Travel Behavior Models Research Articles

Enhancement of the impacts 2050 model to enable a whole system sustainability assessment of rideshare

Emerging concepts, such as Mobility as a Service (MaaS), could evolve to provide sustainable mobility, especially in densely populated urban areas. However, recent studies highlight the challenge of evaluating how the complex interactions of user demographics, mode choice, vehicle automation, governance, and efficiency will impact the sustainability of future mobility. Given this challenge, this research identifies a whole system (STEEP - social, technical, economic, environmental, and political) framework as essential to assess the overall sustainability of emergent urban mobility systems such as rideshare. The need is a single tool that can rapidly explore the long-range sustainability impact of such alternative future mobility scenarios for a given city region. This paper documents enhancements made to Impacts 2050, a strategic-level model of urban mobility, to address this need, including updates to the statistical travel behavior model and the addition of rideshare including trip occupancy. Results obtained with the enhanced Impacts 2050 showed that, while rideshare use increased significantly for some scenarios, its overall mode share remained limited. In addition, though rideshare enabled users to shed car ownership, the overall percentage increase of “no car ownership” was low. An urban mobility sustainability scorecard based on STEEP and generated by output from the enhanced Impacts 2050 is presented.

A case for race and space in auto ownership modeling: A Los Angeles County study

Auto ownership behavior is driven by complex relationships that can vary dramatically across different traveler groups and communities. Differences in auto ownership among racial groups have been of particular interest, given ongoing efforts to advance equity in transportation outcomes. There are a number of studies documenting racial disparities in auto ownership associated with racial and ethnic residential clustering, termed “automobile mismatch.” Yet, these differences in auto ownership behavior by race and residential location are virtually never considered in models of travel behavior, despite calls for the consideration for race in transportation planning and decision making. This study aims to bridge the gap between understandings of the connections between race and space and transportation outcomes, using Los Angeles County as a case study. A series of auto ownership model specifications are used to investigate statistical connections between the racial and ethnic categories of residents, and neighborhoods, revealing systematic variations across racial and spatial dimensions. The composite model, which includes racial and spatial indicators, outperforms the base model, suggesting that the inclusion of race and space explains significantly more information on variations in auto ownership and provides a superior fit to the data. Our findings also suggest that the exclusion of racial and spatial indicators may lead to overestimation of certain effects, and may completely misrepresent the importance of certain household, individual-level, and built environment effects in explaining auto ownership preferences. Given the increasing attention to equity and representation in transportation outcomes, models that exclude considerations for race and space may be poorly positioned to support meaningful transportation equity analyses.

Estimating and Implementing a Vehicle-Type Model in an Activity-Based Travel Model Framework

Characteristics of household vehicles can influence daily travel behavior and can vary greatly by vehicle type. Vehicle type is defined here as a combination of body type, fuel type, and age. Using data compiled primarily from the National Household Travel Survey, a multinomial logit model was developed to predict vehicle type based on characteristics of the household that owns the vehicle. The model was then implemented for the San Francisco Bay Area in the ActivitySim activity-based modeling framework and validated against observed data. The ActivitySim project’s goal is to create and maintain advanced, open-source, activity-based travel behavior modeling software, based on best software development practices, for distribution to the public, free of charge. A naïve 2030 scenario was analyzed to show model response to changes in the vehicle fleet.

Examining Congestion Pricing Scheme Effectiveness Using the Travel Time Congestion Index

This study investigated the effectiveness of congestion pricing (CP) using the travel time congestion index (TTCI), a congestion assessment tool, in Hyderabad, India. Initially, a set of hypothetical mode choice scenarios under the CP scheme were designed to collect car users’ perceptions based on a stated preference (SP) experiment. Based on the SP survey data, discrete travel behavior models were developed to estimate the probable modal trade-off among cars, two-wheelers, and public buses under the generated CP scenarios. Using the existing traffic, geometric, and land-use data from the most congested corridors of the study city, TTCI values were estimated for the base and future conditions, followed by commuter volume estimation for the different conditions using vehicle occupancy factors. Further, the commuter volume derived from modal trade-off under CP scenarios was converted into traffic volume for the identified congested corridors. Finally, the TTCI values were re-estimated using the final traffic volume and compared across worst, best-worst (intermediate), and best case CP scenarios for the base and future years. An annual average growth of 5% in traffic volume was considered. The results show a significant improvement in TTCI values across all identified corridors under CP implementation, indicating its effectiveness toward congestion alleviation. Such demonstration of CP effectiveness could play a major role in making CP a successful travel demand management measure for cities burdened with congestion.

