Roadway Facilities Research Articles

Study on Wind Speed Distribution Law of Mine Roadway Section under Different Conditions

Ventilation is the cornerstone of coal mine safety production, and accurate monitoring of ventilation parameters is the basic guarantee for ventilation technology decision-making. Aiming at the problem that the accuracy of wind speed monitoring in coal mine roadway is difficult to meet the needs of decision-making, this paper studies the position relationship between the average wind speed line and the roadway wall surface by combining theoretical calculation and numerical simulation, and simulates the wind speed distribution law of typical roadway section. The main factors affecting the distribution of wind speed in roadway section include roadway section shape, section size, support form, wind speed, facilities and equipment status, etc. The expression of the position relationship between the average wind speed line of the rectangular and semi-circular arch roadway section and the roadway wall surface is y=elna-1.5. Through 25 groups of model simulation, the wind speed distribution law of different types of roadway sections under different conditions is obtained: the wind speed contour is basically parallel to the roadway wall. The wind speed gradient near the roadway wall is large, and the wind speed gradient in the middle of the roadway is small. The thickness of the boundary layer decreases with the increase of wind speed.

Unveiling pre-crash driving behavior common features based upon behavior entropy

Driving behavior is considered as the primary crash influencing factor, whereas studies claimed that over 90% crashes were attributed by behavior features. Therefore, unveil pre-crash driving behavior features is of great importance for crash prevention. Previous studies have established the correlations between features such as vehicle speed, speed variability, and the probability of crash occurrences, but these analyses have concluded inconsistent results. This is due to the varying operating characteristics among roadway facilities, where given the same driving behavior statistical features, the corresponding traffic states are not identical. In this study, a behavioral entropy index was proposed to address the abovementioned issue. First, through comparing the individual driving behavior with the group distribution, behavioral entropy index was calculated to quantify the abnormality of driving behavior. Then, crash classification models were established by comparing the behavioral entropy prior to crash events and normal driving conditions. The empirical analyses have been conducted based on 1,634,770 naturalistic driving trajectories and 1027 crash events. And models have been carried out for urban roadway sections, urban intersections, and highway sections separately. The results showed that utilizing the behavior entropy instead of the statistical features could enhance the crash classification accuracy by 11.3%. And common pre-crash features of increased behavioral entropy were identified. Moreover, the speed coefficient of variation (QCV) entropy was concluded as the most influencing factor, which can be used for real-time driving risk monitoring and enables individual-level hazard mitigation.

LEVEL OF SERVICE ANALYSIS OF PEDESTRIAN CROSSING FACILITIES AT SIGNALIZED INTERSECTIONS UNDER HETEROGENEOUS TRAFFIC CONDITIONS

While designing roadway facilities the pedestrian crosswalks must be given high importance, because these are the locations where pedestrians interact with vehicular traffic directly. This study aims to determine Pedestrian Perception values (PV) and assess the Level of Service (LOS) for crosswalk facilities provided for pedestrians at signalized intersections in heterogeneous traffic conditions. For this purpose, 24 crosswalks of six signalized intersections were chosen: 16 crosswalk locations were used for model development and eight crosswalk locations were used for model validation. The perception value of pedestrians was collected by conducting a questionnaire survey and traffic data was collected using a videographic survey. The parameters considered for the present study are: pedestrian volume, time is taken by the pedestrian to cross the crosswalk, length of crosswalk, pedestrian crossing speed, vehicular volume, and turning movements. The present research work proposed a mathematical model for Perception Value (PV) to assess the LOS of crosswalks of signalized intersections in urban areas. The results of the study are helpful in enhancing pedestrian safety and comfort at crosswalks.

LEVEL OF SERVICE ANALYSIS OF PEDESTRIAN CROSSING FACILITIES AT SIGNALIZED INTERSECTIONS UNDER HETEROGENEOUS TRAFFIC CONDITIONS

While designing roadway facilities the pedestrian crosswalks must be given high importance, because these are the locations where pedestrians interact with vehicular traffic directly. This study aims to determine Pedestrian Perception values (PV) and assess the Level of Service (LOS) for crosswalk facilities provided for pedestrians at signalized intersections in heterogeneous traffic conditions. For this purpose, 24 crosswalks of six signalized intersections were chosen: 16 crosswalk locations were used for model development and eight crosswalk locations were used for model validation. The perception value of pedestrians was collected by conducting a questionnaire survey and traffic data was collected using a videographic survey. The parameters considered for the present study are: pedestrian volume, time is taken by the pedestrian to cross the crosswalk, length of crosswalk, pedestrian crossing speed, vehicular volume, and turning movements. The present research work proposed a mathematical model for Perception Value (PV) to assess the LOS of crosswalks of signalized intersections in urban areas. The results of the study are helpful in enhancing pedestrian safety and comfort at crosswalks.

