Quality of traffic flow on urban arterial streets and its relationship with safety

The two-fluid model for vehicular traffic flow explains the traffic on arterials as a mix of stopped and running vehicles. It describes the relationship between the vehicles’ running speed and the fraction of running vehicles. The two parameters of the model essentially represent ‘free flow’ travel time and level of interaction among vehicles, and may be used to evaluate urban roadway networks and urban corridors with partially limited access. These parameters are influenced by not only the roadway characteristics but also by behavioral aspects of driver population, e.g., aggressiveness. Two-fluid models are estimated for eight arterial corridors in Orlando, FL for this study. The parameters of the two-fluid model were used to evaluate corridor level operations and the correlations of these parameters’ with rates of crashes having different types/severity. Significant correlations were found between two-fluid parameters and rear-end and angle crash rates. Rate of severe crashes was also found to be significantly correlated with the model parameter signifying inter-vehicle interactions. While there is need for further analysis, the findings suggest that the two-fluid model parameters may have potential as surrogate measures for traffic safety on urban arterial streets.     

A two-fluid model for the normal-state properties of cuprate superconductors

We consider a two-dimensional metal with metallic layers of widthd separated by thick layers with a large dielectric constant ₂. Electronic states near the Fermi surface are renormalized so that their velocity increases significantly, as in the Lindhard model. In contrast, states removed from the FS are renormalized so that their velocity decreases, as described by the Gutzwiller theory. We suggest that these two types of states are decoupled, and can be described by a two-fluid model. The resistivity of the FS fluid, due to elastic scattering, becomes temperature-dependent, decreasing significantly atT=0 and actually vanishing as we approach the Mott transition. The increased velocity accounts for the small London penetration depth found in the superconducting state. This model can also account for the zero bias anomaly observed in some exotic superconductors.     

A two-fluid model for calcium carbonate precipitation in highly supersaturated solutions

Precipitation of calcium carbonate is a common phenomenon in nature, which has attracted attention from researchers due to its importance in biomineralization processes, climatic changes, and especially incrustations in pipelines. In this work, a two-fluid model is proposed for homogeneous crystallization of calcium carbonate in highly supersaturated solutions. By making an analogy between the dynamics of a granular gas and the dynamics of a solid dispersion, the proposed model not only accounts for the interaction forces between the solid particles and dispersion medium, but also for the mass transfer and changes in the particles size during the precipitation reaction. Moreover, by using a numerical scheme based on an iterative algorithm, 3D numerical simulations are performed for the homogeneous crystallization of calcium carbonate, and the results are compared with experimental particles size distributions and curves of pH versus time. The good agreement between theoretical and experimental results indicates that the two-fluid model can be successfully used to evaluate the growth kinetics of calcium carbonate nuclei.     

A random parameters probit model of urban and rural intersection crashes

Intersections are hazardous locations and many studies have been conducted to identify the factors contributing to the frequency and severity of intersection crashes. However, little attention has been devoted to investigating the differences between crashes at urban and rural intersections, which have different road, traffic and environmental characteristics. By applying a random parameters probit model to the data from the Canadian Province of Alberta between 2008 and 2012, we find that urban intersection crashes are more likely to be associated with hit and run behaviours, roads with higher traffic volume, wet surfaces, four lanes and skewed intersections, and crashes on weekdays and off-peak hours, whereas rural crashes are likely to be associated with increases in fatalities and injuries, roads with higher speed limits, special road features, exit and entrance terminals, gravel, curvature and two lanes, crashes during weekends, peak hours and night-time, run-off-road crashes, and police visit to crash scene. Hence, road safety professionals in urban and rural areas should consider these differences when designing and implementing counter-measures to improve intersection safety, especially their safety audits and reviews, enforcement activities and education campaigns, to target the more vulnerable times and locations in the different areas.     

