世界経済・社会統計 = World development indicators

Highway Capacity Manual改訂の動向--テイラ-教授の講演より

Generalized Linear Models (2nd ed.)

Gaining a better understanding of the factors that affect the likelihood of a vehicle crash has been an area of research focus for many decades. However, in the absence of detailed driving data that would help improve the identification of cause and effect relationships with individual vehicle crashes, most researchers have addressed this problem by framing it in terms of understanding the factors that affect the frequency of crashes - the number of crashes occurring in some geographical space (usually a roadway segment or intersection) over some specified time period. This paper provides a detailed review of the key issues associated with crash-frequency data as well as the strengths and weaknesses of the various methodological approaches that researchers have used to address these problems. While the steady march of methodological innovation (including recent applications of random parameter and finite mixture models) has substantially improved our understanding of the factors that affect crash-frequencies, it is the prospect of combining evolving methodologies with far more detailed vehicle crash data that holds the greatest promise for the future.

http://ceprofs.tamu.edu/dlord/Papers/Lord-Mannering_Review.pdf

The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives

Artificial intelligence research is ushering in a new era of sophisticated, mass-market transportation technology. While computers can already fly a passenger jet better than a trained human pilot, people are still faced with the dangerous yet tedious task of driving automobiles. Intelligent Transportation Systems (ITS) is the field that focuses on integrating information technology with vehicles and transportation infrastructure to make transportation safer, cheaper, and more efficient. Recent advances in ITS point to a future in which vehicles themselves handle the vast majority of the driving task. Once autonomous vehicles become popular, autonomous interactions amongst multiple vehicles will be possible. Current methods of vehicle coordination, which are all designed to work with human drivers, will be outdated. The bottleneck for roadway efficiency will no longer be the drivers, but rather the mechanism by which those drivers' actions are coordinated. While open-road driving is a well-studied and more-or-less-solved problem, urban traffic scenarios, especially intersections, are much more challenging.

We believe current methods for controlling traffic, specifically at intersections, will not be able to take advantage of the increased sensitivity and precision of autonomous vehicles as compared to human drivers. In this article, we suggest an alternative mechanism for coordinating the movement of autonomous vehicles through intersections. Drivers and intersections in this mechanism are treated as autonomous agents in a multiagent system. In this multiagent system, intersections use a new reservation-based approach built around a detailed communication protocol, which we also present. We demonstrate in simulation that our new mechanism has the potential to significantly outperform current intersection control technology--traffic lights and stop signs. Because our mechanism can emulate a traffic light or stop sign, it subsumes the most popular current methods of intersection control. This article also presents two extensions to the mechanism. The first extension allows the system to control human-driven vehicles in addition to autonomous vehicles. The second gives priority to emergency vehicles without significant cost to civilian vehicles. The mechanism, including both extensions, is implemented and tested in simulation, and we present experimental results that strongly attest to the efficacy of this approach.

/pdf/a-multiagent-approach-to-autonomous-intersection-management-ndv1gqtnse.pdf

A multiagent approach to autonomous intersection management

Urban safety performance functions are used to predict crash frequencies, mostly based on Negative Binomial (NB) count models. They could be differentiated for considering homogeneous subsets of segments/intersections and different predictors. The main research questions concerned: a) finding the best possible subsets for segments and intersections for safety modelling, by discussing the related problems and inquiring into the variability of predictors within the subsets; b) comparing the modelling results with the existing literature to highlight common trends and/or main differences; c) assessing the importance of additional crash predictors, besides traditional variables. In the context of a National research project, traffic volumes, geometric, control and additional variables were collected for road segments and intersections in the City of Bari, Italy, with 1500 fatal+injury related crashes (2012–2016). Six NB models were developed for: one/two-way homogeneous segments, three/four-legged, signalized/unsignalized intersections. Crash predictors greatly vary within the different subsets considered. The effect of vertical signs on minor roads/driveways, critical sight distance, cycle crossings, pavement/markings maintenance was specifically discussed. Some common trends but also differences in both types and effect of crash predictors were found by comparing results with literature. The disaggregation of urban crash prediction models by considering different subsets of segments and intersections helps in revealing the specific influence of some predictors. Local characteristics may influence the relationships between well-established crash predictors and crash frequencies. A significant part of the urban crash frequency variability remains unexplained, thus encouraging research on this topic.

