Traffic Control Features Research Articles

A hazard-based model to derive travel time under congested conditions

Increasing congestion levels and their elusive impact warrants the development of congestion mitigation strategies. Quantifying congestion and analyzing the spatiotemporal variations is imperative to achieve this target. Travel time is being explored as a measure of congestion with the advent of Intelligent Transportation Systems (ITS) and the deployment of related technologies. Researchers identified congested conditions when the average travel time exceeds 1.33 or 1.66 times free-flow travel time (FFTT). It is well known that travel time under congested conditions (Tc) is more sensitive to land use, road geometry, and traffic control characteristics than the FFTT. Therefore, the extension of FFTT to derive Tc may not be appropriate. This study focuses on developing a hazard-based model to derive Tc. Travel time is modeled using a parametric accelerated failure time (AFT) model. The applicability of the proposed methodology is justified using empirical and simulated datasets. The Tc derived from the AFT model is close to the travel time for the level of service (LOS F). Based on the Tc, a new measure of congestion, termed congestion index (CI), is proposed. The proposed index can quantify the frequency and intensity of congestion on a link or network. The traffic states identified based on CI were mapped on the fundamental diagram (FD) and the macroscopic fundamental diagram (MFD). It was concluded that if travel times are uncertain and unstable under low-density conditions, then the ascending leg of the FD or MFD can be marked congested. Uncertain and unstable travel times indicate that traffic flow is unstable, and therefore, it can be concluded that traffic instabilities significantly affect congestion.

New Intersection Crash Prediction Models for the Second Edition of the Highway Safety Manual

The objective of this research was to develop new intersection crash prediction models for consideration in the second edition of the Highway Safety Manual (HSM), consistent with existing methods in HSM Part C and comprehensive in their ability to address a wide range of intersection configurations and traffic control types in rural and urban areas. The focus of the research was on developing safety performance functions (SPFs) for intersection configurations and traffic control types not currently addressed in HSM Part C. SPFs were developed for the following general intersection configurations and traffic control types: rural and urban all-way stop-controlled intersections; rural three-leg intersections with signal control; intersections on high-speed urban and suburban arterials (i.e., arterials with speed limits greater than or equal to 50 mph); urban five-leg intersections with signal control; three-leg intersections where the through movements make turning maneuvers at the intersections; crossroad ramp terminals at single-point diamond interchanges; and crossroad ramp terminals at tight diamond interchanges. Development of severity distribution functions (SDFs) for use in combination with SPFs to estimate crash severity as a function of geometric design elements and traffic control features was explored; but owing to challenges and inconsistencies in developing and interpreting the SDFs, it was recommended for the second edition of the HSM that crash severity for the new intersection configurations and traffic control types be addressed in a manner consistent with existing methods in Chapters 10, 11, and 12 of the first edition, without use of SDFs.

Mixed logit approach to analyzing pedestrian injury severity in pedestrian-vehicle crashes in North Carolina: Considering time-of-day and day-of-week

Objective The objective of this research is to identify and compare contributing factors to pedestrian injury severities in pedestrian-vehicle crashes considering both time-of-day and day-of-week. Methods The pedestrian-vehicle crash data are collected from 2007 to 2018 in North Carolina with categorical factors of pedestrian, driver, vehicle type, crash group, geography, environment, and traffic control characteristics. The final dataset includes 17,904 observations with 69 categorized variables. Four mixed logit models are developed to analyze the crash dataset with segmentations of weekday daytime, weekday nighttime, weekend daytime, and weekend nighttime. Results A total number of 31 fixed significant factors and 6 random parameter factors to the pedestrian injury severity are detected in four mixed logit models. According to marginal effects, large vehicle involved, pedestrians with age over 65, hit and run, drunk pedestrian, down/dusk light, dark without roadside light, and industrial land use are identified as the contributing factors that result in more than a 0.08 increase in the probability of fatal injury. Compared to the daytime, most factors are found to have more impact on severe injuries in the nighttime. Also, most factors are found to result in more severe injuries on weekends than on weekdays. Conclusions This study identifies and compares the factors to pedestrian injury severity in pedestrian-vehicle crashes considering the temporal variance in time-of-day (i.e., daytime vs. nighttime) and day-of-week (i.e., weekdays vs. weekends). Random effects are explored in mixed logit models. Differences and possible reasons for the significant factors’ impact within and across time-of-day and day-of-week are also investigated. Corresponding countermeasures and suggestions to mitigate the impacts of major factors are also discussed, which give practical guidance to planners and engineers, and provide a solid reference to further explore the temporal variance of the crash data.

