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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. [ABSTRACT FROM AUTHOR]

Transport demand models are widely used to inform policy making and produce forecasts of future demand. A core output derived from demand models is the Value of Travel Time (VTT), which provides insights on the trade-offs that travellers are willing to make in terms of travel time and travel cost. VTT estimates are a critical input to cost-benefit analyses and feasibility assessments of potential projects and thus play a crucial role in transport planning and policy decisions. While much of the early work on VTT made use of revealed preference (RP) data, their use decreased due to growing concerns about reporting errors that may result in omitted observations and measurement errors in the model inputs. As a consequence, VTT measures have, for the last two decades, primarily been estimated using state-preference (SP) surveys. While SP methods can assess the individual trade-offs in a controlled manner, they are prone to behavioural incongruence. More recently, RP data from passively-collected data sources have raised the promise of accounting for some of the limitations of traditional RP surveys due to the minimal (or even no) active input from the respondent. The present study utilises such a dataset that combined a 2-week trip diary captured through smartphone GPS tracking with a household survey containing individual socio-demographic information. A mixed Logit model for mode choice was specified and the estimated parameters were then applied on the National Travel Survey to calculate the VTT estimates. Those estimates were further adjusted based on trip distances to get more representative national VTT values. This process resulted in estimates similar to the official UK guidelines used in transport appraisal that were obtained from SP data, where our results are not affected by concerns about response quality or survey artefacts. The findings hence strengthen the case for shifting towards passively generated RP data sources and are important for transport practitioners. [ABSTRACT FROM AUTHOR]

Departure time choice models quantify the relative impacts of the factors affecting travellers' departure time selection and help design targeted peak-spreading policies. The departure time preference of travellers is traditionally captured using parameters associated with different alternatives along three aspects – outbound, return, and duration. In reality, departure time decisions for outbound and return legs, and the corresponding activity durations, are interrelated in most cases. However, none of the previous departure time choice models has explicitly investigated the impact of this potential correlation on model outputs. To address this gap in the existing literature, we proposed a model structure with a novel polynomial functional form of alternative specific constants (ASCs) that captures this correlation in a joint (outbound and return) departure time choice model. A revealed preference (RP) dataset from Dhaka, Bangladesh, was used to model the joint departure time preferences of the car commuters. The proposed model was then compared with a state-of-the-art model that uses a trigonometric formulation of the ASCs. Results indicate that the proposed formulation yields more behaviourally realistic outputs compared to the trigonometric model by explicitly capturing the correlation between departure time and duration. While the specific outputs are applicable to car commuters residing in Dhaka, Bangladesh, the framework can be applied to better predict departure times and improve the formulations of the peak spreading policies in other contexts as well. [ABSTRACT FROM AUTHOR]

Over the last few decades, there have been two main streams of data used for driving behavior research: trajectory data collected from the field [such as using video recordings and global positioning systems (GPS)] and experimental data from driving simulators (where the behaviors of the drivers are recorded in controlled laboratory conditions). Previous research has shown that the parameters of car-following models developed using simulator data are not directly transferable to the field. In this research, we investigate the differences in detail and compare alternative methods to overcome the problem. Two types of approaches are tested in this regard: (1) econometric approaches for increasing model transferability--Bayesian updating and combined transfer estimation--and (2) joint estimation using both data sources simultaneously. Car-following models based on a stimulus-response framework are developed in this regard, using experimental data collected at the University of Leeds Driving Simulator (UoLDS) and detailed trajectory data collected at California Interstate 80 (I-80), in the US, and the UK Motorway 1 (M1). The estimation results of the initial models show that car-following models using driving-simulator data are closer to the UK (M1) data than the I-80 data but not directly transferable. Performances of the proposed approaches for improving transferability are evaluated using t-tests for individual parameter equivalence and transferability test statistics (TTS). The results indicate that the transferability can be improved after parameter updating, and the combined transfer estimation is found to outperform the other approaches. The findings of this study will enable a more effective usage of the driving simulator data for the estimation of mainstream mathematical models of driving behavior while the techniques used can be applied to other types of econometric models. [ABSTRACT FROM AUTHOR]