Evaluating relocation behavior of establishments: Evidence for the short-term effects of COVID-19

Due to the significant effects of establishments’ relocations on travel patterns and land-use conditions, investigating establishments’ relocation behavior is an important issue. In recent years, many establishments closed or relocated because of a downturn economy, health concerns, interrupted supply chains, and work-from-home caused by the pandemic. Hence, this study aims to propose a modeling approach to assess and compare the relocation behavior of establishments before, during, and after the pandemic. Establishments’ relocation behavior is modeled in two steps: relocation decision and relocation action. The former provides insights into behavioral factors associated with establishment relocation and the latter models likelihood of spatial relocation choice. Using the data collected from the state of Tennessee, USA, the Random Forest classification approach is incorporated to model both steps, where the model validation results showed the promising accuracy of this modeling approach. Moreover, statistical analyses are applied to evaluate the differences between the spatial relocation choices throughout the time. Results showed that in post-Covid conditions, the importance of establishment characteristics on relocation decisions was reduced by half and relocations occurred more due to office profile and accessibility. Results of modeling relocation action indicated the high importance of accessibility, even though the attractiveness of accessibility was reduced by 20.9% in post-Covid analysis. The findings of this study enrich the knowledge on establishment relocation behavior and provide valuable information regarding the effect of the pandemic, which can be used in policy development and travel behavior modeling by urban and transportation planners.

Virtual environments for analyzing travel behavior during the COVID-19 pandemic

The COVID-19 pandemic has put a spotlight on the importance of crowd avoidance to understand behavior during a public health emergency. In terms of travel behavior modeling tools, it is not obvious how spatial elements such as crowding should be displayed in discrete choice experiments. In this paper, we argue in favor of using virtual environments. We describe how we have successfully used static virtual environments in two pandemic-related case studies. Extrapolating from these two applications, we discuss how immersive virtual environments could be used in travel behavior research in general, along with the identification of associated methodological challenges.

Contextual Bayesian optimization of congestion pricing with day-to-day dynamics

Congestion pricing is a common approach to alleviate urban traffic congestion. The design of second-best congestion pricing schemes is typically formulated as non-linear programming and bi-level optimization problems, where the lower-level problem involves either a static or dynamic network equilibrium model. The complexity of these bi-level toll optimization problems increases considerably when incorporating day-to-day dynamic models of travel behavior and dynamic models of network congestion. These models are often operationalized using simulation, and consequently, the toll design problem is a computationally challenging simulation-based optimization problem where the evaluation of a single candidate pricing scheme involves simulating the day-to-day model until convergence. In order to circumvent this issue, we propose a contextual Bayesian optimization (BO) framework, where the BO scheme is embedded within the day-to-day dynamic model by using temporal contextual information. The framework implicitly incorporates the relationship between the objective function across days and uses past days’ observations (function evaluations) as weak priors when constructing the Gaussian process underlying the BO algorithm for the current day’s toll optimization problem, resulting in gains in computational efficiency.The contextual BO approach is applied to the design of distance-based pricing schemes for the morning commute problem. We demonstrate numerically that the scheme converges to the system optimum, and moreover, utilizes a significantly smaller number of simulation evaluations (ten-fold reduction) than the standard approach wherein each function evaluation involves simulating the day-to-day model until convergence. From a policy perspective, we find that the distance-based schemes yield significant welfare gains relative to area-based schemes and show that the design of the distance-based tariff scheme can significantly affect distributional impacts. A suitably designed two-part tariff structure can partially offset the relatively large welfare losses of travelers with longer commute distances while maintaining overall welfare. The proposed contextual BO scheme is also extended to incorporate context specific demand and supply information, which can be of value to policy-makers when evaluating optimal toll design schemes under a wide range of scenarios in a computational tractable manner.

The relation between barrier-free environment perception and campus commuting satisfaction.