Exploring pedestrian injury severities at pedestrian-vehicle crash hotspots with an annual upward trend: A spatiotemporal analysis with latent class random parameter approach

Introduction: With the increasing trend of pedestrian deaths among all traffic fatalities in the past decade, there is an urgent need for identifying and investigating hotspots of pedestrian-vehicle crashes with an upward trend. Method: To identify pedestrian-vehicle crash locations with aggregated spatial pattern and upward temporal pattern (i.e., hotspots with an upward trend), this paper first uses the average nearest neighbor and the spatial autocorrelation tests to determine the grid distance and the neighborhood distance for hotspots, respectively. Then, the spatiotemporal analyses with the Getis-Ord Gi* index and the Mann-Kendall trend test are utilized to identify the pedestrian-vehicle crash hotspots with an annual upward trend in North Carolina from 2007 to 2018. Considering the unobserved heterogeneity of the crash data, a latent class model with random parameters within class is proposed to identify specific contributing factors for each class and explore the heterogeneity within classes. Significant factors of the pedestrian, vehicle, crash type, locality, roadway, environment, time, and traffic control characteristics are detected and analyzed based on the marginal effects. Results: The heterogeneous results between classes and the random parameter variables detected within classes further indicate the superiority of latent class random parameter model. Practical Applications: This paper provides a framework for researchers and engineers to identify crash hotspots considering spatiotemporal patterns and contribution factors to crashes considering unobserved heterogeneity. Also, the result provides specific guidance to developing countermeasures for mitigating pedestrian-injury at pedestrian-vehicle crash hotspots with an upward trend.

Safety Performance of One-Way Arterials

Safety analysts are generally interested in understanding the differences in the safety performance when a two-way street is converted to a one-way operation or vice-versa. Literature exists to understand and predict the safety of two-way streets. However, safety prediction procedures are currently not available for assessing the safety performance of one-way arterials. This research was undertaken to develop safety prediction models for one-way arterials. To accomplish this objective, data collected in California, Illinois, Michigan, Oregon, and Texas were assembled that included a wide range of geometric design features, traffic control features, traffic characteristics, and crash records. The data were used to calibrate predictive models, each of which included a safety performance function (SPF) and several crash modification factors (CMFs). Separate SPFs were developed for fatal and injury crashes (i.e., fatal, incapacitating injury, non-incapacitating injury, and possible injury crash) and property-damage-only crashes. The SPFs were estimated using the negative binomial modeling structure. Severity distribution functions (SDFs) were also calibrated using the fatal and injury data. These functions can be used with the predictive models to estimate the expected crash frequency for each of four injury severity levels.

Investigation of the Factors Affecting the Crash Frequency at Four-Legged Signalized Urban Intersections

Kavşaklar kentsel yol ağı üzerinde çok az yer kaplamalarına rağmen trafik kazalarının yoğunlaştığı bölgeleri oluşturmaktadır. Bu çalışmada Mersin kent merkezinde sinyalizasyon ile yönetilen dört kollu 39 kavşakta, 2014-2016 yılları arasındaki sadece taşıtların karıştığı ölümlü/yaralanmalı trafik kazaları ile trafik hacmi, kavşak geometrisi ve trafik kontrol özellikleri arasındaki ilişki incelenmiştir. Bu amaçla Poisson ve negatif binom (NB) regresyon modelleri kullanılmıştır. Sonuçlar kavşağa yaklaşan toplam şerit sayısının artmasının kazaları arttırdığını göstermiştir. Öte yandan, birincil kavşak kollarındaki trafiğin, ikincil kavşak kollarındaki trafiğe oranının atması ve birincil kavşak kollarındaki orta refüj kavşaklardaki kaza sayısını azaltmaktadır.

