Travel Mode Choice Behavior Research Articles

Latent class choice model with a flexible class membership component: A mixture model approach

This study presents a Latent Class Choice Model (LCCM) with a flexible class membership component. Specifically, it formulates the latent classes using Gaussian-Bernoulli mixture models and investigates the impact of such formulation on the representation of heterogeneity in the choice process, goodness-of-fit measures and out-of-sample prediction accuracy of the choice models. Mixture models are model-based clustering techniques that have been widely used in areas such as machine learning, data mining and pattern recognition for clustering and classification problems. An Expectation-Maximization (EM) algorithm is derived for the estimation of the proposed model. Using two different case studies on travel mode choice behavior, the proposed model is compared to traditional discrete choice models on the basis of parameter estimates’ signs, values of time, statistical goodness-of-fit measures, and cross-validation tests. Results show that mixture models improve the overall performance of latent class choice models by providing better out-of-sample predication accuracy in addition to better representations of heterogeneity without weakening the behavioral and economic interpretability of the choice models.

Transit Users’ Mode Choice Behavior During Light Rail Transit Short-Term Planned Service Disruption

Planned service disruptions (PSDs) of light rail transit (LRT) improve service reliability, extend infrastructure’s life, and reduce the frequency and impact of unplanned service disruption caused by system failure. However, the literature on the impact of LRT PSDs on transit customers’ travel mode choice behavior is scarce relative to that on unplanned service disruptions. This study aimed to investigate transit customers’ mode choice behavior in response to short-term LRT PSD in the City of Calgary, AB, Canada. A stated preference survey was designed to gather respondents’ mode choices under a set of hypothetical scenarios. A mixed multinomial logit model was estimated using stated preference data. Findings of this study include: (i) stated LRT ridership dropped by about 35% during the examined short-term LRT service disruption; (ii) transit customers who hold a LRT payment pass (monthly, subsidized seniors, low income, and students) and are frequent weekend LRT users are more likely to stay with the LRT mode in case of short-term PSD; (iii) the value of time for transit users during short-term LRT PSD was found to be 11.76 $/h and 13.0 $/h for travel time (excluding wait time) and wait time during travel, respectively. A sensitivity analysis was conducted on key variables to predict choice probabilities of transit alternatives. Recommendations are made to improve Calgary Transit customers’ experience during short-term LRT PSDs.

Psychological impacts of COVID-19 pandemic on the mode choice behaviour: A hybrid choice modelling approach

The COVID-19 pandemic is a pivotal moment in the history of mankind, which had a huge impact on the fast-paced world. The uncertainty associated with the plight of the pandemic, pushed the world towards a sense of insecurity and panic. Apart from the disease, the psychological problems connected to the lockdowns has caused an unprecedented change in the thought process of people towards travel. In the present study, we aim to statistically illustrate the change, the pandemic and lockdowns brought upon the travel mode choice behaviour. An Integrated choice and latent variable (ICLV) framework was adapted to understand the impact of the novel behavioural constructs, such as awareness of the disease and people's perception of the strictness of lockdown towards the mode choice in the post pandemic scenario. Different trip types were characterized according to the nature of the trip and their mode choice were assessed separately for the impact of the latent constructs. The results suggest that the awareness of the disease and the perception of strictness of the lockdown implemented play a major role in affecting the change of the mode choice of people. Further, the perception of safety in public transport, characterized by the social distancing and sanitization measures, determine the willingness of people towards the choice of public transit systems. The study concludes with a focus on the policies, which could be implemented for a safe travel in the post lockdown stage.

The impacts of mediating the work environment on the mode choice in work trips

AbstractThe impacts of work characteristics on travel mode choice behavior has been studied for a long time, focusing on the work type, income, duration, and working time. However, there are no comprehensive studies on the influence of travel behavior. Therefore, this study examines the influence of work environment as a mediator of socio-economic variables, trip characteristics, transportation infrastructure and services, the environment and choice of transportation mode on work trips. The mode of transportation consists of three variables, including public transportation (bus rapid transit and mass rapid transit), private vehicles (cars and motorbikes), and online transportation (online taxis and motorbike taxis online). Multivariate analysis using the partial least squares-structural equation modeling method was used to explain the relationship between variables in the model. According to the results, the mediating impact of work environment is significant on transportation choices only for environmental variables. The mediating mode choice effect is negative for public transportation and complimentary for private vehicles and online transportation. Other variables directly affect mode choice, including the influence of work environment.