• Combine driving simulator data with stated choice data. • Propose framework for joint model. • Separates intentions from actions. Drivers approaching lane closures due to roadworks tend to choose a target lane (plan) and seek suitable gaps to execute the plan (action). The plan is however latent or unobserved as the driver may or may not be able to move to the target lane due to the constraints imposed by the surrounding traffic. Hence, only the actions of the driver (as manifested by their final lane occupancies) are observed in the trajectory data. This paper analyses such mandatory lane changing behaviour in a roadworks environment in detail with data from a controlled driving simulator experiment and a simple stated preference survey with the same group of participants. While in the former drivers face similar constraints in implementing the plans as in the real world, in the simple stated choice survey the same drivers elicit their preferred target lanes without a need to put the plan into action. We contrast the findings from the two sources and also show correlations between the latent plan and stated target components in a latent class model. The results provide new insights into lane changing behaviour that may be useful for example for traffic management purposes. Furthermore, using stated choice data potentially reduces the cost of data collection for model development. [ABSTRACT FROM AUTHOR]

The rapid growth in passive mobility tracking technologies has led to departure time choice studies based on GPS data in recent years (e.g. Peer et al., 2013). GPS data however typically has limited sample sizes and is affected by technical issues like signal losses and battery depletion leading to gaps in the data. On the other hand, the rapid growth in mobile phone penetration rates has led to the emergence of alternative passive mobility datasets such as Global System for Mobile communication (GSM) data. GSM data covers much wider proportions of the population and can be used to infer departure time information. This motivates this research where we investigate the potential use of GSM data for modelling departure time choice. We describe practical approaches to extract relevant information from GSM data and propose a modelling framework that accounts for the fact that the desired departure times are unobserved. We assume that the preferred departure times vary randomly across the users and apply the mixed logit framework to jointly estimate the distribution parameters of the preferred departure times and the sensitivities to schedule delay. Comparison of the model results and time valuation metrics derived from the GSM data with similar metrics derived from the GPS data of a subset of the users reveals that the fewer time gaps in the GSM data lead to reliable model outputs. The proposed framework can be used for mobile phone and other passive data sources with unobserved preferred departure times. [ABSTRACT FROM AUTHOR]

Bus Rapid Transit (BRT) has been widely recognised as an affordable and effective mass transport system that can solve various mobility issues in countries that are unable to afford rail-based mass transit options. However, it is extremely challenging to predict the demand for the first BRT service in a city of a developing country with a weak public transport system using aggregate models, given the radical difference in the level of service between the BRT and the existing modes. Further, there can be substantial changes in the activity and travel patterns in a city after the introduction of the BRT which simpler disaggregate level analysis tools are unable to predict. Agent-based simulation tools, which are the state-of-the-art tools for simulating complex travel behaviour, are hence more appropriate for predicting the network conditions after the introduction of a new BRT system. But the application of such simulation tools has been primarily limited to developed countries where the transport landscape and the travel behaviour are very different from the developing countries. To address this gap, this paper presents a demand forecasting model for BRT and integrates it into an activity-based micro-simulation tool in the context of Dhaka, the capital of Bangladesh and one of the fastest growing megacities in the world. The model was developed based on an existing multi-agent, activity-based, travel demand simulator (MATSim). The MATSim implementation in the context of Dhaka focused on two aspects: (1) implementing behaviour models in MATSim to reflect the mode choice in the presence of the proposed BRT (2) integrating multiple data sources (including stated-preference data) for calibrating the mode choice and other components of MATSim to realistically mimic the travel behaviour in the city. Once calibrated, different access scenarios for BRT were simulated using MATSim, and the sensitivity of the outputs to different modelling assumptions is tested. Results from the simulation showed that the marginal utility of travel time, travel cost, and pricing structure of BRT significantly influenced BRT travel demands. Also, BRT demand was found to be the highest (25% of the total trips) in the scenario with multi-modal access/egress connections. While such direct model outputs presented in this paper will be useful for the planners to maximise the ridership of the proposed BRT, the calibrated simulator will be also useful for the evaluation of other innovative transport modes in the context of Dhaka in the future. • Behavioural models are integrated with agent-based microsimulation approach to predict BRT demand in the context of Dhaka, Bangladesh. • The demand for BRT is found to be notably influenced by both the fare structure and multimodal connectivity. • Simulation outputs demonstrate that BRT demand can vary between 0.7% and 25% depending on the adopted policies. • Policy measures to maximise BRT demand include making motorcycles less attractive and ensuring intermodal connectivity. • Customised services, incentives, and targeted planning, including gender-sensitive strategies, are vital for improving BRT ridership. [ABSTRACT FROM AUTHOR]