Datacenter Traffic Control: Understanding Techniques and Tradeoffs

Datacenters provide cost-effective and flexible access to scalable compute and storage resources necessary for today's cloud computing needs. A typical datacenter is made up of thousands of servers connected with a large network and usually managed by one operator. To provide quality access to the variety of applications and services hosted on datacenters and maximize performance, it deems necessary to use datacenter networks effectively and efficiently. Datacenter traffic is often a mix of several classes with different priorities and requirements. This includes user-generated interactive traffic, traffic with deadlines, and long-running traffic. To this end, custom transport protocols and traffic management techniques have been developed to improve datacenter network performance. In this tutorial paper, we review the general architecture of datacenter networks, various topologies proposed for them, their traffic properties, general traffic control challenges in datacenters and general traffic control objectives. The purpose of this paper is to bring out the important characteristics of traffic control in datacenters and not to survey all existing solutions (as it is virtually impossible due to massive body of existing research). We hope to provide readers with a wide range of options and factors while considering a variety of traffic control mechanisms. We discuss various characteristics of datacenter traffic control including management schemes, transmission control, traffic shaping, prioritization, load balancing, multipathing, and traffic scheduling. Next, we point to several open challenges as well as new and interesting networking paradigms. At the end of this paper, we briefly review inter-datacenter networks that connect geographically dispersed datacenters which have been receiving increasing attention recently and pose interesting and novel research problems.

The impact of controller support tools in enroute air traffic control on cognitive error modes: A comparative analysis in two operational environments

Air traffic is increasing worldwide. In order to accommodate the anticipated rapid growth in air traffic in the future, changes are required to increase the capacity of the airspace. Although major structural changes in air traffic management in Europe are still underway through the implementation of 4D trajectories, many changes happened and are happening now through the introduction of new automated features in the enroute air traffic control systems. Examples of such air traffic control features are electronic coordination and conflict detection tools designed for air traffic controllers. Although it is likely that these controller support tools may decrease the possibilities of cognitive error, the introduction of new tasks may also introduce new sources of cognitive error. This paper describes the results of a qualitative analysis conducted in two European Area Control Centers, and compares possible cognitive error modes using the TRACEr method. The results show that for an operational environment equipped with controller support tools, the cognitive errors that may occur, have changed. Errors related to detection, memory, decision-making and action execution may decrease. However, new tasks related to the controller support tools may also introduce new errors such as those related to timely detection of information. Furthermore, the results show that there is a shift in type of errors which may occur during the execution of tasks. System safety may be increased through eliminating or reducing possibilities for cognitive error, increasing error recovery opportunities and indirectly through reducing mental workload related to the execution of tasks.

Methodology to develop crash modification functions for road safety treatments with fully specified and hierarchical models

Crash modification factors (CMFs) for road safety treatments are developed as multiplicative factors that are used to reflect the expected changes in safety performance associated with changes in highway design and/or the traffic control features. However, current CMFs have methodological drawbacks. For example, variability with application circumstance is not well understood, and, as important, correlation is not addressed when several CMFs are applied multiplicatively. These issues can be addressed by developing safety performance functions (SPFs) with components of crash modification functions (CM-Functions), an approach that includes all CMF related variables, along with others, while capturing quantitative and other effects of factors and accounting for cross-factor correlations. CM-Functions can capture the safety impact of factors through a continuous and quantitative approach, avoiding the problematic categorical analysis that is often used to capture CMF variability. There are two formulations to develop such SPFs with CM-Function components – fully specified models and hierarchical models. Based on sample datasets from two Canadian cities, both approaches are investigated in this paper. While both model formulations yielded promising results and reasonable CM-Functions, the hierarchical model was found to be more suitable in retaining homogeneity of first-level SPFs, while addressing CM-Functions in sub-level modeling. In addition, hierarchical models better capture the correlations between different impact factors.

