We develop a simple but modern stated-preference (SP) choice experiment to analyse the impact of traffic images on the estimated value of travel time savings (VTTS). A random subsample of survey respondents view computer-generated images that depict traffic conditions for congested and free-flow portions of hypothetical travel alternatives. The remaining respondents receive otherwise-identical surveys that only verbally describe the attributes of those alternatives. From surveys with images, the estimated VTTS is $7.40 per hour for congested travel time and $5.70 for free-flow travel time, implying a ‘congestion premium’ of 30%. From surveys without images, however, congested and free-flow VTTS estimates are identical at $5.90 per hour. Our results indicate that traffic images, however rudimentary, can substantially influence travel-time valuation, thus compelling further research on incorporating them into SP experimental design.

The LWR model is of interest since it is simple and can successfully reproduce some essential features of traffic flow, such as the formation and propagation of traffic disturbances. In this article, we investigate the LWR model from an uncertainty perspective. We attempt to analyse how reliable the LWR model prediction will be if the fundamental diagram (FD) in use is not accurately specified. To fulfil this end, we postulate a flux function (equivalently a FD) driven by a random free flow speed, which accommodates the uncertain feature observed in the speed–density data. We provide essential mathematical properties and solution schemes of the LWR model with the probabilistic FD. In case studies, the approach to evaluate the uncertainty of traffic disturbance propagation with this model is presented. We find that if FD in a LWR model cannot be perfectly specified, the uncertainty associated with the location of a traffic disturbance would increase over time. In contrast, the magnitude of the traffic disturbance can still be accurately predicted.

This study divides entire airline services into eight service stages and uses a second-order confirmatory factor analysis (CFA) to constitute service quality and to examine the causal relationships between two consecutive service stages from a service chain perspective. Two conceptual frameworks – overall framework and service chain framework are proposed. The former incorporates the constructs of service quality, sacrifice, servicescape, service value, satisfaction, switching barriers, and behavioural intentions combined with seven hypothetical causal relationships. The latter depicts seven hypothetical causal relationships between two consecutive service stages. The proposed models are validated by a case study of Spring Airlines, a low-cost carrier (LCC) based in China. The empirical results support all hypotheses except hypothesis 7 that service quality positively impacts behavioural intentions. Notably, test results for the interrelationships between two consecutive service stages suggest that a lack of satisfaction at a specific service stage will affect customer perception of the consequent service stage. Therefore, to improve the service quality for a service stage, the service quality of all upstream service stages must be improved first. This study also found that service quality has a large effect although not direct on behavioural intentions while sacrifice has the smallest effect. A low-fare strategy may not be effective when an airline fails to deliver high-quality service.

Three new models for the flow–density relationship are proposed in this work. The resulting flow–density curves are concave in the whole range of feasible values for the parameters. These models have four parameters, three of them being the jam density, the free-flow speed and the kinematic wave speed. The fourth parameter is a shape parameter. The models allow for a great flexibility for fitting of real traffic flow and density data. A remarkable property of these models is the fact that they yield a bilinear fundamental diagram when the shape parameter tends to infinity. The models have been tested with freeway data and urban data. The results demonstrate that the models achieve an excellent goodness of fit and yield realistic estimates of the parameters. The models proposed in this work are a valuable tool not only for fitting flow–density data but also for its use in traffic flow dynamic models.

In this study, we employ an ant colony system (ACS) algorithm, coupled with a threshold accepting technique, to develop an ACS-based hybrid algorithm that is capable of efficiently solving an emergency roadway repair time-space network flow problem. To test how well the algorithm may be applied to actual operations, we perform a case study using data from the 1999 Chi-Chi earthquake in Taiwan. To assess the robustness of the model and solution algorithm for different roadway network patterns, we generate 16 problem instances with four different roadway network patterns. The method that uses CPLEX to directly solve the problem and a previously proposed heuristic are used to compare with our algorithm. The test results show that our algorithm is both effective and efficient enough to solve problems with different roadway networks, and therefore, would be useful in practice.

The capacity within an extended theory of planned behaviour (TPB) to change young drivers’ intentions and reduce their commission of driving violations was tested using regression-based statistical simulations. Participants (N = 198) completed questionnaire measures of TPB variables, plus moral norm and anticipated regret, each with respect to 11 different driving violations. One month later, subsequent behavioural performance was measured, again using self-completion questionnaires. Statistical simulations indicated substantial capacity within the extended TPB to reduce driving violations, with maximum changes to all of the cognitive predictors generating large degrees of intention and behaviour change (i.e. d > 0.80). However, the degree of intention change that was generated was greater than the degree of behaviour change, and sensitivity analyses demonstrated that behavioural interventions need to successfully change multiple cognitive variables in order to achieve meaningful reductions in driving violations. Implications of the findings for developing behaviour change interventions are discussed.

We examine the impact of individual-specific information processing strategies (IPSs) on the inclusion/exclusion of attributes on the parameter estimates and behavioural outputs of models of discrete choice. Current practice assumes that individuals employ a homogenous IPS with regards to how they process attributes of stated choice (SC) experiments. We show how information collected exogenous of the SC experiment on whether respondents either ignored or considered each attribute may be used in the estimation process, and how such information provides outputs that are IPS segment specific. We contend that accounting the inclusion/exclusion of attributes will result in behaviourally richer population parameter estimates.

This article addresses model development and computational algorithm design for the probit-based asymmetric stochastic user equilibrium (SUE) problem with elastic demand. Two variational inequality (VI) models are first proposed for the SUE problem and then existence and uniqueness of their solutions are examined. These two VI models are, in reality, built by means of a probit-based stochastic network loading (SNL) map. Since there is no computational procedure available for calculating the SNL map, we thus propose a two-stage Monte Carlo simulation-based method to estimate the SNL map. To compromise computational time with accuracy in the estimation, a lower bound of sample size required by the Monte Carlo simulation is also investigated. Based on these two VI models and Monte Carlo simulation-based method, we design two hybrid prediction–correction (PC) — cost averaging (CA) algorithms for solving the SUE problem. Finally, two numerical examples are carried out to assess performance of the proposed algorithms.

This article studies route choices and traffic equilibria when travel times are risky, and travellers are risk averse and regret averse. It is shown how regret theory, being one of the most popular contenders of expected utility theory throughout the social sciences, can be applied to model risky route choices by means of an expected modified utility function. Subsequently this function is used to study numerically how risk aversion and regret aversion jointly determine equilibrium outcomes in a simple binary route choice situation. It is found that increasing levels of regret aversion lead to equilibrium shifts towards routes whose mean travel time is low, routes that are less risky and especially routes whose worst-case travel time is low compared to that of the competing route. Furthermore, risk aversion and regret aversion are found to reinforce each other's impact on equilibrium towards a situation where safer routes are preferred over riskier (but faster) ones.