Traditional approaches to trip generation modelling rely on household travel surveys which are expensive and prone to reporting errors. On the other hand, mobile phone data, where spatio-temporal trajectories of millions of users are passively recorded has recently emerged as a promising input for transport analyses. However, such data has primarily been used for the development of human mobility models, extraction of statistics on human mobility behaviour, and origin-destination matrix estimation as opposed to the development of econometric models of travel demand. This is primarily due to the exclusion of user demographics from mobile phone data made available for research (owing to privacy reasons). In this study, we address this limitation by proposing a hybrid trip generation model framework where demographic groups are treated as latent or unobserved. The proposed model first predicts the demographic group membership probabilities of individuals based on their phone usage characteristics and then uses these probabilities as weights inside a latent class model for trip generation, with different classes representing different socio-demographic groups. The model is calibrated using the call log data of a sub-sample of users with known demographics and trip rates extracted from their GSM mobility data. The performance of the hybrid model is compared with that of a traditional trip generation model which uses observed demographic variables to validate the proposed methodology. This comparative analysis shows that the model fit and the prediction results of the hybrid model are close to those of the traditional model. The research thus serves as a proof-of-concept that the mobile phone data can be successfully used to develop econometric models of transport planning by having additional information for a subset of the users. [ABSTRACT FROM AUTHOR]

Highlights • Gap-acceptance model accounting for time pressure and associated stress. • Stress levels represented by heart rate and Electrodermal Activity (EDA). • Results indicate increased probability of gap acceptance under time pressure. • Accounting for stress leads to reduced sensitivities towards traffic-related variables. Abstract Driving behaviour is an inherently complex process affected by various factors ranging from network topography, traffic conditions and vehicle features to driver characteristics like age, experience, aggressiveness and emotional state. Among these, the effects of emotional state and stress have received considerable attention in the context of crash analysis and safety research where driving behaviour has been found to be affected by drivers' mental state/stress, cognitive workload and distraction. However, these studies are mostly based on questionnaire surveys and self-reports which can be prone to response bias and reporting/measurement errors. The analyses are also often descriptive in nature. In a parallel stream of research, advances in sensor technologies have made it possible to observe drivers' stress through human physiological responses, e.g. heart rate, electro-dermal activity etc. However, these studies have primarily focused on detecting stress rather than quantifying or modelling its effects on driving decisions. The present paper combines these two approaches in a single framework and investigates the gap-acceptance behaviour of drivers during an intersection crossing, using data collected using a driving simulator. The participants are deliberately subjected to stress induced by time pressure, and their stress levels are measured using two physiological indicators, namely Electrodermal Activity (skin conductance) and heart rate. In addition to statistical analyses, discrete choice models are developed to link the accept-reject choices of a driver with the driver demographics, traffic conditions and stress levels. The results of the models indicate that increased stress levels significantly increase the probabilities of accepting a gap. The improvement in model fit and safety implications derived from model estimates are also discussed. The insights from the results can be used for designing appropriate intervention strategies to improve safety. [ABSTRACT FROM AUTHOR]

In this research, we investigate the acceptability of three new and emerging smart mobility options and quantify the associated willingness-to-pay values in the context of Lisbon using a comprehensive stated preferences (SP) survey. The smart mobility options include shared taxi, one-way car rental, and a novel combination of park-and-ride and school bus facilities. While previous surveys on smart mobility options had investigated limited number of alternatives in isolation, the SP survey used in this research presents the smart mobility options alongside the existing options and their traditional variants like congestion pricing and improved public transport systems. Further, the choice of mode, departure time and occupancy are investigated in a multidimensional framework. This resulted a large choice set (with 9 modes, 5 departure times, and 2 occupancy levels leading to 135 alternatives in total) and required a novel survey design. The main survey administered over the internet and computer aided personal interviews included 2372 valid SP observations from 1248 respondents. Multi-dimensional mixed logit models were used to capture the complex correlations introduced due to the non-traditional survey design. Results indicate a significant preference of one-way car rental and shared taxi for non-commute trips. For commute trips, improved versions of traditional public transport modes are favoured over smart mobility options. These findings, as well as the novel data collection and modelling approach, are expected to provide important information to transportation planners and policy makers working to implement smart mobility options in Lisbon as well as in other cities. [ABSTRACT FROM AUTHOR]

