Different Categories Of Vehicles Research Articles

Development of a High-Precision and Lightweight Detector and Dataset for Construction-Related Vehicles

Effective vehicle detection plays a crucial role in various applications in cities, including traffic management, urban planning, vehicle transport, and surveillance systems. However, existing vehicle detection methods suffer from low recognition accuracy, high computational costs, and excessive parameters. To address these challenges, this paper proposed a high-precision and lightweight detector along with a new dataset for construction-related vehicles. The dataset comprises 8425 images across 13 different categories of vehicles. The detector was based on a modified version of the You Only Look Once (YOLOv4) algorithm. DenseNet was utilized as the backbone to optimize feature transmission and reuse, thereby improving detection accuracy and reducing computational costs. Additionally, the detector employed depth-wise separable convolutions to optimize the model structure, specifically focusing on the lightweight neck and head components. Furthermore, H-swish was used to enhance non-linear feature extraction. The experimental results demonstrated that the proposed detector achieves a mean average precision (mAP) of 96.95% on the provided dataset, signifying a 4.03% improvement over the original YOLOv4. The computational cost and parameter count of the detector were 26.09GFLops and 16.08 MB, respectively. The proposed detector not only achieves lower computational costs but also provides higher detection when compared to YOLOv4 and other state-of-the-art detectors.

CROSSING SPEEDS OF CARS THROUGH SIGNALIZED INTERSECTIONS

The article deals with the problem of traffic in three traffic-light intersections in the street network of Iasi city. Usually, the existing traffic lights do not fully take into account the size, structure and parameters of the queue of vehicles waiting at intersections; which in turn affects the traffic capacity of the intersection. In the study, we used a representative set of measurements taken at the three intersections to interpret the variation in the crossing speeds of the traffic light intersection space by the vehicle columns using the linear regression method. We studied several categories of vehicles queuing before the stop line at the intersection. We made a statistical analysis of the time taken to cross three intersections by vehicles in different starting positions. The main results of the research showed estimates in good agreement with observational data as well as a significant reduction in vehicle speed (to 58%) when there are different categories of vehicles queuing at the intersection. This study can be used in the urban traffic diagnosis method, being part of a traffic study together with urban traffic forecasting and therapy.

Uncovering the CO2 emissions of vehicles: A well-to-wheel approach

Carbon dioxide (CO2) from road traffic is a non-negligible part of global greenhouse gas (GHG) emissions, and it is a challenge for the world today to accurately estimate road traffic CO2 emissions and formulate effective emission reduction policies. Current emission inventories for vehicles have either low-resolution, or limited coverage, and they have not adequately focused on the CO2 emission produced by new energy vehicles (NEV) considering fuel life cycle. To fill the research gap, this paper proposed a framework of a high-resolution well-to-wheel (WTW) CO2 emission estimation for a full sample of vehicles and revealed the unique CO2 emission characteristics of different categories of vehicles combined with vehicle behavior. Based on this, the spatiotemporal characteristics and influencing factors of CO2 emissions were analyzed with the geographical and temporal weighted regression (GTWR) model. Finally, the CO2 emissions of vehicles under different scenarios are simulated to support the formulation of emission reduction policies. The results show that the distribution of vehicle CO2 emissions shows obvious heterogeneity in time, space, and vehicle category. By simply adjusting the existing NEV promotion policy, the emission reduction effect can be improved by 6.5%–13.5% under the same NEV penetration. If combined with changes in power generation structure, it can further release the emission reduction potential of NEVs, which can reduce the current CO2 emissions by 78.1% in the optimal scenario.