Optimization Scheme of Tradable Credits and Bus Departure Quantity for Travelers’ Travel Mode Choice Guidance

To analyze the influence of tradable credits and bus departure quantity on travelers' travel mode choice, this study investigated car travel and bus travel as research objects and established a two-mode day-to-day travel mode choice model based on tradable credits and bus departure quantity. To improve the guiding effect of tradable credits and bus departure quantity, an optimization scheme of tradable credits and bus departure quantity was developed with the goal of minimizing the system total travel time of car travel and the system total comprehensive cost of bus travel. Taking a test transportation network as an example, the influence of no tradable credits scheme, tradable credits scheme, and tradable credits and bus departure quantity scheme on the travelers’ travel mode choice behavior was analyzed. The results showed that the tradable credits and bus departure quantity scheme could reduce the saturation of road traffic and improve bus service quality.

Modal split and commuting patterns on a bottleneck-constrained highway with peak-only bus lane

ABSTRACT In this paper, commuters’ travel mode and departure time choice behaviors on a bottleneck-constrained highway with a peak-only bus lane (PBL) are investigated. The PBL occupies part of the capacity of the bottleneck when it is open. The capacity of the bottleneck for both auto and bus modes is time-varying. As the total number of commuters grows, we consider three regimes with different equilibrium commuting patterns – low, medium and high travel demand. The analytical results for the arrival rates of the two modes are presented and the numerical results are consistent with the analytical results. When the PBL is instituted, the number of bus mode commuters increases and total travel cost decreases with other parameters remaining fixed. The optimal capacity and opening period for the peak-only bus lane and the optimal bus dispatch frequency are discussed in detail.

Modelling the effects of COVID-19 on travel mode choice behaviour in India

The COVID-19 pandemic has resulted in unprecedented changes in the activity patterns and travel behaviour around the world. Some of these behavioural changes are in response to restrictive measures imposed by the Government (e.g. full or partial lock-downs), while others are driven by perceptions of own safety and/or commitment to slow down the spread (e.g. during the preceding and following period of a lock-down). Travel behaviour amidst the stricter of these measures is quite straightforward to predict as people have very limited choices, but it is more challenging to predict the behavioural changes in the absence of restrictive measures. The limited research so far has demonstrated that different socio-demographic groups of different countries have changed travel behaviour in response to COVID-19 in different ways. However, no studies to date have either (a) investigated the changes in travel behaviour in the context of the Global South, or (b) modelled the relationship between changes in transport mode usage and traveller characteristics in order to quantify the associated heterogeneity. In this paper, we address these two gaps by developing mathematical models to quantify the effect of the socio-demographic characteristics of the travellers on the mode-specific trip frequencies before (January 2020) and during the early stages of COVID-19 spread in India (March 2020). Primary data collected from 498 respondents participating in online surveys have been used to estimate multiple discrete choice extreme value (MDCEV) models in this regard. Results indicate – a) significant inertia to continue using the pre-COVID modes, and b) high propensity to shift to virtual (e.g. work from home, online shopping, etc.) and private modes (e.g. car, motorcycle) from shared ones (e.g. bus and ride-share options). The extent of inertia varies with the trip purpose (commute and discretionary) and trip lengths. The results also demonstrate significant heterogeneity based on age, income, and working status of the respondents. The findings will be directly useful for planners and policy-makers in India as well as some other countries of the Global South in better predicting the mode-specific demand levels and subsequently, making better investment and operational decisions during similar disruptions.

