Traffic safety evaluation is one of the most important processes in analyzing transportation systems performance. Traditional methods like statistical models and before-after comparisons have many drawbacks, such as limited time periods, sample size problems, and reporting errors. The advancement of traffic conflict techniques combined with microsimulation offers a potentially innovative way for conducting safety assessment of traffic systems even before safety improvements are implemented. In this paper, simulation-based safety studies are reviewed, and a modified simulation-based surrogate safety measure and a new simulation-based surrogate safety measure that can capture the probability of collisions, as well as the severity of these potential collisions, are proposed. Conceptual and computational logic of the proposed surrogate safety indicators are described in detail. These surrogate safety indices are initially proposed for link-based analysis and should not be used for other purposes, such as intersection safety assessment, without further enhancements, and the use of these indices should be limited to the analysis of linear conflicts. In addition, these link-based indices are extended to be able to conduct aggregate networkwide safety assessments. The proposed indices are validated by means of a well-calibrated traffic simulation model of a section of the New Jersey Turnpike and real accident data from the same section. Preliminary results indicate a strong relationship between the proposed surrogate safety measures and real accident data. Further research is needed to investigate these new surrogate safety indices under different locations and traffic conditions.

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Nonrecurring traffic incidents, such as motor vehicle crashes, increase not only travel delays but also the risk of secondary crashes. Secondary crashes can cause additional traffic delays and reduce safety. Implementation of effective countermeasures to prevent or reduce secondary crashes requires that their characteristics be investigated. However, the related research has been limited, largely because of the lack of detailed incident and traffic data necessary to identify secondary crashes. Existing approaches, such as static methods employed to identify secondary crashes, cannot fully capture potential secondary crashes because of fixed spatiotemporal identification criteria. Improved approaches are needed to categorize secondary crashes accurately for further analysis. This paper develops an enhanced approach for identifying secondary crashes that uses the existing crash database and archived traffic data from highway sensors. The proposed method is threefold: (a) defining secondary crashes, (b) examining the impact range of primary crashes that possibly relate to secondary crashes, and (c) identifying secondary crashes. The proposed methodology establishes a practical framework for mining secondary crashes from existing sensor data and crash records. A case study was performed on a 27-mi segment of a major highway in New Jersey to illustrate the performance of the proposed approach. The results show that the proposed method provides a more reliable and efficient categorization of secondary crashes than commonly used approaches.

Understanding evacuation response behavior is critical for public officials in deciding when to issue emergency evacuation orders for an impending hurricane. Such behavior is typically measured by an evacuation response curve that represents the proportion of total evacuation demand over time. This study analyzes evacuation behavior and constructs an evacuation response curve on the basis of traffic data collected during Hurricane Irene in 2011 in Cape May County, New Jersey. The evacuation response curve follows a general S-shape with sharp upward changes in slope after the issuance of mandatory evacuation notices. These changes in slope represent quick response behavior, which may be caused in part by an easily mobilized tourist population, lack of hurricane evacuation experience, or the nature of the location, in this case a rural area with limited evacuation routes. Moreover, the widely used S-curves with different mathematical functions and the state-of-the-art behavior models are calibrated and compared with empirical data. The results show that the calibrated S-curves with logit and Rayleigh functions fit empirical data better. The evacuation behavior analysis and calibrated evacuation response models from this hurricane evacuation event may benefit evacuation planning in similar areas. In addition, traffic data used in this study may also be valuable for the comparative analysis of traffic patterns between the evacuation periods and regular weekdays and weekends.