Mode Choice Preference Research Articles

Embracing Urban Micromobility: A Comparative Study of E-Scooter Adoption in Washington, D.C., Miami, and Los Angeles

E-scooters have emerged as a popular micromobility option for short trips, with many cities embracing shared e-scooters to enhance convenience for travelers and reduce reliance on automobiles. Despite their rising popularity, there is a lack of clear understanding of how user preferences and adoption practices vary by location. This study aims to explore user and non-user attitudes towards e-scooter use in diverse urban settings. A meta-analysis of data from three surveys (N = 1197) conducted in Washington, D.C., Miami, FL, and Los Angeles, CA, was performed to compare e-scooter users and non-user profiles, mode choice factors, and attitudes and preferences towards e-scooter use. Additionally, machine learning (ML) and SHAP (SHapley Additive exPlanations) analysis were utilized to identify influential factors in predicting e-scooter use in each city. The results reveal that the majority of e-scooter users are 25 to 39 of age, male, with higher income and a bachelor’s degree, and 92% possess a driver’s license. Significant differences in attitudes between e-scooter users and non-users highlight the complexity of perceptions towards e-scooter usage. The ML model indicates that employment status negatively impacts the prediction of e-scooter users, while factors such as living without a car and using non-motorized modes positively influence e-scooter use. Educational background is a significant e-scooter mode choice factor in Washington, D.C. and Miami, whereas attitudinal questions on car and technology usage are influential in Los Angeles. These findings provide valuable insights into the factors shaping e-scooter adoption, informing urban transportation planning and policymaking and enhancing understanding of shared micromobility and its impact on urban mobility.

Analyzing university students’ mode choice preferences by using a hybrid AHP group-PROMETHEE model: evidence from Budapest city

Over the last decades, the analysis of mode choice preferences has become a vital aspect of enhancing the quality of public transport services. Most papers aim to derive conclusions from large-scale surveys. However, for specific homogenous patterns, such as university students, a smaller group of evaluators might also be sufficient. Such a survey can rather be considered as an expert survey, in which few representatives might express the preference of a larger community, thus, a different methodology can be more effective than the traditional statistical techniques. This paper aims to introduce a new approach that combines two multi-criteria decision-making methods, the analytic hierarchy process and the preference ranking organization method for enrichment evaluation, for a hybrid consensual model by aggregating the individual priorities defined by each decision-maker. The introduced model presents advantages in terms of reducing time, cost, and effort compared to statistical methods and requires solely necessary information from the users via objective and subjective evaluations. The model’s effectiveness is tested with real-world data from the city of Budapest; highlighting the significant impact of the underground mode on users’ behavior toward public transport. In addition, the outcomes are compared to other existing results of student preference surveys.

Effects of proactive and reactive health control measures on public transport preferences of passengers – A stated preference study during the COVID-19 pandemic

The outbreak of COVID-19 has caused unprecedented disruptions to public transportation services, passengers' mode choice preferences, and their behavioral pattern. Recent studies have updated the systemic shift from shared travel to private mode due to the perceived higher health risk of shared mobility. However, findings from these studies may not be applicable to public transport-oriented cities with very low rate of private car ownership. In the long term when we arrive at a stabilized ‘New Normal’, the need to promote public transport is likely to be re-emphasized with high priority. This study presents insights into the factors affecting public transport (PT) preferences and potential modal shifts within various modes of PT when proactive and reactive health control measures are implemented. An online stated preference survey was conducted in Hong Kong, where public buses, public light buses, MTR, and taxis are the popular modes of public transport. The regret-based panel mixed multinomial logit model was applied in this study, aiming to capture individual’s regret aversion psychology in the context of the COVID-19 pandemic. The results of our random regret model suggest that travelers generally have lower regrets for the chosen mode when having passenger temperature screening, COVID tracing app, and more frequent disinfection, while seem to be unaware of the in-vehicle air quality. Interaction effects between health control measures and personal characteristics (e.g., age and risk attitudes) were also considered. Trip characteristics, including in-vehicle crowding level, fares, in-vehicle travel time, waiting time at a station, walking time, and trip purpose, were found to influence passengers' choices of travel modes. Policy simulations of possible changes in public transport modal preferences in the presence of various health control measures have also been undertaken. The findings suggest that overall health control measures are appreciated by passengers and do align with promotion of a return to public transport use. However, their effects are quite moderate and should be selectively used for different transport modes. Increasing service frequency can be a promising solution, which reduces crowding and waiting time of passengers using public transport modes, and reduces virus transmission risk. This study contributes to a better understanding of public acceptance and preference toward health control measures in public transport, and call for in-depth cost-benefit analysis in the related fields so that better response can be made in possible future public health crisis.

