Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan.

Non-recurrent congestion disrupts normal traffic operations and lowers travel time (TT) reliability, which leads to many negative consequences such as difficulties in trip planning, missed appointments, loss in productivity, and driver frustration. Traffic incidents are one of the six causes of non-recurrent congestion. Early and accurate detection helps reduce incident duration, but it remains a challenge due to the limitation of current sensor technologies. In this paper, we employ a recurrence-based technique, the Quadrant Scan, to analyse time series traffic volume data for incident detection. The data is recorded by multiple sensors along a section of urban highway. The results show that the proposed method can detect incidents better by integrating data from the multiple sensors in each direction, compared to using them individually. It can also distinguish non-recurrent traffic congestion caused by incidents from recurrent congestion. The results show that the Quadrant Scan is a promising algorithm for real-time traffic incident detection with a short delay. It could also be extended to other non-recurrent congestion types.

Predicting the effectiveness of road safety campaigns through alternative research designs.

INTRODUCTION: A large number of road safety communication campaigns have been designed and implemented in the recent years; however their explicit impact on driving behavior and road accident rates has been estimated in a rather low proportion. METHOD: Based on the findings of the evaluation of three road safety communication campaigns addressing the issues of drinking and driving, seat belt usage, and driving fatigue, this paper applies different types of research designs (i.e., experimental, quasi-experimental, and non-experimental designs), when estimating the effectiveness of road safety campaigns, implements a cross-design assessment, and conducts a cross-campaign evaluation. An integrated evaluation plan was developed, taking into account the structure of evaluation questions, the definition of measurable variables, the separation of the target audience into intervention (exposed to the campaign) and control (not exposed to the campaign) groups, the selection of alternative research designs, and the appropriate data collection methods and techniques. RESULTS: Evaluating the implementation of different research designs in estimating the effectiveness of road safety campaigns, results showed that the separate pre-post samples design demonstrated better predictability than other designs, especially in data obtained from the intervention group after the realization of the campaign. CONCLUSIONS: The more constructs that were added to the independent variables, the higher the values of the predictability were. The construct that most affects behavior is intention, whereas the rest of the constructs have a lower impact on behavior. This is particularly significant in the Health Belief Model (HBM). On the other hand, behavioral beliefs, normative beliefs, and descriptive norms, are significant parameters for predicting intention according to the Theory of Planned Behavior (TPB). PRACTICAL APPLICATIONS: The theoretical and applied implications of alternative research designs and their applicability in the evaluation of road safety campaigns are provided by this study.