A simple method for identifying and correcting crash problems on urban arterial streets

Urban arterials by their nature carry heavy traffic volumes and generate large numbers of motor vehicle crashes. The present study involved review of police crash reports to identify precrash events and driver actions for a sample of crashes on urban arterials and describes a method for reducing such crashes based on analyses of collision patterns and identification of locations with excessive numbers of crashes of a particular type. Police-reported crash data were obtained for three urban arterials in the Washington, DC metropolitan area. A total of 2013 crash reports were analyzed. Seven crash types accounted for nearly 90% of these reports. On each arterial studied, several locations with excessive numbers of crashes of a particular type were identified, and corresponding engineering countermeasures were recommended. Differences between the approach employed in this study and traditional blackspot analyses are discussed.     

A simulator study on the impact of traffic calming measures in urban areas on driving behavior and workload

This study examined the impact of traffic calming measures (TCM) on major roads in rural and urban areas. More specifically we investigated the effect of gate constructions located at the entrance of the urban area and horizontal curves within the urban area on driving behavior and workload. Forty-six participants completed a 34 km test-drive on a driving simulator with eight thoroughfare configurations, i.e., 2 (curves: present, absent) × 2 (gates: present, absent) × 2 (peripheral detection task (PDT): present, absent) in a within-subject design.     

Understanding crash mechanism on urban expressways using high-resolution traffic data

Urban expressways play a vital role in the modern mega cities by serving peak hour traffic alongside reducing travel time for moderate to long distance intra-city trips. Thus, ensuring safety on these roads holds high priority. Little knowledge has been acquired till date regarding crash mechanism on these roads. This study uses high-resolution traffic data collected from the detectors to identify factors influencing crash. It also identifies traffic patterns associated with different types of crashes and explains crash phenomena thereby. Unlike most of the previous studies on conventional expressways, the research separately investigates the basic freeway segments (BFS) and the ramp areas. The study employs random multinomial logit, a random forest of logit models, to rank the variables; expectation maximization clustering algorithm to identify crash prone traffic patterns and classification and regression trees to explain crash phenomena. As accentuated by the study outcome, crash mechanism is not generic throughout the expressway and it varies from the BFS to the ramp vicinities. The level of congestion and speed difference between upstream and downstream traffic best explains crashes and their types for the BFS, whereas, the ramp flow has the highest influence in determining the types of crashes within the ramp vicinities. The paper also discusses about the applicability of different countermeasures, such as, variable speed limits, temporary restriction on lane changing, posting warnings, etc., to attenuate different patterns of hazardous traffic conditions. The study outcome can be utilized in designing location and traffic condition specific proactive road safety management systems for urban expressways.     

Bayesian spatial joint modeling of traffic crashes on an urban road network

This study proposes a Bayesian spatial joint model of crash prediction including both road segments and intersections located in an urban road network, through which the spatial correlations between heterogeneous types of entities could be considered. A road network in Hillsborough, Florida, with crash, road, and traffic characteristics data for a three-year period was selected in order to compare the proposed joint model with three site-level crash prediction models, that is, the Poisson, negative binomial (NB), and conditional autoregressive (CAR) models. According to the results, the CAR and Joint models outperform the Poisson and NB models in terms of model fitting and predictive performance, which indicates the reasonableness of considering cross-entity spatial correlations. Although the goodness-of-fit and predictive performance of the CAR and Joint models are equivalent in this case study, spatial correlations between segments and the connected intersections are found to be more significant than those solely between segments or between intersections, which supports the employment of the Joint model as an alternative in road-network-level safety modeling.     

Multi-level Bayesian safety analysis with unprocessed Automatic Vehicle Identification data for an urban expressway

In traffic safety studies, crash frequency modeling of total crashes is the cornerstone before proceeding to more detailed safety evaluation. The relationship between crash occurrence and factors such as traffic flow and roadway geometric characteristics has been extensively explored for a better understanding of crash mechanisms. In this study, a multi-level Bayesian framework has been developed in an effort to identify the crash contributing factors on an urban expressway in the Central Florida area. Two types of traffic data from the Automatic Vehicle Identification system, which are the processed data capped at speed limit and the unprocessed data retaining the original speed were incorporated in the analysis along with road geometric information. The model framework was proposed to account for the hierarchical data structure and the heterogeneity among the traffic and roadway geometric data. Multi-level and random parameters models were constructed and compared with the Negative Binomial model under the Bayesian inference framework. Results showed that the unprocessed traffic data was superior. Both multi-level models and random parameters models outperformed the Negative Binomial model and the models with random parameters achieved the best model fitting. The contributing factors identified imply that on the urban expressway lower speed and higher speed variation could significantly increase the crash likelihood. Other geometric factors were significant including auxiliary lanes and horizontal curvature.     