Impact of Inconsistent Behavior of Drivers on Estimation of Critical Gap at Multi-Lane Roundabouts

Critical gap (CG) estimation, while taking into consideration inconsistent driver behavior resulting from heterogeneous traffic conditions, is a tedious task. Several methods dealing with CG estimation have been developed in the past but limited research has been done in this regard for developing countries and there is a lot of scope to explore this field considering varying traffic conditions on the roads. In this study, the maximum likelihood method (MLM), one of the most prominent methods of determining CG, has been compared with two other methods developed for traffic conditions prevailing in India. These methods use different techniques for calculating the CG, such as minimization of absolute differences of gaps given by Ahmad et al., and minimization of square root of standard deviation of actual gap value from the predicted CG as per the Indian Highway Capacity Manual (Indo-HCM). This paper discusses the estimation of CG for five different categories of vehicles using these methods to counter the heterogeneity of traffic. Data has been collected on two urban multi-lane roundabouts in India. SOLVER in MS Excel is used to optimize the functions defined in all three methods. The results reveal that the method suggested by Ahmad et al. gives the most consistent results which are close to actual value when driver behavior is inconsistent. The other two methods are also reasonably consistent, with the method given in Indo-HCM being slightly better than MLM. Further, two-way ANOVA was applied to check the consistency of results.

Real-Time Traffic Prediction Using Monte Carlo Simulation: A Case Study of Kantipath Road, Kathmandu, Nepal

This paper deals with the model development for the analysis of traffic data to improve efficiency and sustainability of the existing transportation system of a section of the Kantipath road, Kaiser Library to Jamal. The traffic data of the two extreme locations of the section of the road were taken using digital video recorders. The data were extracted manually and statistically analyzed to test the different statistical distribution of traffic flow using Kolmogorov–Smirnov and Shapiro–Wilk test, and to find out different categories of vehicles along with their flow and peak hour. The Kolmogorov–Smirnov test reveals the traffic flow distribution is the same as Cauchy, Normal, Inverse Gaussian, Logistic, Gamma, and Beta distributions. The dataset of Kaiser Library was used to predict the real-time traffic flow of Jamal employing Monte Carlo Simulation in accordance to normal distribution. The forecasted and observed traffic flows show better agreement. BIBECHANA 19 (2022) 83-89

Impact of change in traffic flow on vehicle non-exhaust PM2.5 and PM10 emissions: A case study of the M25 motorway, UK.

This study quantifies the change in traffic flow on the M25 motorway in the UK due to the COVID-19 outbreak. Moreover, the impact of the change in traffic flow on non-exhaust PM2.5 and PM10 emissions for different categories of vehicle was explored. During the year of the COVID-19 outbreak (March 2020 to February 2021), the total traffic flows of passenger cars (PCs), light goods vehicles (LGVs), heavy goods vehicles (HGVs), and long HGVs on the M25 motorway decreased by 38.6%, 27.6%, 15.9% and 7.2%, respectively, in comparison to the previous year. Correspondingly, the total mass of non-exhaust emissions (PM2.5 and PM10) of PCs, LGVs, HGVs, and long HGVs reduced by 38.7%, 27.3%, 16.2% and 7%, respectively. The traffic flows per year before and during the COVID-19 outbreak of long HGVs were 87.2% and 80.7% less than those of PCs. Correspondingly, the long HGVs emitted 10.2% less but 36.3% more PM2.5 emissions, as well as 10.9% and 66.7% more PM10 emissions than the latter, indicating that long HGVs contribute much more to non-exhaust particles than PCs. In addition, it was found that resuspension of road dust on the M25 motorway was the largest contributor to air pollution among non-exhaust emissions, followed by road wear, tyre wear, and brake wear particles.

Методика корректировки нормативной периодичности технического обслуживания автотранспортных средств на основе данных о фактическом расходе топлива

