A review on travel behaviour modelling in dynamic traffic simulation models for evacuations

Dynamic traffic simulation models are frequently used to support decisions when planning an evacuation. This contribution reviews the different (mathematical) model formulations underlying these traffic simulation models used in evacuation studies and the behavioural assumptions that are made. The appropriateness of these behavioural assumptions is elaborated on in light of the current consensus on evacuation travel behaviour, based on the view from the social sciences as well as empirical studies on evacuation behaviour. The focus lies on how travellers’ decisions are predicted through simulation regarding the choice to evacuate, departure time choice, destination choice, and route choice. For the evacuation participation and departure time choice we argue in favour of the simultaneous approach to dynamic evacuation demand prediction using the repeated binary logit model. For the destination choice we show how further research is needed to generalize the current preliminary findings on the location-type specific destination choice models. For the evacuation route choice we argue in favour of hybrid route choice models that enable both following instructed routes and en-route switches. Within each of these discussions, we point at current limitations and make corresponding suggestions on promising future research directions.     

The call of the road: factors predicting students’ car travelling intentions and behaviour

The most common daily trip for employed persons and students is the commute to and from work and/or place of study. Though there are clear environmental, health and safety benefits from using public transport instead of private vehicles for these trips, a high proportion of commuters still choose private vehicles to get to work or study. This study reports an investigation of psychological factors influencing students’ travel choices from the perspective of the Theory of Planned Behaviour (TPB). Students from three different university campuses (n = 186) completed a cross-sectional survey on their car commuting behaviour. Particular focus was given to whether car commuting habits could add to understanding of commuting behaviour over and above behavioural intentions. Results indicated that, as expected, behavioural intention to travel by car was the strongest TPB predictor of car commuting behaviour. Further, general car commuting habits explained additional variance over and above TPB constructs, though the contribution was modest. No relationship between habit and intentions was found. Overall results suggest that, although student car commuting behaviour is habitual in nature, it is predominantly guided by reasoned action. Implications of these findings are that in order to alter the use of private vehicles, the factors influencing commuters’ intentions to travel by car must be addressed. Specifically, interventions should target the perceived high levels of both the acceptability of commuting by car and the perceived control over travel undertaken by private vehicle.     

Understanding travel behaviour change during mega-events: Lessons from the London 2012 Games

This paper presents results from a longitudinal study of the travel behaviour change associated with the London 2012 Olympic and Paralympic Games (the ‘Games’). The research examines commuter travel behaviour through a panel approach enabling an understanding of individual behaviour across three waves (before, during and after), with the study utilising unique access to a Transport for London panel study (n = 1132). The findings indicate that a substantial amount of change occurred during the Games (54% made at least one change), with reducing or re-timing journeys being the most likely adaptations made. A key objective of this work was to advance the discussion about the theoretical constructs that are most applicable in the study of behaviour change associated with disruptive events, which was done through the application and critical evaluation of the Transtheoretical Model. The insights from the stages of change element of the model were relatively limited but the analysis shows significant differences in the underlying factors explaining change according to the type of change made (reduce, re-time, re-mode and re-route). Whilst the long-term behavioural impacts of events like the Games appear small, the study has uncovered a need to consider these behavioural choices as distinct rather than under the collective term of “travel behaviour change”, as is current practice.     

Allowing for heterogeneous decision rules in discrete choice models: an approach and four case studies

The study of respondent heterogeneity is one of the main areas of research in the field of choice modelling. The general emphasis is on variations across respondents in relative taste parameters while maintaining the assumption of homogeneous utility maximising decision rules. While recent work has allowed for differences in the utility specification across respondents in the context of looking at heterogeneous information processing strategies, the underlying assumption that all respondents employ the same choice paradigm remains. This is despite evidence in the literature that different paradigms work differently well on given datasets. In this article, we argue that such differences may in fact extend to respondents within a single dataset. We accommodate these differences in a latent class model, where individual classes make use of different underlying paradigms. We present four applications using three different datasets, showing mixtures between “standard” random utility maximisation models and lexicography based models, models with multiple reference points, elimination by aspects models and random regret minimisation models. In each of the case studies, the behavioural mixing model obtains significant gains in fit over the base structure where all respondents are hypothesised to use the same rule. The findings offer important further insights into the behavioural patterns of respondents. There is also evidence that what is retrieved as taste heterogeneity in standard models may in fact be heterogeneity in decision rules.     