Bayesian random effect models incorporating real-time weather and traffic data to investigate mountainous freeway hazardous factors

Freeway crash occurrences are highly influenced by geometric characteristics, traffic status, weather conditions and drivers’ behavior. For a mountainous freeway which suffers from adverse weather conditions, it is critical to incorporate real-time weather information and traffic data in the crash frequency study. In this paper, a Bayesian inference method was employed to model one year's crash data on I-70 in the state of Colorado. Real-time weather and traffic variables, along with geometric characteristics variables were evaluated in the models. Two scenarios were considered in this study, one seasonal and one crash type based case. For the methodology part, the Poisson model and two random effect models with a Bayesian inference method were employed and compared in this study. Deviance Information Criterion (DIC) was utilized as a comparison factor. The correlated random effect models outperformed the others. The results indicate that the weather condition variables, especially precipitation, play a key role in the crash occurrence models. The conclusions imply that different active traffic management strategies should be designed based on seasons, and single-vehicle crashes have different crash mechanism compared to multi-vehicle crashes.     

A residential location approach to traffic safety: two case studies from Germany

This paper aims to spatially differentiate the road accident risk associated with living at a certain place of residence. Official accident data usually record the place the accident occurred, but not the casualties’ places of residence. Among those involved in an accident at a certain place there may obviously be some non-residents, such as in-commuters and transients. Hence spatial analysis based on place of accident may not be suitable for drawing conclusions about specific risk levels for people living in certain places. People's risk of encountering an accident in areas other than that where they live may vary with their mobility.We report on two case studies for the German states of North Rhine-Westphalia and Lower Saxony, which are based on casualties’ places of residence. We draw on two data sets both of which have specific advantages and disadvantages. From the data we calculate population-based risk figures on the district level and, for Lower Saxony, on the municipality level. For North Rhine-Westphalia these are categorised by age group and transport mode. We also investigate to what extent accident related analyses can be used to estimate residential related risks. The results show that the risk of being killed or seriously injured in a road accident is considerably lower for the population of agglomeration cores than for the suburban and rural population. Macro-economically this means that suburban and rural areas have markedly higher accident costs than cities.     

A review of the effect of traffic and weather characteristics on road safety

Taking into consideration the increasing availability of real-time traffic data and stimulated by the importance of proactive safety management, this paper attempts to provide a review of the effect of traffic and weather characteristics on road safety, identify the gaps and discuss the needs for further research. Despite the existence of generally mixed evidence on the effect of traffic parameters, a few patterns can be observed. For instance, traffic flow seems to have a non-linear relationship with accident rates, even though some studies suggest linear relationship with accidents. On the other hand, increased speed limits have found to have a straightforward positive relationship with accident occurrence. Regarding weather effects, the effect of precipitation is quite consistent and leads generally to increased accident frequency but does not seem to have a consistent effect on severity. The impact of other weather parameters on safety, such as visibility, wind speed and temperature is not found straightforward so far. The increasing use of real-time data not only makes easier to identify the safety impact of traffic and weather characteristics, but most importantly makes possible the identification of their combined effect. The more systematic use of these real-time data may address several of the research gaps identified in this research.     

