Travel Mode Choice Research Articles

Impact of Carpooling under Mobile Internet on Travel Mode Choices and Urban Traffic Volume: The Case of China

This paper firstly analyzes the comparative advantages of carpooling under the mobile Internet and traditional travel modes, including buses, private cars and taxis, as well as the differences between carpooling under the mobile Internet and traditional carpooling, so as to obtain the factors that affect travelers’ mode choices. Secondly, the mixed logit model is used to describe the travelers’ travel mode choice behavior, which effectively avoids the limitations of the IIA characteristics and preference randomness of the logit model. Finally, we conducted an SP survey on 1077 samples online and offline. After eliminating some invalid samples, 984 valid ones were obtained. Based on these survey data, we analyze the impacts of carpooling under the mobile Internet on the mode shares of traditional travel modes. The results show that for different trip lengths, carpooling under the mobile Internet has different degrees of substitution for buses, taxis and private cars. That is to say, travelers who previously chose buses and other modes will shift to carpooling due to the mobile Internet. In addition, in most cases, the emergence of carpooling under the mobile Internet is helpful to reduce the traffic volume in the urban road network, thus alleviating the urban congestion. However, when the trip length is short and the seat utilization ratio of carpooling under the mobile Internet is low, carpooling under the mobile Internet will increase the traffic volume.

Do Residents Living in Transit-Oriented Development Station Catchment Areas Travel More Sustainably? The Impacts of Life Events

Transit-oriented development (TOD) is an urban designed model aimed at attracting more sustainable travellers. However, not all TOD projects succeed in maintaining a high rate of sustainable travel behaviour. To examine the impacts of TOD on residents’ travel behaviour, this paper applies binary logistic regression to analyse survey data for 1,298 residents living in the TOD areas in Hangzhou collected in 2020. The results show that socioeconomic characteristics, built environment factors, and travel attitudes play important roles in influencing their travel mode choices. Furthermore, the number of children in households and higher levels of car ownership significantly influence residents’ sustainable travel behaviours. However, it appears that only a limited number of factors can convince car users to shift to sustainable modes of travel, such as their workplace being accessible by metro and attitudes towards changes in accessibility. This research study contributes to the existing literature in terms of enhancing the understanding of travel mode choice behaviours, particularly with regard to people who live near public transport infrastructure, as well as formulating evidence-based TOD policies to achieve more sustainable transport systems.

Role of rural built environment in travel mode choice: Evidence from China

Role of rural built environment in travel mode choice: Evidence from China

Indicadores de mobilidade urbana na RMSP a partir da pesquisa OD-Metrô

Abstract The results of the 2017 Origin and Destination (OD) Metro survey for the Metropolitan Region of São Paulo (acronym in Portuguese: RMSP) were published in 2019. Through a spatial analysis of open data from the OD survey, this article aims to identify characteristics of the daily commuting performed by RMSP residents, investigating spatial patterns of travel time, mode choice, motorization rate, mobility and immobility index, considering population distribution according to different income ranges and disaggregated by OD zone. The 2017 results are compared to those of 2007 to investigate what changed and what remained during the decade.

Travel Mode Choice Prediction Using Imbalanced Machine Learning

Travel mode choice prediction is critical for travel demand prediction, which influences transport resource allocation and transport policies. Travel modes are often characterised by severe class imbalance and inequality, which leads to the inferior predictive performance of minority modes and bias in travel demand prediction. In existing studies, the class imbalance in travel mode prediction has not been addressed with a general approach. Basic resampling methods were adopted without much investigation, and the performance was assessed by commonly used metrics (e.g., accuracy), which is not suitable for predicting highly imbalanced modes. To this end, this paper proposes an evaluation framework to systematically investigate the combination of six over/undersampling techniques and three prediction methods. In a case study using the London Passenger Mode Choice dataset, results show that applying over/undersampling techniques on travel mode substantially improves the F1 score (i.e., the harmonic mean of precision and recall) of minority classes, without considerably downgrading the overall prediction performance or model interpretation. These findings suggest that combining over/undersampling techniques and statistical/machine-learning methods is appropriate for predicting travel mode, which effectively mitigates the influence of class imbalance while achieving high predictive accuracy and model interpretation. In addition, the combination of over/undersampling techniques and prediction methods enriches the model options for predicting mode choice, which would better support transport planning.