The COVID-19 pandemic, which began in the last quarter of 2019, has had a significant impact on urban transportation. With increasing demand for urban transport, the internal roads and public spaces of university campuses play an important role in facilitating commuting and communication between various functional zones. While considerable research has been conducted on route planning, pedestrian-vehicle segregation, and safety management in the internal transportation environment of university campuses, empirical investigations exploring barrier-free inclusive campus environment design and the subjective evaluation of road and public space users in the aftermath of the COVID-19 pandemic are lacking. Recent developments in travel behavior models and positive psychology have led to an increased focus on the correlation among subjective perceptions, attitudes, emotions, and commuting satisfaction in urban transportation and planning design. To elucidate this relationship, a study was conducted on the new campus of Central South University in Changsha, Hunan Province, China. Using 312 valid samples, a structural equation model was constructed to analyse the relationship between commuting satisfaction and the barrier-free environment perception of university students regarding the internal transportation environment of the campus. The results revealed that individuals' instantaneous barrier-free environment perceptions and long-term established positive emotions had a significant positive effect on commuting satisfaction. Furthermore, positive emotions were found to mediate the relationship between commuting attitudes induced by COVID-19, barrier-free environment perceptions, and commuting satisfaction. The results of this study provide a theoretical basis for the necessity of accessibility design in the post-COVID era. In addition, this study considers the perspective of users to provide ideas for the planning and construction of barrier-free campus environments that are based on convenient and inclusive design.

Bibliometric Insights into the Implications of Urban Built Environment on Travel Behavior

With the rapid pace of global urbanization, understanding the impact of the urban built environment on travel behavior has become increasingly significant for developing sustainable and efficient transportation systems. This study conducts a bibliometric review of related research over the past two decades (1997–2023), utilizing 1745 publications from the Web of Science database through network analysis and content analysis. It provides a comprehensive quantitative analysis encompassing publication trends, national and institutional collaborations, and keyword evolution clustering perspectives. The results reveal that (1) academic interest in exploring the implications of the urban built environment on travel behavior has grown markedly, especially in the past decade, with emerging technological approaches and research perspectives; (2) the USA, P.R.CHINA, and the United Kingdom are major research forces in this field, with notable contributions from research institutions in P.R.CHINA and the USA; (3) the “Transportation Research Part” series journals demonstrate extensive influence both in terms of publication count and citation count; (4) through keyword co-occurrence network analysis, three development stages along with five major clusters were identified: travel behavior modeling and public health, active transportation and sustainable development, urban development and carbon emissions, land use and transportation integration, and urban transportation systems and machine learning. Overall, sustained research remains warranted within this field, particularly focusing on selecting new built environment metrics while integrating emerging technologies into travel behavior modeling frameworks. The insights from this study have implications for urban transportation planning and policy, offering guidance on future research directions and policymaking.

A negotiation model of individual matching and zonal-based travel behavior in carpooling

AbstractCarpooling is a sustainable and ecologically acceptable transportation mode. Individuals commonly engage in coordination and negotiation processes to find matching partners and typically modify their schedules to enable cooperation. Mutual cooperation between carpooling individuals plays an important role in executing trips. Through cooperation, participants can achieve challenging agreements effectively in a repetitive manner. This paper presents a negotiation mechanism that can match individuals for carpooling using organization and agent-based concepts. It describes a matching model and a carpooling social network. It studies several aspects of multi-zonal individual behavior to identify groups of carpooling candidates. The carpooling social network is simulated on an ongoing basis for each of the following carpooling activities: interaction, negotiation, and trip execution. The interaction process enables communication between individuals within carpooling social groups in order to activate the negotiation process. During the negotiation process, participants typically modify their schedules to support cooperation by considering their personal preferences and constraints. Negotiation leads to matching of individuals based on trip start times, driver selection, detour duration, and carpool group pickup and dropoff sequences. Trip start times are established on travel, social, financial, and schedule-related factors. The carpoolers’ pickup and dropoff sequences that are feasible for an optimal carpool group are projected using specific scoring methods. Carpooling community candidates are recognized via outcomes projected using the FEATHERS activity–based model. The framework is implemented through the Janus multi-agent system.

Distinguishing the determinants of low-cost and high-cost sustainable travel behaviors

