A Data Driven Approach to Quantify the Impact of Crashes

The growth of data has begun to transform the transportation research and policy, and open a new window for analyzing the impact of crashes. Currently for the crash impact analysis, researchers tend to rely on reported incident duration, which may not always be accurate. Further, impact of the crashes could linger a much longer time at upstream, even if the records are correct for the crash spot and it is a challenge to quantify the impact of a crash from the complex dynamics of the recurrent and non-recurrent congested condition. Therefore, a difference-in-speed approach is developed in this research to estimate the true crash impact duration using stationary sensor data and incident logs. The proposed method used the Kalman filter algorithm to establish traveler’s anticipated travel speed under incident-free condition and then employ the difference-in-speed approach to quantify the temporal and spatial extent of the crash. Moreover, potential applications such as statistical models for predicting the impact duration and total delay were developed in this research. Later, an analysis on distribution of travel rate was performed to describe and numerically show to what extent crashes influenced travel rates compared with the normal conditions at different periods of the day and by the crash types. This study can help to shape incident management policies for different types of crashes at different periods and illustrates the usages of data to improve the understanding of crashes, their impact, and their distribution in a spatial-temporal domain

Modeling of Short Term and Long Term Impacts of Freeway Traffic Incidents using Historical Data

Traffic incidents are major contributors to non-recurring traffic congestion in most urban areas in United States. In addition to losses in terms of injury and property damage, freeway incidents also produce negative effects on the system including increased travel delays, fuel consumption and vehicle emissions. Incident management strategies are aimed at reducing the impacts caused by such incidents. Development of guidelines or models to quantify the impacts of these incidents on the society can aid in analyzing the effectiveness and economic feasibility of such incident management strategies. The first objective of this study is to calibrate models that relate the short term marginal impacts caused by freeway incidents with incident characteristics such as incident duration and the number of lanes blocked. These models will help in quantifying the impacts of freeway incidents on the system as a part of the evaluation of incident management strategies or other related freeway operation projects. Historical incident data from a Las Vegas freeway is used to calibrate these statistical models. Additionally, freeway operation-related information is obtained from the web-based Dashboard system maintained by the Regional Transportation Commission of Southern Nevada (RTC). Different statistical regression models calibrated relate freeway travel times, fuel consumption and emissions as functions of incident characteristics including incident duration, number of lanes blocked and time of day. Statistical measures of performance are used to evaluate the models and appropriate models are selected for recommendation. An additional component included in the impacts is the effect of the incident on the opposing direction of flow (rubbernecking). The second objective of this research is to calibrate the influence of incidents and their corresponding impacts. In this study, various travel time reliability indices are used in quantifying the long term impacts of freeway incidents. Travel time reliability is an important planning tool both from the user point of view as well as transportation planners. The findings of this part of the research can help in operational and economic evaluation of freeway safety and incident management projects from the point of travel time reliability. The models can also be used to quantify system-wide impacts of incident to provide economic justification for acquisition of funding for such projects. This contribution of this research is two-fold. First, statistical models are calibrated for quantifying the short-term impacts of freeway incidents on travel time, fuel consumption and vehicular emissions exclusively from field data as opposed to simulation and/or mathematical models. These marginal impacts can be used by transportation agencies and public organizations in the evaluation of incident management strategies. Also, given that these models are based on historical field data, accuracy is improved over existing models that are based on computer simulation. The second contribution of this research is in providing models that quantify the long-term impacts of incidents in terms of travel time reliability. This quantification is a principal benefit since models specific to traffic incident impacts and travel time reliability have rarely been explored previously. In addition, this analysis is also based on field data unlike the very few previous studies and is therefore an improvement in the understanding of relationships between travel time reliability and incident characteristics