Evaluation of photo radar program in British Columbia

This article presents the results of an evaluation of the speed and traffic safety effects of the photo radar program in British Columbia (BC) after 1 year of full operation. Traffic speed data were collected from the photo radar units and from induction loops installed across the province. Traffic collision and injury data were obtained from police investigation reports and from BC ambulance services records. The study employed a number of analytical frameworks, including simple before and after comparison, time-series cross-sectional analysis, and interrupted time series analysis. The study revealed a dramatic reduction of speed at photo radar deployment sites. A reduction of 2.4 km/h in mean speed was also observed at selected monitoring sites where enforcement was not likely to be present. The reduction of speed was accompanied by a decrease in collisions, injuries and fatalities. The analysis found a 25% reduction in daytime unsafe speed related collisions, an 11% reduction in daytime traffic collision victims carried by ambulances and a 17%, reduction in daytime traffic collision fatalities.     

Traffic accident segmentation by means of latent class clustering

Traffic accident data are often heterogeneous, which can cause certain relationships to remain hidden. Therefore, traffic accident analysis is often performed on a small subset of traffic accidents or several models are built for various traffic accident types. In this paper, we examine the effectiveness of a clustering technique, i.e. latent class clustering, for identifying homogenous traffic accident types. Firstly, a heterogeneous traffic accident data set is segmented into seven clusters, which are translated into seven traffic accident types. Secondly, injury analysis is performed for each cluster. The results of these cluster-based analyses are compared with the results of a full-data analysis. This shows that applying latent class clustering as a preliminary analysis can reveal hidden relationships and can help the domain expert or traffic safety researcher to segment traffic accidents.     

Evaluation of the impacts of traffic states on crash risks on freeways

The primary objective of this study is to divide freeway traffic flow into different states, and to evaluate the safety performance associated with each state. Using traffic flow data and crash data collected from a northbound segment of the I-880 freeway in the state of California, United States, K-means clustering analysis was conducted to classify traffic flow into five different states. Conditional logistic regression models using case-controlled data were then developed to study the relationship between crash risks and traffic states. Traffic flow characteristics in each traffic state were compared to identify the underlying phenomena that made certain traffic states more hazardous than others. Crash risk models were also developed for different traffic states to identify how traffic flow characteristics such as speed and speed variance affected crash risks in different traffic states. The findings of this study demonstrate that the operations of freeway traffic can be divided into different states using traffic occupancy measured from nearby loop detector stations, and each traffic state can be assigned with a certain safety level. The impacts of traffic flow parameters on crash risks are different across different traffic flow states. A method based on discriminant analysis was further developed to identify traffic states given real-time freeway traffic flow data. Validation results showed that the method was of reasonably high accuracy for identifying freeway traffic states.     

Cross-layer queuing analysis on multihop relaying networks with adaptive modulation and coding

Multihop relaying is one of the promising techniques in future generation wireless networks. The adaptive modulation and coding (AMC) mechanisms can be applied in order to increase the spectral efficiency of wireless multihop networks. However, most of these mechanisms concentrate on the physical layer without taking the queuing effects at the data link layer into account, whose performances are overestimated. Therefore the cross-layer analytical framework is presented in analysing the quality-of-service (QoS) performances of the decode-and-forward (DF) relaying wireless networks, where the AMC is employed at the physical layer under the conditions of unsaturated traffic and finite-length queue at the data link layer. Considering the characteristics of DF relaying protocol at the physical layer, the authors first propose modelling a two-hop DF relaying wireless channel with AMC as an equivalent Finite State Markov Chain (FSMC) in queuing analysis. Then, the performances in terms of queuing delay, packet loss rate and average throughput are derived. The numerical results show that the proposed analytical method can be efficiently applied for studying the issues including the relay deployment and the cross-layer design in the multihop relaying networks.     

Performance evaluation of a multi-product system under CONWIP control

Analytical models of multi-product manufacturing systems operating under CONWIP control are composed of closed queuing networks with synchronization stations. Under general assumptions, these queuing networks are hard to analyze exactly and therefore approximation methods must be used for performance evaluation. This research proposes a new approach based on parametric decomposition. Two-moment approximations are used to estimate the performance measures at individual stations. Subsequently, the traffic process parameters at the different stations are linked using stochastic transformation equations. The resulting set of non-linear equations is solved using an iterative algorithm to obtain estimates of key performance measures such as throughput, and mean queue lengths. Numerical studies indicate that the proposed method is computationally efficient and yields fairly accurate results when compared to simulation.     