PurposeMost research on sustainable tourism has been devoted to understanding the determinants of tourists' sustainable behavior on a unidimensional construct, overlooking the importance of behavioral costs in sustainable travel behavior. To shed light on this issue, this study aims to quantitatively differentiate sustainable travel behaviors based on behavioral costs and to examine the impact of psychological factors on both low-cost and high-cost sustainable travel behaviors.Design/methodology/approachA survey of 470 tourists used Rasch analysis to measure the behavioral costs associated with sustainable travel behavior and partial least squares structural equation modeling (PLS-SEM) to test hypotheses.FindingsThe results indicate that the value-identity-personal norm model explains more variance in low-cost sustainable travel behaviors than in high-cost sustainable travel behaviors. This supports the central tenet of the low-cost hypothesis and also suggests that values and self-identity factors have a stronger influence on low-cost sustainable travel behavior. However, personal norms have a stronger influence on high-cost behaviors.Practical implicationsThis research highlights the importance for tourism and destination managers to distinguish between different categories of sustainable travel behavior and to analyze their determinants separately. This allows for the development of tailored messages for specific groups of tourists based on the psychological drivers of sustainable travel behavior.Originality/valueThis study provides insights into the determinants of sustainable travel behaviors with different behavioral costs and highlights the importance of analyzing different categories of behaviors separately.

Unraveling the mode substitution of dockless bike-sharing systems and its determinants: A trip level data-driven interpretation

Understanding the mode substitution of shared micro-mobility systems is essential for assessing their societal and environmental impact and developing improvement planning instruments. This study carries out a fine-grained analysis of the mode substitution of dockless bike sharing (DLBS) in relation to other transport modes at the trip level, leveraging multi-modal route planning techniques, transaction data of bike-sharing, and travel behavior modeling. More importantly, the study leverages interpretable machine learning to reveal the complex effects of built environment factors on the mode substitution patterns of DLBS based on multiple data sources. The results indicate that the probabilities of DLBS replacing other transport modes present large heterogeneity among different trips and in different urban contexts, which can be successfully quantified by the proposed approach at the trip level. The average substitution rates of bike-sharing to bus, metro, walking and ride-hailing in Shanghai are estimated to be 0.356, 0.116, 0.347 and 0.181, respectively. Built environment factors such as presence of transit systems can explain the variations in the substitution rates of DLBS to a certain transport mode in different urban contexts. Especially, the effects of some built environment factors show complex nonlinear and threshold patterns revealed by the data-driven method. The effects of key built environment factors are quantitatively interpreted and their practical implications discussed.

Spatial Models of Travel Behavior and Land Use Restriction

This paper develops a spatial econometric model of transportation mode choice and tests the association between zoning and other built environment variables and the choice of auto and non-auto transportation. We provide an extensive review of spatial econometrics and demonstrate the importance of using models that treat space formally when investigating urban transportation behavior. Using a unique combination of travel, employment, and built environment datasets from Denver, Colorado, we confirm previous results that built environment variables have a small association with choice of transportation mode and show the benefits of formal spatial modeling to the traditional probit model.

Utilising physiological data for augmenting travel choice models: methodological frameworks and directions of future research

ABSTRACT Recent technological and methodological advances have led to the possibility of a wider range of data being incorporated into travel choice models. In particular, physiological data such as eye-tracking information, skin conductance, heart rate recordings and electroencephalogram (EEG) have emerged as promising sources of information that could be used to gain insights into the decision-making process as well as the decision-maker's state of mind. However, research on methodologies to utilise these data sources and to integrate them with mobility data for advancing state-of-the-art travel behaviour models is still very limited. In this paper, we discuss the key benefits of using these emerging sources of physiological data, review applications of different types of physiological data and highlight their strengths and weaknesses. Particular attention is paid to two different generic frameworks for integrating these types of data into econometric choice models of travel behaviour. The first framework involves using physiological sensor data as indicators of latent variables while in the second framework, they are used as exogenous variables. We identify the research gaps and outline the directions for future methodological and applied research required to better utilise the physiological data for travel choice models.

Debiased Machine Learning for Estimating the Causal Effect of Urban Traffic on Pedestrian Crossing Behavior

Before the transition of automated vehicles (AVs) to urban roads and subsequently unprecedented changes in traffic conditions, the evaluation of transportation policies and futuristic road design related to pedestrian crossing behavior is of vital importance. Recent studies analyzed the non-causal impact of various variables on pedestrian waiting time in the presence of AVs. However, we mainly investigate the causal effect of traffic density on pedestrian waiting time. We develop a double/debiased machine learning (DML) model in which the impact of the confounders variable influencing both a policy and an outcome of interest is addressed, resulting in unbiased policy evaluation. Furthermore, we try to analyze the effect of traffic density by developing a copula-based joint model of the two main components of pedestrian crossing behavior, pedestrian stress level and waiting time. The copula approach has been widely used in the literature for addressing self-selection problems, which can be classified as a causality analysis in travel behavior modeling. The results obtained from copula approach and DML are compared based on the effect of traffic density. In the DML model structure, the standard error term of the density parameter is lower than that of the copula approach and the confidence interval is considerably more reliable. In addition, despite the similar sign of effect, the copula approach estimates the effect of traffic density lower than DML, because of the spurious effect of the confounders. In short, the DML model structure can flexibly adjust the impact of confounders by using machine learning algorithms and is more reliable for planning future policies.