Understanding the constraints that individuals face during their spatial choices is important from a policy perspective. Such constraints, however, are often overlooked in the choice set generation process during model development. In order to address that gap, the current study proposes a probabilistic choice set formation based on Manski's framework assuming that the actual choice set of an individual is latent (unobserved). Though latent class models with heterogeneous choice sets have been used previously in the context of mode and route choice, their application in the context of spatial choices have been hindered due to the inherently large choice sets making the problem computationally intractable. To address this issue, we propose to computationally simplify the problem by utilising the geography-derived notions of Activity Spaces to delineate a range of potential choice sets per individual helping us to capture both issues of spatial awareness and time-space constraints. In order to account for the latent nature of the true choice set, we propose a Latent Class Choice Modelling (LCCM) framework to allocate the individuals probabilistically into the different resulting choice sets, with each class having a different choice set and a different set of parameters. Thus the LCCM is able to capture heterogeneity in the choice sets and in the sensitivities, at the same time. The proposed LCCM framework is empirically tested on joint mode and shopping destination choices captured through a GPS smartphone application. It is compared to a base MNL model estimated on the global choice set, an LCCM capturing heterogeneity only in the sensitivities and a LCCM with latent consideration choice sets, similarly to the proposed model, but with generic parameters across classes. Our proposed specification is able to outperform all of the remaining models, while also providing insights on the factors affecting individuals to be constrained in their location choices across space hinting to cases of spatial cognition, the importance of the home and workplace geography and the individual's socioeconomic status. Such insights can be important for developing more behaviourally realistic models that can be used by planners and policy makers to formulate more effective measures that better relate to the underlying population. Furthermore, the analysis provides insights into the discrepancies that can emerge by accounting for latent consideration sets in willingness-to-pay measures and demand elasticities, which could have significant implications in the effectiveness of policy measures. [ABSTRACT FROM AUTHOR]

Acceleration is an important driving manoeuvre that has been modelled for decades as a critical element of the microscopic traffic simulation tools. The state-of-the art acceleration models have however primarily focused on lane based traffic. In lane based traffic, every driver has a single distinct lead vehicle in the front and the acceleration of the driver is typically modelled as a function of the relative speed, position and/or type of the corresponding leader. On the contrary, in a traffic stream with weak lane discipline , the subject driver may have multiple vehicles in the front. The subject driver is therefore subjected to multiple sources of stimulus for acceleration and reacts to the stimulus from the governing leader . However, only the applied accelerations are observed in the trajectory data, and the governing leader is unobserved or latent . The state-of-the-art models therefore cannot be directly applied to traffic streams with weak lane discipline. This prompts the current research where we present a latent leader acceleration model. The model has two components: a random utility based dynamic class membership model (latent leader component) and a class-specific acceleration model (acceleration component). The parameters of the model have been calibrated using detailed trajectory data collected from Dhaka, Bangladesh. Results indicate that the probability of a given front vehicle of being the governing leader can depend on the type of the lead vehicle and the extent of lateral overlap with the subject driver. The estimation results are compared against a simpler acceleration model (where the leader is determined deterministically) and a significant improvement in the goodness-of-fit is observed. The proposed models, when implemented in microscopic traffic simulation tools, are expected to result more realistic representation of traffic streams with weak lane discipline. [ABSTRACT FROM AUTHOR]

Increasing commute distances often lead to increased auto-dependency and is a major problem in many developed as well as developing countries. While in developed countries, the propensity to commute long distances generally originates from the preference to work in the core of the city and live in the suburb or periphery, in developing countries, the trend is often quite the opposite. For example, in Bangladesh, people generally have a strong preference to live at the heart of the major cities even if they work at the peripheral areas of the city, in another city or in a rural area. Further, it is also not uncommon to maintain split-families where the earning member of the family lives near the workplace while the rest of the family is based in a big city (subject to affordability). These phenomena lead to substantial increase in Vehicle Miles Traveled (VMT) and add burden to the transport infrastructure. The focus of the research is to explore the key factors that induce middle and upper-middle class commuters in Bangladesh to live away from their workplace and/or maintain split-families. A case study is conducted using Stated Preference (SP) surveys conducted among the faculty members of two universities: one located at the periphery of the capital city and the other quite far away. Discrete Choice Models are developed using the collected data. Results reveal that albeit some differences, for both cases, the choices are strongly driven by quality of the education institutes and the house rent. Factors like gender, income and car-ownership, which traditionally play a strong role in the context of developed countries, are found to be of less significance. The models, though estimated with limited data, provide useful insights about the factors that drive residential location choices in the context of a developing country and can help in formulating policies for encouraging people to live closer to their workplaces and thereby reduce commuter VMT. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose a methodology to develop OD matrices using mobile phone Call Detail Records (CDR) and limited traffic counts. [•] The methodology is demonstrated using real data and supported by validation results. [•] The approach is more economic than the traditional approaches of OD estimation. [Copyright &y& Elsevier]