Adapting interactive stated response techniques to a self-completion survey

We report here on how we adapted Interactive Stated Response Methods to use in a self-completion survey of 454 California households to measure the market for electric vehicles in California. Electric vehicles are a novel product and have many features unfamiliar to consumers, in particular their home recharging capability and limited range. Reflexive techniques were designed to draw households into a deeper exploration of the lifestyle implications of electric vehicles than can be done in more typical self-completion surveys, and to stimulate key decision processes previously observed in detailed gaming interviews with 51 households. Reflexive self-completion techniques provide a middle course between typical large sample quantitative surveys and small sample, detailed gaming interviews. One of the benefits of the more intensive techniques was that participants reported finding the surveys interesting and return rates were high for a self-completion approach – over sixty percent. We review previous transportation and sociological methods which inspired our design, describe the design goals and features of our research, and summarize research results pertinent to testing the validity of our approach.     

Applying behavioural theories to studying the influence of climate change on young people’s future travel intentions

Transport policy in the UK is seeking to promote the development of low carbon transport technology and to encourage people to choose to use low carbon travel options. This paper draws on existing behavioural theories to study young people’s travel behaviour intentions and the influence on these from their knowledge of, and willingness to act on, climate change. The study involved a series of focus groups with young people aged 11-18 years, where attitudes to transport modes, attitudes towards climate change and travel behaviour intentions were discussed. Knowledge and values are established as the key determinants of young people’s attitudes and behaviour intentions towards transport in the context of climate change. More specifically it is established that young people’s values emphasise speed and freedom and that it is important to young people that the mode of transport they choose is reflective of the image they want to portray.     

When strike comes to town… anticipated and actual behavioural reactions to a one-day,pre-announced,complete rail strike in the Netherlands

We conducted secondary analysis on data collected among rail users, days before and after a national rail strike in the Netherlands. Our aim was to compare anticipated and actual behavioural reactions to the rail strike, investigate associations with traveller and trip characteristics, and perceived behavioural control and satisfaction with the chosen alternative. Forty-four percent of the people who had anticipated to travel by train on the day of the strike abandoned their trip, 24% switched to car as driver, 14% switched to another mode (as passenger), 18% stayed with the train and rescheduled the planned activity to another day. Almost half of people who had anticipated travelling by car expected to change behaviour as well. Multinomial logistic regression showed low preference for car among rail users. Considerable marginal effects were found for several variables, e.g.: young people and females were less likely to switch to car; short and middle distance trips were less likely abandoned or switched to another day; commute and business trips were more likely done by car, and business trips less likely cancelled. Despite high levels of perceived behavioural control and satisfaction with the chosen alternative, permanent modal shift as result of this strike is not expected.     

Analysing Demand for Suburban Trips: A Mixed RP/SP Model with Latent Variables and Interaction Effects

We analyse mode choice behaviour for suburban trips in the Grand Canary island using mixed revealed preference (RP)/stated preference (SP) information. The SP choice experiment allowed for interactions among the main policy variables: travel cost, travel time and frequency, and also to test the influence of latent variables such as comfort. It also led to discuss additional requirements on the size and sign of the estimated model parameters, to assess model quality when interactions are present. The RP survey produced data on actual trip behaviour and was used to adapt the SP choice experiment. During the specification searches we detected the presence of income effect and were able to derive willingness-to-pay measures, such as the subjective value of time, which varied among individuals. We also studied the systematic heterogeneity in individual tastes through the specification of models allowing for interactions between level-of-service and socio-economic variables. We concluded examining the sensitivity of travellers’ behaviour to various policy scenarios. In particular, it seems that contrary to political opinion, in a crowded island policies penalising the use of the private car seem to have a far greater impact in terms of bus patronage than policies implying direct improvements to the public transport service.     

Sample size requirements for stated choice experiments

Stated choice (SC) experiments represent the dominant data paradigm in the study of behavioral responses of individuals, households as well as other organizations, yet in the past little has been known about the sample size requirements for models estimated from such data. Traditional orthogonal designs and existing sampling theories does not adequately address the issue and hence researchers have had to resort to simple rules of thumb or ignore the issue and collect samples of arbitrary size, hoping that the sample is sufficiently large enough to produce reliable parameter estimates, or are forced to make assumptions about the data that are unlikely to hold in practice. In this paper, we demonstrate how a recently proposed sample size computation can be used to generate so-called S-efficient designs using prior parameter values to estimate panel mixed multinomial logit models. Sample size requirements for such designs in SC studies are investigated. In a numerical case study is shown that a D-efficient and even more an S-efficient design require a (much) smaller sample size than a random orthogonal design in order to estimate all parameters at the level of statistical significance. Furthermore, it is shown that wide level range has a significant positive influence on the efficiency of the design and therefore on the reliability of the parameter estimates.     

