The Experts below are selected from a list of 204 Experts worldwide ranked by ideXlab platform
Fred L Mannering - One of the best experts on this subject based on the ideXlab platform.
-
unobserved heterogeneity and the statistical analysis of Highway accident data
Analytic Methods in Accident Research, 2016Co-Authors: Fred L Mannering, Venky N Shankar, Chandra R BhatAbstract:Highway Accidents are complex events that involve a variety of human responses to external stimuli, as well as complex interactions between the vehicle, roadway features/condition, traffic-related factors, and environmental conditions. In addition, there are complexities involved in energy dissipation (once an accident has occurred) that relate to vehicle design, impact angles, the physiological characteristics of involved humans, and other factors. With such a complex process, it is impossible to have access to all of the data that could potentially determine the likelihood of a Highway accident or its resulting injury severity. The absence of such important data can potentially present serious specification problems for traditional statistical analyses that can lead to biased and inconsistent parameter estimates, erroneous inferences and erroneous accident predictions. This paper presents a detailed discussion of this problem (typically referred to as unobserved heterogeneity) in the context of accident data and analysis. Various statistical approaches available to address this unobserved heterogeneity are presented along with their strengths and weaknesses. The paper concludes with a summary of the fundamental issues and directions for future methodological work that addresses unobserved heterogeneity. Language: en
-
a study of factors affecting Highway accident rates using the random parameters tobit model
Accident Analysis & Prevention, 2012Co-Authors: Panagiotis Ch Anastasopoulos, Fred L Mannering, Venky N Shankar, John E. HaddockAbstract:A large body of previous literature has used a variety of count-data modeling techniques to study factors that affect the frequency of Highway Accidents over some time period on roadway segments of a specified length. An alternative approach to this problem views vehicle accident rates (Accidents per mile driven) directly instead of their frequencies. Viewing the problem as continuous data instead of count data creates a problem in that roadway segments that do not have any observed Accidents over the identified time period create continuous data that are left-censored at zero. Past research has appropriately applied a tobit regression model to address this censoring problem, but this research has been limited in accounting for unobserved heterogeneity because it has been assumed that the parameter estimates are fixed over roadway-segment observations. Using 9-year data from urban interstates in Indiana, this paper employs a random-parameters tobit regression to account for unobserved heterogeneity in the study of motor-vehicle accident rates. The empirical results show that the random-parameters tobit model outperforms its fixed-parameters counterpart and has the potential to provide a fuller understanding of the factors determining accident rates on specific roadway segments.
-
empirical assessment of the impact of Highway design exceptions on the frequency and severity of vehicle Accidents
Accident Analysis & Prevention, 2010Co-Authors: Nataliya V Malyshkina, Fred L ManneringAbstract:Compliance to standardized Highway design criteria is considered essential to ensure roadway safety. However, for a variety of reasons, situations arise where exceptions to standard-design criteria are requested and accepted after review. This research explores the impact that such design exceptions have on the frequency and severity of Highway Accidents in Indiana. Data on Accidents at carefully selected roadway sites with and without design exceptions are used to estimate appropriate statistical models of the frequency and severity of Accidents at these sites using recent statistical advances with mixing distributions. The results of the modeling process show that presence of approved design exceptions has not had a statistically significant effect on the average frequency or severity of Accidents - suggesting that current procedures for granting design exceptions have been sufficiently rigorous to avoid adverse safety impacts. However, the findings do suggest that the process that determines the frequency of Accidents does vary between roadway sites with design exceptions and those without.
Venky N Shankar - One of the best experts on this subject based on the ideXlab platform.
-
unobserved heterogeneity and the statistical analysis of Highway accident data
Analytic Methods in Accident Research, 2016Co-Authors: Fred L Mannering, Venky N Shankar, Chandra R BhatAbstract:Highway Accidents are complex events that involve a variety of human responses to external stimuli, as well as complex interactions between the vehicle, roadway features/condition, traffic-related factors, and environmental conditions. In addition, there are complexities involved in energy dissipation (once an accident has occurred) that relate to vehicle design, impact angles, the physiological characteristics of involved humans, and other factors. With such a complex process, it is impossible to have access to all of the data that could potentially determine the likelihood of a Highway accident or its resulting injury severity. The absence of such important data can potentially present serious specification problems for traditional statistical analyses that can lead to biased and inconsistent parameter estimates, erroneous inferences and erroneous accident predictions. This paper presents a detailed discussion of this problem (typically referred to as unobserved heterogeneity) in the context of accident data and analysis. Various statistical approaches available to address this unobserved heterogeneity are presented along with their strengths and weaknesses. The paper concludes with a summary of the fundamental issues and directions for future methodological work that addresses unobserved heterogeneity. Language: en
-
a study of factors affecting Highway accident rates using the random parameters tobit model
Accident Analysis & Prevention, 2012Co-Authors: Panagiotis Ch Anastasopoulos, Fred L Mannering, Venky N Shankar, John E. HaddockAbstract:A large body of previous literature has used a variety of count-data modeling techniques to study factors that affect the frequency of Highway Accidents over some time period on roadway segments of a specified length. An alternative approach to this problem views vehicle accident rates (Accidents per mile driven) directly instead of their frequencies. Viewing the problem as continuous data instead of count data creates a problem in that roadway segments that do not have any observed Accidents over the identified time period create continuous data that are left-censored at zero. Past research has appropriately applied a tobit regression model to address this censoring problem, but this research has been limited in accounting for unobserved heterogeneity because it has been assumed that the parameter estimates are fixed over roadway-segment observations. Using 9-year data from urban interstates in Indiana, this paper employs a random-parameters tobit regression to account for unobserved heterogeneity in the study of motor-vehicle accident rates. The empirical results show that the random-parameters tobit model outperforms its fixed-parameters counterpart and has the potential to provide a fuller understanding of the factors determining accident rates on specific roadway segments.