/pdf/the-variability-of-urban-safety-performance-functions-for-2amvh75wnj.pdf

The variability of urban safety performance functions for different road elements: an Italian case study

This paper introduces a new approach to addressing two of the most challenging issues in road safety research, namely, how to account for unobserved heterogeneity and how to identify latent subpopulations in data. Compared to the approaches of applying random effects/parameters models and finite mixtures, the proposed approach employs a Bayesian semi-parametric methodology based on Dirichlet process mixtures. Our method has four noteworthy advantages: (i) it allows examining the robustness of distributional assumptions in random effects/parameters models; (ii) it allows identifying latent clusters in data; (iii) it enables identification of outliers (extreme observations) while allowing accommodating them in analyses without compromising the quality of estimates; and (iv) it is capable of estimating the number of latent clusters in data using an elegant mathematical structure. In this paper, we evaluate the proposed method on a railway grade crossing crash dataset with hierarchical (multilevel) structure, at municipality level, from Canada for the years 2008 to 2013. We use cross-validation predictive densities and pseudo Bayes factor for Bayesian model selection. While confirming the need for the multilevel modeling approach, the results pointed out the inadequacy of the parametric assumption. In fact, our proposed method improved model fitting significantly for the municipality-level data. In a fully probabilistic framework, we also identified the expected number of latent clusters with similar unknown/unmeasured features among 81 Canadian municipalities. It is possible thus to further investigate the reasons behind such similarities and dissimilarities, which could have important policy implications in terms of safety management process.

A Flexible Modeling Approach Using Dirichlet Process Mixtures: Application to Multi-Level Railway Grade Crossing Crash Data

The average American male age 16 and over is 5-ft, 9-in. (69 inches) tall and the average American female age 16 and over is 5-ft. 4-in. (64 inches). Utilizing data from the National Center for Health Statistics, it was found that only 19.7% of males are 72-in or taller and only 0.15% females reach or exceed 72-in. The statistics on height distribution were compared with the information obtained from the Fatal Accident Reporting System Encyclopedia (FARS) on the number of deaths from single-vehicle rollover accidents as correlated to occupant height. Since individuals 72-in or taller have less headroom (defined as the vertical space between the interior of the roof and the bottom of the seat) in vehicles than the average individual, they therefore have a greater probability of having head injuries when the vehicle rolls over. The average head clearance for the average American male and female is 3.67-in and 4.38-in, respectively; with the head clearance for a 72-in male and female being 2.57-in and 3.60-in, respectively. The results of this study show that male (t-value=4.72, p<0.001) and female (t-value=1.41, p=0.16) drivers 72-in or taller are more likely to be killed in rollover accidents as based on their population percentage; the results for female drivers are less conclusive due to the small sample size. In addition, vehicles were divided into different categories and examined to see if vehicles for certain categories had increased fatalities for male drivers 72-in or taller. It was found that seven of the thirteen vehicle categories showed a higher than average fatality rate for 72-in or taller drivers (all t=values≥1.96, p≤0.05).

http://ceprofs.civil.tamu.edu/dlord/Papers/Howson_et_al._Rollover_Accidents.pdf

Dominique Lord

Papers

The variability of urban safety performance functions for different road elements: an Italian case study

A Flexible Modeling Approach Using Dirichlet Process Mixtures: Application to Multi-Level Railway Grade Crossing Crash Data

The Effect of Driver Height on the Death Rate in Single-Vehicle Rollover Accidents

Traffic Safety Diagnostic and Application of Countermeasures for Rural Roads in Burkina Faso

Alcohol-impaired motorcyclists versus car drivers: A comparison of crash involvement and legal consequence from adjudication data.