The ‘Four Dimensions of Behaviour’ framework: a tool for characterising behaviours to help design better interventions

This paper sets out the rationale and structure of a tool for assisting policy-makers and practitioners to understand behavioural challenges and open up thinking on the design of effective ‘behaviour change’ interventions. The ‘Four Dimensions of Behaviour’ (4DB) framework is based on the theoretical and empirical research in a range of policy domains including transport and pro-environmental behaviour more generally. The 4DB framework characterises multifaceted behaviours along dimensions of actor, domain, durability and scope. Its application in workshop or structured settings opens up diverse and non-exclusive discussion on designing interventions to match salient behavioural characteristics. The use of the 4DB framework in the transport domain is demonstrated for travel behaviours of interest to policy-makers using examples of buying plug-in vehicles (PiVs), commuting by bicycle, eco-driving and making business trips by train.     

Small area estimates of daily person-miles of travel: 2001 National Household Transportation Survey

The National Household Transportation Survey (NHTS) was designed at the national level, and for most states it does not have a large enough sample to produce reliable estimates, especially for subdomains (e.g., age groups) within a state. Using the 2001 NHTS, we produced small area estimates (SAEs) of the percentage of persons among four age groups (17 or younger, 18–39, 40–54, and 55 or older) having high daily person-miles of travel (more than 87.5 miles a day, which is the 90th percentile for daily person-miles traveled) and associated prediction intervals for all 50 states and the District of Columbia. The survey weighted hierarchical Bayes (Folsom et al., Proc of the Sect on Surv Res Methods of the Am Stat Assoc 371–375, 1999) small area estimation (SAE) methodology was used to produce state-level SAEs. This paper describes the methodology and shows that SAE can be an effective technique for producing reliable state-level estimates from large, national surveys like the NHTS. In particular, the prediction interval relative widths for SAEs were, on average, 31–48% narrower than the corresponding design-based confidence interval widths, whereas for small states the reduction was around 47–63%.     

Can induced traffic be measured by surveys?

This paper addresses the feasibility of measuring induced traffic by means of appropriately designed surveys. The problems which confront any attempt to measure induced traffic are described and discussed. They include: inherent variability of traffic data, difficulty in establishing what would have happened in the absence of the scheme, uncertainties in the attribution of cause and difficulty in determining the most appropriate time to conduct surveys. Some of these problems have no solution while others can be reduced with an appropriate programme of surveys and control studies. Calculations are made of the sample sizes required to achieve various levels of target accuracy. It is concluded that, given an appropriate programme of traffic counts including control studies and extensive screenlines, it should be possible at relatively modest cost and with reasonable precision, to measure the increases in traffic associated with a scheme and to identify how much of that increase is due to rerouteing. Any further disaggregation of this extra traffic into that which is due to change of mode, change of destination and increased frequency, is much more problematic. The basic traffic counts would need to be supplemented by a programme of public transport surveys, registration plate marking and/or roadside interviews, which would add very considerably to the cost of the exercise. However, even with considerable expenditure, adequate precision may be difficult to achieve and attribution of cause impossible to make.     

Behavioural intentions as predictors of very specific behaviour

This paper proposes an alternative approach to understanding travel behaviour —the study of behavioural intentions. The communality of this and the revealed behaviour approach are outlined and the use of controlled experimental methods illustrated in an analysis of empirical data on behavioural intentions of citizens with respect to changes in the levels of transport service, and the introduction of an area licensing scheme for travel in a central city area in Australia.This research was completed while Jordan Louviere was at Cambridge Systematics Inc., Boston, Massachusetts, U.S.A.     

An agent-based approach to modelling driver route choice behaviour under the influence of real-time information

This paper presents an agent-based approach to modelling individual driver behaviour under the influence of real-time traffic information. The driver behaviour models developed in this study are based on a behavioural survey of drivers which was conducted on a congested commuting corridor in Brisbane, Australia. Commuters’ responses to travel information were analysed and a number of discrete choice models were developed to determine the factors influencing drivers’ behaviour and their propensity to change route and adjust travel patterns. Based on the results obtained from the behavioural survey, the agent behaviour parameters which define driver characteristics, knowledge and preferences were identified and their values determined. A case study implementing a simple agent-based route choice decision model within a microscopic traffic simulation tool is also presented. Driver-vehicle units (DVUs) were modelled as autonomous software components that can each be assigned a set of goals to achieve and a database of knowledge comprising certain beliefs, intentions and preferences concerning the driving task. Each DVU provided route choice decision-making capabilities, based on perception of its environment, that were similar to the described intentions of the driver it represented. The case study clearly demonstrated the feasibility of the approach and the potential to develop more complex driver behavioural dynamics based on the belief–desire–intention agent architecture.     

Modelling users’ behaviour of a carsharing program: Application of a joint hazard and zero inflated dynamic ordered probability model

This paper presents an econometric model for the behaviour of carsharing users. The econometric model is developed to jointly forecast membership duration, the decision to become an active member in a particular month, and the frequency of monthly usage of active members. The model is estimated using the membership directory and monthly transaction data of a carsharing program, ‘Communauto Inc.’, based in Montréal, Canada. The model shows a high degree of fit to the observed dataset and provides many behavioural details of carsharing users. The results are instructive to carsharing planners in devising efficient policies.     

