Investigating effects of asphalt pavement conditions on traffic accidents in Tennessee based on the pavement management system (PMS)

Pavement maintenance is essential for ensuring good riding quality and avoiding traffic congestion, air pollution, and accidents. Improving road safety is one of the most important objectives for pavement management systems. This study utilized the Tennessee Pavement Management System (PMS) and Accident History Database (AHD) to investigate the relationship between accident frequency and pavement distress variables. Focusing on four urban interstates with asphalt pavements, divided median types, and 55 mph speed limits, 21 Negative Binomial Regression models were developed for predicting various types of traffic accident frequencies based on different pavement condition variables, including rut depth (RD), International Roughness Index (IRI), and Present Serviceability Index (PSI). The modeling results indicated that the RD models did not perform well, except for predicting accidents at night and accidents under rain weather conditions; whereas, IRI and PSI were always significant prediction variables in all types of accident models. Comparing the models goodness‐of‐fit results, it was found that the PSI models had a better performance in crash frequency prediction than the RD models and IRI models. This study suggests that the PSI accident prediction models should be considered as a comprehensive approach to integrate the highway safety factors into the pavement management system. Copyright © 2010 John Wiley & Sons, Ltd.     

Real-time crash prediction for expressway weaving segments

Weaving segments are potential recurrent bottlenecks which affect the efficiency and safety of expressways during peak hours. Meanwhile, they are one of the most complicated segments, since on- and off-ramp traffic merges, diverges and weaves in the limited space. One effective way to improve the safety of weaving segments is to study crash likelihood using real-time crash data with the objective of, identifying hazardous conditions and reducing the risk of crashes by Intelligent Transportation Systems (ITS) traffic control. This study presents a multilevel Bayesian logistic regression model for crashes at expressway weaving segments using crash, geometric, Microwave Vehicle Detection System (MVDS) and weather data. The results show that the mainline speed at the beginning of the weaving segments, the speed difference between the beginning and the end of weaving segment, logarithm of volume have significant impacts on the crash risk of the following 5–10 min for weaving segments. The configuration is also an important factor. Weaving segment, in which there is no need for on- or off-ramp traffic to change lane, is with high crash risk because it has more traffic interactions and higher speed differences between weaving and non-weaving traffic. Meanwhile, maximum length, which measures the distance at which weaving turbulence no longer has impact, is found to be positively related to the crash risk at the 95% confidence interval. In addition to traffic and geometric factors, wet pavement surface condition significantly increases the crash ratio by 77%. The proposed model along with ITS, e.g., ramp metering, Dynamic Message Sign (DMS), and high friction surface treatment can be used to enhance the safety of weaving segments in real-time.     

Evaluating speed consistency between successive elements of a two-lane rural highway

The V₈₅ speed reflects the 85th percentile speed of vehicles in a traffic stream passing a given element on the highway section. The large amount of speed differential in V₈₅ between the two successive elements along a highway reflects lack of speed consistency in the highway section. This lack of speed consistency can result in increased crash risk. Recent research points to several problems associated with the conventional method of measuring ΔV₈₅ between successive highway elements: (1) failure to reflect individual vehicle speed profiles; and (2) failure to account for inter-dependence in individual vehicle speeds between successive elements. These problems have serious implications for justifying safety treatment when conventional ΔV₈₅ measure is applied. A number of researchers have suggested that the estimated speed differential based on individual vehicle speed profiles in successive elements is significantly higher than that obtained using the conventional approach. In this paper, we assess the safety implications of using the conventional ΔV₈₅ and introduce a hierarchical model for considering individual vehicles speed consistency. These findings lead to important implications for introducing engineering treatments to improve safety along in two-lane rural highways based on the criteria of speed consistency.     