The role of personal norms in the choice of mode for commuting

Research in travel behavioural studies have shown that social forces play a significant role in travel decision making like socio-economic factors. Several studies have tested models with different subjective variables to investigate their effects on travel behaviour. Understanding the influence of social norm and personal norm on travel mode choice preference is essential in promoting sustainable travel, considering the reported importance of these social forces in decision-making. This study draws upon the theories underpinning these sociological constructs and the integrated choice and latent variable (ICLV) framework to develop an ICLV model by incorporating social norm and personal norm as latent variables to investigate their impact on transport mode choice decisions. The results of the ICLV model is consistent with the findings in similar studies and extend the literature in transport mode choice modelling. The results indicate that internalised norms or personal norms have a significant influence on individual behaviour, and positively influence travel behaviour. This strengthens the claim that activated norms and pro-environmental behaviour (PEB) influence overt behaviour by inducing a sense of obligations to act. The results also suggest that individuals with pro-environmental attitude are likely to travel with sustainable travel modes. Thus, reinforcing the importance of sociological and psychological factors in decision-making. Unlike studies involving social norms and personal norms, this research is novel because it applies the ICLV framework to empirically investigate the impact of these sociological constructs on travel mode choice behaviour by incorporating them as latent variables in an ICLV model. The contribution of this study to the literature is that it shows that personal norm has a substantial positive impact on sustainable travel. The impact of this study could be situated in the framework of sustainable transport. The findings are relevant for policymaking, the development of policies meant to create awareness of the consequences of travel behaviour could promote the development of strong personal norms, and consequently influence travel decision making. This can be useful for promoting sustainable travel as the findings shed light on the characteristics of individuals most likely to travel by active modes or PT.

Travelers’ preferences regarding autonomous mobility in the State of Qatar

Autonomous mobility is gaining increasing attraction in the international setting that results in a growing body of literature on various aspects. Preferences regarding the use of autonomous vehicles as an alternative transport mode are investigated in western and advanced countries as it provides the notion about what fraction of population will more inclined towards such technology and under what circumstances. This study focused on investigating travel preferences regarding autonomous mobility (in the form of privately owned AV (PAV) and shared AV (SAV) (such as on-demand taxi service). As a case study, the State of Qatar is studied and analyzed. A stated preference survey was designed and executed in a web-based questionnaire platform. Overall, data from n = 315 respondents were analyzed, and travel mode choice behavior was modeled using a logit model for the examination of travelers’ preferences. The results indicate that there exists a reluctance to prefer PAV and SAV, over a regular car (NC) while public transport (PT) is the least preferred mode. The comfort level that a vehicle can offer is found to be one of the most important factors for Qatari residents. Students and unemployed individuals have shown an inclination towards SAV while full-time employed individuals have shown a higher preference for PAVs. These results are helpful for government agencies and AVs marketing companies to develop plans and policies in line with travelers’ preferences.

Quantifying Travel Physical Energy Expenditure for Urban Travellers – A Case Study of Beijing

Travel physical energy expenditure for travellers has impact on travel mode choice behaviour. However, quantitative study on travel physical energy expenditure is rare. In this paper, the concept of travel physical energy expenditure coefficient has been presented. A case study has been carried out of young travellers in Beijing to get the value of physical energy expenditure per unit time under three transport modes, walking, car and public transportation. A series of experiments have been designed and conducted, which consider influence factors including age, gender, travel mode, riding posture, luggage level and crowded level. By analysing the travel data of money, travel time and physical energy expenditure, we determined that the value of travel physical energy expenditure coefficient δ is 0.058 RMB/KJ, which means that travellers can pay 0.058 RMB to reduce 1 KJ physical energy expenditure. Next, a travel mode choice model has been proposed using a multinomial logit model (MNL), considering economic cost, time cost and physical energy cost. Finally, the case study based on OD from Xizhimen to Tiantongyuan in Beijing was conducted. It is verified that it will be in better agreement with the actual travel behaviour when we take the physical energy expenditure for different types of travellers into account.

Understanding transport mode choice for commuting: the role of affect

ABSTRACTThis study examines the relationship between positive and negative user valence and transport mode choice behaviour. We integrate latent attitudes ‘affect’ and ‘salience’ into transport mode choice models using the framework of integrated choice and latent variable modelling and simultaneous maximum likelihood estimation methods. The results are consistent with findings in similar travel behaviour and behavioural economics literature. The study extends the findings of previous research and has demonstrated that user sentiments about public transport mode and salient public transport experiences have a significant impact on travel mode choice behaviour. It was found that private motorised users are more sensitive to overcrowding and anti-social behaviours on PT than active and PT travellers. Key attitudinal indicators influencing individual transport choice behaviour are established to guide public policy. The key indicators of Affect and Salience must be analysed and addressed through public policy to enhance PT user experience and develop services and facilities to increase the utility of PT in-vehicle travel time.