Predicting transport mode choice preferences in a university district with decision tree-based models

Modeling and prediction of mode choice are essential to support more sustainable and safer transportation decisions. There is plenty of literature in this decade showing that machine learning (ML) models have been effective predicting techniques, although not easily interpretable. When these techniques are used, there is a lack of connection with the data-gathering step, which is crucial to the technique selection and appropriate analysis of results. Based on a systematic literature review on mode choice studies, we present a methodology that interconnects the data-gathering process as a fundamental part of the descriptive phase when ML classification methods are used to predict mode choice preferences. The case study presented occurs in a university context whose descriptive phase shows interesting behavior patterns and highly imbalanced data in terms of mode choice. We show how decision tree methods allow us to tackle this issue in a contextualized manner and permit sensitivity analysis to test policies promoting changes in the modal split that aim for more sustainable mobility for the community of the university.

Integrating Autonomous Busses as Door-to-Door and First-/Last-Mile Service into Public Transport: Findings from a Stated Choice Experiment

Autonomous busses and on-demand (OD) services have the potential to improve the public transport system. However, research on potential traffic impacts is still ongoing, mainly because of a lack of existing use cases of autonomous driving as part of public transport. The availability of revealed preference data for mode choice decisions is thus very limited. Therefore, we conducted a stated choice experiment to assess mode choice preferences with regard to use cases as the main mode of transport and as the solution for the first and last mile. We also distinguished between OD and schedule-based (sched.) services. The target population of the survey is the population of Baden-Württemberg, a state in southwestern Germany. The responses of 1,434 people were analyzed using a nested logit approach. On this basis, we established exemplary utility functions and descriptively derived recommendations for efficient forms of deploying autonomous busses in addition to already existing well-developed public transport systems. It was found that, under the given conditions, public transport pass owners without a car in their household would be the most interested in using autonomous busses. Car owners without a smartphone see less benefit. It was also shown that the recruiting method of the respondents is crucial. Those reached via social media were significantly more positive than those contacted via an online panel. Further evaluations show that autonomous busses are rated similarly to existing public transport and consequently have particularly high potential on medium distances, especially if their deployment leads to shorter access routes.

Car Use: A Matter of Dependency or Choice? The Case of Commuting in Noord-Brabant

Car use in the sprawled urban region of Noord-Brabant is above the Dutch average. Does this reflect car dependency due to the lack of competitive alternative modes? Or are there other factors at play, such as differences in preferences? This article aims to determine the nature of car use in the region and explore to what extent this reflects car dependency. The data, comprising 3,244 respondents was derived from two online questionnaires among employees from the High-Tech Campus (2018) and the TU/e-campus (2019) in Eindhoven. Travel times to work by car, public transport, cycling, and walking were calculated based on the respondents’ residential location. Indicators for car dependency were developed using thresholds for maximum commuting times by bicycle and maximum travel time ratios between public transport and car. Based on these thresholds, approximately 40% of the respondents were categorised as car-dependent. Of the non-car-dependent respondents, 31% use the car for commuting. A binomial logit model revealed that higher residential densities and closer proximity to a railway station reduce the odds of car commuting. Travel time ratios also have a significant influence on the expected directions. Mode choice preferences (e.g., comfort, flexibility, etc.) also have a significant, and strong, impact. These results highlight the importance of combining hard (e.g., improvements in infrastructure or public transport provision) and soft (information and persuasion) measures to reduce car use and car dependency in commuting trips.