沥青路面抗滑性能影响因素识别研究进展

沥青路面作为公路建设的重要内容,其抗滑性能是道路安全问题的核心,探究其影响因素可以为提高路面抗滑性能提供依据。国内外学者对此做了大量研究,也取得了相应的进展。本文对国内外的研究成果进行了系统的归纳总结,将公路沥青路面抗滑性能影响因素分为了沥青混合料的材料特性、环境因素、车辆特性3个主要方面,其中在沥青混合料的材料特性方面,研究认为集料的结构特性与混合料配合比对抗滑性的影响较大;环境因素方面,研究认为温度对于抗滑性的影响较大;车辆特性对抗滑性的影响方面,研究认为主要体现在轮胎特性与荷载方面。此外随着行车速度的增加,路面的抗滑性能呈现降低趋势。     

公路路线设计中运行速度理论检验的方法

我国目前的公路路线设计采用设计速度理论,设计速度是个定值,而多年的实践表明,车辆实际的运行速度总是随公路线形等条件的改变而变化,所以设计速度理论本身存在一定的局限性,需要采用动态的运行速度对公路的路线设计进行安全性检验,以确保行车安全。文章结合设计实例,探讨公路路线设计中运行速度理论检验的方法。     

Disaggregate safety evaluation for signalized intersections and evaluation tool

This study was to evaluate traffic safety of four‐legged signalized intersections and to develop a spreadsheet tool for identifying high‐risk intersections taking into consideration vehicle movements, left‐turn signal phase types, and times of day. The study used data from Virginia and employed count data models and the empirical Bayes (EB) method for safety evaluation of such intersections. It was found that crash pattern defined by vehicle movements involved in a crash and time of day are important factors for intersection crash analysis. Especially for a safety performance function (SPF), a model specification (Poisson or NB), inclusion of left‐turn signal types, type of traffic flow variables, variable functional forms, and/or magnitudes of coefficients turned out to be different across times of day and crash patterns. The spreadsheet application tool was developed incorporating the developed SPFs and the EB method. As long as Synchro files for signal plans and crash database are maintained, no additional field data collection efforts are required. Adjusting the developed SPFs and the spreadsheet for recent traffic and safety conditions can be done by applying the calibration methods employed in the SafetyAnalyst software and the Highway Safety Manual. Implementing the developed tool equipped with streamlining data entry would greatly improve accuracy and efficiency of safety evaluation of four‐legged signalized intersections in localities and highway agencies that cannot operate the SafetyAnalyst. Copyright © 2010 John Wiley & Sons, Ltd.     

公路路面沥青综合表面处治层质量评价方法研究

文章基于公路路面沥青综合表面处治试验路段性能指标检测,确定使用构造深度和饱和度评价公路路面沥青综合表面处治的使用性能,并利用主成分分析法对公路路面沥青综合表面处治层进行了质量评价。结果表明,该评价方法是合理、可行的。     

Vehicle mass as a determinant of fuel consumption and secondary safety performance

One interaction between environmental and safety goals in transport is found within the vehicle fleet where fuel economy and secondary safety performance of individual vehicles impose conflicting requirements on vehicle mass from an individual’s perspective. Fleet characteristics influence the relationship between the environmental and safety outcomes of the fleet; the topic of this paper. Cross-sectional analysis of mass within the British fleet is used to estimate the partial effects of mass on the fuel consumption and secondary safety performance of vehicles. The results confirmed that fuel consumption increases as mass increases and is different for different combinations of fuel and transmission types. Additionally, increasing vehicle mass generally decreases the risk of injury to the driver of a given vehicle in the event of a crash. However, this relationship depends on the characteristics of the vehicle fleet, and in particular, is affected by changes in mass distribution within the fleet. We confirm that there is generally a trade-off in vehicle design between fuel economy and secondary safety performance imposed by mass. Cross-comparison of makes and models by model-specific effects reveal cases where this trade-off exists in other aspects of design. Although it is shown that mass imposes a trade-off in vehicle design between safety and fuel use, this does not necessarily mean that it imposes a trade-off between safety and environmental goals in the vehicle fleet as a whole because the secondary safety performance of a vehicle depends on both its own mass and the mass of the other vehicles with which it collides.     