Shift of reference point and implications on behavioral reaction to gains and losses

It is widely recognized that individual decision-making is subject to the evaluation of gains and losses around a reference point. The estimation of discrete choice models increasingly use data from stated choice experiments which are pivoted around a reference alternative. However, to date, the specification of a reference alternative in transport studies has been fixed, whereas it is common to observe individuals adjusting their preferences according to a change in their reference point. This paper focuses on individual reactions, in a freight choice context, to a negative change in the reference alternative values, identifying the behavioural implications in terms of loss aversion and diminishing sensitivity. The results show a significant adjustment in the valuation of gains and losses around a shifted reference alternative. In particular, we find an average increase in loss aversion for cost and time attributes, and a substantial decrease for punctuality. These findings are translated to significant differences in the willingness to pay and willingness to accept measures, providing supporting evidence of respondents’ behavioural reaction.     

Simulation of electric vehicle driver behaviour in road transport and electric power networks

The integration of electric vehicles (EVs) will affect both electricity and transport systems and research is needed on finding possible ways to make a smooth transition to the electrification of the road transport. To fully understand the EV integration consequences, the behaviour of the EV drivers and its impact on these two systems should be studied. This paper describes an integrated simulation-based approach, modelling the EV and its interactions in both road transport and electric power systems. The main components of both systems have been considered, and the EV driver behaviour was modelled using a multi-agent simulation platform. Considering a fleet of 1000 EV agents, two behavioural profiles were studied (Unaware/Aware) to model EV driver behaviour. The two behavioural profiles represent the EV driver in different stages of EV adoption starting with Unaware EV drivers when the public acceptance of EVs is limited, and developing to Aware EV drivers as the electrification of road transport is promoted in an overall context. The EV agents were modelled to follow a realistic activity-based trip pattern, and the impact of EV driver behaviour was simulated on a road transport and electricity grid. It was found that the EV agents’ behaviour has direct and indirect impact on both the road transport network and the electricity grid, affecting the traffic of the roads, the stress of the distribution network and the utilization of the charging infrastructure.     

A prototype fuel-efficiency support tool

An effective way to reduce fuel consumption in the short run is to induce a change in driver behaviour. If drivers are prepared to change their driving habits they can complete the same journeys within similar travel times, but using significantly less fuel. In this paper, a prototype fuel-efficiency support tool is presented which helps drivers make the necessary behavioural adjustments.The support tool includes a normative model that back-calculates the minimal fuel consumption for manoeuvres carried out. If actual fuel consumption deviates from this optimum, the support tool presents advice to the driver on how to change his or her behaviour. To take account of the temporal nature of the driving task, advice is generated at two levels: tactical and strategic.Evaluation of the new support tool by means of a driving simulator experiment revealed that drivers were able to reduce overall fuel consumption by 16% compared with ‘normal driving’. The same drivers were only able to achieve a reduction of 9% when asked to drive fuel efficiently without support; thus, the tool gave an additional reduction of 7%. Within a simulated urban environment, the additional reduction yielded by the support tool rose to 14%. The new support tool was also evaluated with regard to secondary effects.     

Tests of assignment accuracy: An interurban case study

This paper is concerned with methods of testing the accuracy of traffic assignments. It focuses on the fact that whereas assignment models are usually based on a behavioural hypothesis about drivers' route choice (e.g. cost or time minimisation) the test of the accuracy of the assignment is the extent to which observed link loadings are reproduced. This inconsistency opens up the doubt that an apparently “accurate” assignment on this basis may be a result of compensating errors. It is difficult to apply the same test to accuracy of route choices as is applied to accuracy of link loadings (e.g. chi square, correlation coefficient) and hence a new measure is devised here which can be applied both to comparisons between observed and predicted route choices and comparisons between observed and predicted loadings. It is, moreover, possible to devise a test of significance for this measure so that one can test whether a predicted assignment is significantly different from what one, might have observed on the basis of chance observation. A case study is carried out to test the proposed method. Traffic flows between 72 origins and destinations on either side of the Pennine Mountains in Britain are assigned to a network using different assignment techniques with varied parameters. In all, one hundred and ninety assignments are carried out and the degree of correspondence between observed and predicted route choices and link loadings is measured. The results tend to confirm that the link loadings criterion is not a very stable criterion and that the route choice correspondence criteria seems to behave in a sensible way. A simulation exercise is carried out which produces the probability distribution of the “route-fit” index for different assumed sample levels. The paper concludes by suggesting avenues for further research.