Travel Mode Choice Research Articles

A Traffic Equilibrium Model for Multi-Modal Networks with Uncertain Demands

In this study, an effective travel cost (ETC) traffic equilibrium model is proposed for multi-modal networks with uncertain demands. The multi-modal networks are transformed into supernetworks and travel demands are assumed to be closed intervals. Passenger flows and travel costs are also formulated as closed intervals to capture the effects of uncertain demands. The ETC concept is introduced and regarded as a choice criterion to develop an equilibrium model which captures travellers’ travel mode and route choice behavior under interval travel costs. The model is formulated as a variational inequality problem, and the method of successive average algorithm is adapted to interval mathematics to obtain the results in the form of interval variables. Illustrative examples are also presented to demonstrate the model’s characteristics and its differences from the traditional equilibrium model, in which the expected travel cost is regarded as the choice criterion.

Influence of Activity-Travel Participation, Travel Mode Choice, and Multitasking Activities on Subjective Well-Being Using R

Multitasking activities (MTA) are typically thought to enhance general subjective well-being (SWB). However, performing MTA while operating a private vehicle is frequently challenging. Public transportation (PT) can provide an additional option to engage in more pleasurable activities while traveling. Several studies have been conducted on the engagement of different activities while using different transport modes and its influence on physical, social, and mental health. Moreover, numerous studies have been carried out on motorized transport and MTA that resulted in accidents, fatalities, injuries, and even disasters. In addition, several experts studied the influence of health parameters on daily activities. There have, however, only been a few studies on MTA while on PT and its influence on SWB. Therefore, the current study aims to investigate the travel mode choice, the performance of onboard MTA, and its influence on overall SWB. Using random sampling techniques, data on 732 individuals and 191 households—representing 0.029% of the overall population of Bandung, Indonesia—were gathered. Two different models were developed between independent, intermediate, and dependent variables. Statistical Package for Social Sciences (SPSS) was used for descriptive statistics, whereas R software was used for the multilevel linear regression analysis. The model estimation results show that MTA mediates the relationship among socio-demographic and economic variables, built environment, trip and travel parameters, and SWB. A unit increase in PT lines can provide a 1.5% greater opportunity to participate in more onboard MTA; however, a unit increase in MTA can enhance SWB by 5.1% where both the models show satisfactory coefficient of determination (R2). A unit increase in motorized transport caused a 12.9% negative association with MTA and 10.9% with SWB. A unit increase in NMT and PT are 21.7% and 10.2% positively associated with MTA and 19.2% and 13.1% positively associated with SWB. The current study helps policymakers to develop a policy based on PT which allows the individuals to engage in more MTA that enhance SWB and target sustainable transportation system.

Study on Induced Passenger Flow Forecast for Intercity High-Speed Rail

To forecast the induced passenger flow of intercity high-speed rail, two related multinomial logit models are applied to describe travelers’ travel choice behavior under changes in the travel environment. The first model describes different choices of travel mode by travelers between origin–destination pairs, while the second model describes the choice of travel frequency by different travelers. By introducing the consumption surplus variable of travelers, the two selected models are correlated and the model is calibrated using data from a behavioral survey and an intention survey. The results show that the polynomial logit model is applicable for forecasting induced passenger flow after changes in the travel environment. The consumption surplus of travelers is the key factor influencing changes in travel frequency, and the social characteristics of travelers and the economic development features of origin and destination cities have significant impacts on travel frequency. In-transit time is the key factor affecting the demand elasticity of travel frequency. The range of demand elasticity for short-distance business and non-business travel is between −0.61 and −0.79. The results of the demand elasticity analysis were applied to predict that the travel frequency of people from Nanjing to Huai’an, Suqian, Lianyungang, and Yancheng will increase by about 45% after the opening of the Nanjing-Huai’an intercity high-speed railway.

How do adolescents experience the decision-making process on travel mode choice?

Active travel can contribute to multiple health benefits in youth. Previous research has identified several factors influencing travel behavior. This study investigates how adolescents process these factors during their decision-making process on travel mode choice. Semi-structured interviews were conducted with 13 adolescents (11-14 years) and analysed using deductive-inductive thematic analysis. Four themes were generated from which the decision-making process on mode choice was conceptualized according to adolescents. The step-by-step process in which travel mode options were gradually reduced, was dependent on the context (Theme 1), the perceived availability of travel mode choice options, which was influenced by factors beyond the individual's perceived control (Theme 2), and on adolescents' pros/cons assessment to identify the most convenient option. Adolescents' habitually used travel mode influenced the decision at several stages throughout the process (Theme 4). To promote healthy travel behaviors, interventions should consider contextual circumstances, balance between enabling autonomous choices and promoting advantages of active travel modes, address individual travel modes and include practices that foster conscious processing of decision-making.