一种恶劣天气下高速公路车流稳健控制方式

高速公路的安全运营受恶劣天气影响显著,尤其是在极端恶劣的天气情况下。在车辆跟驰理论的基础上提出了一种车流控制方式,并结合车间距和车流安全的影响因素,推导得出了恶劣天气下能见度与高速公路安全行车速度的关系。研究为恶劣天气条件下高速公路的不间断安全运营提供了理论依据。     

Rear‐end crash potential estimation in the work zone merging areas

This paper proposed a methodology to estimate rear‐end crash potential of the merging vehicles traveling in the merge lane, on the basis of the traffic data extracting from the available videotapes. First, we developed a binary logit model to identify drivers' merging behavior in the work zone merging area. Subsequently, the occurrence potential of rear‐end crash based on time‐to‐collision was computed between the merging vehicle and its neighboring vehicles. The overall crash potential of the merging vehicle was finally determined. It was found that the crash potential decreases with the remaining distance to work zone. Moreover, there will be a rear‐end crash potential of 4.0% if the merging vehicle fails to complete merging at the end of work zone merging area. If the merging vehicle takes an early merge, there will be a lower rear‐end crash potential (1.2%). These findings suggest that we should encourage merging vehicles to take early merges for improving the traffic safety in the work zone merging areas. Copyright © 2012 John Wiley & Sons, Ltd.     

An optimal variable speed limits system to ameliorate traffic safety risk

Active Traffic Management (ATM) systems have been emerging in recent years in the US and Europe. They provide control strategies to improve traffic flow and reduce congestion on freeways. This study investigates the feasibility of utilizing a Variable Speed Limits (VSL) system, one key part of ATM, to improve traffic safety on freeways. A proactive traffic safety improvement VSL control algorithm is proposed. First, an extension of the METANET (METANET: A macroscopic simulation program for motorway networks) traffic flow model is employed to analyze VSL’s impact on traffic flow. Then, a real-time crash risk evaluation model is estimated for the purpose of quantifying crash risk. Finally, optimal VSL control strategies are achieved by employing an optimization technique to minimize the total crash risk along the VSL implementation corridor. Constraints are setup to limit the increase of average travel time and the differences of the posted speed limits temporarily and spatially. This novel VSL control algorithm can proactively reduce crash risk and therefore improve traffic safety. The proposed VSL control algorithm is implemented and tested for a mountainous freeway bottleneck area through the micro-simulation software VISSIM. Safety impacts of the VSL system are quantified as crash risk improvements and speed homogeneity improvements. Moreover, three different driver compliance levels are modeled in VISSIM to monitor the sensitivity of VSL effects on driver compliance. Conclusions demonstrated that the proposed VSL system could improve traffic safety by decreasing crash risk and enhancing speed homogeneity under both the high and moderate compliance levels; while the VSL system fails to significantly enhance traffic safety under the low compliance scenario. Finally, future implementation suggestions of the VSL control strategies and related research topics are also discussed.     

A Bayesian Network model for contextual versus non-contextual driving behavior assessment

Driving behavior is generally considered to be one of the most important factors in crash occurrence. This paper aims to evaluate the benefits of utilizing context-relevant information in the driving behavior assessment process (i.e. contextual driving behavior assessment approach). We use a Bayesian Network (BN) model that investigates the relationships between GPS driving observations, individual driving behavior, individual driving risks, and individual crash frequency. In contrast to prior studies without context information (i.e. non-contextual approach), the data used in the BN approach is a combination of contextual features in the surrounding environment that may contribute to crash risk, such as road conditions surrounding the vehicle of interest and dynamic traffic flow information, as well as the non-contextual data such as instantaneous driving speed and the acceleration/deceleration of a vehicle. An information-aggregation mechanism is developed to aggregates massive amounts of vehicle GPS data points, kinematic events and context information into drivel-level data. With the proposed model, driving behavior risks for drivers is assessed and the relationship between contextual driving behavior and crash occurrence is established. The analysis results in the case study section show that the contextual model has significantly better performance than the non-contextual model, and that drivers who drive at a speed faster than others or much slower than the speed limit at the ramp, and with more rapid acceleration or deceleration on freeways are more likely to be involved in crash events. In addition, younger drivers, and female drivers with higher VMT are found to have higher crash risk.     

