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 Current methods of vehicle coordination, which are all designed to work with human drivers, will be outdated.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

This research deals with the roadway aspect, by investigating the safety and operational aspects of exclusive truck facilities. Determining the need for truck facilities established criteria such as the vehicular volumes, safety elements, and financial feasibility. This paper only deals with the first two. The research approach developed level of service relationships using the simulation software VISSIM. The safety investigation consisted of an evaluation of crashes on the New Jersey Turnpike comparing car-only lanes with mixed flows of trucks and cars. Results of the safety investigation indicate that the crash rate in the outer lanes (mixed flow of cars and trucks) is almost double that in the inner lanes (cars only), given the same exposure. Although inconclusive, this outcome may indicate that truck traffic had an influence on crashes. Results of the operational analysis indicate that the capacity of a two-lane exclusive truck roadway under ideal conditions approaches 1,500 trucks (defined as 3-or more axle vehicles) per lane per hour.

Safety and operational aspects of exclusive truck facilities

Roadway designs intend to provide a safe facility addressing mobility concerns, accommodating the physical and social environment and within financial constraints. Sometimes, trade-offs among these may be needed to deliver the desired project and designers need tools to estimate the safety implications from such decisions. The work completed here aimed to develop a set of recommendations to be used in evaluating safety implications from design element trade-offs. The effort focused on developing crash-prediction models and accident modification factors (AMFs) for multilane rural roads regarding lane width, shoulder width, and median width and type. The available data limited these models to four-lane roadways with 12-ft lanes. Separate models were developed for divided and undivided facilities as well as for single-vehicle, multivehicle, and all crashes involving both total and injury crashes. The AMF values recommended here were compared to those proposed in the Highway Safety Manual. Most of the proposed values are in accordance with previous work and demonstrate the effect of these elements on crash occurrence.

https://ceprofs.civil.tamu.edu/dlord/Papers/Stamatiadis_et_al._NCHRP_15-27.pdf

Safety Impacts of Design Element Trade-Offs for Multilane Rural Highways

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.

Evaluating alternative variations of Negative Binomial–Lindley distribution for modelling crash data

As one of the major analysis methods, statistical models play an important role in traffic safety analysis They can be used for a wide variety of purposes, including establishing relationships between variables and understanding the characteristics of a system The purpose of this paper is to document a new type of model that can help with the latter This model is based on the Generalized Waring (GW) distribution The GW model yields more information about the sources of the variance observed in datasets than other traditional models, such as the negative binomial (NB) model In this regards, the GW model can separate the observed variability into three parts: 1) the randomness, which explains the model’s uncertainty; 2) the proneness, which refers to the internal differences between entities or observations; and, 3) the liability, which is defined as the variance caused by other external factors that are difficult to be identified and have not been included as explanatory variables in the model The study analyses were accomplished using two observed datasets to explore potential sources of variation The results show that the GW model can provide meaningful information about sources of variance in crash data and also performs better than the NB model

Applying the Generalized Waring model for investigating sources of variance in motor vehicle crash analysis

Over the last 20 to 30 years, there has been a significant amount of tools and statistical methods that have been proposed for analyzing crash data. Yet, the Poisson-gamma (PG) is still the most commonly used and widely acceptable model. The Poisson-Weibull (PW) distribution has only been applied once in highway safety and has not been used so far in the context of generalized linear model (GLM). This paper documents the application of the PW GLM for modeling motor vehicle crashes. The objectives of this study were to evaluate the application of the PW GLM for analyzing motor vehicle crashes and compare the results with the traditional PG model. To accomplish the objectives of the study, several PG and PW GLMs were developed and compared using two crash datasets. The results of this study show that the PW GLM performs as well as the PG GLM in terms of goodness-of-fit (GOF) statistics.

Dominique Lord

Papers

Safety and operational aspects of exclusive truck facilities

Safety Impacts of Design Element Trade-Offs for Multilane Rural Highways

Evaluating alternative variations of Negative Binomial–Lindley distribution for modelling crash data

Applying the Generalized Waring model for investigating sources of variance in motor vehicle crash analysis

Examining the Poisson-Weibull Generalized Linear Model for Analyzing Crash Data