysis and generalise MTLDNN to other travel behaviour topics.

Research on Heterogeneous Traveler Travel Mode Choices with Differences under a Mixed Traffic Environment.

Autonomous vehicles (AVs) have been made possible by advances in sensing and computing technologies. However, the high cost of AVs makes privatization take longer. Therefore, companies with autonomous vehicles can develop shared autonomous vehicle (SAV) projects. AVs with a high level of automation require high upgrade and use costs. In order to meet the needs of more customers and reduce the investment cost of the company, SAVs with different levels of automation may coexist for a long time. Faced with multiple travel modes (autonomous cars with different levels of automation, private cars, and buses), travelers' travel mode choices are worth studying. To further differentiate the types of travelers, this paper defines high-income travelers and low-income travelers. The difference between these two types of travelers is whether they have a private car. The differences in time value and willingness to pay of the two types of travelers are considered. Based on the above considerations, this paper establishes a multi-modal selection model with the goal of maximizing the total utility of all travelers and uses the imperial competition algorithm to solve it. The results show that low-income travelers are more likely to choose buses and autonomous vehicles with lower levels of automation, while high-income travelers tend to choose higher levels of automation due to their high value of travel time.

Optimization of ecological sports innovation teaching path based on multiple intelligence evaluation model

Abstract Rural sports tourism is an emerging industry that integrates rural tourism and sports tourism and plays an important role in broadening the field of sports and cultural consumption and driving rural economic development. Under the background of big data, how to better integrate rural sports culture and sports tourism becomes an important issue to improve the quality of rural tourism. This paper analyzes the big data of rural sports tourism on the Internet review platform based on big data processing technology, combined with the investigation and research on the current situation of rural sports tourism resources in a town. The big data analysis shows that rural sports tourists are only 7% more female than male, and the overall gender ratio is relatively balanced. In terms of age composition, the largest number of tourists were between 36 and 45 years old, accounting for 42.6%. In addition, rural sports tourists aged 26 to 60 accounted for 82.6% of all, indicating that rural sports tourism is dominated by middle-aged and elderly people. In terms of travel time, the largest number of rural sports tourists chose 2 to 3 days, accounting for 33.8%, and only 2.3% traveled for more than 7 days. In the choice of travel mode, 37.6% of tourists are organized by travel agencies, accounting for the largest number of travelers. From the big data emotional analysis of the evaluation words of sports tourists, the most frequent occurrence is characteristic, reaching 435 times, and the least frequent is danger, but also 89 times. Meanwhile, convenience and health also appear more frequently. Comprehensive analysis of the above, this paper believes that rural sports tourism should promote more local characteristics of sports culture and highlight differentiation while strengthening personal safety protection measures for tourists.

A world of fully autonomous mobility options: On long-distance travel mode choice

What might long-distance travel behaviour look like in a world of fully autonomous mobility options? Within this context, which of the transportation options, including autonomous cars, pilotless planes, driverless trains, and driverless buses would people choose for their long-distance travel? We examine the extent to which people's attitudes towards fully autonomous mobility options, habits, and the level of satisfaction with current travel modes, as well as individual and socioeconomic variables, influence the choice/preference for fully autonomous modes. Data for this study were collected through an online survey (n = 811). A choice model was used to analyse the effects of attitudinal factors and other variables on the probability of choosing four fully autonomous alternatives. The results of the model show that (1) the preferred order of autonomous mobility options is: Autonomous Car (AC), Autonomous Train (AT), Autonomous Flight (AF), and Autonomous Bus (AB), (2) AT was evaluated the safest, most relaxing, and most economical alternative, and (3) both attitudes and habit influence the choice of fully autonomous vehicles. However, sensitivity analyses show that attitudes will have a greater impact on travel behaviour compared to habits. Policy-wise, a demographically differentiated campaign for changing attitudes can be employed.

Travel Pattern Analysis on Switching Behavior in Response to the COVID-19 Pandemic

The COVID-19 pandemic has significantly affected people’s daily life across the globe. This paper aims to understand the change of individual travel behavior during the pandemic. We administer a survey to record people’s travel habits before, during and after the stay-at-home order in New York City. Respondents’ information, including travel mode, travel frequency, trip purpose and demographics, is gathered to study switching behavior regarding travel patterns during the pandemic. Results show that among all travel mode choices, public transits are the most affected by the pandemic while bikes and automobiles are the least affected by the pandemic.