Assessment of the effects of highway geometric design features on the frequency of truck involved crashes using bivariate regression

Given the enormous losses to society resulting from large truck involved crashes, a comprehensive understanding of the effects of highway geometric design features on the frequency of truck involved crashes is needed. To better predict the occurrence probabilities of large truck involved crashes and gain direction for policies and countermeasures aimed at reducing the crash frequencies, it is essential to examine truck involved crashes categorized by collision vehicle types, since passenger cars and large trucks differ in dimensions, size, weight, and operating characteristics. A data set that includes a total of 1310 highway segments with 1787 truck involved crashes for a 4-year period, from 2004 to 2007 in Tennessee is employed to examine the effects that geometric design features and other relevant attributes have on the crash frequency. Since truck involved crash counts have many zeros (often 60–90% of all values) with small sample means and two established categories, car-truck and truck-only crashes, are not independent in nature, the zero-inflated negative binomial (ZINB) models are developed under the bivariate regression framework to simultaneously address the above mentioned issues. In addition, the bivariate negative binomial (BNB) and two individual univariate ZINB models are estimated for model validation. Goodness of fit of the investigated models is evaluated using AIC, SBC statistics, the number of identified significant variables, and graphs of observed versus expected crash frequencies. The bivariate ZINB (BZINB) models have been found to have desirable distributional property to describe the relationship between the large truck involved crashes and geometric design features in terms of better goodness of fit, more precise parameter estimates, more identified significant factors, and improved predictive accuracy. The results of BZINB models indicate that the following factors are significantly related to the likelihood of truck involved crash occurrences: large truck annual average daily traffic (AADT), segment length, degree of horizontal curvature, terrain type, land use, median type, lane width, right side shoulder width, lighting condition, rutting depth (RD), and posted speed limits. Apart from that, passenger car AADT, lane number, and indicator for different speed limits are found to have statistical significant effects on the occurrences of car-truck crashes and international roughness index (IRI) is significant for the predictions of truck-only crashes.     

The impact of flooding on road transport: A depth-disruption function

Transport networks underpin economic activity by enabling the movement of goods and people. During extreme weather events transport infrastructure can be directly or indirectly damaged, posing a threat to human safety, and causing significant disruption and associated economic and social impacts. Flooding, especially as a result of intense precipitation, is the predominant cause of weather-related disruption to the transport sector. Existing approaches to assess the disruptive impact of flooding on road transport fail to capture the interactions between floodwater and the transport system, typically assuming a road is fully operational or fully blocked, which is not supported by observations. In this paper we develop a relationship between depth of standing water and vehicle speed. The function that describes this relationship has been constructed by fitting a curve to video analysis supplemented by a range of quantitative data that has be extracted from existing studies and other safety literature. The proposed relationship is a good fit to the observed data, with an R-squared of 0.95. The significance of this work is that it is simple to incorporate our function into existing transport models to produce better estimates of flood induced delays and we demonstrate this with an example from the 28^th June 2012 flood in Newcastle upon Tyne, UK.     