How is the acceptance of new energy vehicles under the recurring COVID-19 — A case study in China

COVID-19 threatens human life and health, and affects travel frequency and travel mode choice. Clarifying the main drivers of the purchase intention of new energy vehicles (NEVs) under the epidemic is important for formulating phasing policy regarding the promotion of green mobility and the construction of smart cities. Based on this, this study introduces variables such as perceived risk and trust under the epidemic, aiming to investigate the main drivers of the purchase intention of NEVs in China under epidemic through an extended technology acceptance model. The data covered the public in urban, suburban, and rural areas of China (n = 884) and were analyzed using structural equation model. Specifically, trust had a significant positive effect after the epidemic. Perceived risk did not directly affect the public’s intention to purchase new energy vehicles, but had an indirect effect through attitude and perceived utility, attitude and trust, and social interaction and trust. In addition, the significant positive impacts of subsidy and non-subsidy incentive are further validated, which reinforces the current view. Considering the limited literature related to the purchase intention of NEVs under the epidemic in developing countries, it is hoped that these findings will provide new perspectives for future research, and provide guidance for phasing policy formulation.

An exploratory analysis of factors that encourage students to choose sustainable modes in travelling to schools: evidence from Victoria, Australia

ABSTRACT Travelling using sustainable modes, such as active transport and public transport, improves students’ well-being and mental health, and bolsters sustainability. This study aims to investigate factors that encourage students to use sustainable modes which are public transport and active transport. The random parameters (mixed) logit was employed using the Victorian Integrated Survey of Travel and Activity (VISTA) data from Victoria, Australia to comprehensively explore trip attributes, socio-demographics, and built environment factors impacts on school travel mode choice. The inadequacy of model validation in previous research was addressed with the inclusion of the model accuracy test, which arguably is as equally important as behavioural inference. The result showed that travel distance and destination to primary school were the biggest contributors to mode choice probability, negatively affecting sustainable mode choice. The model produced an excellent overall accuracy (85.80%). This research elaborated on the inference of factors affecting mode choice and thus provided recommendations to help develop policies supporting sustainable transportation plans to improve students’ well-being.

Understanding travel mode choice through the lens of COVID-19: a systematic review of pandemic commuters

ABSTRACT The COVID-19 pandemic disrupted travel behaviours for very large numbers of people including those who shifted to teleworking and those without the option to work from home. While there is much valuable transport research that has examined the former category, it is still unknown how certain people such as health sector employees and delivery drivers changed their physical commuting in transport contexts that were radically different from those existing normally in urban areas. Based on a systematic review of 36 scientific publications on commuting during pandemic, this study pursues a dual objective. First, by examining the interrelated institutional, physical, and socio-psychological processes that supported or hindered low-carbon transport the study revealed that (A) public transport (PT) reduced service levels and concerns related to COVID were positively associated with substantial shifts away from PT towards car and active travel; (B) this positive association was found to be even stronger in the existence of pre-pandemic habit of car use for commute and strong negative emotions like fear triggered by environmental changes and health risks. Second, by synthesising the key findings from the literature, this study provides significant implications for how mode choice is modelled through the Theory of Planned Behavior and Norm Activation Model. By questioning whether the pandemic commuters had a “normal” set of travel mode alternatives to choose from, the study draws attention to the nuances of mode “choice” versus mode “use” and moves beyond the assumption that commuting always results from individuals making choices. It also argues that the role of (negative) emotions along with the importance of proximity to, or separation from, other bodies on how people commute should be considered in future research. Finally, the crucial role of COVID-19 in changing travel-related norms and the resulting long-term implications for policy interventions require further investigation by future research.