Identifying the Critical Risk Factors for Road Crashes Based on Large-Scale Safety Audits in India

Road transportation is considered one of the most essential factors for the growth of a country. Due to the exponential growth in population and fast urbanization, the existing roadway facilities are facing a huge surge in vehicle growth. But this development is also accompanied by the concerning growth of road crashes. To mitigate road crashes, one needs to identify the potential risk factors involved with them. This requires detailed accident data, which is sometimes difficult to acquire and in addition, there is a high probability of accidents in many places due to various hazardous situations, but there may not be an accident record yet. Road Safety Audit is an alternative approach that can overcome these obstacles. Hence, the present study has attempted to highlight various risk factors involved with road crashes for four different road facilities type, viz. Straight, intersection, curve, and road with culvert and flyovers (RCF) by conducting the Road Safety Audit for 216 black spots. Analytic Hierarchy Process (AHP) was performed on the data collected from the audit process to determine the weightage of involvement of each factor in road crashes and identify the most significant ones.

An equilibrium approach for compensating public–private partnership concessionaires for reduced tolls during roadway maintenance

This study considers the possibility of exploiting excess capacity along concurrent tolled roadway facilities operating in a public–private partnership for the purpose of alleviating traffic congestion during improvement action execution on a public roadway facility. Increased access to the tolled facility is enabled by reducing or suspending tolls during periods of roadway improvement action and appropriately compensating the tolled facility concessionaire for expected losses. The problem of determining an optimal reduced toll pricing strategy and compensation level is referred to as the Maintenance Timing and Price Discovery (MTPD) problem. A multi-level optimization model and iterative solution method are proposed to solve this problem. As toll prices often depend on traffic levels, the model also determines the optimal timing and time-of-day and day-of-week for executing the set of required improvement actions over the planning horizon to ensure a lowest total cost. The multi-level model consists of two bilevel models. Solution of the greater model is attained at a Stackelberg equilibrium between these two bilevel models. An additional Stackelberg equilibrium is sought between upper and lower levels of each of these two bilevel models individually. The MTPD formulation and general solution methodology are applied on an illustrative example from which computational results are obtained. Insights from the results show that this strategy for alleviating traffic congestion during roadway maintenance or other improvement activities can lead to substantial savings in total agency and user costs.

Design of Traffic Intersection

bstract: Transportation contributes to the economic, industrial, social and cultural development of any country. Traffic congestion is a major problem due to the unexpected growth of private vehicles in a country. The intersection is the most important part in any highway, Because the traffic congestion is due to this. An intersection is defined as the general area where two or more highways join or cross, within which are included the roadway and roadside facilities for traffic movements in that area. In this project, an intersection at Tirupur was taken for analysis. All the necessary traffic parameters of the intersection was collected and analyzed. The intersection is designed as per IRC standards ensuring safe and efficient movement of vehicles at intersection

Traffic Density Estimation Methods for Uninterrupted Roadway Facilities: Review and Guidelines

Traffic density is an essential parameter for assessing the performance of uninterrupted facilities during planning, design and operations. This study reviews and evaluates existing density estimation methods. The density estimation methods are classified into two categories: definition of concept (DOC) methods and automated methods. The traditional approach includes aerial photographic, fundamental traffic-flow equation, Edie’s, and cumulative input-output methods. The automated approach includes the occupancy method, filtering method, and integrated method. In addition, the strengths, weaknesses, opportunities, and threats (SWOT) analysis for the preceding methods is conducted. The SWOT analysis will be helpful to researchers and practitioners in selecting the appropriate density estimation method based on its relative merits. Finally, guidelines for using various density methods for heterogeneous and homogeneous traffic are presented.

Modeling faults among mountainous highway fatal crash-involved drivers with different route familiarity levels