ASSOCIATION OF RESIDENTS’ STEP COUNT WITH NEIGHBORHOOD BUILT ENVIRONMENT AND TRAVEL BEHAVIOR

To deeply understand walkable environments, this study analyzed associations between residents' daily step counts and their living environments. The primary characteristic of this study is the analysis incorporating travel behavior in addition to neighborhood built environment. Specifically, we developed and compared three multi-level regression models: 1) neighborhood environment model; 2) travel behavior model; 3) model that integrated both elements. As a result, the integrated model estimated residents' step counts more precisely, implying the other models might overestimate direct associations of the two elements. We also found the amount of model improvement varied depending on the group of age and gender.

Study on the analysis of travel behavior: A review

Travel behavior analysis involves the utilization and examination of travel demand through the application of theories and analytical techniques derived from diverse scientific disciplines. There are many methods used by researchers in analyzing travel behavior. Mode selection is the stage where the transportation planning process is responsible for determining the travel load or knowing the number of people and goods, then selecting the available transportation models that serve as the starting point of the destination. Travel factors have a strong influence on a person’s travel behavior, (trip maker behavior), including income, vehicle ownership, vehicle condition, settlement density of one’s socioeconomic conditions. This paper describes the disadvantages and advantages of travel behavior modeling analysis and factors that influence the choice of modes based on travel behavior, so that in the future researchers can use it as an illustration in determining the right analysis.

Data-driven cooling, heating and electrical load prediction for building integrated with electric vehicles considering occupant travel behavior

The access of electric vehicles facilitates in the fluctuation and diversification of building load, accurate load prediction contributes to investigating the operation and optimization of energy supply systems for building integrated with electric vehicles (EVs). This study proposes a multivariate load prediction model for building/EVs considering occupant travel behavior. Firstly, travel variables of dissimilar occupants are obtained by fitting distribution, Markov chain and machine learning method. Besides, these variables are sampled to construct the occupant travel behavior model based on Monte Carlo method. In addition, occupancy rate and hourly load of different buildings are determined according to the occupant travel behavior. Ultimately, data-driven approaches, such as artificial neural network, long short term memory network (LSTM) and LSTM with temporal pattern attention (TPA-LSTM), are applied to construct the load prediction model for building/electric vehicles. Results indicated that building load constructed in accordance with the occupancy rate of occupants' travel behavior exhibits a favorable cooperativity with the EVs charging load. TPA-LSTM model has a high prediction accuracy, and the maximum correlation coefficient reaches 0.987. In general, this study provides an effective tool for accurate load prediction of building integrated with EVs.

An activity-based model for transit network design and activity location planning in a three-party game framework

Over the past few decades, the activity-based approach has received extensive attention in travel behaviour modelling in order to understand the underlying motivation of trip making. In this study, we optimize the transit network and activity location plan with explicit consideration of the correlations between passengers’ activity choices and travel choices by using the activity-based approach. Considering the conflicting interests of three stakeholders (i.e., the government, the transit company and the passengers) involved in transit network design and activity location planning, a bi-level programming model is formulated to depict the three-party game, and multi-objective optimization is proposed. A Pareto genetic algorithm is adopted to solve the model, and a numerical example is provided to illustrate the application of the proposed model and solution algorithm. The results show that under different transit network designs and activity location plans, passengers’ activity and travel choices, as well as the system-wide indicators including space-time accessibility of activity locations, social welfare and subsidy expenses vary significantly.

Attitude changes, modelling travel behaviour, and ex ante project evaluations

The literature has increasingly recognized that people’s attitudes can change for many reasons. This paper argues that attitude changes can have important implications for modelling travel behaviour and ex ante evaluations of candidate policy options. Limiting ourselves to the transport system and the built environment, we discuss why attitudes could change, how these changes are not addressed in current aggregate travel behaviour models, mainly the four step model, and how they could influence the assessment of candidate policy options. We suggest related avenues for future research. The core point we want to make is that attitude changes are not included in aggregate models, which leads to an underestimation of the impacts (often: benefits) of unconventional transport policy options.