An integrated estimation approach to incorporate latent variables through SEM into discrete mode choice models to analyze mode choice attitude of a rider

Mode Choice preference of each individual commuter depends on a number of factors. The classic models for choice estimation have majorly focused on the quantifiable variables such as travel cost, travel time, socio-economic characteristics of riders, trip characteristics, and mode characteristics. This paper aims at analyzing the hidden impact of latent variables integrated with observed variables on mode choice attitude of a rider for an integrated public transport system with environment-friendly feeder mode. The study is in a tier II type metropolitan city of Vadodara, Gujarat. For the purpose a new class of model, Integrated Choice and Latent Variable (ICLV) model is introduced. It integrates the Structural Equation Modelling (SEM) through Analysis of Moment Structures (AMOS) approach for the analysis of latent variable with the classic type choice model analyzed through Biogeme regression. The study precisely captures the attitude of a rider towards the need for comfort and convenience, safety and security, services and facilities, and riding attraction and quality class of latent variables. 26 of initially considered 41 latent variables clubbed into four latent constructs through Confirmatory Factor Analysis (CFA) and SEM approach were incorporated in the ICLV analysis. The results explain the riders’ attitude driven choice for comfort and convenience and service and facilities over the safety and security and riding quality aspects of the mode. The shift demand governed by these latent variables helps to develop a methodological framework, which sets up a base for transport system design oriented policies’ implications as future scope of the research.

Modeling the COVID-19 Travel Choices in Colombia and India: A Hybrid Multiple Discrete-Continuous Nested Extreme Value Approach.

The COVID-19 pandemic has affected many daily activities, primarily as a result of the perceived contagion risk and government restrictions to mitigate the spread of the virus. To this end, drastic changes in the trip choices for commuting to work have been reported and studied, mostly through descriptive analysis. On the other hand, modeling-based research that can simultaneously understand both changes in mode choice and its frequency at an individual level has not been much used in existing studies. As such, this study aims to understand the changes in mode-choice preference and the frequency of trips, comparing pre-COVID with during-COVID scenarios, in two different countries of the Global South: Colombia and India. A hybrid multiple discrete-continuous nested extreme value model was implemented using the data obtained from online surveys in Colombia and India during the early COVID-19 period of March and April 2020. This study found that, in both countries, utility related to active modes (more used) and public transportation (less used) changed during the pandemic. In addition, this study highlights potential risks in likely unsustainable futures where there may be increased use of private vehicles such as cars and motorcycles, in both countries. It was also identified that perceptions toward government responses had a significant impact on the choices in Colombia, though this was not the case in India. These results may help decision makers focus on public policies to encourage sustainable transportation by avoiding the detrimental long-term behavioral changes resulting from the COVID-19 pandemic.

Mode Choice Preferences of Female University Students and Impacts of Ride-Sharing Thereon

The equality of gender is accounted to be an indispensable element when it comes to sustainable transportation infrastructure planning in an inclusive society. Special attention in this regard must be required to reach the growing transportation demands of the urban population- especially in a developing country like Bangladesh. In this inclusive society, females constitute a major portion of the students as well as the workforce and contribute significantly to the economy. Although females have to rely on the different modes available in the existing transportation system for their day-to-day travel, they have different preferences among the alternative modes in terms of accessibility and safety. To assess the travel behavior of female students, this study explores the elements those influence travel choices (travel cost, travel time, security, and comfort) and evaluates the effect of ride-sharing facilities on their choice of mode. Data were collected from 360 female university students through an online questionnaire. The research focuses on two divisions of female students: staying at the residence hall (residents) and not staying at the residence hall (non-residents). Descriptive and inferential statistical methods were utilized to explain female travel behaviors and determine the importance of ridesharing services in their mode selection (rickshaw, bus, autorickshaw, bicycle, private car, and ridesharing). The study reveals that female students (both resident and non-resident) prefer rickshaws over other modes and the inception of ride-sharing services had a consequential effect on their choice of modes and travel behavior. This analysis will deliver beneficial information to relevant authorities and policymakers in evaluating existing transportation systems and will indicate the required mitigations that can be made to ensure social equity. Journal of Engineering Science 13(2), 2022, 43-49

Assessing environmental benefits from shared micromobility systems using machine learning algorithms and Monte Carlo simulation

Shared micromobility systems (SMSs) are paving the way for new, more convenient travel options while also lowering transportation-related greenhouse gas (GHG) emissions. However, few studies have used real-world trip data to estimate SMSs’ environmental benefits, especially for dockless scooter-sharing services. To this end, we proposed a system to estimate the GHG emission reduction affected by SMSs. First, several machine learning (ML) algorithms were utilized to identify citizens’ travel mode choice preferences, and then the mode substituted by each shared micromobility trip was estimated. We compared the ML algorithms’ estimation results and selected those from the random forest, lightGBM, and XGBoost model for further estimating GHG reductions. Second, the Monte Carlo simulations were used to simulate the substituted mode at the trip level to improve the reliability of the final GHG reduction estimation. Finally, the environmental benefits were calculated based on the trip distances and the travel modes that were substituted. Instead of estimating a specific number, we obtained a probabilistic outcome for the environmental benefits while considering the level of uncertainty. Our results suggest that SMSs have positive environmental impacts and have the potential to facilitate the decarbonization of urban transport. According to these findings, implications and suggestions on extending SMSs’ environmental benefits are proposed.

