Road Traffic Volume Research Articles

Brief design and numerical calculations for a secondary road

Based on the traffic volume survey, the standard traffic volume conversion is considered, and then the road traffic volume is predicted. Through the calculation, the maximum annual average daily traffic volume in the design life is obtained, and then the road grade and design speed of the design road are determined. The Annual Average Daily Traffic (AADT) is converted into Directional Design-Hour Volume (DDHV), and then the number of lanes of the road is determined. Collect the terrain data of the route area through the field survey, and then carry on the interior design. The route schemes are compared and the optimal route scheme is selected. AUTOCAD and a professional road design software were used to assist in the design of road line selection, horizontal curve design, vertical section design, cross section design and drainage design. Through the calculation, the design parameters such as the maximum synthetic slope, the average slope and the minimum length of superelevation transition section are obtained. Afterwards, the parameters in horizontal curve, vertical curve and superelevation-widening were checked manually.

Privileging Electric Vehicles as an Element of Promoting Sustainable Urban Mobility—Effects on the Local Transport System in a Large Metropolis in Poland

The main purpose of this article was to determine the impact on the equilibrium of the local transport system from privileging EVs by permitting them to use bus lanes. The study used two sets of data: information on infrastructure and traffic management; and information on the recorded road network loads and traffic volumes generated by a given shopping centre—the E. Leclerc shopping centre (an important traffic generator within the city of Łódź, Poland). These sets were then used to develop a microsimulation traffic model for the shopping centre and the associated effects on the localised transport system. The model was constructed by means of the PTV Vissim software tool. An initial simulation was conducted that formed a basis for subsequent scenarios (in total, 17 simulations were performed). On the basis of the conducted analyses, it was established that—for the researched part of the transport system—privileging the still rather uncommon battery electric vehicles (BEVs) engendered a marginal deterioration of traffic conditions. At the same time, allowing BEVs to use bus lanes within the chosen research area had no negative impact on bus journey times.

Identifying the Optimum Forecasting Horizon to Apply the Singular Spectrum Analysis on Daily Road Traffic Volume Forecasts

The paper delivers an assessment of Singular Spectrum Analysis (SSA) forecasting ability for short- and medium-term forecasting horizon, on real time traffic volume data. The key study goal is to estimate forecasting pertinency for daily traffic volume, based upon measurements at toll station. The suggested methodology is tested on real data from Moschohorion and Pelasgia Toll Station – Greece, utilizing custom developed forecasting software toolbox. Applied research results confirm an advanced forecasting ability of proposed methodology for short-term forecasting horizon against medium term forecasting horizon, when performance is compared upon the statistical criteria of the coefficient of determination R2. The obtained results present that SSA forecasting model could provide a competent forecasting methodology for road traffic volume data.

Evaluation of a New Intersection Design, “Shifting Movements”

Several unconventional designs have been suggested to enhance traffic operation and safety at intersections. However, the operational benefits of implementing some of them are achieved only under certain traffic conditions. For instance, the operational performance of the restricted crossing U-turn (RCUT) intersection design manifests only under highly unbalanced traffic conditions. The RCUT intersection outperforms conventional intersections that are subjected to high major traffic and light minor traffic volumes, while its operational performance fades at intersections with moderate to heavy minor road traffic. In this technical paper, a new innovative four-leg intersection design has been proposed to replace the RCUT intersection under moderate and heavy minor road traffic volumes. The new intersection design which has been named the “Shifting Movements” (SM) intersection has a low number of conflict-points compared with conventional intersections, but similar to the RCUT intersection. Therefore, similar safety benefits are expected to be achieved by the implementation of the SM intersection. Operational evaluations and comparisons between conventional, RCUT, and SM intersections have been conducted in the microscopic simulation environment. Different traffic volume levels and left-turn proportions have been assumed to represent the peak hour with moderate to high left-turn traffic. The results indicate that the SM intersection design significantly outperforms conventional and RCUT intersections that are subjected to high traffic volumes in average control delay and throughput.