Impact of traffic oscillations on freeway crash occurrences

Traffic oscillations are typical features of congested traffic flow that are characterized by recurring decelerations followed by accelerations (stop-and-go driving). The negative environmental impacts of these oscillations are widely accepted, but their impact on traffic safety has been debated. This paper describes the impact of freeway traffic oscillations on traffic safety. This study employs a matched case-control design using high-resolution traffic and crash data from a freeway segment. Traffic conditions prior to each crash were taken as cases, while traffic conditions during the same periods on days without crashes were taken as controls. These were also matched by presence of congestion, geometry and weather. A total of 82 cases and about 80,000 candidate controls were extracted from more than three years of data from 2004 to 2007. Conditional logistic regression models were developed based on the case-control samples. To verify consistency in the results, 20 different sets of controls were randomly extracted from the candidate pool for varying control-case ratios. The results reveal that the standard deviation of speed (thus, oscillations) is a significant variable, with an average odds ratio of about 1.08. This implies that the likelihood of a (rear-end) crash increases by about 8% with an additional unit increase in the standard deviation of speed. The average traffic states prior to crashes were less significant than the speed variations in congestion.     

Predicting the frequency of median barrier crashes on Pennsylvania interstate highways

Median barrier warrant criteria were developed in the 1970s and generally remain unchanged today. Vehicle travel, including both traffic volumes and operating speeds, have increased over this same time period. Encroachments into the median, and subsequent collisions with vehicles traveling in the opposite travel lanes, result in high severity crashes. Median barrier is typically used to prevent cross-median crashes; median barrier selection is based on median width and traffic volumes. Quantifiable information regarding the effects of median barrier installation and its placement on crash frequency is limited. This paper investigates median barrier crash frequency on Pennsylvania Interstate highways, including separate models for the Turnpike and all other Interstate-designated highways. Negative binomial regression models were used to develop predictive crash frequency tools. Traffic volume, horizontal alignment, interchange ramp presence, and median barrier offset distance from the travel lanes were used to estimate median barrier crash frequency. The analytical methodology developed in this research can be used, in concert with other prediction models, to assess the consequences of median barrier placement decisions.     

灰色关联分析法在交通噪声影响因素分析中的应用

交通噪声污染已成为现代城市严重的环境污染问题之一。利用灰色关联分析方法，对南方某城市的交通噪声和其影响因子进行分析，得出各影响因子与该城市交通噪声的关联度，找出了对该城市交通噪声影响较大的影响因子。并对研究结果进行了阐述。     

Management of traffic congestion in adaptive traffic signals using a novel classification-based approach

Traffic congestion is a critical problem which makes roads busy. Traffic congestion challenges traffic flow in urban areas. A growing urban area creates complex traffic problems in daily life. Congestion phenomena cannot be resolved only by applying physical constructs such as building bridges and motorways and increasing road capacity. It is necessary to build technological systems for transportation management to control the traffic phenomenon. In this article, a new idea is proposed to tackle traffic congestion with the aid of machine learning approaches. A new strategy based on a tree-like configuration (i.e. a decision-making model) is suggested to handle traffic congestion at intersections using adaptive traffic signals. Different traffic networks with different sizes, varying from nine to 400 intersections, are examined. Numerical results and discussion are presented to prove the efficiency and application of the proposed strategy to alleviate traffic congestion.     

Multi-scale analysis of generalised processor sharing queues with long-range-dependent traffic inputs and variable service rates

An analytical technique is provided to estimate queue-length and delay distributions for multi-queue systems using generalised processor sharing discipline with time-correlated variable service rates, based on two-dimensional multi-level decoupling. First, temporal decomposition is used to convert the time-correlated queuing problem into a set of sub-problems over several timescales. Subsequently, queue decomposition exploits the queue weight dependencies to convert a multi-queue problem into a set of single-queue problems. The core of the analysis lies in estimating the multi-scale service rate models for each of these queues. The authors show the hierarchy of this estimation and the dependency of the queue service rate on the unused capacity of the other queues and their weights. Simulation and analytical results on queue and delay survivor functions are in good agreement.     