Analysis of Travel Mode Choice Behavior Considering the Indifference Threshold

An effective way to optimize traffic structures is by changing travel costs, thereby moving travelers from private transportation to public transportation. However, according to the existing studies, the traveler will not transfer from one mode to another unless the change in travel utility is greater than the indifference threshold. Therefore, the “indifference threshold” is one of the most important factors influencing a traveler’s choice of behavior. This study defines the “indifference threshold” as the traveler’s sensitivity to changes in travel utilities. In the framework of the theory of planned behavior (TPB), a structural equation model (SEM) considering the indifference threshold is established to analyze a traveler’s mode choice behavior. The analysis results showed that a travelers’ sensitivity to changes in travel utilities has the greatest impact on mode-choice behavior intentions and mode choice behavior. Perceptual behavior control has the strongest influence on travel choice behavior. In addition, in order to further explore the heterogeneity of a traveler’s behavior, the travelers were subdivided into four types, by establishing a latent class model (LCM) considering the indifference threshold. Finally, different traffic management suggestions are proposed for different types of travelers.

Travel mode choice: a data fusion model using machine learning methods and evidence from travel diary survey data

ABSTRACTIn this paper, we present a series of machine learning approaches for better understanding people’s travel mode choice. The widely used Logit model is dependent on the assumption that the utility items are independent, violating this assumption caused inconsistent parameter estimations and biased predictions. To improve the prediction accuracy of mode choice, this paper employs the data fusion model based on stacking strategy and proposes a hybrid model of the unsupervised Denoising Autoencoder (DAE) combining with the supervised Random Forest (RF). A variety of features that may impact mode choice behavior are ranked and selected by using the feature selection algorithms. The proposed model, which is particularly useful and powerful in the choice behavior analysis and outperforms other widely used classifiers, is verified by travel diary data from Germany and Switzerland. The results can be used for better understanding and effectively modeling of human travel mode choice behavior.

Research on the Choice Behavior of Taxis and Express Services Based on the SEM-Logit Model

With the development of Internet technology, online car-hailing is booming in China, which has profoundly affected people’s travel structures. In order to seek the sustainable development of taxi and online car-hailing services from the perspective of passenger mode choice behavior, the mechanism of passengers’ decision-making procedures and their travel mode choice behaviors were analyzed. To study the influence of latent variable factors on passenger choice behavior, this paper firstly designed a questionnaire, and a structural equation model (SEM) was established for the preliminary study of the relationship between the latent variables and the behavioral intentions using the online survey data. Then, the latent variables were introduced into the Logit model, setting up the SEM-Logit model to explore the mode choice patterns between taxis and online car services. The results showed that the SEM-Logit model with the latent variables is better than a general Logit model in terms of the model precision and hit ratio. Meanwhile, after introducing the latent variables, it was found that convenience, comfort, and economy factors have a significant influence on the model, and the explanatory power of the model increases accordingly.

Applying an ensemble-based model to travel choice behavior in travel demand forecasting under uncertainties

ABSTRACT The application of travel demand models to transportation planning has triggered great interests in issues that potentially improve the accuracy of model forecasts. These forecasts, however, are subject to various sources of input and model uncertainties. Focusing on travel choice behavior, this paper draws attention to the use of an ensemble-based model for addressing these uncertainties. A random multinomial logit (RMNL) model is developed by assembling a collection of multinomial logit (MNL) models. The bootstrapping procedure and the random feature selection are employed to capture the uncertainties in the model. A case study of investigating travel mode choice behaviors that illustrates situations necessitating the RMNL model is presented. Results suggest that the uncertainty related to predictions is reduced and the prediction accuracy is much improved. The RMNL model is computationally efficient and provides useful interpretations by estimating variable significance. Also, the RMNL model is able to deal with high-dimensional data.