The Influence of The Covid-19 Pandemic on Mode Choice Preference in Jakarta

The COVID-19 pandemic has had an enormous global impact in only a few months. It coerces the government in any country to impose some strict policies to stop COVID-19 from spreading, i.e., stay-at-home requirements or household lockdowns. Travel behaviours are essentially impacted due to such measures. This study focuses on changes in travel behaviour caused by the COVID-19 outbreak in Jakarta. The data was taken through an online survey of 1138 respondents. The questionnaire in this study includes questions about the mode choice containing the purpose of the trip, frequency, travel distance, and several other supporting attribute factors in the pre-pandemic period until the early months of the pandemic. Results clarified that people's travel behaviour was considerably contrastive between those two different times, i.e., the frequency of being outside, transport apps used, and Eid and Christmas Homecoming Tradition. Moreover, the top destinations are grocery stores indicated for primary movement only. In this case, it can be seen that there is a shifting mode for people's daily movement, from what was previously public to private vehicles. Distance, activities, driving license, and vehicle ownership were essential considerations for mode choice throughout the COVID-19 outbreak. The findings of this study may be helpful in transportation planning and establishing policies in the future.

Incorporating habitual effects into mode choice modeling in light of Mobility-as-a-Service in tourism transport : An empirical analysis in China

ABSTRACT Mobility-as-a-Service (MaaS) system becomes a significant part of urban transportation network systembased on the information and communication technologies, but rarely receivedattention in tourism transport. This paper explored the impact of travel modepreferences on habitual choice behavior for MaaS in tourism, and an onlinesurvey and stated preference experiments were designed and carried out. Respondents were divided into three categoriesincluding strictly preference (SP) group, weak preference (WP) group, and nopreference (NP) group, accounting for 25.09%, 72.24%, and 2.67% respectively. Estimation results of the mode choice random parameterlogit (RPL) models indicate that explanatory variables of the alternatives (e.g.,the total travel time, in-vehicle cost, and walking distance) only affect thetravel mode choices for WP group, which are insensitive to the SP group and NP group. Marginal effect analysisshows that mode choice preferences for MaaS are influenced as tour experienceand daily travel habits change.

Modelling of pre and post Covid-19’s impact on employee’s mode choice behavior

Public transportation is one of the most affected systems by the pandemic. The utilization of public transit during the pandemic made the people feel unsafe. So, the use of private transportation modes for daily mobility has increased. This study aims to understand the COVID-19 impact on the employee's mode choice. The survey methodology adopted in this study is a web-based survey in which questionnaires are distributed via various social media platforms and collected respondents' opinions. After collecting the responses, statistical analysis of socio-demographic characteristics, mode choice preferences, and factors affecting the mode choice were performed. From the results, it is observed that there is a mode shift from public transportation to private transportation to avoid the spread of COVID-19, and also there is a marginal increase in non-motorized transportation modes post COVID-19. The finding indicates the factors related to the spread of the infection, are the most important factors to consider when choosing a mode of transportation following COVID-19. Multinomial logistic regression and artificial neural network models were developed to analyze the mode choice of travelers pre and post COVID-19.

Gendered mode choice preferences and characteristics for educational trips in Abbottabad, Pakistan: An empirical investigation

Educational trips are a major component of traffic in peak hours on working days. For proper trip forecasting, gendered preferences and dynamics must be understood. This study identifies gender-based model choices, preferences, and travel characteristics among school-going children and investigates the satisfaction level regarding public transport in Abbottabad, Pakistan. Using Slovin’s sampling method, a total of 370 samples were collected. Descriptive statistics, chi-square tests, and paired sample t-tests were conducted to determine the significant differences between genders and their travel characteristics. For public transport satisfaction, an index was constructed using indicators extracted from a rigorous literature review. The results show a significant difference between the mode choices and preferences of boys and girls. There was no significant difference in travel time, distance, and cost, but boys tended to make more trips than girls. The satisfaction level shows that girls had a more negative opinion about public transport than boys. This study argues that transport and urban planners must incorporate both gender preferences for developing transportation alternatives for students.