Analysis of Injury Severity in Crashes on Ramps and at Crossroad Ramp Terminals

Travel along a ramp can present drivers with complex alignment changes and decision points. These changes and complexities can increase the potential for conflict or crash, especially for larger vehicles. A few studies have examined the effect of variables on ramp crash frequency. However, none of these studies considered the effect of variables on crash severity distributions. Relatively little or no information is available about the safety effects of design elements on the severity of ramp crashes. In some cases, countermeasures (such as longitudinal barriers) are implemented to reduce fatal crashes, but the effect of these treatments on less severe crashes is not well understood. New research has been conducted to develop severity distribution functions (SDFs) for ramp segments and signalized and unsignalized crossroad ramp terminals for predicting the proportion of crashes in each severity category as a function of roadway geometric design elements and traffic control features. The SDFs were calibrated with data from California, Maine, and Washington State. The findings from this research show that barrier presence, the number of through lanes, area type, and ramp type influence the proportion of high-severity crashes on ramp segments. At the same time, access point frequency, left-turn operation, the presence of nonramp public street leg, and area type influence the proportion of high-severity crashes at crossroad ramp terminals. These SDFs can be applied along with safety performance functions and crash modification factors to obtain more precise estimates of the safety effects of design decisions.

INTERCHANGES SAFETY: FORECAST MODEL BASED ON ISAT ALGORITHM

The ISAT algorithm (Interchange Safety Analysis Tool), developed by the Federal Highway Administration (FHWA), provides design and safety engineers with an automated tool for assessing the safety effects of geometric design and traffic control features at an existing interchange and adjacent roadway network. Concerning the default calibration coefficients and crash distributions by severity and type, the user should modify these default values to more accurately reflect the safety experience of their local/State agency prior to using ISAT to perform actual safety assessments. This paper will present the calibration process of the FHWA algorithm to the local situation of Oriental Sicily. The aim is to realize an instrument for accident forecast analyses, useful to highway managers, in order to individuate those infrastructural elements that can contribute to improve the safety level of interchange areas, if suitably calibrated.

Posted speed limit: To include or not to include in operating speed models

Models of motorized vehicle operating speeds are used to assess the design consistency of planned or existing highway alignments or to quantify the potential speed outcomes of a design or traffic control decision. These models generally estimate the mean or 85th-percentile speed of free-flow vehicles as a function of the geometric design elements, traffic control, or other features present along the roadway. Models for measures of speed dispersion (e.g., standard deviation of speed) have been considered in more recent research. There are differing viewpoints concerning the inclusion of the posted speed limit as an independent variable in operating speed models. Some have excluded posted speed limit from operating speed prediction models, indicating that it was highly correlated with the geometric design elements also included as independent variables in the model. The other viewpoint is that the posted speed limit logically influences operating speeds, and should therefore be included in speed prediction models. This paper explores this issue using an econometric modeling approach, considering irrelevant variables, multicollinearity, omitted variable bias, and endogeneity bias. The results suggest that the posted speed limit should be treated as an exogenous variable in models of operating speed, including models of speed magnitude and speed dispersion.

Investigation cause of accident using Terrestrial Laser scanner

The aim of traffic reconstruction is to recreate motor vehicle collision scenes in order to analyze the dynamics of the collision events, to provide evidence in court cases and allow the manufacturers to evaluate the vehicle’s design. However, at the scene of the accident it is not known exactly the amount of information that will be required for the analysis and often relevant data are missing. The emergence of terrestrial laser scanning has enabled the 3D documentation of accident events in a safer way, as information can be collected without any lane closures or traffic interruptions, and in a more flexible and faster way, as a single user can operate the instrument in complete darkness or direct sunlight. The notion of this paper is to model accident scene using terrestrial laser scanning data. Terrestrial laser scanning provides the capability of recording such infrastructures in a fast and reliable way, where a number of different information, including traffic control features, volumes, accident characteristics and detailed spatial data, can be easily obtained. This information can then be integrated within traffic management integrated systems and be used for accident prevention purposes.