The complexity of changes in modal choice: A quasi-experimental study

BackgroundChanges in modal choice is argued to be one way to reduce the emission of greenhouse gases. Increasing modal choices in favour of more environmentally friendly travel modes requires a better understanding of how these choices are actually made. The first aim of this study is therefore to examine how modal choice is related to subjective experiences as perceived accessibility, perceived satisfaction, and habit, both before and after an intervention promoting public transport. The second aim is to examine how modal choice is affected by the intervention. Finally, the third aim is to examine how subjective experiences as perceived accessibility, perceived satisfaction, and habit are affected by the intervention. MethodThe design used is a before- and after-study with free public travel passes as the intervention (30- or 14-days free travel pass). Altogether, 52 travelers – distributed on two intervention groups and one control group – participated in the study. The 30-days free travel pass group constitutes 18 participants, the 14-days free travel pass group constitutes 19 participants while the control group constitutes 15 participants. During the before-period the participants were asked to register their modal choice using a digitalized application downloaded on their smart phones (the TravelVu app), to complete a short app-based questionnaire, and a web-based questionnaire. During the after-period, they were once again asked to register their modal choice and to complete a web-based questionnaire. All data collected were analyzed by variance or correlation analyses using the change between before- and after period as the dependent variable. ResultsThe results show that walking was more common than the use of public transport and car, which in turn were more common than the use of bicycle. Perceived accessibility, perceived satisfaction, general health, life quality as well as habit were all rated fairly high. Over time, the use of public transport increased while the use of car decreased overall. With increased use of public transport, the perceived accessibility decreased, but on the other hand, the life quality increased. There was no difference in perceived accessibility, perceived satisfaction, or habit between the three groups, but the participants became overall more satisfied with the standard of their experiences of their everyday travel (cognitive evaluation). At the same time, they became less reflective of their choice of travel mode and less interested in trying out new alternative travel modes during the after- compared to the before period. ConclusionThe intervention did not affect the modal choice or the subjective experiences. Over time, the participants did however increase their use of public transport and their cognitive evaluation of their everyday travel overall, while they decreased their use of car and became less reflective and less interested in trying out new alternative travel modes. These changes might be attributed to their participation in the present study.

Understanding patients heterogeneity in healthcare travel and hospital choice - A latent class analysis with covariates

Access to health care is key to well-being, and it is increasingly clear that aggregated accessibility analysis is hard to reflect people's actual healthcare behaviour. This paper employs a patient-based healthcare travel survey to obtain a nuanced picture of how healthcare travel varies across patients. The existing literature shows transportation is an essential factor in accessing health care; however, most studies focus on separate healthcare travel mode choices or hospital choices for certain segments of patients, making it difficult to derive clear profiles of patients. Also, the attitudinal factors in healthcare travel have long been neglected. This research explores the joint hospital choice and travel behaviour of patients. We conducted an online survey with patients in Shanghai to identify the heterogeneity in healthcare travel behaviour and hospital choice. A latent class model with covariates is adopted to identify different patient types that exhibited distinct hospital choices and healthcare travel behaviour. Attitudinal factors are included in our model to form clear-separated clusters. Four categories of patients are identified: public transit patients, car-oriented patients, near-hospital patients, and non-downtown hospital patients, which differ in sociodemographic characteristics, healthcare-seeking behaviour, and public transit accessibility. Our research shows that a substantial share of non-downtown hospital patients should not be underestimated in healthcare travel demand analysis. The behaviour of public transit and non-downtown patients requires improvement of quality and public transit accessibility in non-downtown tertiary hospitals. Our study contributes to a better understanding of the market segments of patients and tailored healthcare and transport policies to meet patient healthcare travel demand.

Analysis of mode choice affects from the introduction of Doha Metro using machine learning and statistical analysis

The aim of this study was to investigate the possible influences of the operation of the new Doha Metro on the travel mode choice behavior in Doha City, Qatar. Revealed preference (RP) and stated preference (SP) survey questionnaires were designed to collect the necessary data. The questions considered different trip conditions and socioeconomic factors of travelers. Three different mode choices were considered in this study: private cars, taxi services, and metro. Two statistical models and one machine learning model were used to analyze the current and future mode choices: discrete choice binary logit (BL) and multinomial logit (MNL) models as well as extreme gradient boosting (XGBoost). Furthermore, the SHapley Additive exPlanations (SHAP) method was used to rank the input features based on their importance according to the mean SHAP value. The results showed that the XGBoost model outperforms the other two models in terms of predicting the travel mode choice as well as in terms of its accuracy. The results showed that various trip characteristics are significant in determining the mode choice, including the number of travelers and bags, journey time, and reimbursement of parking fees. Furthermore, different socioeconomic characteristics proved to be significant for the current and future mode choices, including nationality, income, age, employment status, and vehicle ownership.