Objective With the constrained topography and road geometry, adverse weather conditions and restricted roadway facilities, mountainous highway crash rates and fatality rates are much higher. Considering the potential influence of driver’s route familiarity level on driving behavior and fault assignment, this research investigates high- and low- route familiarity level drivers (HRF and LRF drivers) fault assignment in mountainous highway fatal crashes in Yunnan Province of China by examining factors of driver, crash/environment and pre-crash behaviors. Methods Yunnan Province is famous for its tourism, and tourism can also bring in many drivers with low-route familiarity levels. Spatial distance away from residence-based method is used for identifying route familiarity levels of the drivers in this study. We employed two separate binary logistic regression models to investigate the effects of the explanatory variables on the likelihoods that the HRF or LRF drivers were at fault in the mountainous highway fatal crashes. Results The results show that driver under alcohol influence, sharp turn, dawn/dusk and left turning are 4 common factors that significantly influence both HRF and LRF drivers’ fault assignments. Factors including driver age, driver seatbelt use, weather condition, road type, section type, lighting condition and pre-crash behaviors have different or opposite influences on HRF and LRF drivers’ fault assignments. HRF drivers are much easier to be distracted under the conditions that are without the need of extra attention. LRF drivers have much more difficulties in figuring out, responding to and making timely driving behavior adjustment to ensure their driving safety on the high-risk sections like tunnels, continuous long downhills and sharp turns. Street parking/backing and left turning of the LRF drivers are very serious problems on the mountainous highways in China. Conclusions There is a large difference of significant factors contributing to the fault assignment of HRF and LRF drivers in mountainous highway fatal crashes. Some more effective and targeted countermeasures are put forward for HRF and LRF drivers and transportation managers to improve mountainous highway traffic safety.

Heterogeneous ensemble learning for enhanced crash forecasts – A frequentist and machine learning based stacking framework

Introduction: This study aims to increase the prediction accuracy of crash frequency on roadway segments that can forecast future safety on roadway facilities. A variety of statistical and machine learning (ML) methods are used to model crash frequency with ML methods generally having a higher prediction accuracy. Recently, heterogeneous ensemble methods (HEM), including “stacking,” have emerged as more accurate and robust intelligent techniques providing more reliable and accurate predictions. Methods: This study applies “Stacking” to model crash frequency on five-lane undivided (5 T) segments of urban and suburban arterials. The prediction performance of “Stacking” is compared with parametric statistical models (Poisson and negative binomial) and three state-of-the-art ML techniques (Decision tree, random forest, and gradient boosting), each of which is termed as the base-learner. By employing an optimal weight scheme to combine individual base-learners through stacking, the problem of biased predictions in individual base-learners due to differences in specifications and prediction accuracies is avoided. Data including crash, traffic, and roadway inventory were collected and integrated from 2013 to 2017. The data are split into training (2013–2015), validation (2016), and testing (2017) datasets. After training five individual base-learners using training data, prediction outcomes are obtained for the five base-learners using validation data that are then used to train a meta-learner. Results: Results of statistical models reveal that crashes increase with the density (number per mile) of commercial driveways whereas decrease with average offset distance to fixed objects. Individual ML methods show similar results – in terms of variable importance. A comparison of out-of-sample predictions of various models or methods confirms the superiority of “Stacking” over the alternative methods considered. Conclusions and practical applications: From a practical standpoint, “stacking” can enhance prediction accuracy (compared to only one base-learner with a particular specification). When applied systemically, stacking can help identify more appropriate countermeasures.

City parks and slow streets: A utility-based access and equity analysis

During the spring and summer of 2020, cities across the world responded to the global COVID-19 pandemic by converting roadway facilities into open pedestrian spaces. These conversions improved access to public open space, but measuring the variation in that improvement among different populations requires clear definitions of access and methods for measuring it. In this study, we evaluate the change in a utility-based park accessibility measure resulting from street conversions in Alameda County, California. Our utility-based accessibility measure is constructed from a park activity location choice model we estimate using mobile device data – supplied by StreetLight Data, Inc. – representing trips to parks in that county. The estimated model reveals heterogeneity in inferred affinity for park attributes among different sociodemographic groups. We find, for example, that neighborhoods with more lower-income residents and those with more residents of color show a greater preference for park proximty while neighborhods with higher incomes and those with more white residents show a greater preference for park size and amenities. We then apply this model to examine the accessibility benefits resulting from COVID-19 street conversions to create a set of small park-like open spaces; we find that this has been a pro-social policy in that Black, Hispanic, and low-income households receive a disproportionate share of the policy benefits, relative to the population distribution.

Modeling the impact of the COVID-19 pandemic on speeding at rural roadway facilities in Maine using short-term speed and traffic count data

The COVID-19 pandemic caused a significant change in traffic operations and safety. For instance, various U.S. states reported an increase in the rate of fatal and severe injury crashes over this duration. In April and May of 2020, comprehensive stay-at-home orders were issued across the country, including in Maine. These orders resulted in drastic reductions in traffic volume. Additionally, there is anecdotal evidence that speed enforcement had been reduced during pandemic. Drivers responded to these changes by increasing their speed. More importantly, data show that speeding continues to occur, even one year after the onset of the pandemic. This study develops statistical models to quantify the impact of the pandemic on speeding in Maine. We developed models for three rural facility types (i.e., major collectors, minor arterials, and principal arterials) using a mixed effect Binomial regression model and short duration speed and traffic count data collected at continuous count stations in Maine. Our results show that the odds of speeding by more than 15 mph increased by 34% for rural major collectors, 32% for rural minor arterials, and 51% for rural principal arterials (non-Interstates) during the stay-at-home order in April and May of 2020 compared to the same months in 2019. In addition, the odds of speeding by more than 15 mph, in April and May of 2021, one year after the order, were still 27% higher on rural major collectors and 17% higher on rural principal arterials compared to the same months in 2019.