Extracting Road Traffic Volume in the City before and during covid-19 through Video Remote Sensing

Continuous, automatic measurements of road traffic volume allow the obtaining of information on daily, weekly or seasonal fluctuations in road traffic volume. They are the basis for calculating the annual average daily traffic volume, obtaining information about the relevant traffic volume, or calculating indicators for converting traffic volume from short-term measurements to average daily traffic volume. The covid-19 pandemic has contributed to extensive social and economic anomalies worldwide. In addition to the health consequences, the impact on travel behavior on the transport network was also sudden, extensive, and unpredictable. Changes in the transport behavior resulted in different values of traffic volume on the road and street network than before. The article presents road traffic volume analysis in the city before and during the restrictions related to covid-19. Selected traffic characteristics were compared for 2019 and 2020. This analysis made it possible to characterize the daily, weekly and annual variability of traffic volume in 2019 and 2020. Moreover, the article attempts to estimate daily traffic patterns at particular stages of the pandemic. These types of patterns were also constructed for the weeks in 2019 corresponding to these stages of the pandemic. Daily traffic volume distributions in 2020 were compared with the corresponding ones in 2019. The obtained results may be useful in terms of planning operational and strategic activities in the field of traffic management in the city and management in subsequent stages of a pandemic or subsequent pandemics.

All roads lead to sustainability: Opportunities to reduce the life-cycle cost and global warming impact of U.S. roadways

The preservation and renewal of our transport system come at a significant cost to governmental agencies and contribute significantly towards global greenhouse gas emissions. Across fourteen “average” contexts classified by the U.S. Department of Transportation, this study probabilistically evaluates the life-cycle cost and global warming impact of alternative abatement strategies available to transportation agencies in managing their pavement infrastructure. The research relies on two algorithmic approaches to optimize the management of pavements: a trigger-value technique and a reinforcement learning algorithm. Invariant to traffic volume, our results show that by simply spending more “up front”, transportation agencies not only reduce the global warming impact of their pavements but also save money over their life-cycle. Surprisingly, these savings are particularly noteworthy for lower traffic volume roads typically managed by local municipalities with limited budgets. A higher “up front” investment reduces the global warming impact and life-cycle cost for a typical urban interstate by 19% and 2%, respectively; those life-cycle cost savings grow to nearly 16% for a local urban road while its global warming impact is also mitigated by 16%. We also show that more frequent rehabilitation of lower traffic-volume pavements not only increases agency expenditures but also their global warming impact. Conversely, the global warming impact of larger traffic-volume roadways, which are typically managed by state transportation agencies, can be reduced through increased rehabilitation spending. Our findings provide practitioners with valuable information around the life-cycle cost and savings of available pavement management strategies.

Comparison of skid resistance of EAC pavement with different maximum aggregate grain size

ABSTRACT The paper presents a comparison of friction coefficients and MPD (Mean Profile Depth) parameters of EAC (Exposed Aggregate Concrete) pavements with a maximum aggregate size of 8, 11 and 14 mm. In order to obtain for all tested EAC pavements the same conditions that simulate the phenomenon of polishing, tests were performed using a FAP (Friction After Polishing) machine in laboratory conditions. Macrotexture measurements were made on the surface of each sample using an Elatexure laser profile. In addition, three types of coarse aggregates were selected based on their resistance to polishing, which was determined on the basis of PSV (Polished Stone Value). Based on ANOVA, a significant influence of maximum aggregate size on both the friction coefficient and the MPD parameter was demonstrated. Along with the increase in the aggregate maximum grain size in EAC, the increase in MPD value was recorded. Despite of this fact, no effect of resistance to the polishing of aggregates with PSV from 48 to 52 on skid resistance was demonstrated. However, the decision to apply individual solutions should take into account road classification in the road network system, traffic volume and speed limits.

Roads pose a significant barrier to bee movement, mediated by road size, traffic and bee identity

Abstract Roads are a major driver of environmental stress, yet we know surprisingly little about how roads impact the movement of insect pollinators, and consequent pollination. We investigated the influence of roads on pollinator movement and pollination by examining patterns of pigment transfer between focal plants of two species, Coreopsis verticillata and Monarda fistulosa. We asked whether roads reduced pigment transfer, and what characteristics of roads were important in driving this reduction. We also evaluated whether pollinator assemblage differed between species, and if this mediated the effect of roads on pigment transfer. Plants across a road from a pigment‐added plant received significantly less pigment than plants on the same side of the road. This effect was stronger for coreopsis than for monarda. The mean body size of visitors to coreopsis was significantly smaller than that of visitors to monarda, suggesting that smaller bees are more limited by roads. Road width was the best predictor of pigment transfer, with a smaller effect of traffic volume; further research is needed to fully disentangle the effects of different road characteristics. Roadside habitat had little influence on pigment transfer, and roadside plants did not receive significantly less pigment than plants in contiguous habitat. Synthesis and applications. This study demonstrates that roads pose substantial barriers to bee movement, reducing pollen flow between plants located across roadways from one another. Road characteristics, particularly width and traffic volume, mediated this effect, as did bee size. Our results suggest that the effects of roads on pollinators and pollination can be mitigated by many of the same design strategies currently being implemented to reduce human traffic accidents, offering the opportunity for win–win scenarios.

