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 Current methods of vehicle coordination, which are all designed to work with human drivers, will be outdated.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

In the research documented here, researchers develop information to communicate the safety and economic impacts of converting frontage roads from two-way to one-way Researchers studied five locations in Texas where frontage roads were converted from two-way operation to one-way operation Researchers identified a comparison site in Texas that remained two-way for comparison to four of the treatment (conversion) sites Researchers developed accident modification factors (AMFs) related to frontage road conversion segments that roadway designers can use to guide frontage road conversion project planning Researchers developed AMFs based on non-property-damage-only (non-PDO) crashes for segments and interchange intersections Researchers developed 12 AMFs and provide confidence intervals around the estimates Researchers describe how the AMFs were developed, associated caveats, and how to apply the AMFs Researchers also document the process used to build the safety database from electronic data-sets and printed crash reports Finally, researchers provide recommendations to facilitate and expedite future crash analysis Researchers attempted to investigate the economic impacts of frontage road conversion by analyzing gross sales data, appraisal data, employment data, and surveys of business owners/managers and customers Only the appraisal data and survey information were at the parcel level along the corridors and survey sample size was limited

Safety and economic impacts of converting two-way frontage roads to one-way : methodology and findings

Developing reliable statistical models is critical for predicting motor vehicle crashes in highway safety studies. However, the conventional statistical method ignores model uncertainty. Transportation safety analysts typically select a single “best” model from a series of candidate models (called model space) and proceed as if the selected model is the true model. This paper proposes a new approach for deriving more reliable and robust crash prediction models than the conventional statistical modeling method. This approach uses the Bayesian model averaging (BMA) to account for model uncertainty. The derived BMA crash model is an average of the candidate models included in the model space weighted by their posterior model probabilities. To examine the applicability of BMA to the Poisson and negative binomial (NB) regression models, the approach is applied to the crash data collected on 338 rural interstate road sections in Indiana over a five-year period (1995 to 1999). The results show that BMA was successfully applied to Poisson and NB regression models. More importantly, in the presence of model uncertainty, the proposed approach can provide better prediction performance than single models selected by conventional statistical techniques. Thus, this paper provides transportation safety analysts with an alternative methodology to predict motor vehicle crashes when model uncertainty is suspected to exist.

Application of the Bayesian Model Averaging in Predicting Motor Vehicle Crashes

There is a significant need to explicitly include highway safety in the highway design process, in that safety has so far often been generally limited to qualitative assessments of design alternatives. To receive proper consideration, safety needs to be dealt with quantitatively. Unfortunately, highway safety is seldom, if ever, explicitly considered during the highway design process. This omission can be attributed to various factors, the most important one being the lack of available tools needed for estimating the number of crashes during this process. To help fill this void, the objective of this study is to develop a tool that would allow the estimation of crashes for four types of ramp design configuration. The tool proposed in this work will be useful during the project planning process where it can be implemented to identify cost-effective interchange ramp design configurations. Only ramps in non-frontage-road settings were analyzed for this study. The four basic ramp design configurations analyzed were: 1) diagonal ramp, 2) non-free-flow loop ramp, 3) free-flow loop ramp, and 4) outer connection ramp. Predictive models were proposed for the four ramp design configurations. Predictive models, developed from data collected in Washington for the years 1993-1995 inclusively, were calibrated using data from Texas. Crash data, traffic flows and geometric design characteristics were collected at 31 ramps for the years 1998 to 2000. Calibration factors were estimated using a procedure recently proposed in the Interactive Highway Safety Design Model (IHSDM). An example detailing the application of the tool for the design of ramp configurations is presented in this study.

Estimating the Safety of Four Ramp Design Configurations

Network segmentation is foundational and critical to traffic safety analysis. Existing approaches to conduct segmentation require engineering judgement and are subject to a lack of standard metrics for assessing segmentation performance. This paper presents a novel methodology for data-driven analytics of crash distribution, crash aggregation, and network segmentation. It provides general solutions to determine optimal segment lengths for rigorous safety analysis, and extends the knowledge of crash distribution and aggregation for innovating segment-based safety analysis. The methodology is based on a redesigned spectral analysis of crash density in the spatial frequency domain (SFD) in which frequency components represent the natural patterns how crashes occur along roadways. By proposing the one-dimensional spatial frequency domain analysis (SFDA), this paper reveals the characteristic of power spectral concentration within the low frequency band for crash distribution. Based on this finding, this paper further proposes the power spectral segment length (PSSL) for determining optimal segment lengths and the power spectral percentage (PSP) for assessing the segmentation performance. Based on those new concepts and inferences, the paper proposes the low-pass filtering (LPF) method that outperforms the sliding window (SW) method, and the improved wavelet-based method that identifies high-risk segments properly. Those new techniques are easy to implement and ready for practical application. This research illustrates that interdisciplinary and innovative analytics combined with high-quality data collected by intelligent transportation infrastructure can reshape the fundamental knowledge and conventional paradigms in traffic safety.

/pdf/examining-network-segmentation-for-traffic-safety-analysis-2onaf1tp4i.pdf

Dominique Lord

Papers

Safety and economic impacts of converting two-way frontage roads to one-way : methodology and findings

Application of the Bayesian Model Averaging in Predicting Motor Vehicle Crashes

Estimating the Safety of Four Ramp Design Configurations

Application of the Bayesian Model Averaging in Predicting Motor Vehicle Crashes

Examining Network Segmentation for Traffic Safety Analysis With Data-Driven Spectral Analysis