Cross-Border Travel Behavior Analysis of Hong Kong-Zhuhai-Macao Bridge Using MXL-BMA Model

The Hong Kong-Zhuhai-Macao Bridge (HZMB) is an important transportation facility connecting Hong Kong, Zhuhai, and Macao. Thus, analyzing the characteristics of cross-border behavior becomes crucial for enhancing the smart travel experience of the HZMB. Discrete choice models (e.g., logit models) are commonly used to describe travel mode choice behavior. Multinomial logit (MNL) is subjected to the independence of irrelevant alternatives (IIA) assumption. Nested logit (NL) model does not consider the heterogeneity of travel individuals. Mixed logit (MXL) model can overcome the above limitations, but it may neglect model uncertainty. Therefore, a Bayesian model averaging (BMA) approach is applied to model travel mode choice behavior considering using revealed preference/stated preference (RP/SP) fusion data collected by questionnaires online. A structural equation model (SEM) is adopted to explore the potential relationship between latent variables, and two travel modes (i.e., cross-border bus and cross-border private car) are selected to analyze the cross-border travel mode choice of the HZMB. The results reveal that the MXL-BMA approach can better explain the cross-border travel mode choice behavior. And the transportation modes arriving and departing the HZMB have a significant impact on the travel mode choice of the HZMB. The findings of this study can provide suggestions for designing personalized travel services for travelers across the HZMB.

A Bayesian hierarchical approach to the joint modelling of Revealed and stated choices

Revealed and stated choice data are fundamental inputs to understanding individuals’ preferences. Owning to the distinctive characteristics and complementary nature of these two types of data, making joint inference based on their combined information content represents an attractive approach to preference studies. However, complications may arise from the different decision protocols under the two distinct choice contexts. In this study, a Bayesian hierarchical model is proposed to make joint inference from combined RP and SP data, with special attention paid to capturing the behavioural differences between the two choice contexts. In addition to the well-recognised issues of decision inertia and scale differences, the proposed model also takes into account other behavioural characteristics such as a decision-maker ignoring situation constraints, non-attending attributes, and misinterpreting attributes. An empirical analysis of a combined RP and SP dataset of travel mode choices is used to demonstrate the advantageous features of the model. Upon examining the empirical evidence, two main advantages emerge: the model provides direct measures of the effect of behavioural issues arising from ignoring situation constraints and non-attending attributes, as well as evidence for the misinterpretation of attributes.

Assessing the role of public transportation to foster city bike tourism. The case of Italy

Sustainability issues promote the combination of greener transport means when visiting urban environments. This paper focuses on bike tourists by analysing their choice of cities as a destination and the decision to use public transportation to connect within-destination places during holidays. By using data from an Italian survey on bike tourism in 2020, a bivariate probit model is used to study the role of socio-demographics, bike-related factors, travel characteristics, and cycling and accommodation features. The odds of visiting cities are positively affected by the length of stays, available commercial and bike recovery services, while the negative effect of road traffic is confirmed. Instead, combining bikes with public transportation is more likely for low-cost tourists, mainly lodging in B&Bs, and for people with higher sensitivity to bike-related services. From a transport policy perspective, our results support claims for sustainability measures jointly affecting tourists’ decisions on destinations and travel mode choices.

A traveler-centric mobility game: Efficiency and stability under rationality and prospect theory.

In this paper, we study a routing and travel-mode choice problem for mobility systems with a multimodal transportation network as a "mobility game" with coupled action sets. We formulate an atomic routing game to focus on the travelers' preferences and study the impact on the efficiency of the travelers' behavioral decision-making under rationality and prospect theory. To control the innate inefficiencies, we introduce a mobility "pricing mechanism," in which we model traffic congestion using linear cost functions while also considering the waiting times at different transport hubs. We show that the travelers' selfish actions lead to a pure-strategy Nash equilibrium. We then perform a Price of Anarchy and Price of Stability analysis to establish that the mobility system's inefficiencies remain relatively low and the social welfare at a NE remains close to the social optimum as the number of travelers increases. We deviate from the standard game-theoretic analysis of decision-making by extending our mobility game to capture the subjective behavior of travelers using prospect theory. Finally, we provide a detailed discussion of implementing our proposed mobility game.

Can Bike-Sharing Reduce Car Use in Alexandroupolis? An Exploration through the Comparison of Discrete Choice and Machine Learning Models

The implementation of bike-sharing systems (BSSs) is expected to lead to modifications in the travel habits of transport users, one of which is the choice of travel mode. Therefore, this research focuses on the identification of factors influencing the shift of private car users to BSSs based on stated preference survey data from the city of Alexandroupolis, Greece. A binary logit model is employed for this purpose. The estimation results indicate the impacts of gender, income, travel time, travel cost and safety-related aspects on the mode shift, through which behavioural insights are derived. For example, car users are found to be twice as sensitive to the cost of BSSs than to that of car. Similarly, they are highly sensitive to BSS travel time. Based on the behavioural findings, policy measures are suggested under the following categories: (i) finance, (ii) regulation, (iii) infrastructure, (iv) campaigns and (v) customer targeting. In addition, a secondary objective of this research is to obtain insights from the comparison of the specified logit model with a machine learning approach, as the latter is slowly gaining prominence in the field of transport. For the comparison, a random forest classifier is also developed. This comparison shows a coherence between the two approaches, although a discrepancy in the feature importance for gender and travel time is observed. A deeper exploration of this discrepancy highlights the hurdles that often occur when using mathematically more powerful models, such as the random forest classifier.