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Travel time variability is determined by variations in demand and capacity. Knowledge about these demand and supply factors can help improve the reliability of travel time and hence the quality of traveling. The precise nature of the relationship between, for example, variations in inflows and travel time variation is still largely unknown. This paper uses empirical traffic data from both Regiolab-Delft in the Netherlands and the Beijing Olympic area to analyze the variability of travel times depending on inflow conditions. Preliminary analysis shows that travel time variability is a function of inflow characterized by two so-called critical inflows (critical transition inflow λ1 and critical capacity inflow λ2, which are both lower than capacity). These critical inflow levels subdivide traffic into a fluent traffic region, a transition traffic region, and a capacity traffic region. Variation of inflow has little or no effect on travel time variation below λ1. But both demand and capacity variations have a positive correlation with travel time variability in between λ1 and λ2. When volumes are above λ2, the inflow slightly affects the travel time variability. Under all inflow levels, the variation in capacity appears to have more impact on travel time variability than does the variation of traffic flow.

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Traffic accident is one of the main causes of the increasing congestion in traffic networks. Due to the fact that traffic accidents reduce the capacity of a freeway and that their effect and duration are rather unpredictable at the moment they occur, it is expected that they contribute for great proportion to less reliable travel times. In this paper we present an empirical travel time reliability analysis on a basis of a large dataset of registered traffic accident data and empirical traffic flow data. The preliminary results show that travel time accidents result in both higher travel time variability and higher probability of traffic breakdown on freeways and thereby higher travel time unreliability.

Uncertainty of travel times and the impact on travel choice behavior has been recognized as an increasingly important research direction in the past decade. This paper proposes an extension to the popular scheduling approach to modeling traveler's departure time choice behavior under uncertainty, with the main focus on a richer representation of uncertainty. This more general approach incorporates a separate term to reflect the risk aversion associated with uncertainty. Recognizing the correlation between expected schedule delay and travel time variability, the schedule delay components in the generalized approach are defined in terms of expected travel time, which differs from the scheduling approach. This approach is developed based on the analytical investigation of the relationship between the expected schedule delay and the mean and standard deviation of travel time. An analytical equivalence was found between the scheduling approach and the general approach given a departure time t. To investigate the empirical performance of the generalized approach, two state preference (SP) data sets are used; one from China with a symmetric travel time distribution and the other from Australia with an asymmetric distribution. Both studies show empirical evidence of an equivalence in respect of statistical fit between the generalized and the scheduling approaches, as found from analytical investigations. The Chinese study gives support in the generalized model to including both the mean-variance and the scheduling effects; whereas the Australian study finds only the mean-variance specification has statistical merit. Despite the different travel contexts, it is noteworthy in both empirical settings, that the parameter estimate for arriving earlier than the preferred arrival time (PAT) in the generalized model is positive. This suggests that commuters tend to prefer to arrive earlier in order to guarantee he/she will not be late. This paper contributes to a better understanding of performances of different reliability measures and their relationships. The practical value of the various unreliability measures is provided showing that these indicators are easy to obtain for inclusion in project appraisal.2

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