Will COVID-19 be the end for the public transit? Investigating the impacts of public health crisis on transit mode choice.

COVID-19 had an unprecedented impact on transit demand and usage. Stiff and vigilant hygiene safety requirements changed travellers’ mode choice preferences during the COVID-19 pandemic. Specifically, transit modal share is radically impacted. Therefore, quantitative measurements on transit demand impact are urgently needed to facilitate evidence-based policy responses to COVID-19. Thus, we collected 1000 random samples through a web-based survey in the Greater Toronto Area (GTA), Canada, on traveler’s modal choices behavior during the COIVD-19 pandemic. The paper presents an analysis with this firsthand dataset to understand transit users' behavioral adaptation resulting from the spreading of COVID-19 in 2020. We found that the transit frequency dropped by 21% to 71% for various socioeconomic groups in the GTA during the pandemic. The transit modal share dipped for all trip purposes. For private vehicle owners, around 70% of transit users switched to their private vehicles. More than 60% of those without cars switched to active transport for all travel purposes. Also, ride-hailing services are the second most popular substitution of transit for them. More than 80% of the respondents agreed with all transit safety policies, such as mandatory face-covering listed in the survey. Moreover, a similar proportion of the respondents agreed to return to public transit in the future. Multinomial, nested, and mixed logit models are estimated to capture relationships between modal choices and various factors. We found that the daily number of new COVID-19 cases impacts the choice of transit negatively. However, vaccine availability and mandatory face-covering onboard positively affect travellers’ choices of riding transit during the pandemic.

Predicting the travel mode choice with interpretable machine learning techniques: A comparative study

Prediction of mode choice for travelers has been the subject of keen interest among transportation planners. Traditionally, mode choice analysis is conducted by statistical models or simple machine learning (ML) paradigms. Although statistical analysis approaches have a good theoretical basis and interpretability, they are built on several unrealistic assumptions regarding the distribution of data, which may lead to biased model predictions. On the other hand, the ML methods widely used in this regard have poor interpretability and fail to capture the behavioral aspects. To fill this gap, this study proposes a systematic machine learning (ML) framework for a better understanding of traveler’s mode choice decisions. Five different ML models (Logistic Regression, Random Forests, Decision Tree, Multilayer Perceptron, Light Gradient Boosting Decision Tree (LightGBDT)) were developed to model the travel mode choices of travelers using three years of Dutch National Travel Survey data. Empirical results of various performance evaluation metrics (overall accuracy, average precision, precision-recall curves) showed that LightGBDT outperformed other models for both under and over-sampling strategies. To overcome the blackbox criticism of ML models and to improve their interpretability, variable importance and SHAP dependency analysis were also conducted. The analysis showed that predictors that significantly influence the travel mode decisions of travelers include trip distance, travelers’ age and annual income, number of cars/bicycles owned, and trip density. The results can be used for better understanding and effective modeling of travelers’ mode choice preferences.

Does the bus rapid transit reduce motorcycle use? Evidence from the Jakarta metropolitan area, Indonesia

Countries of lower average income, higher population density, and higher level of urbanization tend to have higher motorcycle ownership levels. Motorcycles bring safety and environmental challenges to Global South cities. The Jakarta metropolitan area in Indonesia adopted bus rapid transit (BRT) in 2004, but motorcycle ownership growth continued since. This empirical study examines what predicted motorcycle use in 2018, fourteen years after the opening of the BRT system TransJakarta, with particular attention paid to the potential impacts of the BRT on motorcycle use. It contributes to the understanding of high-capacity transit’s impacts on motorcycle travel in a Global South context. It tested literature review-based hypotheses with multinomial logit and left-censored tobit models predicting mode choice among motorcycle-based and bus-based modes as well as trip distance of motorcycle-based modes. The main dataset analyzed in this research contains 4,978 commuting-to-the-office first-leg trips. Model results suggested: proximity to the BRT is associated with office commute mode choice preference of the BRT over both motorcycle-based modes controlling for poverty rate, population density, and trip distance, and the effect is stronger on private motorcycles; similarly, proximity to the BRT is associated with reduced office commute trip distance in trips made by both motorcycle-based modes controlling for poverty rate and population density, and the effect is stronger on private motorcycles; village-level poverty rate is positively associated, and village-level population density is negatively associated, with private motorcycle trip distance. Such findings are generally in line with the literature. As the modal share of BRT is still low, transport policies in motorcycle-stressed cities with BRT could promote transit ridership by raising the relative costs of using private motorcycles such as parking costs, reducing the relative travel time of the BRT by increasing frequency and speeds, and improving the user-friendliness of first mile and last-mile trips connecting the BRT.