Research on the Improved Neural Network of Coal Price Forecast Based on Co-integration Theory

China is a big coal consumer, and changes in coal prices will have a huge impact on the entire market economy system. In this paper, aiming at the prediction of coal price, the grey correlation analysis method is firstly used to describe the correlation degree between the main factors and the coal price, and the main influencing factors are sorted according to the correlation coefficient. Finally, it is determined that a coal price prediction system needs to be established on such indicators as coal reserves, raw coal production, total coal consumption, the proportion of coal in the energy mix, coal road traffic volume, coal imports, etc. Next, the quantitative indicators are combined with the economic cycle and cointegration theory to study, with the help of cointegration theory to improve the traditional neural network, so that it is more suitable for the prediction of coal prices. Finally, this paper obtains an effective coal price prediction model, which can better predict and analyze the future coal price and can play an important reference role in the formulation of government policies.

Non-Stationary Modeling of Microlevel Road-Curve Crash Frequency with Geographically Weighted Regression

Vehicle crashes on roads are caused by many factors. However, the influence of these factors is not necessarily homogenous across locations, which is a challenge for non-stationary modeling approaches. To address this problem, this paper adopts two types of methods allowing parameters to fluctuate among observations, that is, the random parameter approach and the geographically weighted regression (GWR) approach. With road curvature, curve length, pavement friction, and traffic volume as independent variables, vehicle crash frequencies are modeled by two non-spatial methods, including the negative binomial (NB) model and random parameter negative binomial (RPNB), as well as three spatial methods (GWR approach). These models are calibrated in microlevel using a dataset of 9415 horizontal curve segments with a total length of 1545 kilometers for a period of three years (2016–2018) over the State of Indiana. The results revealed that the GWR approach can capture spatial heterogeneity and therefore significantly outperforms the conventional non-spatial approach. Based on the Akaike Information Criterion (AICc), geographically weighted negative binomial regression (GWNBR) was proved to be a superior approach for statewide microlevel crash analysis.

Possibility of incorporating waste plastic film flakes into warm-mix asphalt as a bitumen extender

A warm-mix asphalt (WMA) produced at 100 °C and incorporating waste plastic film flakes was evaluated. The investigation comprised two stages analysing the WMA and a conventional HMA with the same plastic material. The first stage established the WMA composition. The second stage comprised performance evaluation, for which the incorporation of plastic slightly increased stiffness, and fatigue resistance was similar for both mixtures. WMA with plastic was the worst in the uniaxial compression test and the best in the wheel-tracking test. The conclusion is that WMA incorporating waste plastic is a promising solution for low to intermediate traffic volume road infrastructures.

Influence Range and Traffic Risk Analysis of Moving Work Zones on Urban Roads

There is a body of literature on the influence range and traffic risk of fixed work zones. However, relatively few studies have examined the effect of ubiquitous moving operating vehicles, such as road cleaners, on urban roads. The influence of low speed moving work zones on road traffic flow and traffic risk is still unclear. In this work, we used simulations to establish an urban expressway three lanes VISSIM model, and selected the road traffic volume and speed of the moving work zone as the independent variables. We analyzed the range of influence of the moving work zone on the rear vehicles in the left, middle and right lanes of the urban expressway and the traffic risk variation law caused by the moving work zone. The results show that the left lane was indirectly affected by the moving work zone when the traffic volume reached 2000 pcu/h. The influence of the moving work zone on the middle lane was controlled by the traffic volume and the speed of the moving work zone. Both the left and middle lanes were mainly impacted by vehicles changing lane from the right lane. Regardless of the traffic volume and the speed of the moving work zone change, the vehicles 200 m behind a moving work zone will be directly affected in the right lane. Furthermore, the average traffic risk is the highest within 50 m of the moving work zone in the right lane. When the traffic volume decreases and the speed of the moving work zone increases, the average traffic risk decreases gradually. These results provide a scientific basis for the operation and management of moving working vehicles on urban roads.

Method for Obtaining Better Traffic Survey Data

Road traffic surveys determine the number and type of vehicles passing by a specific point over a certain period of time. The manual estimation of the number and type of vehicles from images captured by a camera is the most commonly used method. However, this method has the disadvantage of requiring high amounts of manpower and cost. Recently, methods of automating traffic volume surveys using sensors or deep learning have been widely attempted, but there is the disadvantage that a person must finally manually verify the data in order to ensure that they are reliable. In order to address these shortcomings, we propose a method for efficiently conducting road traffic volume surveys and obtaining highly reliable data. The proposed method detects vehicles on the road from CCTV (Closed-circuit television) images and classifies vehicle types using deep learning or a similar method. After that, it automatically informs the user of candidates with a high probability of error and provides a method for efficient verification. The performance of the proposed method was tested using a data set collected by an actual road traffic survey company. As a result, we proved that our method shows better accuracy than the previous method. The proposed method can reduce the labor and cost in road traffic volume surveys, and increase the reliability of the data due to more accurate results.