In recent years, planners have started considering Machine Learning (ML) techniques as an alternative to discrete choice models (CM). ML techniques are primarily data-driven and typically achieve better prediction accuracy compared to CM. However, it is hypothesized that since the ML techniques do not have the strong grounding to economic theory as the CMs, they may not perform well in contexts that are radically different from the 'training' scenario. It is also hypothesized that the relative prediction performance may be affected by the metrics used for comparing the models. This research aims to test these two hypotheses empirically by modelling vehicle ownership choices using household survey data from Dhaka, Bangladesh collected in 2004, 2010 and 2019. The performances of CM (multinomial logit) and ML techniques (neural networks and gradient boosting trees) have been compared using log-likelihood and mean absolute percentage error of market shares. The results indicate that the multinomial logit model (MNL) with a piecewise linear transformation of the household income, has the best performance in terms of log-likelihood and mean absolute percentage error of market shares. This is followed by Neural Networks (NN) and Gradient Boosting Trees (GBT). The results thus provide empirical evidence that the ML techniques do not consistently outperform CM. Moreover, the difference in the performance of the models further increases if the prediction scenario is substantially different. This reinforces the hypothesis that CMs, with their behavioural underpinning, are better suited for long-term forecasting than data-driven ML approaches, especially if the population and network attributes are expected to change substantially. These findings will be useful for planners and policy makers in the selection of the appropriate tool for forecasting travel demand. [ABSTRACT FROM AUTHOR]

There is a growing interest in the travel behaviour modelling community in using alternative methods to capture the behavioural mechanisms that drive our transport choices. The traditional method has been Random Utility Maximisation (RUM) and recent interest has focussed on Random Regret Minimisation (RRM), but there are many other possibilities. Decision Field Theory (DFT), a dynamic model popular in mathematical psychology, has recently been put forward as a rival to RUM but has not yet been investigated in detail or compared against other competing models like RRM. This paper considers arguments in favour of using DFT, reviews how it has been used in transport literature so far and provides theoretical improvements to further the mechanisms behind DFT to better represent general decision making. In particular, we demonstrate how the probability of alternatives can be calculated after any number of timesteps in a DFT model. We then look at how to best operationalise DFT using simulated datasets, finding that it can cope with underlying preferences towards alternatives, can include socio-demographic variables and that it performs best when standard score normalisation is applied to the alternative attribute levels. We also present a detailed comparison of DFT and Multinomial Logit (MNL) models using stated preference route choice datasets and find that DFT achieves significantly better fit in estimation as well as forecasting. We also find that our theoretical improvement provides DFT with much greater flexibility and that there are numerous approaches that can be adopted to incorporate heterogeneity within a DFT model. In particular, random parameters vastly improve the model fit. [ABSTRACT FROM AUTHOR]

• We examine the underlying structure of both decision field theory (DFT) and the multi-attribute linear ballistic accumulator (MLBA) and consider how to operationalise these models for use in transport modelling. • We provide a number of methodological improvements for both models as well as a detailed section on the identification of both models. • Both models perform well on typical transport choice datasets. • Results from simulated datasets demonstrate that both models can incorporate alternative specific coefficients and constants. • We test both DFT and MLBA on revealed preference data for the first time, finding that both models have a better model fit than standard choice models in both estimation and out-of-sample validation. Interest in behavioural realism has gradually led to the introduction of alternatives to random utility models (RUMs) as a paradigm for representing choice behaviour, with notable interest, for example, in random regret minimisation (RRM). These more general models continue to rely on a framework where a single value function is calculated for each alternative in each choice setting, and the choice probabilities are calculated by comparing these value functions across alternatives. By contrast, research in mathematical psychology has used a more dynamic approach, where the preference value of each alternative updates over time in a given situation while the decision maker is deliberating about the choice to make. These accumulator models are well suited to accommodating a variety of context effects, and have been shown to give good performance for data collected in laboratory-based settings. The present paper considers two such accumulator models, namely decision field theory (DFT) and the multi-attribute linear ballistic accumulator (MLBA), and addresses limitations that have prevented their use in travel behaviour research. The methodological additions include the ability to capture the influence of socio-demographics, the presence of underlying preferences for specific alternatives, and/or the representation of attributes that have opposite effects on choice probabilities. We develop what we believe to be the first in-depth simultaneous comparison of DFT and MLBA with typical discrete choice models, and test both DFT and MLBA on a revealed preference dataset. We find that each model outperforms typical RUM and RRM implementations for both in-sample estimation and out-of-sample prediction, including in a large scale simulation experiment. [ABSTRACT FROM AUTHOR]