FHR-NSGA-III: A hybrid many-objective optimizer for intercity multimodal timetable optimization considering travel mode choice

FHR-NSGA-III: A hybrid many-objective optimizer for intercity multimodal timetable optimization considering travel mode choice

Effects of risk perception on commuters’ mode choice behavior during the COVID-19 pandemic: Evidence from Shenzhen, China

Coronavirus disease 2019 (COVID-19) has influenced and is still influencing people’s daily lives worldwide. The infection rate and health and safety countermeasures may influence people’s epidemic risk perception, thus influencing their travel mode choice. It is critical to understand epidemic risk perception and its impact on travel mode choice behavior to ensure the timeliness and effectiveness of health and safety policy implementation. In this study, we conducted an online survey to investigate how commuters’ epidemic risk perception affects travel mode choice behavior in Shenzhen. The online survey mainly includes Likert scale questions and a stated preference experiment with three scenarios to capture commuters’ epidemic risk perceptions and travel mode choice behaviors. First, confirmatory factor analysis was used to obtain three dimensions of epidemic risk perception from the Likert scale questions. Then, three subpopulation groups, including risk-sensitive commuters, risk-insensitive commuters and cautious commuters, were classified based on their risk perception using clustering analysis. Finally, a latent class choice model was estimated to capture the influencing factors of mode choice behavior. Three classes of mode choice behavior were identified, including those who can accept using public transportation, those who avoid using public transportation and tend to bicycles/electric bicycles and those who avoid using public transportation and tend to personal cars. The obtained results can provide valuable support for decision-making during and after an epidemic. Firstly, by offering insights into the intricate risk perception characteristics of Shenzhen residents, these findings can contribute to the effective management of epidemic risks during an epidemic. Secondly, by revealing the factors that influence individuals' preferences for travel modes, these results play a pivotal role in enhancing the market share of the public transportation system after the epidemic.

What drives them to drive? Mode choice for holiday travel in Poland and its determinants

The article presents insights into holiday travel and its determinants in Poland. The purpose of the study was to analyze Polish citizens’ modal split and its determinants. Raw data from a pilot survey conducted in 2015 were used as the source material. To identify the determinants of travel mode choice for holiday trips, a multilevel multinomial logit model was utilized. This approach made it possible to include the hierarchical structure of the data, in which respondents are clustered within municipalities. The results reveal that apart from the decision-maker’s socioeconomic characteristics and household attributes, trip characteristics significantly determine Polish citizens’ choice of holiday travel mode. Moreover, the inclusion of municipality-level predictors substantially improved the accuracy of the model. The analysis revealed that the severity of the environmental consequences of motorized transport perceived by respondents also significantly influences their travel mode choice for holiday trips.

An empirical analysis of the factors influencing Scottish residents’ compliance with COVID-19 travel restrictions

This study uses survey data to investigate the factors affecting compliance with the Scottish Government’s COVID-19 travel restrictions. The survey contained self-reported data (n = 819) regarding various aspects of Scottish residents’ COVID-19 travel behaviour. For a question gauging compliance with COVID-19 travel restrictions, ∼57% of respondents complied fully, ∼35% complied mostly and ∼7% were slightly compliant or non-compliant. A random parameters ordered probit model, with allowances for heterogeneity in the means of random parameters, was estimated to unveil the factors affecting levels of compliance. The model estimation results showed that various sociodemographic characteristics of respondents, including age, gender and household income significantly affected compliance. Other significant independent variables were dominated by perceptual and behavioural characteristics of respondents, such as public trust in the Scottish Government, COVID-19 risk perceptions, COVID-19 information channels and travel mode choices at different stages of the pandemic. Two variables gauging car ownership (those with no car) and COVID-19 information channels (received through online sources) produced statistically significant random parameters, which suggests heterogeneous levels of compliance among these groups. In addition, one instance of heterogeneity in the mean of the online sources random parameter was discovered, such that levels of compliance among this group were dependent on a variable gauging highest education level. Policy implications are discussed in terms of tailoring the Scottish Government’s public health messaging to certain demographics, while the most effective channels for delivering public health information, in the case of COVID-19 and future pandemics, are also explored.

A Study on the Formation and Distribution Mechanisms of the Demand for Shared Electric Vehicles

With the decarbonization of the transportation sector and the diversification of travel demand, the development of shared electric vehicles has become crucial. Based on survey data of travel mode and destination of shared electric vehicles in Beijing, this paper aims to explore the formation and distribution mechanisms of the demand for shared electric vehicles. First of all, a multi-index and multi-cause (MIMIC) model was established to quantify the psychological latent variables that cannot be directly observed and to analyze the mechanisms between individual socio-demographic attributes and latent variables. Secondly, these psychological latent variables were added to mixed logit (ML) models as explanatory variables to form hybrid choice models to explore the travel mode choice behavior and travel destination choice behavior, respectively, when using shared electric vehicles for leisure travel. The results show that potential users of shared electric vehicles are characterized by higher education, employees of enterprises, no car availability and high driving years, and most of them travel for the purpose of connecting to transport hubs. Latent variables such as individual carbon trading, subjective norms, risks and behavioral intentions all affect the demand for shared electric vehicles; in-car time, out-of-car time, travel cost and the number of subway stations have negative impacts on the demand, while mall properties and the number of parking lots have positive impacts on the demand. Furthermore, the use of shared electric vehicles is highly correlated with the use of cars and subways, and part of the travel demand could be transferred to shared electric vehicles by taking certain measures.