Travel Mode Choice Behavior for High-Speed Railway Stations Based on Multi-Source Data

The service quality of connecting travel mode at high-speed railway (HSR) stations plays an essential role in the travel chain. Most travel choice behavior studies are based on survey data and focus on special scenarios, such as going to work or school. Few studies have addressed travel mode choices for connecting to HSR stations. Based on multi-source data, this study aims to investigate the influences of travel time, cost, reliability, detour, and land use on connecting mode choice behaviors at HSR stations. Taking the Xi’anbei railway station as an example, this study analyzes the behavior characteristics of connecting modes including conventional buses, subway transit, and taxis, and builds a connecting travel mode choice behavior model for arrival/departure at HSR stations based on a generalized additive model (GAM). The results show that connecting travelers at HSR stations prefer to choose subways and taxis during the morning and evening peak hours. The perceived costs for arriving travelers are less sensitive than those for departing travelers. Arriving travelers are significantly more sensitive to reliability than departing travelers. Connecting travelers are less sensitive to cost and more sensitive to reliability than commuting travelers. The research results can provide suggestions and guidelines for connecting facility planning in HSR stations.

Understanding Influencing Factors of Travel Mode Choice in Urban-Suburban Travel: A Case Study in Shanghai

After the rapid expansion of the subway system over the past two decades, some cities are preparing to build more suburban railways. The emergence of suburban railways is bound to change the choice of suburban passenger transportation. This paper studies the factors that affect the choice of travel mode at the construction stage of suburban railways, aiming to design a more rational suburban railway network and urban public transport service system. Taking Shanghai as an example, this study first surveyed revealed preference (RP) and stated preference (SP) among urban-suburban travelers. Then, we used discrete choice models (DCM) and machine learning algorithms to build a travel mode choice model based on data collection and analysis. Furthermore, the importance of each factor was analyzed, and the effects were predicted under several traffic demand management schemes. Finally, this study proposed some strategies for increasing the share of public transport. On the one hand, it is suggested that Shanghai should continue to develop suburban railways and maintain low pricing of public transport services. Considering the construction and operation costs, the government needs to provide certain subsidies to stabilize prices. On the other hand, as passengers are very sensitive to the “last mile” trips in their suburban railway travel, transport planners should strengthen the connection from and to the suburban railway stations by developing services such as shared bikes and shuttle buses. In addition, the results indicated that some traffic demand management measures can also contribute to a larger share of public transport.

Understanding the role of household modality style on first and last mile travel mode choice and public transit station choice

From a public transit perspective, the first and last mile (FLM) problem emerges from a lack of adequate connectivity between stations and trip origins and destinations. The empirical literature on FLM travel has shown that passengers travel longer distances to access stations with good transit services. However, previous studies are yet to explore how individual household members interact to find ways to satisfy household travel needs (i.e., intra-household interactions) and how this is shaped by variations in household preference. To this end, capturing the intra-household interactions, this study aims to account for the heterogeneity in group decision making in FLM travel mode and station choice by considering two distinct decision rules when making a travel choice: (1) selecting the best overall outcome; and (2) avoiding frustration or regret. Latent class choice models are estimated by using a household travel survey for Brisbane, Australia. Results identified two household modality style groups: (1) Multimodal travellers accessing their nearest stations and walking averse for accessing non-nearest stations: Such households are likely to select the best overall outcome when making choices, given that they have fewer cars and participate in more joint travel; (2) car-oriented: Individuals within this household modality style are more likely to avoid regret or frustration when making travel mode and station choices as they tend to own more cars and participate less joint travel. The utility of this study lies in its capacity to guide policymakers to better target specific population groups and search for the appropriate transport integration policies.

Residents' Choice of Transportation Mode Based on Multiple Logit Model

In recent years, with the promotion of urbanization, China's urban structure has become larger and larger, and most cities have only one urban center. Commercial and residential areas are mainly concentrated in the city center, with high population density, which leads to great traffic pressure. Therefore, the factors affecting the residents' travel mode can be analyzed. The multiple logit model based on the random utility theory is used to analyze the travel mode of residents, and then the suggestions to improve the urban transportation facilities and land use mode are put forward, which is helpful to solve some urban congestion problems and reduce the urban traffic pressure. This paper takes Hangzhou, which is located in Zhejiang Province in East part of China as an example to analyze the travel mode of residents. Based on the investigation of local residents' choice of travel mode, three general travel modes, namely private cars, public transportation and non-motor vehicles, were selected as logarithmic model parameters for coefficient calibration, and the influence of gender, age, travel purpose, income and other factors on the choice of travel mode were analyzed. The study found that men are more likely to use private cars. The higher the monthly income, the higher the probability of private car travel. If the public transportation is more convenient in the place of departure and destination, the probability of choosing public transportation is also higher.