A conceptual framework for road safety and mobility applied to cycling safety

Scientific literature lacks a model which combines exposure to risk, risk, and the relationship between them. This paper presents a conceptual road safety framework comprising mutually interacting factors for exposure to risk resulting from travel behaviour (volumes, modal split, and distribution of traffic over time and space) and for risk (crash and injury risk). The framework's three determinants for travel behaviour are locations of activities; resistances (generalized transport costs); needs, opportunities, and abilities. Crash and injury risks are modelled by the three ‘safety pillars’: infrastructure, road users and the vehicles they use. Creating a link in the framework between risk and exposure is important because of the ‘non-linear relationship’ between them, i.e. risk tends to decrease as exposure increases. Furthermore, ‘perceived’ risk (a type of travel resistance) plays a role in mode choice, i.e. the perception that a certain type of vehicle is unsafe can be a deterrent to its use. This paper uses theories to explain how the elements in the model interact. Cycling is an area where governments typically have goals for both mobility and safety. To exemplify application of the model, the paper uses the framework to link research on cycling (safety) to land use and infrastructure. The model's value lies in its ability to identify potential consequences of measures and policies for both exposure and risk. This is important from a scientific perspective and for policy makers who often have objectives for both mobility and safety.     

A mixed generalized ordered response model for examining pedestrian and bicyclist injury severity level in traffic crashes

This paper proposes an econometric structure for injury severity analysis at the level of individual accidents that recognizes the ordinal nature of the categories in which injury severity are recorded, while also allowing flexibility in capturing the effects of explanatory variables on each ordinal category and allowing heterogeneity in the effects of contributing factors due to the moderating influence of unobserved factors. The model developed here, referred to as the mixed generalized ordered response logit (MGORL) model, generalizes the standard ordered response models used in the extant literature for injury severity analysis. To our knowledge, this is the first such formulation to be proposed and applied in the econometric literature in general, and in the safety analysis literature in particular.

The MGORL model is applied to examine non-motorist injury severity in accidents in the USA, using the 2004 General Estimates System (GES) database. The empirical findings emphasize the inconsistent results obtained from the standard ordered response model. An important policy result from our analysis is that the general pattern and relative magnitude of elasticity effects of injury severity determinants are similar for pedestrians and bicyclists. The analysis also suggests that the most important variables influencing non-motorist injury severity are the age of the individual (the elderly are more injury-prone), the speed limit on the roadway (higher speed limits lead to higher injury severity levels), location of crashes (those at signalized intersections are less severe than those elsewhere), and time-of-day (darker periods lead to higher injury severity).     

Application of propensity scores and potential outcomes to estimate effectiveness of traffic safety countermeasures: Exploratory analysis using intersection lighting data

More than 5.5 million police-reported traffic crashes occurred in the United States in 2009, resulting in 33,808 fatalities and more than 2.2 million injuries. Significant funds are expended annually by federal, state, and local transportation agencies in an effort to reduce traffic crashes. Effective safety management involves selecting highway and street locations with potential for safety improvements; correctly diagnosing safety problems; identifying appropriate countermeasures; prioritizing countermeasure implementation at selected sites; and, evaluating the effectiveness of implemented countermeasures. Accurate estimation of countermeasure effectiveness is a critical component of the safety management process. In this study, a statistical modeling framework, based on propensity scores and potential outcomes, is described to estimate countermeasure effectiveness from non-randomized observational data. Average treatment effects are estimated using semi-parametric estimation methods. To demonstrate the framework, the average treatment effect of fixed roadway lighting at intersections in Minnesota is estimated. The results indicate that fixed roadway lighting reduces expected nighttime crashes by approximately 6%, which compares favorably to other, recent lighting-safety research findings.     

Culture related to road traffic safety: A comparison of eight countries using two conceptualizations of culture

