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Travellers account for variability in transport system performance when they make choices about routes, modes and destinations. Modellers try to quantify travel time reliability through various dispersion measures, most commonly the standard deviation of travel time. However, standard deviation is only one attribute of the nuanced travel time distribution. This paper considers whether standard deviation is sufficient to describe the travellers' understanding and value of travel time reliability and how we might include other aspects of variability such as the frequency of exceeding a lateness threshold or the likelihood of rare events. Car drivers in New South Wales, Australia, were asked to reconstruct the distribution of their commuting time and identify a lateness threshold. Further, we asked them about their preferences in a series of stated choice experiments using three representations of travel time reliability pivoted around their regular commute. The results show reliability ratios consistent with those in the literature for all three presentations. Moreover, the standard error of the estimated coefficient on the risk of rare events indicates that standard deviation alone may not sufficiently capture travellers' preferences towards travel time reliability. [ABSTRACT FROM AUTHOR]

Path choice modelling is typically conducted by considering a subset of paths, not the universal set of all feasible paths as this is computationally challenging. This study proposes a two-stage modelling approach. In the first stage, it develops a new probabilistic importance sampling protocol by using fuzzy logic. In the second stage, it tested different structures of the discrete choice models, where different strategy attributes are considered along with the traditional variables. The results prove that the new sampling protocol performs better than traditional sampling protocol. Again, the inclusion of the strategy attributes proves to yield better prediction. The results of the study recommend considering different strategy attributes in the path generation process as well as in the transit assignment models. The study also discusses the effect of the choice set size on the model performance. Household travel survey data of south-east Queensland, Australia is used to develop the models. [ABSTRACT FROM AUTHOR]

• Investigates alternative ways to obtain detailed street environment data for route choice analysis. • Compares objective and subjective measures for pedestrian route choice behaviour. • Empirically tests pedestrian route choice behaviours in a case location in Australia. • Finds that subjective measures capture different construct than those measured objectively. Difficulties in obtaining detailed street environment data is identified as a major obstacle in pedestrian route choice studies. Scholars have used either objectively or subjectively measured street environment data without testing their suitability as a substitute for each other in the route choice literature. This study aims to overcome these gaps by investigating the relevance of subjective data as a less-expensive proxy for objective data—together with identifying the factors affecting the degree of disconcordance between them. Subjective street environment data was collected from 178 pedestrians in Brisbane, Australia. Participants were intercepted and requested to draw their walking routes. They were asked to rate the importance of different street environment attributes influencing the chosen route. A range of objective attributes of these routes was derived through virtual audit and spatial analyses. The concordance of 13 seemingly related street environment variables was tested using 'Kappa coefficient' and '% agreement' methods. Additionally, 13 multinomial logistic regression models were estimated, one for each variable, to identify different factors affecting the level of disconcordance. Results indicate a relatively poor agreement between objective and subjective attributes. However, an objective provisioning of some attributes can improve the perception of pedestrians about these attributes. Estimated regression models show that all groups are to some extent at-risk of being mismatched, suggesting the need for population-based policy interventions to improve perceptions. Findings highlight that subjective measures capture different construct of the street environment than those measured objectively, hence, using these two measures are not comparable. [ABSTRACT FROM AUTHOR]

Growing interest in sustainable transportation systems has driven decision-makers toward policies and investments aimed at promoting cycling, but little to no effort has been made toward incorporating bicycle transport in transport planning models. This study contributes toward this direction by estimating a bicycle route choice model in value-of-distance space from a large sample of 3384 cycling trips that were traced with GPS devices in the Copenhagen Region. The novelty of this study lies in (i) observing cyclists' behavior in a cycling-oriented country, (ii) exploiting rich data about the cycling environment, (iii) estimating the model in value-of-distance rather than preference space, and (iv) not focusing only on preferences for traditional variables (e.g., distance, turns, hilliness, intersections, motorized road characteristics), but also on perceptions and preferences for bicycle facilities (e.g., bicycle lanes, bicycle paths, bicycle traces) and land-use designations (e.g., residential, industrial, sports, scenic areas). The findings from the model show that: (i) cyclists exhibit heterogeneous preferences for avoiding right and left turns, cycling the wrong way, using roundabouts and bridges, and cycling alongside residential and scenic areas; (ii) cyclists dislike cycling on unpaved and hilly surfaces and alongside larger roads; (iii) cyclists have clear perceptions about different types of bicycle facilities, with a preference for bicycle lanes and segregated paths; (iv) cyclists have clear perceptions about land-use designations, with a preference for cycling alongside sports and scenic areas; (v) time-of-day and air temperature contribute to the perceptions of cyclists and their preferences for bicycle facilities and land-use designations. [ABSTRACT FROM AUTHOR]

This work builds upon the thought that individuals allocate higher levels of importance to some particular features of the route, so called anchor points. Previous route choice models have either ignored the effects of anchor points (route-based models), or have given an exclusive attention to their effects and ignored the behavioral accuracy and practicality of these models (anchor-based models). In this work we argue that the consideration of both route-level attributes and anchor points would enhance the behavioral aspect of route choice models as well as their estimation and prediction abilities. Global Positioning System traces have been used to investigate the effect of bridges as anchor points for trips between Montreal and its Northern suburb, Laval. A classic Nested Logit and a nested Logit Kernel model have been estimated, in which interdependencies among routes crossing the same bridge are captured through the nested structure and the adopted factor analytic approach, respectively. A Metropolis-Hastings path-sampling algorithm is applied, for the first time, on a large road network with more than 40,000 nodes and 19,000 links to provide the consideration choice set. Estimates are then compared to three alternate models, representing route-based and anchor-based formulations; namely Path-Size Logit, Extended Path-Size Logit, and Independent Availability Logit models. Empirical results showed that the proposed nested structures with MH sampling provide better estimates and also perform better in the validation step with respect to comparative models. Findings underscore the importance of considering anchor points in conjunction with route level attributes in route choice decisions. [ABSTRACT FROM AUTHOR]