Exploring endogeneity of macroscopic speed parameters: empirical study during low volume conditions in construction work zones

This paper uses a simultaneous equation modeling approach to explore the relationships between mean speed, standard deviation of speed and work zone design characteristics. Data for model estimation were collected in 17 work zones on four-lane, divided freeways in Pennsylvania and Texas. The three-stage least squares estimator was used, as it is both consistent and efficient in the presence of endogeneity and error covariance. The fixed effects and random effects estimators with instrumental variables were also used to address possible unobserved heterogeneity. Results pointed towards a truly simultaneous structure, where mean speed is determined by exogenous design and traffic control features and the standard deviation of speed and the standard deviation of speed is dependent on design and traffic control features as well as mean speed. A number of work zone design and traffic control features directly influenced both speed parameters. Findings of this research indicate the relationship between speed magnitude, speed dispersion and work zone design and traffic control features is more complex than documented generalizations made in current work zone design and traffic control decision processes.

Severity Distribution Functions for Freeway Segments

To date, the focus of modeling efforts for freeway safety has been on developing safety prediction functions and crash modification factors, with only limited consideration for crash severity distributions. As a result, relatively little is known about the safety effects of design elements such as lane width, rumble strips, and longitudinal barriers on crash severity. In some cases, countermeasures are implemented with the intent to reduce fatal crashes, but the effect of these treatments on less severe crashes is not well understood. Research was conducted to develop severity distribution functions (SDFs) to predict the proportion of crashes in each severity category as a function of roadway geometric design elements and traffic control features. The SDFs were calibrated with freeway segment data from California, Maine, and Washington State. The findings from this research show that barrier presence, increased traffic volume, increased lane width, and urban area type reduce the proportion of high-severity crashes. At the same time, the presence of rumble strips and horizontal curvature increases the proportion of high-severity crashes. These SDFs can be applied along with safety prediction functions and crash modification factors to obtain more precise estimates of the safety effects of design decisions.

Speed Optimization in Unplanned Traffic Using Bio-Inspired Computing and Population Knowledge Base

Bio-Inspired Algorithms on Road Traffic Congestion and safety is a very promising research problem. Searching for an efficient optimization method to increase the degree of speed optimization and thereby increasing the traffic Flow in an unplanned zone is a widely concerning issue. However, there has been a limited research effort on the optimization of the lane usage with speed optimization. The main objective of this article is to find avenues or techniques in a novel way to solve the problem optimally using the knowledge from analysis of speeds of vehicles, which, in turn will act as a guide for design of lanes optimally to provide better optimized traffic. The accident factors adjust the base model estimates for individual geometric design element dimensions and for traffic control features. The application of these algorithms in partially modified form in accordance of this novel Speed Optimization Technique in an Unplanned Traffic analysis technique is applied to the proposed design and speed optimization plan. The experimental results based on real life data are quite encouraging.

Older drivers’ crashes in Queensland, Australia

BackgroundThe growing proportion of older adults in Australia is predicted to comprise 23% of the population by 2030. Accordingly, an increasing number of older drivers and fatal crashes of these drivers could also be expected. While the cognitive and physiological limitations of ageing and their road safety implications have been widely documented, research has generally considered older drivers as a homogeneous group. Knowledge of age-related crash trends within the older driver group itself is currently limited. ObjectiveThe aim of this research was to identify age-related differences in serious road crashes of older drivers. This was achieved by comparing crash characteristics between older and younger drivers and between sub-groups of older drivers. Particular attention was paid to serious crashes (crashes resulting in hospitalisation and fatalities) as they place the greatest burden on the Australian health system. MethodUsing Queensland Crash data, a total of 191,709 crashes of all-aged drivers (17–80+) over a 9-year period were analysed. Crash patterns of drivers’ aged 17–24, 25–39, 40–49, 50–59, 60–69, 70–79 and 80+ were compared in terms of crash severity (e.g., fatal), at fault levels, traffic control measures (e.g., stop signs) and road features (e.g., intersections). Crashes of older driver sub-groups (60–69, 70–79, 80+) were also compared to those of middle-aged drivers (40–49 and 50–59 combined, who were identified as the safest driving cohort) with respect to crash-related traffic control features and other factors (e.g., speed). Confounding factors including speed and crash nature (e.g., sideswipe) were controlled for. Results and discussionResults indicated that patterns of serious crashes, as a function of crash severity, at-fault levels, road conditions and traffic control measures, differed significantly between age groups. As a group, older drivers (60+) represented the greatest proportion of crashes resulting in fatalities and hospitalisation, as well as those involving uncontrolled intersections and failure to give way. The opposite was found for middle-aged drivers, although they had the highest proportion of alcohol and speed-related crashes when compared to older drivers. Among all older drivers, those aged 60–69 were least likely to be involved in or the cause of crashes, but most likely to crash at interchanges and as a result of driving while fatigued or after consuming alcohol. Drivers aged 70–79 represented a mid-range level of crash involvement and culpability, and were most likely to crash at stop and give way signs. Drivers aged 80 years and beyond were most likely to be seriously injured or killed in, and at-fault for, crashes, and had the greatest number of crashes at both conventional and circular intersections. Overall, our findings highlight the heterogeneity of older drivers’ crash patterns and suggest that age-related differences must be considered in measures designed to improve older driver safety.