Influence of road traffic vehicle volume with damage and road safety

Increasing population and vehicles on the road will be accompanied by increased transportation needs so that the traffic density cannot be avoided. Generally, happens on a national road route, which is a connecting road between the provincial capital and a national strategic highway. The traffic density is potentially a bottleneck, road damage, and traffic accidents. The research aims to determine the influence of vehicle volume against the damage and safety of roads in one of the national roads in West Java. The Data sourced from road planning and supervision, the police sector, and field Survey Board. The methods used Bina Marga method to determine the number of road damage, RCI, and IRI methods to assess the value of road conditions, and to determine the influence of inter-variable, road safety inspection refers to the test of the roadway to evaluate roadworthiness of road functions. The results of the study gained that the impact of vehicle volume on road traffic damage and categorized accidents was insignificant. The dominant factor in the traffic accident that occurs caused by carelessness and unorderly driving. These roads in the road category are still eligible and recommended for routine maintenance.

Performance Evaluation of Studded Tire Ruts for Asphalt Mix Designs in a Cold Region Environment

Studded tire wear is one of the most important contributing factors that govern pavement life on high traffic volume roads in south-central Alaska. It has been estimated that the annual cost to repair damage caused by studded tires in Alaska is approximately $13.7 million. Polymer modified asphalt binders and hard aggregate are commonly used in mix designs in the State of Alaska Department of Transportation and Public Facilities projects in an attempt to resist studded tire wear. This research provides a field performance-based analysis of the use of hard aggregate and polymer modified binders related to the reduction of the impact of studded tire wear. To analyze the performance of modification in asphalt mix designs, roadway data was gathered from the Alaska Department of Transportation Pavement Management System Database and roads were grouped into deterioration families based on mix design properties. Relationships of each deterioration are presented to show cumulative average annual daily traffic versus total rut depth, comparing mix designs with and without hard aggregate and polymer modification. Findings indicated that both polymer modification and hard aggregate can be used to reduce the impact of studded tire wear. The benefits of polymer modification and hard aggregate proved more significant as cumulative traffic volumes increase. Recommendations from the performance analysis included continuing the use of hard aggregate and polymer modified binders on Alaskan roadways with continuous research on the use of more highly modified asphalt binders to further reduce the impact of studded tire wear.

Severely injured patients do not disappear in a pandemic: Incidence and characteristics of severe injuries during COVID-19 lockdown in Finland

Background and purpose — COVID-19 lockdowns have resulted in noteworthy changes in trauma admissions. We report and compare the incidence and characteristics of severe injuries (New Injury Severity Score [NISS] > 15) during the COVID-19 lockdown in Finland with earlier years. Methods — We retrospectively analyzed incidence rate, injury severity scores, injury patterns, and mechanisms of injury of all severely injured patients (NISS >15) in 4 Finnish hospitals (Tampere University Hospital, Kuopio University Hospital, Central Finland Hospital, Mikkeli Central Hospital) during the 11-week lockdown period (March 16–May 31, 2020) with comparison with a matching time period in earlier years (2016–2018). These 4 hospitals have a combined catchment area of 1,150,000 people or roughly one-fifth of the population of Finland. Results — The incidence rate of severe injuries during the lockdown period was 4.9/105 inhabitants (95% CI 3.7–6.4). The incidence rate of severe injuries during years 2016–2018 was 5.1/105 inhabitants (CI 3.9–6.5). We could not detect a significant incidence difference between the lockdown period and the 3 previous years (incidence rate difference –0.2 (CI –2.0 to 1.7). The proportion of traffic-related accidents was 55% during the lockdown period and 51% during previous years. There were no detectable differences in injury patterns. During the lockdown period, the mean age of patients was higher (53 years vs. 47 years, p = 0.03) and the rate of severely injured elderly patients (aged 70 or more) was higher (30% vs. 16%). Interpretation — Despite heavy social restrictions, the incidence of severe injuries during the lockdown period was similar to previous years. Notably, a decline in road use and traffic volumes did not reduce the number of severe traffic accidents. Although our data is compatible with a decrease of 2.0 to an increase of 1.7 severely injured patients per 105 inhabitants, we conclude that severely injured patients do not disappear even during pandemic and stabile hospital resources are needed to treat these patients.