Shared Autonomous Vehicles as Last-Mile Public Transport of Metro Trips

The “last-mile problem” of public transportation is one of the main obstacles affecting travelers who choose to utilize public transport. Although autonomous vehicles (AVs) have made much progress, they have not been officially put into commercial use. This paper adopts stated preference experiments to explore the impact of shared AVs on the last-mile travel behavior of metro users and takes Wuhan as an example for case analysis. First of all, this paper establishes a structural equation model (SEM) based on the theory of planned behavior to explore latent psychological variables, including travelers’ attitudes (ATTs), subjective norms (SNs), perceived behavior control (PBC), and behavioral intention of use (BIU) toward AVs. These latent psychological variables are incorporated into the latent class (LC) logit model to establish a hybrid model with which to study the factors and degree of influence on the travel mode choices of travelers for the last mile of their metro trips. The results show that travelers have preference heterogeneity for the travel mode choices for the last mile of metro trips. Through the analysis of LCs, education, career, and income significantly impact the classification of LCs. The latent psychological variables towards AVs have a significant impact on the travel behavior of respondents, but the impacts vary among different segments. Elastic analysis results illustrate that a 1% increase in the travel cost for shared AVs in segment 1 leads to a 7.598% decrease in the choice probability of using a shared AV. Respondents from different segments vary significantly in their willingness to pay for their usage, and the value of travel time for high-income groups is relatively higher.

Persona Design Methodology for Work-Commute Travel Behaviour Using Latent Class Cluster Analysis

The present study proposes a new methodology that combines quantitative and qualitative data for the generation of representative personas for commuters. The profiles can be used to better understand their travel behaviour and mode choices. The research is based on the example of the region of Agder in Norway and aims to overcome the persona development shortcomings identified by previous researchers. Data from a regional travel behaviour survey (N= 1 849) is analysed using latent class cluster analysis (LCCA), and enriched with qualitative input from 32 interviews, and information provided by an expert panel. This results in a set of 20 representative persona profiles for the case study region. The proposed methodology is easily replicable in other urban networks and has the potential to provide insight into the mobility behaviour and needs of specific groups of people in order to adapt the transport services and encourage climate-friendly behaviour.

The post-COVID-19 road-based tourism market: Negotiation of travel constraints

The novel coronavirus (COVID-19) not only transformed people’s choice of travel mode but also resulted in travel constraints and related behaviour. This study investigated travel constraints and constraint negotiation in post-COVID-19 road-based tourism in Indonesia. An online questionnaire survey was administered to Indonesian road tourists including self-drive tourists between May to July 2022. The resulting 510 completed questionnaires were analysed using Structural Equation Modelling. Road trips were more popular than flights during the recovery stage of COVID-19. Intrapersonal travel constraints were the largest problem for road-based tourism. This research advances the understanding of road trip travel behaviour in Indonesia and travel constraints after a crisis. The study applies the constraint negotiation model in a road-based travel context. Our study is among the first to combine road tourism psychology and tourism crisis/disaster management in the context of road-based tourism, which may be useful to policymakers and to tourism industry stakeholders in general.

Does high-speed rail mitigate peak vacation car traffic to tourist city? Evidence from China

Tourist travel contributes greatly to transport problems in attractive tourist cities. To take full advantage of high-speed rail (HSR) for alleviating massive car traffic during the peak vacation period, this paper analyses the travel modes of domestic visitors to Shaoxing before and after the operation of HSR. Scenario-based comparison and a random-coefficients structure Mixed Logit (MXL) model with error components were adopted to analyze the travel mode change and the factors explaining tourists’ travel mode choices. Our findings show that the HSR modal share increased substantially at the expense of express buses, more than cars. Also, HSR was found to be less competitive than cars on toll-free days for medium short travel distances. The MXL model results indicate that HSR was more likely to be used over automobiles by young people, females, and one-destination travellers, for longer travel distances, and with high service frequency to Shaoxing. Besides, online booking services were highly associated with HSR use. Driving was favoured over HSR by higher income level groups, when travelling with family or friends, on toll-free national holidays. Current government policy to waive road tolls during the peak holiday period further induced car traffic to tourist cities. When individual taste was considered, tourists showed a similar preference in their valuation of the travel time variable, while were heterogenous in their preference for low per-distance cost. Our findings suggest that the adjustment of the road-toll policy, pre-booking design for targeted tourists, and measures to reduce the total travel time of HSR should be considered to promote HSR as well as impede the use of cars during peak periods. This study offers empirical evidence of achieving effective travel demand management and reducing car dependence through HSR and complementary measures.

