This paper focuses on the effect of weather conditions on daily traffic intensities (the number of cars passing a specific segment of a road). The main objective is a general examination of whether or not weather conditions uniformly alter daily traffic intensities in Belgium, or in other words whether or not the road usage on a particular location determines the size of the effects of various weather conditions. This general examination is a contribution which allows policy makers to assess the appropriateness of countrywide versus local traffic management strategies. In addition, a secondary goal of this paper is to validate findings in international literature within a Belgian context. To achieve these goals, the effects of weather conditions on both upstream (towards a specific location) and downstream (away from a specific location) traffic intensities of three traffic count locations, typified by a different road usage, are analyzed. The most interesting results of this study for policy makers are the heterogeneity of the weather effects between different traffic count locations, and the homogeneity of the weather effects on upstream and downstream traffic at a certain location. The results also indicated that snowfall, rainfall and wind speed clearly have a diminishing effect on traffic intensity, while maximum temperature has an increasing effect on traffic intensity.Further generalizations of the findings will be possible by studying weather effects on local roads and by shifting the scope towards travel behavior.

Weather can influence travel demand, traffic flow, and traffic safety. A hypothesis—the type of weather determined the likelihood of a change in travel behavior, and changes in travel behavior because of weather conditions depended on trip purpose—was assayed. A stated adaptation study was conducted in Flanders (the Dutch-speaking region of Belgium). A survey, completed by 586 respondents, was administered both on the Internet and as a traditional paper-and-pencil questionnaire. To ensure optimal correspondence between the survey sample composition and the Flemish population, observations in the sample were weighted. To test the main hypotheses, Pearson chi-square independence tests were performed. Results from both the descriptive analysis and the independence tests confirm that the type of weather matters and that changes in travel behavior in response to these weather conditions are highly dependent on trip purpose. This dependence of behavioral adjustments on trip purpose provides policy makers with a deeper understanding of how weather conditions affect traffic. Further generalizations of the findings are possible by shifting the scope toward revealed travel behavior. Triangulation of both stated and revealed travel behavior on the one hand and traffic intensities on the other hand is a key challenge for further research.

Due to a variety of reasons, the previous century is characterized by an extraordinary growth in car use that has continued into the current century. This has resulted in serious environmental repercussions. Despite technological advancements, the externalities remain an ecological threat that can not be discarded by policy makers. Therefore, it is essential that policy makers focus on reducing car use and on stimulating the shift towards more environment-friendly transport modes. In this study, Q-methodology is adopted as the technique to segment people, and to ascertain which approaches and determinants matter to medium distance travel. Segmentation is important, as policy measures will be more efficient and effective if they are fine-tuned on specific target groups. The analysis revealed that four discourses preponderate the paradigm of environmentally sustainable transport: travelers who use public transport as a dominant alternative, car-dependent travelers, travelers with a positive perception of using public transport, and travelers with a preference for car use. Concerning rational, economic motives, individuals evaluate travel time reliability as most important. To increase the reliability policy makers should consider the use of separate bus lanes and traffic light manipulation. In addition, public transport can be made even more attractive, when costs of cars are made more variable by road or congestion charging. When the s motives are discussed, the differences between the different groups of travelers were more pronounced. Next to increasing the benefits of using public transport, policy makers should also pay attention to removing psycho-social barriers.

The objective of this study is to examine the effect of road pricing on people's tendency to adapt their current travel behavior. To this end, the relationship between changes in activity-travel behavior on the one hand and public acceptability and its most important determinants on the other are investigated by means of a stated adaptation experiment. Using a two-stage hierarchical model, it was found that behavioral changes themselves are not dependent on the perceived acceptability of road pricing itself, and that only a small amount of the variability in the behavioral changes were explained by socio-cognitive factors. The lesson for policy makers is that road pricing charges must surpass a minimum threshold in order to to entice changes in activity-travel behavior and that the benefits of road pricing should be clearly communicated, taking into account the needs and abilities of different types of travelers. Secondly, earlier findings concerning the acceptability of push measures were validated, supporting transferability of results. In line with other studies, effectiveness, fairness and personal norm all had a significant direct impact on perceived acceptability. Finally, the relevance of using latent factors rather than aggregate indicators was underlined.

In this paper, daily traffic counts are explained and forecast by different modeling philosophies: an approach using autoregressive integrated moving average (ARIMA) models with explanatory variables (i.e., the ARIMAX model) and approaches using a seasonal autoregressive integrated moving average (SARIMA) model as well as a SARIMA model with explanatory variables (i.e., the SARIMAX model). Special emphasis is placed on the investigation of seasonality in daily traffic data and on the identification and comparison of holiday effects at different sites. To get insight into prior cyclic patterns in the daily traffic counts, spectral analysis provides the required framework to highlight periodicities in the data. The analyses use data from single inductive loop detectors, which were collected in 2003, 2004, and 2005. Four traffic count locations are investigated in this study: an upstream and a downstream traffic count site on a highway used extensively by commuters, and an upstream and a downstream traffic count site on a highway typically used for leisure travel. The different modeling techniques show that weekly cycles appear to determine the variation in daily traffic counts. The comparison between seasonal and holiday effects at different site locations reveals that both the ARIMAX and the SARIMAX modeling approaches are valid frameworks for identifying and quantifying possible influencing effects. The techniques yield the insight that holidays have a noticeable impact on highways extensively used by commuters, while having a more ambiguous impact on highways typically used for leisure travel. Future research challenges are the modeling of daily traffic counts on secondary roads and the simultaneous modeling of underlying reasons for travel and revealed traffic patterns.