Showing papers by "Dominique Lord published in 2022"
TL;DR: In this paper , a Negative Binomial-Lindley (NB-L) model is proposed to estimate the expected crash frequency for a specific site, which is a mixture of the NB and Lindley distributions and has shown superior fit compared to the NB model.
11 citations
TL;DR: In this paper , a comparative analysis among previously developed and two newly proposed parameterisations of the NB-L distribution, the negative binomial weighted Lindley (NB-WLindley) and NB-QL, was conducted.
Abstract: Several studies have reported the superior performance of the Negative Binomial–Lindley (NB-L) compared to the commonly used Negative Binomial distribution. Consequently, different parameterisations of the NB-L distribution have been introduced to further improve crash data modelling. However, little is known on how these models perform for different data domains. This study is documenting a comparative analysis among previously developed and two newly proposed parameterisations of the NB-L distribution, the negative binomial weighted Lindley (NB-WLindley) and the negative binomial quasi-Lindley (NB-QL). The results show that the NB-WLindley distribution performed better for the majority of data domains. Also, its generalised linear model (NB-WLindley GLM) showed superior statistical performance relative to the NB GLM and NB-L GLM. The results of this study contribute to the advancement of current predictive models used in transportation safety and provide insights for safety analysts and researchers when these models should be used.
8 citations
TL;DR: In this paper , the authors proposed a finite mixture NB-L model to analyze data generated from heterogeneous subpopulations with many zero observations and a long tail, and used the FMNB-L to estimate statistical models for Texas four-lane freeway crashes.
5 citations
TL;DR: In this article , the authors proposed the Grouped Random Parameters Negative Binomial-Lindley (G-RPNB-L) model to account for the unobserved heterogeneity in crash data with many zero observations.
5 citations
TL;DR: In this paper , the authors examined the safety of the fastest routes suggested by navigation systems and found that the shortest route can differ from the safest one, where taking a route to decrease travel time by 8% was associated with a 23% higher risk of being involved in a crash.
Abstract: Automotive navigation systems are one of the driver assistance technologies with the primary objective of directing drivers to their desired destinations. Although recent versions of automotive navigation systems help users minimize their travel time, there are certain situations in which the shortest route is not necessarily the safest one. Navigating through local roads that carry higher risks of crashes—roads with poor geometric designs, drainage problems, lack of illumination, wildlife crossing danger, and interruptions in traffic flow—is an example of the unintended consequences of routing to ensure minimum travel time. This study is designed to examine the safety of the fastest routes suggested by navigation systems. Road network connecting five metropolitan areas in Texas, including more than 29,000 road segments, is studied. The results of comparing the safest and shortest route between pairs of origins and destinations showed that the shortest route can differ from the safest, where taking a route to decrease travel time by 8% was associated with a 23% higher risk of being involved in a crash. The findings indicate the safest route varies according to different weather conditions. To incorporate safety in route-finding, a centralized, predictive algorithm is introduced for static and dynamic safe route-finding that can complement the existing navigation systems. The requirements for implementing such a system are identified as: (1) availability of real-time traffic flow and incident data for dynamic route-finding systems, (2) more accurate crash prediction models, and (3) a methodology for dealing with the tradeoffs between travel time and safety to find the optimal route.
3 citations
10 Mar 2022
TL;DR: In this article , the authors present a survey of the state of the art in the field of data visualization and data visualization, focusing on the following topics, e.g., data visualization.
Abstract: ............................................................................................................................... xiv EXECUTIVE SUMMARY ..........................................................................................................
1 citations