Modelling total duration of traffic incidents including incident detection and recovery time

Traffic incidents are key contributors to non-recurrent congestion, potentially generating significant delay. Factors that influence the duration of incidents are important to understand so that effective mitigation strategies can be implemented. To identify and quantify the effects of influential factors, a methodology for studying total incident duration based on historical data from an ‘integrated database’ is proposed. Incident duration models are developed using a selected freeway segment in the Southeast Queensland, Australia network. The models include incident detection and recovery time as components of incident duration. A hazard-based duration modelling approach is applied to model incident duration as a function of a variety of factors that influence traffic incident duration. Parametric accelerated failure time survival models are developed to capture heterogeneity as a function of explanatory variables, with both fixed and random parameters specifications. The analysis reveals that factors affecting incident duration include incident characteristics (severity, type, injury, medical requirements, etc.), infrastructure characteristics (roadway shoulder availability), time of day, and traffic characteristics. The results indicate that event type durations are uniquely different, thus requiring different responses to effectively clear them. Furthermore, the results highlight the presence of unobserved incident duration heterogeneity as captured by the random parameter models, suggesting that additional factors need to be considered in future modelling efforts.     

Relationship between quality of life and child traffic fatalities

Traffic injuries are the leading cause of morbidity and mortality of children around the world. Economic development and quality of life are main components of public health. Using canonical correlation analysis, this study examined the relationship between quality of life and traffic fatality among children. Data were collected from 19 countries by using World Health Organization and OECD statistics. The results indicated that there is a strong relationship between quality of life and children traffic fatality. Growing quality of life has become protective against child traffic accident mortality. Increasing in gross national income per capita, life expectancy at birth number of years, road motor vehicles per thousand population, and share of persons of working age (15-64 years) in employment also increases children traffic safety.     

Image tracking of motorcycles and vehicles on urban roads and its application to traffic monitoring and enforcement

Abstract

Image tracking has increasingly gained attention for use in vision‐based traffic monitoring and surveillance applications. For many cities in Asia countries, it is desirable to detect multiple motorcycles as well as cars for urban traffic monitoring and enforcement. In this paper, a novel contour initialization and tracking algorithm is presented to track multiple motorcycles and vehicles at any position on the roadway. This method has the capability to detect moving vehicles of various sizes and to generate their initial contours for image tracking. The proposed method is not constrained by lane boundaries or vehicle size. To track vehicles on roadways, dynamic models are designed to predict the horizontal and vertical positions of vehicle contours. A Kalman filter is designed to update the prediction based on real‐time image measurement. Practical experimental studies using video clips are presented to evaluate the performance of the proposed method. Traffic parameters such as traffic flow, vehicle speeds and traffic density are obtained with satisfactory accuracy.     

Crash risk analysis for Shanghai urban expressways: A Bayesian semi-parametric modeling approach

Urban expressway systems have been developed rapidly in recent years in China; it has become one key part of the city roadway networks as carrying large traffic volume and providing high traveling speed. Along with the increase of traffic volume, traffic safety has become a major issue for Chinese urban expressways due to the frequent crash occurrence and the non-recurrent congestions caused by them. For the purpose of unveiling crash occurrence mechanisms and further developing Active Traffic Management (ATM) control strategies to improve traffic safety, this study developed disaggregate crash risk analysis models with loop detector traffic data and historical crash data. Bayesian random effects logistic regression models were utilized as it can account for the unobserved heterogeneity among crashes. However, previous crash risk analysis studies formulated random effects distributions in a parametric approach, which assigned them to follow normal distributions. Due to the limited information known about random effects distributions, subjective parametric setting may be incorrect. In order to construct more flexible and robust random effects to capture the unobserved heterogeneity, Bayesian semi-parametric inference technique was introduced to crash risk analysis in this study. Models with both inference techniques were developed for total crashes; semi-parametric models were proved to provide substantial better model goodness-of-fit, while the two models shared consistent coefficient estimations. Later on, Bayesian semi-parametric random effects logistic regression models were developed for weekday peak hour crashes, weekday non-peak hour crashes, and weekend non-peak hour crashes to investigate different crash occurrence scenarios. Significant factors that affect crash risk have been revealed and crash mechanisms have been concluded.     

