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 Current methods of vehicle coordination, which are all designed to work with human drivers, will be outdated.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 investigates a strategy to ameliorate the largest source of unintentional injuries in the United States, motor vehicle crashes, by improving the ability of our most vulnerable drivers, the elderly, to safely negotiate the most dangerous and demanding of all traffic situations—intersections. Studies have shown that more crashes occur at intersections, resulting in more injuries and fatalities, than in any other driving situation; and this risk is exacerbated for older persons who, with their declining functional abilities but increasing frailty, represent the fastest growing segment of the driving public. Coupled with an overwhelming reliance by seniors on private vehicle travel to meet their personal mobility needs, these trends make it imperative to somehow enhance the proficiency of older drivers at intersections. The present research suggests a way to meet this goal through improvements in highway design and operations, specifically the use of the modern roundabout. Compared to conventional intersections, roundabouts have the demonstrated potential to significantly reduce the most injurious (angle) type of crashes and slow the operating speed of all vehicles, while maintaining a high capacity for moving traffic through an intersection. If all drivers, and especially older drivers, would increase their use of these highway facilities, and use them properly, a system-wide savings in traffic injuries and fatalities is a very high probability. Accordingly, this research sought to a) identify elements of roundabout design and operations that were problematic for older drivers, and b) develop recommendations and guidelines for countermeasures with the potential to improve the comfort, confidence, and safety of seniors in using roundabouts. To accomplish the objectives of this study, a series of focus groups (Phase I), in which drivers above the age of 65 provided feedback, were held to determine potential design elements at roundabouts, such as highway geometrics, traffic signs, and pavement markings that may be problematic to older drivers. Following the identification of these elements, structured interviews (Phase II), using participants in the same age group, were conducted to evaluate potential countermeasures that could be implemented for

http://ceprofs.tamu.edu/dlord/Papers/TTI_Report_CTS-05-01.pdf

Reducing Older Driver Injuries at Intersections Using More Accommodating Roundabout Design Practices

Can Autonomous Vehicles Enhance Traffic Safety at Unsignalized Intersections

This study analyzed driver’s eye movements while driving on public roads under different lighting conditions. Four driving environments were examined: 1) a segment of highway without street lighting, 2) a segment of highway with street lighting, 3) the approach to a stop-sign controlled intersection without street lighting, and 4) the approach to signalized intersection with street lighting. This study used eye movement data that had been previously collected for 11 drivers navigating a pre-set route at night, and evaluated the probabilities of drivers looking at specific areas on the road scene using binary logistic regression modeling. Drivers’ eye movements were affected by different highway features (segment vs. intersection) and peripheral environments (presence/absence of street lighting). Drivers made more glances to the center of the travel lane while driving on the segments and under street lighting. The probabilities of drivers looking at locations off the eye-tracker’s screen (away from the roadway) decreased when approaching intersections. Drivers’ eye movements significantly changed on close approach to the intersections, particularly when approaching the signalized intersection with the more complex traffic environment.

Effect of Driving Environment on Drivers’ Eye Movements: Re-Analyzing Previously Collected Eye-Tracker Data

Texas is considered to be an “opportunity” state by the Federal Highway Administration (FHWA), due to the high number of pedestrian crashes. Data from the Fatality Analysis Reporting System (FARS) show that the number of pedestrian fatal crashes in Texas is the third highest in the U.S and is significantly higher than the national average. The research team explored the Texas Department of Transportation (TxDOT) Crash Record Information System (CRIS) database to identify characteristics of crashes involving pedestrians in Texas. A Classification and Regression Tree (CRT) analysis of all pedestrian crashes was conducted to find the significant factors influencing the severity of crashes involving pedestrians in Texas. The classification tree identified that light condition, road class, traffic control, right shoulder width, involvement of a commercial vehicle, pedestrian age, and the collision manner, have the most influence on the severity of pedestrian crashes.

https://ceprofs.civil.tamu.edu/dlord/papers/iragavarapu_et_al_cart.pdf

Dominique Lord

Papers

Reducing Older Driver Injuries at Intersections Using More Accommodating Roundabout Design Practices

Can Autonomous Vehicles Enhance Traffic Safety at Unsignalized Intersections

Effect of Driving Environment on Drivers’ Eye Movements: Re-Analyzing Previously Collected Eye-Tracker Data

Analysis of Injury Severity in Pedestrian Crashes Using Classification Regression Trees

Pavement Safety-Based Guidelines for Horizontal Curve Safety