How Has Anticipated Post-Pandemic Ride-Sourcing Use Changed During the COVID-19 Pandemic? Evidence from a Two-Cycle Survey of the Greater Toronto Area

The COVID-19 pandemic has significantly affected activity-travel behavior in cities across the world, and in particular, travel mode choices. Studies on the topic have attributed shifts in modal preferences to the changes in attitudes toward different modes of travel that resulted from the pandemic. A common theme is that attitudes toward so-called individual modes of travel, such as private vehicles and active modes, have become more positive. In contrast, attitudes toward shared modes have become more negative. Ride-sourcing represents a relatively unique middle ground, as it combines the attributes of individual and shared modes. Given the potential for the availability of these services to influence activity-travel behavior and the operations of transportation networks before the pandemic, the potential nature of post-pandemic ride-sourcing use has important implications for transportation planning. This study uses data from a web-based, two-cycle survey to examine anticipated post-pandemic ride-sourcing usage among pre-pandemic ride-sourcing users in the Greater Toronto Area. The results highlight how anticipated post-pandemic ride-sourcing usage has changed as the pandemic has progressed, including the extent to which the determinants of anticipated usage have shifted. Notably, it was observed that changes in perceptions of risk during the pandemic influence anticipated post-pandemic ride-sourcing use. Furthermore, changes in ride-sourcing use in response to the pandemic and the utilization of ride-sourcing during the pandemic were also found to influence anticipated post-pandemic usage. Overall, the results of this study underscore the potential for post-pandemic ride-sourcing usage to differ from that of pre-pandemic usage.

Contributing Factors to the Changes in Public and Private Transportation Mode Choice after the COVID-19 Outbreak in Urban Areas of China

The COVID-19 pandemic has tremendously affected the whole of human society worldwide. Travel patterns have greatly changed due to the increased risk perception and the governmental interventions regarding COVID-19. This study aimed to identify contributing factors to the changes in public and private transportation mode choice behavior in China after COVID-19 based on an online questionnaire survey. In the survey, travel behaviors in three periods were studied: before the outbreak (before 27 December 2019), the peak (from 20 January to 17 March 2020), and after the peak (from 18 March to the date of the survey). A series of random-parameter bivariate Probit models was developed to quantify the relationship between individual characteristics and the changes in travel mode choice. The key findings indicated that individual sociodemographic characteristics (e.g., gender, age, ownership, occupation, residence) have significant effects on the changes in mode choice behavior. Other key findings included (1) a higher propensity to use a taxi after the peak compared to urban public transportation (i.e., bus and subway); (2) a significant impact of age on the switch from public transit to private car and two-wheelers; (3) more obvious changes in private car and public transportation modes in more developed cities. The findings from this study are expected to be useful for establishing partial and resilient policies and ensuring sustainable mobility and travel equality in the post-pandemic era.

An investigation on Travel mode preferences of university students travelling to home during vacation: An Indian experience

University travel undertaken by students needs to be analyzed for improved mobility planning in India. Universities with residential accommodation facilities for students become one of the largest trip generators particularly during vacation as most of the students travel to their respective hometown. This study provides an insight into the travel mode choices of typical Indian university students travelling to home during vacation, by examining the travel patterns of students belonging to National Institute of Technology, Durgapur, an Indian higher education institution situated in a tier-II industrial Indian town. Initially, a web-based travel behavior survey was designed to elicit socio-economic, mode-specific information from a total of 618 students. Based on the collected data, multinomial logit model was developed to analyze the mode choice decisions of the students. Results indicate that family income, vehicle ownership, travel-characteristics (whether travelling alone or with others) and time of travel significantly affect the mode preferences of student-travelers. It was also observed that with economic prosperity, the use of flexible and comfortable modes of transport like car and bus increases. Family vehicle ownership was also found to play a major role on car usage. Interestingly, bus was found to be the least preferred mode for night-time travel by students travelling alone. The proposed methodology and study findings would help to understand the university students’ travel characteristics while travelling to home during vacations and devise necessary transportation planning policies.