Location of vehicle identification sensors to monitor travel-time performance

Traffic agencies are increasingly using automatic toll collection technologies to collect fees on toll roads. Associated with such technologies are toll tags on vehicles that identify the vehicle and its account for charging the appropriate toll, and roadside readers (or overhead readers) that read the toll tags. A by-product of such technologies is the data collected on traffic measures such as travel times from the point when the vehicle is first tagged to the point it leaves the system. That is, the toll reader becomes a traffic sensor that measures travel times of some tagged vehicles and volumes of such vehicles that go from intermediate origins to intermediate destinations. Two problems have been studied on the optimal locations of such automatic vehicle identification (AVI) readers on traffic network using criteria: (i) to maximise the total vehicle-miles monitored, and (ii) to minimise the variance of predicted travel times. Optimisation models are formulated through which it is shown that these problems are NP-hard. Subsequently, greedy heuristics are proposed. Exact optimal solutions, using CPLEX, and approximate solutions, using the heuristics are compared for several small hypothetical networks for a case study dealing with a real application as well as in Harris County, Texas, USA.     

帧中继与ATM互通的流量参数映射与流量控制

讨论了帧中继与ATM互通的流量参数映射、流量控制等问题,设计了一种流量控制方法,提出了对帧中继流量进行整形的必要性及其实现方法。     

Impact of traffic congestion on road accidents: A spatial analysis of the M25 motorway in England

Traffic congestion and road accidents are two external costs of transport and the reduction of their impacts is often one of the primary objectives for transport policy makers. The relationship between traffic congestion and road accidents however is not apparent and less studied. It is speculated that there may be an inverse relationship between traffic congestion and road accidents, and as such this poses a potential dilemma for transport policy makers. This study aims to explore the impact of traffic congestion on the frequency of road accidents using a spatial analysis approach, while controlling for other relevant factors that may affect road accidents. The M25 London orbital motorway, divided into 70 segments, was chosen to conduct this study and relevant data on road accidents, traffic and road characteristics were collected. A robust technique has been developed to map M25 accidents onto its segments. Since existing studies have often used a proxy to measure the level of congestion, this study has employed a precise congestion measurement. A series of Poisson based non-spatial (such as Poisson-lognormal and Poisson-gamma) and spatial (Poisson-lognormal with conditional autoregressive priors) models have been used to account for the effects of both heterogeneity and spatial correlation.The results suggest that traffic congestion has little or no impact on the frequency of road accidents on the M25 motorway. All other relevant factors have provided results consistent with existing studies.     

驾驶员对交通标志认知水平的试验研究

交通标志是道路交通系统的重要组成部分,在管制、引导交通流方面起着举足轻重的作用。本研究旨在探究各类交通标志在真实环境中的使用效果;本文通过城市道路实车试验和问卷调查相结合的试验方法,对驾驶员在日常驾驶中使用标志的情况和标志的认知水平进行了考察;研究认为,驾驶员对城市道路中设置的交通标志总体上是信赖的,并且,随着驾驶经验的积累,驾驶员对标志认知水平也在不断提高。     

Limits of asymptotic solutions in low-velocity impact of composite plates

This paper presents a study of the low velocity impact response of composite plates by using dimensional analysis and lumped-parameter models. Simple analytical expressions are obtained and used to construct a characterization diagram that shows the relationship between the maximum normalized impact force and two non-dimensional parameters. For a given impact event, it is shown that the non-dimensional parameters can be obtained by analytical, computational or experimental methods. Once these parameters are determined, the characterization diagram can be used to estimate the type of response, and maximum impact force. Furthermore, the diagram can be used to select adequate simple models for a given impact situation. It is shown that these simple models provide very good approximations of the impact response for a pre-determined wide range of impact parameters.     

交通声景的概念与特点解析

交通噪声是城市主要噪声源之一,在解读了原始声景研究范畴与研究内容后,通过对道路交通特点的分析,提出了交通声景的概念,并从人、声音、环境三个角度阐述了交通声景的研究内容。通过对交通声景与建筑声景研究对象、调节手段进行比较后,对交通声景的特点进行了解析。交通声景的设计在人们的日常生活中占有重要的地位,建立一个恰当的交通声景评价体系需要大量的分析与积累。