A prediction and behavioural analysis of machine learning methods for modelling travel mode choice

The emergence of a variety of Machine Learning (ML) approaches for travel mode choice prediction poses an interesting question to transport modellers: which models should be used for which applications? The answer to this question goes beyond simple predictive performance, and is instead a balance of many factors, including behavioural interpretability and explainability, computational complexity, and data efficiency. There is a growing body of research which attempts to compare the predictive performance of different ML classifiers with classical Random Utility Models (RUMs). However, existing studies typically analyse only the disaggregate predictive performance, ignoring other aspects affecting model choice. Furthermore, many existing studies are affected by technical limitations, such as the use of inappropriate validation schemes, incorrect sampling for hierarchical data, a lack of external validation, and the exclusive use of discrete metrics. In this paper, we address these limitations by conducting a systematic comparison of different modelling approaches, across multiple modelling problems, in terms of the key factors likely to affect model choice (out-of-sample predictive performance, accuracy of predicted market shares, extraction of behavioural indicators, feature importance analysis, and computational efficiency). The modelling problems combine several real world datasets with synthetic datasets, where the data generation function is known. The results indicate that the models with the highest disaggregate predictive performance (namely Extreme Gradient Boosting (XGBoost) and Random Forests (RF)) provide poorer estimates of behavioural indicators and aggregate mode shares, and are more expensive to estimate, than other models, including Deep Neural Networks (DNNs) and Multinomial Logit (MNL). It is further observed that the MNL model performs robustly in a variety of situations, though ML techniques can improve the estimates of behavioural indices such as Willingness To Pay (WTP).

Investigating correlates of personal and freight road transport energy consumption: A case study of England

In most countries worldwide, the transport sector is responsible for a large proportion of energy consumption, the emissions of which have adverse effects on the environment and human health. It is therefore important to understand the determinants of road transport energy consumption in an attempt to minimise these adverse effects. This paper examines the association which road transport energy consumption, for both personal and freight uses, has with a number of area-level factors, covering a host of socio-economic, built environment and travel mode choice variables. We considered England as our case study, using local authority level data. A random parameters multilevel regression model was utilised in order to accommodate the hierarchical structure of the data, with local authorities nested within major areas of England, and to address unobserved heterogeneity more fully. We paid a particular attention to understand the association between levels of active travel and road transport energy consumption, as this is less-understood. Most notably, gross disposable household income per capita had a positive association with personal road transport energy consumption, and the proportion of walking and cycling had a negative association with both personal and freight consumption. The analysis presented here may be useful in modelling the effect that anticipated changes might have on road transport energy consumption, for instance, new transport developments. In particular, local authorities may consider making a concerted effort at promoting active travel as this was found to be highly negatively associated with road transport energy consumption. As well as this, an insight into the disparity in transport energy consumption between geographical areas is provided, which may otherwise go unobserved.

Development of Structural Equation Model for Travel Mode Choice Behaviour of Working Population in Thiruvananthapuram City

Development of Structural Equation Model for Travel Mode Choice Behaviour of Working Population in Thiruvananthapuram City

A greener last mile: Analyzing the carbon emission impact of pickup points in last-mile parcel delivery

This paper analyzes the carbon emission impact of pickup points in last-mile parcel delivery. Pickup points provide customers and delivery companies with an alternative to attended home delivery. The delivery company can drop a parcel off at the pickup point, such as a service desk in a grocery store or a parcel locker, from where the customer collects the parcel. Because of the potential efficiency gains for the delivery vehicle, pickup points are often presented as a sustainable alternative to home delivery. The efficiency gains for the delivery vehicle need to be weighed against customers traveling to the pickup point by car, however. The mathematical analysis presented in this paper integrates continuous approximation techniques to assess the potential for improved delivery route efficiency with multinomial logistic regression for estimating the travel distance and mode choice of customers collecting their parcels. The results challenge the suggestion that pickup points are a universally sustainable alternative to home delivery. The potential for a net positive carbon emission impact is greatest when pickup points are established in urban settings, while in rural settings, the carbon emission benefits derived from improved delivery route efficiency are quickly offset by the carbon footprint associated with customer travel.