Mode choice analysis of school trips using random forest technique

Mode choice analysis of school trips becomes important due to the fact that these trips contribute to the second largest share of peak hour traffic. This scenario is more relevant in India, which has almost 265 million students enrolled in different accredited urban and rural schools of India, from Class I to XII as per the UDISE report of 2019-20. Thus, it becomes necessary to understand what mode of transport will be mostly used for school trips in order to design an efficient transportation system. Modal attributes and socio-economic characteristics are mostly considered as explana-tory variables in travel mode choice models. Multinomial Logit (MNL) model is one of the classic models used in the development of mode choice models. These logistic regression models predict outcomes based on a set of independent variables. With the recent advances in machine learning, transportation problems are getting a wide arena of methods and solutions. Among them the method of ensemble learning is finding a prominent place in contemporary modelling. This study explores the potential of using ensembles of random decision trees in mode choice analysis by Random Forest Technique with a comparative analysis on conventional method. It was observed that Random Forest method outperforms MNL method in predicting the mode choice preference of students. The high accuracy of machine learning models is mainly due to its ability to consider complex nonlinear relationship between socio-economic attributes and travel mode choice. These models can learn and identify pattern characteristics extracted from sample data and form adaptive structures through computational process thereby offering insights into the relationships between variables that random utility models cannot recognize. This study considered activity -travel information, personal data and household characteristics of students as attributes for model development and observed that the age of the student and distance of school from home plays a significant role in deciding the mode choice of school trips.

Exploring Public Perception Toward Travel and COVID-19 Preventive Measures: Insights From the Early Stages of Lockdown in India.

The paper reports evidence related to peoples' perceptions and attitude during the early stages of COVID-19 related lockdown in India in four major aspects: strategies and preventive measures, long-distance travel, essential services, and post-lockdown travel. A five-stage survey instrument was designed and circulated through various online modes to make it convenient to the respondents and also to achieve a greater geographical coverage within a short period of time. The survey responses were analyzed using statistical tools and the results are translated into potential policy recommendations which may be useful to implement effective interventions during future pandemics of similar nature. The findings highlight a high rate of awareness among the people about the COVID-19, lack of supply of protective equipment such as masks, gloves, and personal protective equipment kits during the early stage of lockdown in India. However, several heterogeneities were also observed across a few socio-economic groups which emphasize the need for targeted campaigns in a diverse country such as India. The findings also suggest the need for arranging safe and hygienic long-distance trips for a section of the society when such lockdown measures are extended for long periods. The observations related to mode choice preferences during the post-lockdown recovery period indicate a potential shift of public transport patronage to the personal modes.

Designing Better Public Transport: Understanding Mode Choice Preferences Following the COVID-19 Pandemic

Transport behaviour has evidently changed following the COVID-19 pandemic, with lower usage across multiple modes of public transport and an increasing use of private vehicles. This is problematic as private vehicle use has been linked to an increase in traffic-related air pollutants, and consequently global warming and health-related issues. Hence, it is important to capture transport mode choice preferences following the pandemic, so that potential service changes can be made to address the lower usage. In total, 1138 respondents took part in an online discrete choice experiment methodology to quantify the utility of public transport service attributes in decision making around the choice of public transport. The data resulted in the development of three models using a multinomial logit model in R. For respondents on personal or commuting journeys, the mode of transport had no effect on utility. Results found that fare cost was the most important factor driving transport mode preference, when a range of choices were available. Following this, keeping fare cost consistent, faster journey times were preferred to stronger access to transport (i.e., through the provision of more bus stops/stations). The provision of operational relevant information to the journey was only significantly valued by commuters and travellers who could claim their journey as a business expense. Finally, when cost became less relevant (i.e., for travellers on expensed journeys), there was a significantly strong preference for taxi and road vehicle transport over all other transport modes. The results from this empirical research are discussed and the implications of recent transport policy are discussed, and recommendations of public transport service design are made.