Research on Travel Mode Choice Behaviors Based on Evolutionary Game Model Considering the Indifference Threshold

Research on Travel Mode Choice Behaviors Based on Evolutionary Game Model Considering the Indifference Threshold

Understanding the Effects of Improving Transportation on Pilgrim Travel Behavior: Evidence from the Lhasa, Tibet, China

Effective travel demand management measures provide the opportunity to fully utilize limited transportation resources, especially in underdeveloped areas. It is increasingly recognized that the improvement in existing transportation infrastructure and the optimization of traffic demand management method would result in a complicated urban transportation system with multiple travel modes. This paper aims to investigate the relationship between transportation improvements (e.g., pedestrian flow, free bus for the elderly, and parking space planning) and the mode choice behavior of pilgrimages in the Lhasa of Tibet, China. This study employed a distinctive survey conducted among pilgrims in Lhasa, including both individual questionnaires and interviews from 2010 to 2016. The analysis was undertaken using a multinomial logit model to identify the extent to which transportation improvements could affect the pilgrim’s travel mode choice behavior. The results show that transportation improvements, as an operational method in underdeveloped areas, play an important role in motivating the pilgrimage to travel that can increase the attractiveness of private car use, and make pedestrian traffic more prominent. However, improvements in the public transport need to be conducted to attract more travelers. These results confirm that increasing the attractiveness of low-carbon transportation (e.g., buses, walking, and cycling) to the public can reduce the usage on private vehicles and maintain the development of sustainable transportation in underdeveloped areas with limited transportation resources.

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

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

Park and ride study at light rapid transit (LRT) stations in Puchong, Malaysia

This paper explored the factors influencing commuter’s preferences of using park and ride (PnR), by investigating their travel mode choice behaviour, and assessing the effectiveness of PnR facilities. Binary logistic regression (BLR) was used to develop travel mode choice model which provides insight into commuter’s considerations contributing to the utilisation of PnR facility. Several scenarios were modelled to investigate the effect of policy changes on commuter’s travel mode shift, which could be essential in formulating effective strategy to promote the use of PnR. The results revealed working trip, short travel time (< 15 mins) and medium long travel distance (1–3 km), were the main considerations of commuter using PnR. The significant influencing factors were determined as travel distance from origin to departing station and from arriving distance to destination, trip purpose, frequency of using light rapid transit (LRT), education level and occupation. 3 scenarios were modelled which revealed that provision of parking subsidy, medium travel distance (around 10 km) and medium travel time (around 20 mins) encouraged commuter to use PnR while travelling in group discourage the use of PnR.

Research on Passenger’s Travel Mode Choice Behavior Waiting at Bus Station Based on SEM-Logit Integration Model

To improve the mode share of public transport and reduce the transition to private transport of passengers waiting at bus station, the mechanism of passengers’ decision-making procedure and influence factors of the travel mode choice were analyzed. Some latent variables such as safety, comfort, convenience, flexibility and economy were selected to reflect the satisfaction degree of passengers on the service level of public transport. Taking Jinan City as an example, the questionnaire of passengers’ travel choice behavior at bus station was designed and carried out. Based on the structure equation model (SEM), the relationship between the satisfaction degree and some latent variables such as safety and comfort was discussed. The SEM method analysis shows that, of the influence level of the latent variables to the service level of public transport, flexibility is the most significant variable affecting passenger’s satisfaction degree followed by safety, convenience, comfort and economy. Travel mode choice model of passengers waiting at bus station was established with an integration approach of SEM and nested logit (NL) model. The SEM-NL integration model results reveal that gender, monthly income, purpose of the trip, travel distance, safety and convenience service level have a significant effect on the choice of the upper model (public transport or private transport). Passenger’s age, vehicle ownership and bus ride frequency have great influence on the choice of the lower mode (ORB: original route bus; ARB: alternative route bus; Taxi; and Shared bike). Sensitivity analysis reveals that the transition probabilities from private transport to public transport can reach the highest point (respectively, 69.85%, 68.84% and 35.51%) when safety service reaches level 4, convenience service reaches level 3, or comfort service reaches level 2, indicating that the safety level equal to 4, convenience level equal to 3 and comfort level equal to 2 are the key threshold to increase the public transport mode share. Some proposals such as ensuring good accessibility of public transport, shortening the transfer distance of different routes, creating a comfortable travel environment and integrating bus ticket system have been put forward for the sustainable development of public transport system.