Experiences with Establishing a Simulation Scenario of the City of Duisburg with Real Traffic Volume

This article presents the experience of building a simulation scenario of the whole city of Duisburg using real traffic data. The establishment of the simulation scenario is based on road network and traffic volume. In most cases, it is hard to collect all data sources with high precision. Moreover, it is time-consuming to set up a realistic traffic scenario. Even with available data, conversion, calibration, and validation all take a large effort. With the increase of the respective simulation area, the difficulty and workload rise. In this study, a simulation scenario of the whole city of Duisburg with the road network area of 232 km2 and Origin/Destination (OD) matrix area over 800 km2 was established in the software package SUMO. Four cases with different networks and traffic volumes were built and compared with real traffic data collected from induction loops. The percentage of simulated traffic volume in real traffic volume range can be up to 72.22%.

Analysis and Evaluation of Ramp Metering: From Historical Evolution to the Application of New Algorithms and Engineering Principles

In the modern era, characterized by intense urbanization and frequent travel between interconnected communities, the constant expansion of cities, associated with high densities and growing need for traveling, has led to a significant increase in road traffic volumes. More than ever, road traffic today requires effort to be managed effectively in order to improve performance and safety conditions, given the greater probability of unpleasant events such as accidents or road congestion with related delays and the increased stress levels of the user and infrastructure. Fortunately, there are already various engineering tools, such as ramp metering, that can be used for this purpose. Ramp metering allows for achieving the aforementioned desired benefits, including improving mobility, reliability, efficiency, and safety, and even reducing environmental impact. It also has been shown to be cost-effective from the existing literature. Further research will be necessary to strengthen the quality, efficacy, and efficiency of ramp metering, especially considering the fast-paced progress in technology (e.g., connected autonomous vehicles and drones used for surveys) and new challenging scenarios (e.g., congested industrial areas and emergency vehicles). This review’s scope is to present a general overview of principal ramp metering solutions, focusing on current research studies in the last couple of years and highlighting some of the main algorithms used for this purpose, depending on diverse scenarios. With this article, the authors desire to present the subject of ramp metering, providing a general overview of its story, evolution, and recent analytical models.

Statewide Application of Wrong-Way Driving Crash Risk Modeling and Countermeasures Optimization Algorithm to Identify Optimal Locations for Countermeasure Deployment on Florida Limited Access Facilities

Wrong-way driving (WWD) crashes can have significant impacts on freeway safety and operations. Deploying intelligent transportation systems (ITS) WWD countermeasures at freeway exit ramps can effectively reduce WWD crash risk (WWCR), but these countermeasures are expensive. In this paper, a WWCR segment model and WWD countermeasures optimization algorithm are developed for all Florida limited access facilities (56 roadways with 1,375 exits) to identify the optimal locations for ITS countermeasure deployment. These were previously developed for specific toll-road networks within Florida, but never for a statewide network. Multiple WWCR models were investigated, with the final Poisson model using four-exit segments and 5 years of WWD event data. This model showed that more WWD events and higher crossing road traffic volumes increased WWCR, while certain interchange designs increased or decreased WWCR. Sixty-three segments containing 169 exit ramps without ITS WWD countermeasures were identified as WWD hotspots; these ramps were compared with the 169 ramps with the highest WWCR selected by the optimization algorithm. The algorithm selected 96 ramps not in the hotspots (improved resource utilization of 56.8%) and provided a 38.6% increase in WWCR reduction. Comparing WWD detection and turnaround data from 31 sites with rectangular flashing beacon ITS countermeasures to the optimization indicated a significant positive association between WWCR and turnaround percentage (higher WWCR at sites with lower turnaround percentage), verifying the accuracy of the optimization. By showing the transferability, scalability, accuracy, and benefits of this approach, this paper can help agencies reduce WWD and improve freeway safety and operations.

Urban Traffic Detectors Data Mining for Determination of Variations in Traffic Volumes

This paper analyses road traffic volumes in the urban environment for the purpose of traffic planning and creation of traffic models. For modelling traffic in a certain area, the initial information about transport demand and distribution in given area is required. The demand for transport is further re-distributed to the transport network and measured against the current road traffic volumes / intensity of traffic. Traffic volumes over time are characterized by various periodic and non-periodic influences (variations). By studying these variations, the tools can be specified for making the final estimate of traffic volumes for a specific time period, a specific type of road or specific vehicle category, and for improving the traffic models for a specific area. In this paper, the authors study time variations in traffic volumes using the data obtained from vehicle detectors for monitoring traffic located on roads in the city of Ceske Budejovice, the Czech Republic.