Smart Roadway Facility Deployment for the Accident Reduction Focusing on Highway Rest Areas

Smart Roadway Facility Deployment for the Accident Reduction Focusing on Highway Rest Areas

Impact of operating speed measures on traffic crashes: Annual and daily level models for rural two-lane and rural multilane roadways

A significant association between crash severity and operating speed is known to exist. However, the findings related to the speed-crash association are inconclusive. Some studies found that higher speed is associated with a higher number of crashes, whereas other studies found the opposite result. Some of the critical issues in this research problem result from study design, the definition of operating speed measures, types and granularity of operating speed measures, spatial correlation, and design standards of different roadway facilities. The road safety profession will benefit greatly from informative research on the impact of vehicle operating speed, roadway design elements, and traffic volume on crash outcomes. This study investigated the speed-crash association in both annual and daily level datasets to determine how roadway characteristics interact with various speed measures to impact the likelihood of crash occurrences on both annual and daily levels. For annual models, the average operating speed is positively associated with both fatal and injury and property damage only (PDO) crashes. However, for daily models, this association is mostly negative and insignificant. The standard deviation of operating speed is positively associated with crash occurrences for both daily and annual models. The findings of this study can provide additional insights into the speed-crash association literature.

Alleviating Traffic Signal Design at Kacherippadi Junction

Abstract: Improved traffic management and control systems are widely reported to be cost effective investments. As vehicular traffic began to increase, the congestion on the street to begin to hamper thus safe and efficient movement of traffic. The problem of traffic accidents and congestion in urban roads is being viewed with grave concern in the recent years. The main cause for this problem improper planning of road network and other roadway facilities and poor traffic planning. Simply retiming signals can provide significant benefits by reducing vehicle stops, travel times, and fuel consumption. The installation of advanced traffic management systems (ATMS) can provide even greater savings. However, many hardware and software obstacles are impeded the actual implementation of advanced traffic management systems. For this project we have chosen an intersection at Kacherippadi, where traffic congestion is more and we are planning to do a traffic signal, for that we counted traffic in that area, after analyzing and examining the speed data, average vehicle at a peak time, number of vehicles per day. Pedestrian movements, to reduce the congestion problems

Comprehensive Investigation of Commercial Motor Vehicle Crashes along Roadway Segments in Kentucky

The negative binomial (NB) model, traditionally used for safety performance function (SPF) development, suffers from a fixed over-dispersion parameter and is only valid for over-dispersed data (i.e., data exhibiting greater variance than the mean). A more flexible approach that handles over-dispersed data, under-dispersed data, and excess zero counts, in addition to exhibiting varying dispersion parameter as a function of site-specific characteristics, is the zero-inflated heterogeneous Conway–Maxwell–Poisson (ZI-HTCMP) model, which is an extension of Conway–Maxwell–Poisson (CMP)-based models. This study develops fatal + injury (FI) commercial motor vehicle (CMV) crash-specific SPFs along four roadway segment facilities in Kentucky, U.S., (urban multilane, rural multilane, urban two-lane, and rural two-lane segments). The traditional NB and newly introduced CMP-based models—ZI-HTCMP, zero-inflated Conway–Maxwell–Poisson (ZI-CMP), heterogeneous Conway–Maxwell–Poisson (HTCMP), and CMP—were compared using 14,967 CMV-related crashes on Kentucky’s road segments (between 2015 and 2019) and various roadway variables, for example, shoulder width, annual average daily traffic (AADT), and heavy vehicle percentage (HVP). From the developed SPFs, AADT and HVP >10% significantly increased FI CMV-related crashes on all four segment facilities. Various goodness-of-fit (GOF) statistics, including Akaike information criterion (AIC), mean absolute deviance (MAD), and mean square prediction error (MSPE), were used for model assessment and selection. For all four roadway facilities, CMP-based models showed better model fitting and prediction performance than the NB models. Furthermore, ZI-HTCMP was the best-fit model for urban multilane segments, which had high representation of zero-crash sites. CMP-family models could be used for effectively predicting FI CMV-related crashes (with excess zeros) on road segments.