To increase or to decrease the price? Managing public transport queues during COVID-19 in the presence of strategic commuters.

The impact of COVID-19 on urban travel behavior has been unprecedented. It has significantly influenced the travel mode choices of different urban commuters in various countries across the globe. Given that the public transport providers need to tradeoff between minimizing the spread of COVID-19 and providing an affordable travel choice in this environment, we develop a strategic queueing model to analyze the effect of different pricing strategies on the commuter behavior. In particular, we consider a Markovian queue in front of a public transport ticket counter wherein strategic commuters arrive at the service facility and make joining or balking decisions based on their derived utilities. In contrast to conventional wisdom, we suggest that the public transport provider needs to decrease the price to filter out the wealthy commuters who possess feasible alternative travel options from using public transport and promote the commuters with no alternatives in using public transport.

Active travel to school: a longitudinal millennium cohort study of schooling outcomes

ObjectivesAssess longitudinal associations between active travel during the school commute and later educational outcomes.SettingEngland, Wales and Northern Ireland.Participants6778 children, surveyed at ages 7, 11, 14 and 17.Primary and secondary outcomesSchool-leaver...

Understanding cycling dissonance in travel mode choice for work trip chains: The role of modality styles

Nearly 15 percent of workers in Australia live within a cyclable distance of their workplace but commute by private vehicles. Adopting the concept of cycling dissonance, we investigate the role of modality styles alongside built environment characteristics on commuters’ travel mode choice for work trip chains. Using travel survey data and employing latent class choice modelling, results reveal two modality styles: (1) driving and walking oriented commuters; and (2) multimodal commuters. Commuters in the first modality style are mostly those who are aged 20 and above, have dependent children, own a motor vehicle(s), and are insensitive to trip chain complexity. The second modality style captures those who are younger, relatively inelastic to mode-specific travel time, and prefer sustainable transport including cycling for non-complex trip chains. High population density around home residences encourages the choice of cycling for commuters falling within the second modality style, indicating that they are the most likely candidates to undergo a mode shift from motorised transport to cycling. Findings enable us to distinguish commuters by their latent modal preference and better identify the spatial concentrations of candidate commuters with a greater potential for a mode shift, which may be helpful for prioritising cycling-supportive investment in policymaking.

Sentiment Analysis on Multimodal Transportation during the COVID-19 Using Social Media Data

This paper aims to leverage Twitter data to understand travel mode choices during the pandemic. Tweets related to different travel modes in New York City (NYC) are fetched from Twitter in the two most recent years (January 2020–January 2022). Building on these data, we develop travel mode classifiers, adapted from natural language processing (NLP) models, to determine whether individual tweets are related to some travel mode (subway, bus, bike, taxi/Uber, and private vehicle). Sentiment analysis is performed to understand people’s attitudinal changes about mode choices during the pandemic. Results show that a majority of people had a positive attitude toward buses, bikes, and private vehicles, which is consistent with the phenomenon of many commuters shifting away from subways to buses, bikes and private vehicles during the pandemic. We analyze negative tweets related to travel modes and find that people were worried about those who did not wear masks on subways and buses. Based on users’ demographic information, we conduct regression analysis to analyze what factors affected people’s attitude toward public transit. We find that the attitude of users in the service industry was more easily affected by MTA subway service during the pandemic.

Alternative travel mode for commuters with disability in Accra: The lived experiences of Uber and taxi users

This paper explores the alternative travel mode choice among persons with disabilities (PWDs) in the city of Accra. With a limitation in the ownership of private cars among PWDs, public transport plays a critical role in connecting them to various life enhancing opportunities. Despite its importance, the unresponsive nature of Ghana's public transport services and poor attitudes towards the disability community have induced the need for a reliable alternative travel mode hence the need to examine factors that influence this trend in Accra. Using a qualitative research design, this study engaged 28 visually and 22 physically impaired participants through convenience sampling and snowballing. Findings revealed that the lower fees charged for conventional public transport services informed their patronage but the ease, convenience, exclusive services and the friendly reception from Uber drivers remained the key attraction for the increasing trend in their patronage of such services. Unfortunately, higher fees associated with Uber services translated to few trips and fewer social engagements. The study recommends an overhaul of Ghana’s public transport system by ensuring strict compliance to the transport provision of Ghana’s Disability Act given its potential to increase PWDs’ travel options as seen in the enjoyment of lower transport fares.