Modelling of the pavement acoustic longevity in Hong Kong through machine learning techniques

Traffic noise emission has long been a pervasive environmental and ecological problem, especially in the metropolitan cities with large-scale traffic network and high population density. Low noise road surface (LNRS) has been actively developed and applied as an effective measure to maintain the quieter environment of mobility service system. However, when LNRS is applied for noise abatement, the relationship between the acoustic performance and degradation of pavement has not been fully understood yet. To this end, this study aims to model the acoustic longevity of asphalt pavement as a function of the thickness, binder content, maximum aggregate size, and air void content of the pavement surface, as well as vehicle speed based on the long-term tyre-road noise data collected from 270 asphalt pavement sections in Hong Kong. Two machine learning techniques, namely artificial neural networks (ANN) and support vector machines (SVM), were employed and compared. It was found that both ANN and SVM could successfully model the pavement acoustic performance with acceptable model performance metrics. A case study showed that the ANN model was more aligned with the aging mechanisms of porous road surface, but the SVM model showed better training performance. The predicted acoustic deterioration rates of the porous surface case varied from −0.1 to 0.28 dB(A)/month rather than keeping a constant linear increasing trend, depending on pavement ageing periods and vehicle speed levels. The two-dimension sensitivity analysis (2D-SA) revealed the relative importance of pavement age and vehicle speed in controlling the acoustic performance.     

Sight distance analysis of highways using GIS tools

Analyzing the distance visible to a driver on the highway is important for traffic safety, especially in maneuvers such as emergency stops, when passing another vehicle or when vehicles cross at intersections. This analysis is necessary not only in the design phase of highways, but also when they are in service. For its use in this last phase, a procedure supported by a Geographic Information System (GIS) has been implemented that determines the highway distances visible to the driver. The use of a GIS allows the sight distance analysis to be integrated with other analyses related to traffic safety, such as crash and design consistency analyses. In this way, more complete analyses could be made and costs shared. Additionally, with the procedure proposed it is possible to use data regarding the trajectory of a vehicle obtained on a highway with a Global Positioning System (GPS) device. This application is very useful when highway design data are not available. The procedure developed and its application in a case study are presented in this article.     

Assessing the impact of reduced visibility on traffic crash risk using microscopic data and surrogate safety measures

Due to the difficulty of obtaining accurate real-time visibility and vehicle based traffic data at the same time, there are only few research studies that addressed the impact of reduced visibility on traffic crash risk. This research was conducted based on a new visibility detection system by mounting visibility sensor arrays combined with adaptive learning modules to provide more accurate visibility detections. The vehicle-based detector, Wavetronix SmartSensor HD, was installed at the same place to collect traffic data. Reduced visibility due to fog were selected and analyzed by comparing them with clear cases to identify the differences based on several surrogate measures of safety under different visibility classes. Moreover, vehicles were divided into different types and the vehicles in different lanes were compared in order to identify whether the impact of reduced visibility due to fog on traffic crash risk varies depending on vehicle types and lanes. Log-Inverse Gaussian regression modeling was then applied to explore the relationship between time to collision and visibility together with other traffic parameters. Based on the accurate visibility and traffic data collected by the new visibility and traffic detection system, it was concluded that reduced visibility would significantly increase the traffic crash risk especially rear-end crashes and the impact on crash risk was different for different vehicle types and for different lanes. The results would be helpful to understand the change in traffic crash risk and crash contributing factors under fog conditions. We suggest implementing the algorithms in real-time and augmenting it with ITS measures such as VSL and DMS to reduce crash risk.     

基于可拓方法的沥青路面使用性能评价

沥青路面使用性能评价是路面养护的重要组成部分,一直以来多采用单一综合评价指标进行评价。文章以湖南某高速公路实测数据为依据,将可拓方法用于路面使用性能评价当中,通过选择合理的评价指标,确定各评价指标的关联度,从而精确评价路面使用性能,为沥青路面养护提供参考。     

多年冻土区公路路面路基管理系统设计

我国多年冻土地区公路受气温及水分变化等影响,公路病害发生严重。为了使公路养护部门合理利用资金,最大限度避免冻土病害,有必要建立冻土区公路路基路面评价预测系统。文章介绍了冻土区公路路基的评价指标和评价方法。在常规路基路面评价预测的方法上,还使用了马尔可夫等预测方法,以适合冻土区公路路面破坏机理复杂、影响因素多的实际情况。