Chandra R Bhat - One of the best experts on this subject based on the ideXlab platform.
-
unobserved heterogeneity and the statistical analysis of Highway accident data
Analytic Methods in Accident Research, 2016Co-Authors: Fred L Mannering, Venky N Shankar, Chandra R BhatAbstract:Highway Accidents are complex events that involve a variety of human responses to external stimuli, as well as complex interactions between the vehicle, roadway features/condition, traffic-related factors, and environmental conditions. In addition, there are complexities involved in energy dissipation (once an accident has occurred) that relate to vehicle design, impact angles, the physiological characteristics of involved humans, and other factors. With such a complex process, it is impossible to have access to all of the data that could potentially determine the likelihood of a Highway accident or its resulting injury severity. The absence of such important data can potentially present serious specification problems for traditional statistical analyses that can lead to biased and inconsistent parameter estimates, erroneous inferences and erroneous accident predictions. This paper presents a detailed discussion of this problem (typically referred to as unobserved heterogeneity) in the context of accident data and analysis. Various statistical approaches available to address this unobserved heterogeneity are presented along with their strengths and weaknesses. The paper concludes with a summary of the fundamental issues and directions for future methodological work that addresses unobserved heterogeneity. Language: en
Karsten Baass - One of the best experts on this subject based on the ideXlab platform.
-
seasonal variation in frequencies and rates of Highway Accidents as function of severity
Transportation Research Record, 1997Co-Authors: Bruce Brown, Karsten BaassAbstract:After a study of the identification of dangerous Highway locations in Quebec, the appropriateness of comparisons with U.S. statistics was questioned. It was noted that between 30 and 50 percent of Highway Accidents in Quebec are associated with harsh meteorological conditions, including rain, snow, hail, and icy conditions, and it was implied that these conditions would contribute to a poorer safety record. To better understand the seasonal variation of Highway Accidents, monthly numbers and rates of Highway Accidents of differing severities were examined. The analysis uses all police-reported Accidents associated with numbered roads in the Monteregie region of Quebec between 1989 and 1992. Monthly rates of victims per 100 million vehicle km traveled are calculated, as well as the frequency (number) of victims deceased, severely injured, and with minor injuries. Material-damage-only Accidents are similarly tabulated. The lowest numbers and rates of death and serious injury occur in winter months. However,...
Flavio Bazzana - One of the best experts on this subject based on the ideXlab platform.
-
on the impact of average speed enforcement systems in reducing Highway Accidents evidence from the italian safety tutor
Economics of Transportation, 2019Co-Authors: Mattia Borsati, Michele Cascarano, Flavio BazzanaAbstract:Abstract At the end of 2005, Autostrade per l’Italia (ASPI) and the Italian traffic police progressively deployed along the Italian tolled motorway network an average speed enforcement system, named Safety Tutor, able to determine the average speed of vehicles over a long section to encourage drivers to comply with speed limits and improve safety. The aim of this study was to empirically test the extent to which Safety Tutor led to a reduction in both total and fatal Accidents on Italian Highways during the period of 2001–2017. To do so, we carried out a generalized difference-in-differences estimation using a unique panel dataset that exploits the heterogeneous accident data within all tolled motorway sectors in a quasi-experimental setting. To deal with the potential endogeneity of the non-random placement of Safety Tutor sites, we utilized an instrumental variable strategy by using the network of motorway sectors managed by ASPI and its controlled concessionaires from 2005 onwards (i.e., when the technology was available) as an instrument to predict Safety Tutor adoption. We found that a 10% increase in Safety Tutor coverage led to an average reduction in total Accidents of 3.9%, whereas there is no evidence of a significant causal effect of Safety Tutor in reducing fatal Accidents.