The majority of previous cross-country studies of human factors relevant to traffic safety have not operationalized and measured culture. Also studies in this vein have mostly been carried out in Europe and the United States. The aim of the study was to examine country cluster differences, based on the Culture's Consequences framework, in road traffic risk perception, attitudes towards traffic safety and driver behaviour in samples from Norway, Russia, India, Ghana, Tanzania, Uganda, Turkey and Iran. An additional aim was to examine cluster differences in road traffic culture as symbol use and to investigate whether this theoretical cultural framework predicts risk perception, attitudes towards traffic safety and driver behaviour in the country clusters. The sample consisted of a total of 2418 individuals who were obtained by convenience sampling in the different countries. The countries segmented into four Culture's Consequences clusters; Norway, Russia and India, Sub-Saharan Africa, and Near East countries. The findings showed that Norwegians reported overall safer attitudes towards traffic safety and driver behaviour than the remaining country clusters. Individuals in Africa reported the highest risk perception. The countries also differed substantially in road traffic culture as symbol use. Contrary to established cultural theory, prediction models revealed that cultural factors were stronger predictors of driver behaviour than of risk perception. Also, the social cognitive risk constructs (i.e. risk perception and attitudes) solely explained variance in driver behaviour in the Norwegian and Russia/India clusters. Previous empirical efforts, which aimed to demonstrate that culture is important for the risk perception criterion, may have focused on a criterion variable that is not strongly related to driver behaviour. Furthermore, countermeasures aimed to influence social cognition may have stronger applicability in countries with a more individualistic western cultural orientation.     

A full Bayes multivariate intervention model with random parameters among matched pairs for before-after safety evaluation

The objective of this study is to evaluate the safety performance of a sample of intersections that have been improved with the implementation of certain safety countermeasures in the Greater Vancouver area. A full Bayes approach is utilized to determine the effectiveness of the improvements using a before–after design with matched (yoked) comparison groups. A multivariate Poisson-lognormal intervention model is used for the analysis of crash counts by severity levels. The model is extended to incorporate random parameters to account for the correlation between sites within comparison-treatment pairs. The full Bayes analysis revealed that incorporating such design features as matched comparison groups in the specification of safety performance functions can significantly improve the fit, while reducing the estimates of the extra-Poisson variation. As well, such extended models can be used to account for heterogeneity due to unobserved road geometrics, traffic characteristics, environmental factors and driver behavior. The results showed that the overall odds ratios for injuries and fatalities (I + F) and property damage only (PDO) imply significant reductions in predicted crash counts of 23% and 15%, respectively. The corresponding credible intervals were (12%, 33%) and (6%, 24%) at the 0.95 confidence level. The majority of the site-level odds ratio exhibited reductions in both I + F and PDO predicted crash counts. However, only some of these reductions were significant. As well, the effectiveness of the treatment seems to vary by severity level from one location to another. For I + F, the crash reduction factors were 29%, 15% and 21% for improving signal visibility, left turn phase improvement and left turn lane installation, respectively. The corresponding crash reduction factors for PDO were 21%, 4% and 20%, respectively.     

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.     

Effects on speed and safety of point-to-point speed enforcement systems: Evaluation on the urban motorway A56 Tangenziale di Napoli

In this paper, we evaluated the effects on speed and safety of the point-to-point (P2P) speed enforcement system activated on the urban motorway A56 in Italy. The P2P speed enforcement is a relatively new approach to traffic law enforcement that involves the calculation of the average speed over a section. To evaluate the speed effects, we performed a before–after analysis of speed data investigating also effects on non-compliance to speed limits. To evaluate the safety effects, we carried out an empirical Bayes observational before-and-after study.     

A concurrent model reduction approach on spatial and random domains for the solution of stochastic PDEs

A methodology is introduced for rapid reduced‐order solution of stochastic partial differential equations. On the random domain, a generalized polynomial chaos expansion (GPCE) is used to generate a reduced subspace. GPCE involves expansion of the random variable as a linear combination of basis functions defined using orthogonal polynomials from the Askey series. A proper orthogonal decomposition (POD) approach coupled with the method of snapshots is used to generate a reduced solution space from the space spanned by the finite element basis functions on the spatial domain. POD methods have been extremely popular in fluid mechanics applications and have subsequently been applied to other interesting areas. They have been shown to be capable of representing complicated phenomena with a handful of degrees of freedom. This concurrent model reduction on the random and spatial domains is applied to stochastic partial differential equations (PDEs) in natural convection processes involving randomness in the porosity of the medium and the Rayleigh number. The results indicate that owing to the multiplicative nature of the concurrent model reduction, extremely large computational gains are realized without significant loss of accuracy. Copyright © 2005 John Wiley & Sons, Ltd.