This paper explores the impact of COVID-19 on shopping behavior in two neighboring developing economies: Bangladesh and India. While the previous studies investigating the impact of COVID-19 on shopping behavior have relied on Revealed Preference (RP) data, this paper combines RP and Stated Preference (SP) data to develop joint RP-SP discrete choice models. This makes it possible to quantify the relative impact of the situational contexts on the choice of shopping modes of households and to capture the associated heterogeneity arising from the characteristics of the households. Further, comparison of the data and the estimated model parameters of the two countries with substantial socio-cultural similarities provide insights about how differences in the state of e-commerce can lead to different levels of inertia in continuing the pre-COVID behavior. The results will be useful to planners and policymakers for predicting the shopping modes in different future scenarios and formulating effective restriction measures. [ABSTRACT FROM AUTHOR]

• We provide a general overview of quantum probability and discuss how it might be incorporated into choice models. • We develop models based on a quantum probability framework, finding that they can provide better model fit than traditional models on three travel behaviour datasets. • We demonstrate that models with a quantum framework can incorporate efficient transformations for adjustments in the choice context, resulting in effective models for capturing the different sensitivities to attributes in best compared to worst choice, as well as capturing ordering effects in a typical route choice dataset. There has been an increasing effort to improve the behavioural realism of mathematical models of choice, resulting in efforts to move away from random utility maximisation (RUM) models. Some new insights have been generated with, for example, models based on random regret minimisation (RRM, μ -RRM). Notwithstanding work using for example Decision Field Theory (DFT), many of the alternatives to RUM tested on real-world data have however only looked at only modest departures from RUM, and differences in results have consequently been small. In the present study, we address this research gap again by investigating the applicability of models based on quantum theory. These models, which are substantially different from the state-of-the-art choice modelling techniques, emphasise the importance of contextual effects, state dependence, interferences and the impact of choice or question order. As a result, quantum probability models have had some success in better explaining several phenomena in cognitive psychology. In this paper, we consider how best to operationalise quantum probability into a choice model. Additionally, we test the quantum model frameworks on a best/worst route choice dataset and demonstrate that they find useful transformations to capture differences between the attributes important in a most favoured alternative compared to that of the least favoured alternative. Similar transformations can also be used to efficiently capture contextual effects in a dataset where the order of the attributes and alternatives are manipulated. Overall, it appears that models incorporating quantum concepts hold significant promise in improving the state-of-the-art travel choice modelling paradigm through their adaptability and efficient modelling of contextual changes. [ABSTRACT FROM AUTHOR]

• Joint use of electroencephalography (EEG) and virtual reality in the cycling context. • Developed hybrid choice model using dynamic data in virtual reality. • Demonstrated potential of the employment of neural data (alpha wave) in a hybrid model framework as an indicator of risk. Road risk analysis is one of the key research areas in transport, where the impact of perceived risk on choices, especially in a dynamic setting, has been long recognised. However, due to the lack of dynamic data and the difficulty in capturing risk perception, existing studies typically resort to static and stated approaches to infer the experienced level of risk of individuals. In this paper, we aimed to address this research gap through developing a hybrid choice model that jointly employed dynamic data on cycling behaviour in virtual reality and neural data to evaluate how the fluctuations in momentary risk perception influence the behaviour of cyclists. The results of the developed model confirm our hypotheses, demonstrating that cyclists reduce their speed when approaching a junction as the potential for a collision with passing cars increases. Moreover, the latent component allowed us to establish a link between the neural data, the amplitude of alpha brainwaves, and objective risk measures. In line with our hypothesis, we found that decreased alpha amplitude is associated with higher perceived risk, which in turn increases the likelihood of braking. The implications of our study are manifold. On the one hand, it shows the ability of virtual reality to elicit complex cyclists' behaviour and the feasibility of a joint collection of dynamic neural and choice data. On the other hand, we demonstrated the potential of the employment of neural data in a hybrid model framework as an indicator of risk that allowed us to gain a better understanding of cycling behaviour and associated neural processing. These promising findings pave the way for future studies that aim to explore the advantages of neuroscientific inputs in the choice models. [ABSTRACT FROM AUTHOR]