Estimation and prediction of average vehicle occupancies using traffic accident records

As congestion management strategies begin to put more emphasis on person trips than vehicle trips, the need for vehicle occupancy data has become more critical. The traditional methods of collecting these data include the roadside windshield method and the carousel method. These methods are labor-intensive and expensive. An alternative to these traditional methods is to make use of the vehicle occupancy information in traffic accident records. This method is cost effective and may provide better spatial and temporal coverage than the traditional methods. However, this method is subject to potential biases resulting from under- and over-involvement of certain population sectors and certain types of accidents in traffic accident records. In this dissertation, three such potential biases, i.e., accident severity, driver’s age, and driver’s gender, were investigated and the corresponding bias factors were developed as needed. The results show that although multi-occupant vehicles are involved in higher percentages of severe accidents than are single-occupant vehicles, multi-occupant vehicles in the whole accident vehicle population were not overrepresented in the accident database. On the other hand, a significant difference was found between the distributions of the ages and genders of drivers involved in accidents and those of the general driving population. An information system that incorporates adjustments for the potential biases was developed to estimate the average vehicle occupancies (AVOs) for different types of roadways on the Florida state roadway system. A reasonableness check of the results from the system shows AVO estimates that are highly consistent with expectations. In addition, comparisons of AVOs from accident data with the field estimates show that the two data sources produce relatively consistent results. While accident records can be used to obtain the historical AVO trends and field data can be used to estimate the current AVOs, no known methods have been developed to project future AVOs. Four regression models for the purpose of predicting weekday AVOs on different levels of geographic areas and roadway types were developed as part of this dissertation. The models show that such socioeconomic factors as income, vehicle ownership, and employment have a significant impact on AVOs.

Beyond the Pay: Current Illicit Activities of the Armed Forces in Central America

The growth of criminal gangs and organized crime groups has created unprecedented challenges in Central America. Homicide rates are among the highest in the world, countries spend on average close to 10 percent of GDP to respond to the challenges of public insecurity, and the security forces are frequently overwhelmed and at times coopted by the criminal groups they are increasingly tasked to counter. With some 90 percent of the 700 metric tons of cocaine trafficked from South America to the United States passing through Central America, the lure of aiding illegal traffickers through provision of arms, intelligence, or simply withholding or delaying the use of force is enormous. These conditions raise the question: to what extent are militaries in Central America compromised by illicit ties to criminal groups? The study focuses on three cases: Nicaragua, El Salvador, and Honduras. It finds that: Although illicit ties between the military and criminal groups have grown in the last decade, militaries in these countries are not yet “lost’ to criminal groups. Supplying criminal groups with light arms from military stocks is typical and on the rise, but still not common. In general the less exposed services, the navies and air forces, are the most reliable and effective ones in their roles in interdiction. Of the three countries in the study, the Honduran military is the most worrying because it is embedded in a context where civilian corruption is extremely common, state institutions are notoriously weak, and the political system remains polarized and lacks the popular legitimacy and political will needed to make necessary reforms. Overall, the armed forces in the three countries remain less compromised than civilian peers, particularly the police. However, in the worsening crime and insecurity context, there is a limited window of opportunity in which to introduce measures targeted toward the military, and such efforts can only succeed if opportunities for corruption in other sectors of the state, in particular in law enforcement and the justice system, are also addressed. Measures targeted toward the military should include: Enhanced material benefits and professional education opportunities that open doors for soldiers in promising legitimate careers once they leave military service. A clear system of rewards and punishments specifically designed to deter collusion with criminal groups. More effective securing of military arsenals. Skills and external oversight leveraged through combined operations, to build cooperation among those sectors of the military that have successful and clean records in countering criminal groups, and to expose weaker forces to effective best practices.

Estimation and Prediction of Average Vehicle Occupancies using Traffic Accident Records

As congestion management strategies begin to put more emphasis on person trips than vehicle trips, the need for vehicle occupancy data has become more critical. The traditional methods of collecting these data include the roadside windshield method and the carousel method. These methods are labor-intensive and expensive. An alternative to these traditional methods is to make use of the vehicle occupancy information in traffic accident records. This method is cost effective and may provide better spatial and temporal coverage than the traditional methods. However, this method is subject to potential biases resulting from under- and over-involvement of certain population sectors and certain types of accidents in traffic accident records. In this dissertation, three such potential biases, i.e., accident severity, driver¡¯s age, and driver¡¯s gender, were investigated and the corresponding bias factors were developed as needed. The results show that although multi-occupant vehicles are involved in higher percentages of severe accidents than are single-occupant vehicles, multi-occupant vehicles in the whole accident vehicle population were not overrepresented in the accident database. On the other hand, a significant difference was found between the distributions of the ages and genders of drivers involved in accidents and those of the general driving population. An information system that incorporates adjustments for the potential biases was developed to estimate the average vehicle occupancies (AVOs) for different types of roadways on the Florida state roadway system. A reasonableness check of the results from the system shows AVO estimates that are highly consistent with expectations. In addition, comparisons of AVOs from accident data with the field estimates show that the two data sources produce relatively consistent results. While accident records can be used to obtain the historical AVO trends and field data can be used to estimate the current AVOs, no known methods have been developed to project future AVOs. Four regression models for the purpose of predicting weekday AVOs on different levels of geographic areas and roadway types were developed as part of this dissertation. The models show that such socioeconomic factors as income, vehicle ownership, and employment have a significant impact on AVOs.