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 Current methods of vehicle coordination, which are all designed to work with human drivers, will be outdated.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

Identification of Factors Contributing to the Decline of Traffic Fatalities in the United States from 2008 to 2012

This paper addresses two key issues when applying full Bayesian methods for traffic safety analysis, namely, (a) how to incorporate available information from previous studies (past experiences) for the specification of informative hyper-priors in accident data modeling; and (b) what are the practical implications of the choice of hyper-priors on the results of a road safety analysis. To address these issues, different simulation scenarios where defined and tested using crash data collected at 3-legged rural intersections in California and crash data collected for rural 4-lane highway segments in Texas. The performance of different hyper-priors was investigated, including Gamma and Uniform probability distributions. In this paper, we first illustrate how to incorporate an integrated summary of the available evidences from previous studies for the development of informative hyper-priors. By doing so, we show how the accuracy of parameter estimates are considerably improved, in particular when working with limited accident data, i.e., crash datasets with low mean and small sample size. The results however show that the past knowledge incorporated in the hyper-priors can only slightly improve the accuracy of the hotspot identification methods. In addition, when the number of roadway elements or years of crash data is relatively large, the hyper-prior choice does not have a significant impact on the final results of a traffic safety analysis.

Evaluation of Alternative Hyper-priors for Bayesian Road Safety Analysis

The purpose of the Truck Facility Guidebook is to assist Texas Department of Transportation (TxDOT) engineers in understanding and replicating the process used by researchers to determine the need for truck treatments. This Guidebook provides criteria to assist TxDOT in choosing among the three types of truck facilities: 1) lane restrictions, 2) dedicated truck lanes, and 3) exclusive truck roadways. The Guidebook primarily focuses on the third type, exclusive truck roadways, and guides users through a nine-step process to determine the need for these facilities. The nine steps are as follows: (1) Acquire truck and non-truck volume; (2) Predict future truck and non-truck volume; (3) Determine desired level of service; (4) Determine number of truck roadway lanes; (5) Acquire crash data and estimate crash costs; (6) Determine initial construction cost; (7) Determine costs of delay and fuel consumption; (8) Determine user perspectives and other measures of acceptability; and (9) Total all benefits and costs.

https://ibtta.org/sites/default/files/Strategies%20for%20Seperating%20Trucks%20from%20Passenger%20Vehicle%20Truck%20Facility%20Guidebook%20TTI.pdf

Strategies for Separating Trucks from Passenger Vehicles: Truck Facility Guidebook

The government of Burkina Faso has recently been making important macroeconomic changes to encourage the economic growth of the country. To maintain this growth, the government has implemented a transportation program to improve road network efficiency and safety. A 2000 study to improve the safety of rural roads in Burkina Faso is described. The primary objectives were to assess traffic safety problems and propose countermeasures to reduce the number and severity of collisions on rural roads. Many rural roads were evaluated on site; all accident data and important socioeconomic variables were collected; and key staff members from various governmental and private agencies were interviewed. The study has shown that traffic safety problems in Burkina Faso are multidimensional, involving inefficient traffic safety management and policy, inadequate road networks, untrained drivers, and defective vehicles. Several traffic safety countermeasures have been proposed for immediate, short-, and long-term application. The most important countermeasures are to create a new institutional framework for improving traffic safety management and train the key personnel responsible for implementing these countermeasures. For the short term, the countermeasures mainly relate to roadway infrastructure improvements and better enforcement tools. For the long term, the countermeasures include a review of current highway traffic laws and their application, evaluation of existing countermeasures, and driver training improvement.

/pdf/traffic-safety-diagnostics-and-application-of-5fb0mcoouf.pdf

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

There has been considerable research conducted on the development of statistical models for predicting motor vehicle crashes on highway facilities. Many of these developments were performed for the likelihood-based or frequentest modeling approach. Over the last few years, there has been a significant increase in the application hierarchical Bayes method for modeling motor vehicle crashes. Whether the inferences are estimated using the likelihood-based or the Bayesian method, the most common probabilistic structure used for modeling this type of data remains the traditional Poisson-gamma (or Negative Binomial) distribution. Crash data collected for highway safety studies often have the unusual attributes of being characterized by low sample mean values and, due to the prohibitive costs of collecting data, small sample sizes. Previous studies have shown that the dispersion parameter of Poisson-gamma models can be seriously mis-estimated when the models are estimated using the maximum likelihood method (MLE) for these extreme conditions. Despite important work done on this topic for the MLE, nobody has so far examined how low sample mean values and small sample sizes affect the posterior mean of the dispersion parameter of Poisson-gamma models estimated using the hierarchical Bayes method. The inverse dispersion parameter (posterior mean) plays an important role in various types of highway safety studies, such as building confidence intervals for comparing the safety performance of different highway design alternatives and the application of the empirical Bayes (EB) method for refining the long-term mean of a highway entity, and is particularly important for practitioners who are not familiar with Bayesian methods. It is therefore vital to determine the conditions in which the inverse dispersion parameter may be mis-estimated for this category of models. To accomplish the objectives of this study, a series of Poisson-gamma distributions are simulated using different values describing the mean, the dispersion parameter, the sample size, and the prior specification. Vague and non-vague prior specifications are tested for determining the magnitude of the biases introduced by low sample mean values and small sample sizes. A series of Poisson-lognormal distributions are also simulated, in the light of recent work done by statisticians on this mixed distribution. The study shows that a dataset characterized by a low sample mean combined with a small sample size can seriously affect the estimation of the posterior mean of the dispersion parameter when a vague prior specification is used to characterize the gamma hyper-parameter. The risk of a mis-estimated posterior mean can be greatly minimized when an appropriate non-vague prior distribution is used. Finally, the study shows that Poisson-lognormal models are recommended over Poisson-gamma models whenever crash data characterized by low sample mean values are used for developing crash prediction models.

Effects of Low Sample Mean Values and Small Sample Size on Estimation of Fixed Dispersion Parameter of Poisson-Gamma Models for Motor Vehicle Crashes: Bayesian Perspective

Dominique Lord

Papers

Identification of Factors Contributing to the Decline of Traffic Fatalities in the United States from 2008 to 2012

Evaluation of Alternative Hyper-priors for Bayesian Road Safety Analysis

Strategies for Separating Trucks from Passenger Vehicles: Truck Facility Guidebook

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

Effects of Low Sample Mean Values and Small Sample Size on Estimation of Fixed Dispersion Parameter of Poisson-Gamma Models for Motor Vehicle Crashes: Bayesian Perspective