Exploring the Public Health of Travel Behaviors in High-Speed Railway Environment during the COVID-19 Pandemic from the Perspective of Trip Chain: A Case Study of Beijing-Tianjin-Hebei Urban Agglomeration, China.

The outbreak and spreading of COVID-19 since early 2020 have dramatically impacted public health and the travel environment. However, most of the studies are devoted to travel behavior from the macro perspective. Meanwhile, few researchers pay attention to intercity travel behavior. Thus, this study explores the changes in the travel behavior of intercity high-speed railway travelers during the COVID-19 pandemic from the perspective of the individual. Using the smartphone data, this study first extracts the trip chains by proposing a novel method including three steps. The trip chain can describe the whole process of traveling, including individual characteristics, travel time, travel distance, travel mode, etc. Then, a Multinomial Logit model is applied to analyze the trip chains which verified the validity by using studentized residual error. The study finds that intercity travel behavior has changed in gender, age, travel mode choice, and travel purpose by comparing the trip chains between May 2019 and May 2021 in the Beijing-Tianjin-Hebei urban agglomeration. The method proposed in this study can be used to assess the impact of any long-term emergency on individual travel behavior. The findings proposed in this study are expected to guide public health management and travel environment improvement under the situation of normalized COVID-19 prevention and safety control.

Identifying the Determinants of Anticipated Post-Pandemic Mode Choices in the Greater Toronto Area: A Stated Preference Study.

The COVID-19 pandemic had a significant impact on travel mode choices in cities across the world. Driven by perceptions of risk and the fear of infection, the pandemic resulted in an increased preference for private vehicles and active modes and a reduced preference for public transit and ride-sourcing. As travel behavior and modal preferences evolve, a key question is whether the pandemic will result in long-term changes to travel mode choices. This study uses data from a web-based survey to examine the factors influencing mode choices for non-commuting trips in the post-pandemic era. Specifically, it uses stated preference data to develop a random parameter mixed logit model, which is used to compare the elasticity of key variables across different income and age groups. The results of the study highlight the influence of sociodemographic attributes and pre-pandemic travel habits on anticipated post-pandemic mode choices. Additionally, the results suggest that frequent users of private vehicles, public transit, and active modes are likely to continue to use these modes post-pandemic. Furthermore, the results highlight the potential for the perception of shared modes to influence post-pandemic mode choice decisions. The results of the study offer insights into policy measures that could be applied to address the increased use of private vehicles and reduced use of transit during the pandemic, while also emphasizing the need to ensure that certain segments of the population can maintain a sufficient level of mobility and access to transport.

Joint Impacts of Pricing Strategies and Persuasive Information on Habitual Automobile Commuters’ Travel Mode Shift Responses

Persuasive information developed by smartphone applications is a potential tool that can be utilized in order to increase the effectiveness of the impact of pricing strategies on triggering sustainable travel mode choice behavior. In order to address the joint impacts of pricing strategies and persuasive information on habitual automobile commuters’ travel mode shift responses, a stated-preference survey was conducted in Beijing’s inner district, from which over 1000 responses were collected. Four separate multivariable multilevel logistic regression models were estimated for more and less habitual automobile commuters when subjected to congestion pricing and reward strategies. The model estimation results showed that the influence of persuasive information was more effective in promoting travel mode shifts among more habitual automobile commuters with regard to reward strategies compared to congestion pricing. The results also showed that the impact of sociodemographic characteristics, commuter travel characteristics, the amount of congestion pricing or monetary award, and types of persuasive information on travel mode shift decisions under these strategies were deemed to be significantly different between more and less habitual automobile commuters. These findings suggest that more effective reward strategies can be explored by providing personalized and differentiated travel feedback information (e.g., pollution emission information and physical activity information), particularly for less habitual automobile commuters. This study also provides some degree of insight regarding the question as to how to design future congestion pricing, i.e., with respect to formulating differentiated charge rates according to the travel characteristics of habitual automobile commuters, as well as in developing complementary persuasive information that focuses on addressing public acceptability and fairness rather than travel feedback information.