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 Current methods of vehicle coordination, which are all designed to work with human drivers, will be outdated.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

The left-turn procedures in the Highway Capacity Manual are complex, and some of the equations are presented with little explanation of how they were obtained and the assumptions they embody. This paper is an analytic exploration of two of the four equations used to estimate gq, the amount of green time needed to discharge the opposing queue, and gf, the amount of green time available for through vehicles before the first left-turning vehicle enters the intersection. The investigation reveals surprising hidden assumptions underlying Equation 9-17 for gq, which lead to errors when the opposing flow includes left turns. In the case of Equation 9-20 for gf, the theoretical results are consistently about 5 s larger but otherwise in reasonable agreement with the empirical equation and provide insight into how it could be improved. An analytic approximation is offered either as a replacement or as a framework for an improved empirical model.

Analysis of Two Left-Turn Equations from the Highway Capacity Manual

Developing Methodology for Identifying, Evaluating, and Prioritizing Systemic Improvements

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. 

Navigating to safety: Necessity, requirements, and barriers to considering safety in route finding

In Texas, high-occupancy vehicle (HOV) lanes have emerged as an integral part of the state's current and future transportation system to aid urban mobility. As a result, the issue of HOV lane design and the influence of design on safety has become the focus of much attention in the transportation community. This document provides guidance for future design of freeways with HOV lanes based on an analysis of crash data from Dallas, Texas. This guidance indicates desirable corridor characteristics when considering HOV lane implementation and recommends roadway cross-sections when implementing HOV lanes similar to those in operation in Dallas, Texas.

/pdf/guidance-for-future-design-of-freeways-with-high-occupancy-k9od1e40rw.pdf

Guidance for future design of freeways with high- occupancy vehicle (hov) lanes based on an analysis of crash data from dallas, texas

In recent years the development and use of crash prediction models for roadway safety analyses have received substantial attention. These models, also known as safety performance functions (SPFs), relate the expected crash frequency of roadway elements (intersections, road segments, on-ramps) to traffic volumes and other geometric and operational characteristics. A commonly practiced approach for applying intersection SPFs is to assume that crash types occur in fixed proportions (e.g., rear-end crashes make up 20% of crashes, angle crashes 35%, and so forth) and then apply these fixed proportions to crash totals to estimate crash frequencies by type. As demonstrated in this paper, such a practice makes questionable assumptions and results in considerable error in estimating crash proportions. Through the use of rudimentary SPFs based solely on the annual average daily traffic (AADT) of major and minor roads, the homogeneity-in-proportions assumption is shown not to hold across AADT, because crash proportions vary as a function of both major and minor road AADT. For example, with minor road AADT of 400 vehicles per day, the proportion of intersecting-direction crashes decreases from about 50% with 2,000 major road AADT to about 15% with 82,000 AADT. Same-direction crashes increase from about 15% to 55% for the same comparison. The homogeneity-in-proportions assumption should be abandoned, and crash type models should be used to predict crash frequency by crash type. SPFs that use additional geometric variables would only exacerbate the problem quantified here. Comparison of models for different crash types using additional geometric variables remains the subject of future research.

Dominique Lord

Papers

Analysis of Two Left-Turn Equations from the Highway Capacity Manual

Developing Methodology for Identifying, Evaluating, and Prioritizing Systemic Improvements

Navigating to safety: Necessity, requirements, and barriers to considering safety in route finding

Guidance for future design of freeways with high- occupancy vehicle (hov) lanes based on an analysis of crash data from dallas, texas

Differences in the performance of safety performance functions estimated for total crash count and for crash count by crash type