Integrating multi-scale inspection, maintenance, rehabilitation, and reconstruction decisions into system-level pavement management systems

We are presenting a pavement management framework that involves multi-scale decisions, including system-level budget allocations, group-level inspections, and facility-level maintenance, rehabilitation, and reconstruction (MR&R) strategies. We consider two objectives: (i) to minimize the total discounted cost for the main stakeholders, represented by the agency and users; and (ii) to minimize the discounted user costs under a limited agency budget. Inspection technology innovations have enabled us to investigate the conditions of a group of multiple roadway facilities almost simultaneously, using instrumented vehicles that can travel at the same speed as traffic. Previous related studies have primarily focused on, at most, a bi-level structure with all the managerial activities, such as inspection and MR&R at the facility level and the allocation of a pooled budget at the system level. However, this cannot reflect the current group-level inspection practice, positioned between the facility and system levels. The proposed methodology adopts a bottom-up approach to account for heterogeneous facility- and group-specific properties, and it optimally facilitates determination of the multi-scale decisions to minimize the discounted societal costs. We address the complex multi-scale problem efficiently by adopting the Markov Decision Process, function approximation, and Lagrangian relaxation. As a numerical study, we analyze a real-world roadway network, including three expressway groups and one local road group, near Daejeon City in Korea. The system-level optimal solutions for the multi-scale decisions are determined, and the impacts of the grouping method are investigated.

Enhanced Safety Performance Function for Highway Segments in Oklahoma

AbstractA safety performance function (SPF) is a statistical equation to predict the expected crash frequency for a certain roadway facility, mainly as a function of traffic exposure indicators. Ro...

A random parameters regional quantile analysis for the varying effect of road-level risk factors on crash rates

Many studies have been devoted to investigate the spatial variations (heterogeneities) in the effects of risk factors on crash likelihood. However, investigations mainly target the safety effects on the mean of the crash data (distribution). Less attention was paid to investigate the spatial nonstationary effects on the different quantiles of the crash data distribution. In this study, a conditional quantile-based Bayesian hierarchical random parameter Tobit model is proposed to investigate the regional varying effects of road-related factors on crash rate at different quantiles of the crash rate distribution. A specific roadway facility type, urban two-lane two-way roadway segments in Florida, with crash and road related data for a three-year period is used for a case study. The results show that: 1) the regression coefficients of all of the selected risk factors vary over a wide range among 34 counties on every investigated quantiles of crash rate distribution; 2) in each county, the regression coefficients of all of the factors vary over investigated quantiles of the crash rate distribution, and for the same factor, the coefficients present different ranges of the variation in different counties; 3) the 50th-quantile conditional quantile-based Bayesian hierarchical random parameter Tobit model outperforms the conditional mean-based Bayesian hierarchical random parameter Tobit model, Bayesian quantile Tobit model and Bayesian Tobit model in terms of the prediction accuracy measured by the MAE, and 75th-quantile conditional quantile-based Bayesian hierarchical random parameter Tobit model is outstanding in terms of the goodness-of-fit measured by the DIC. These findings suggest the importance of investigating the regional nonstationary effects of risk factors for different quantiles of the crash rate distribution. The practical implications of the proposed conditional quantile-based Bayesian hierarchical random parameter Tobit model in terms of data prediction, parameters interpretation and safety effects explanation are highlighted at the end of this paper.

Prediction of vehicle occupants injury at signalized intersections using real-time traffic and signal data

Intersections are among the most dangerous roadway facilities due to the existence of complex movements of traffic. Most of the previous intersection safety studies are conducted based on static and highly aggregated data such as average daily traffic and crash frequency. The aggregated data may result in unreliable findings because they are based on averages and might not necessarily represent the actual conditions at the time of the crash. This study uses real-time event-based detection records, and crash data to develop predictive models for the vehicle occupants' injury severity. The three-year (2017–2019) data were acquired from the arterial highways in the City of Tallahassee, Florida. Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) classifiers were used to identify the important factors on the vehicle occupants' injury severity prediction. The performance comparison of the two classifiers revealed that the XGBoost has a higher balanced accuracy score than RF. Using the XGBoost classifier, five topmost influential factors on injury prediction were identified. The factors are the manner of the collision, through and right-turn traffic volume, arrival on red for through and right-turn traffic, split failure for through traffic, and delays for through and right-turn traffic. Moreover, the partial dependency plots of the influential variables are presented to reveal their impact on vehicle occupant injury prediction. The knowledge gained from this study will be useful in developing effective proactive countermeasures to mitigate intersection-related crash injuries in real-time.