Analysis of Pedestrian Exposure to Risk in Relation to Crossing Behavior

The objective of this research was to analyze pedestrian exposure to risk along urban trips in relation to pedestrian crossing behavior. First, an appropriate microscopic indicator was selected for the estimation of pedestrian exposure to risk while a person crossed a road at isolated locations. This indicator expressed exposure as the number of vehicles encountered by pedestrians during the crossing of a single uncontrolled road lane and could be further adapted and applied for various road design and traffic control features. Moreover, the number and type of crossings along a pedestrian trip could be identified on the basis of the trip length and topology, whereas the set of choices of alternative crossing locations for each crossing decision could also be defined. The crossing probability associated with each alternative location along the trip could then be estimated by means of a sequential logit model. A method is presented for the estimation of exposure to risk for pedestrians along a trip in relation to their crossing behavior. The proposed approach was demonstrated in a pilot implementation for a typical pedestrian trip in the center of Athens, Greece, for four scenarios combining different traffic conditions and pedestrian walking speed. The results showed that exposure to risk for pedestrians along a trip was significantly affected by the pedestrians' crossing choices, as well as by road and traffic characteristics. Results also revealed that pedestrians with increased walking speed might have partly compensated for their exposure to risk so that the risk was not significantly affected by traffic volume. Moreover, specific locations with increased pedestrian exposure to risk could be identified for each trip. The proposed microscopic analysis of exposure to risk proved advantageous compared with existing macroscopic ones. This microscopic analysis revealed the different possible definitions and aspects of pedestrian exposure, with useful implications for road safety analysis.

Modeling Speed Behavior of Passenger Cars and Trucks in Freeway Construction Work Zones: Implications on Work Zone Design and Traffic Control Decision Processes

Current work zone design and traffic control guidance is based on desirable speed outcomes; knowledge related to actual speed outcomes of design and traffic control decisions is limited. The objective of this research is to investigate relationships among speed behavior, roadway geometrics, and traffic control in freeway construction work zones. Four speed measures are modeled: 85th percentile passenger car speed, 85th percentile truck speed, passenger car speed deviation, and truck speed deviation. The research objective is accomplished through specification and estimation of a first-order autoregressive seemingly unrelated regression model to account for contemporaneous correlations between equation disturbances and positive autocorrelation. Data for model estimation were collected at 119 locations in 17 Pennsylvania and Texas work zones. Model estimation results show that several work design and traffic control features influence speed magnitudes and deviations. Findings also indicate that relationships among speed magnitude, speed deviation, work zone design, and traffic control are more complex than general engineering intuitions expressed in current work zone design guidance.

Artificial Neural Network Speed Profile Model for Construction Work Zones on High-Speed Highways

Speed profile models can be used as a diagnostic tool by highway engineers. Previous work has investigated relationships between operating speed and geometric roadway elements for permanent roadway conditions on predominantly two-lane rural roads. However, little research of this type has been directed specifically toward construction work zones. In this study, a speed profile model for construction work zones on high speed highways was developed using artificial neural networks and made available for use by practitioners through a MS EXCEL interface. The model inputs include horizontal and vertical alignment variables, cross section dimensions and traffic control features. A linear reference system is used for model input and output. Three categories of vehicles—cars, trucks, and all vehicles—were used in this study. Models for the 15th percentile speed, mean speed, and 85th percentile speed were developed.