Abstract. The use of reasonable standards for the technical operation of motor vehicles is the key to improving the efficiency of the transport process. Due to the fact that a number of standards for the technical operation of vehicles have not been updated for many years, an urgent problem is the improvement of methods for rationing the technological parameters of the technical operation of vehicles. The purpose of the study is: improving the efficiency of the operation of motor vehicles based on the development and implementation of a methodology for adjusting the frequency of maintenance by the amount of actual fuel consumption. The approach proposed by the authors is based on the hypothesis that the work performed by the nodes and aggregates of vehicles is equivalent to the total fuel consumption. Therefore, the use of this parameter as an argument for the function of determining the frequency of preventive actions allows us to more fully take into account the operating conditions of vehicles, the nature of the transport work performed, technical, resource and design features. Methods of mathematical analysis and statistical processing of the results of field surveys were used to develop a methodology for adjusting the frequency of preventive effects by the amount of total fuel consumption. As a result of the work done: a new methodological approach to the appointment of the frequency of preventive actions by the amount of total fuel consumption is proposed; a methodology for adjusting the basic norms of the frequency of maintenance of motor vehicles based on data on actual fuel consumption is developed, which allows to establish the values of total fuel consumption that determine the need for preventive maintenance. The scientific novelty of the work is: – methodical approach to the appointment of the frequency of preventive actions by the amount of total fuel consumption; – methodology for determining the values of total fuel consumption, determining the need to perform preventive actions; – the values of the coefficient of reduction of fuel consumption when driving on a standard driving cycle to the conditions of traffic on the highway, defined for different categories of vehicles and necessary for the practical application of the developed methodology. The practical significance of the results of the study is determined by a new approach to the appointment of the frequency of maintenance, which allows more fully to take into account the actual operational factors affecting the resource characteristics of vehicles. Based on the results of practical application of the developed methodology, it was found that when operating KAVZ-4235 buses in the city, the frequency of TO-2 can be increased by 30%, and outside the suburban area by 45%. Based on the data obtained, recommendations have been developed for the practical application of the presented methods for correcting the regulatory periodicity of the total fuel consumption. Forecast calculations of the frequency of technical services of the categories of vehicles under consideration have been performed.

Development of a Hybrid Artificial Neural Network-Particle Swarm Optimization Model for the Modelling of Traffic Flow of Vehicles at Signalized Road Intersections

The tremendous increase in vehicular navigation often witnessed daily has elicited constant and continuous traffic congestion at signalized road intersections. This study focuses on applying an artificial neural network trained by particle swarm optimization (ANN-PSO) to unravel the problem of traffic congestion. Traffic flow variables, such as the speed of vehicles on the road, number of different categories of vehicles, traffic density, time, and traffic volumes, were considered input and output variables for modelling traffic flow of non-autonomous vehicles at a signalized road intersection. Four hundred and thirty-four (434) traffic datasets, divided into thirteen (13) inputs and one (1) output, were obtained from seven roadsites connecting to the N1 Allandale interchange identified as the busiest road in Southern Africa. The results obtained from this research have shown a training and testing performance of 0.98356 and 0.98220. These results are indications of a significant positive correlation between the inputs and output variables. Optimal performance of the ANN-PSO model was achieved by tuning the number of neurons, accelerating factors, and swarm population sizes concurrently. The evidence from this research study suggests that the ANN-PSO model is an appropriate predictive model for the swift optimization of vehicular traffic flow at signalized road intersections. This research extends our knowledge of traffic flow modelling at a signalized road intersection using metaheuristics algorithms. The ANN-PSO model developed in this research will assist traffic engineers in designing traffic lights and creation of traffic rules at signalized road intersections.

Predictive Modeling of Insurance Claims Using Machine Learning Approach for Different Types of Motor Vehicles

The main objective of this research paper is to build an appropriate mathematical model that helps in forecasting third party claim amount for different categories of vehicles based on the chosen characteristics of the data. In actuarial research, predicting the insurance claim amount for different vehicle categories is a challenging task, and minimal empirical research studies were done to forecast the claims. In the present study, the annual time series historical data were collected for a period of 34 years. We had built the machine learning predictive models to modeling the claim amount with different categories of vehicles effectively. In this context, we exhibited the feasibility of using a statistical machine learning approach such as Linear regression Model, the Exponential Smoothing Model, autoregressive integrated moving average (ARIMA), artificial neural network (ANN), and hybrid ARIMA-ANN models to predict the various categories of vehicles claim amount. The data were analyzed, compared, and the empirical analysis showed that Artificial Neural Network is a better predictive model among the other time series models based on performance evaluation metrics RMSE and MAPE with lesser variance. Therefore, the machine learning approach for forecasting third party claim amounts will help the Insurance Companies in India to provide a better predictive model, which ensures better claims settlement and management for different categories of vehicles.

Analyzing Electric Vehicle Load Impact on Power Systems: Modeling Analysis and a Case Study for Maldives

Electric vehicles (EVs) can have massive benefits in energy sector especially for a small island country like the Maldives that imports oil with high transportation costs while power could have been generated from abundantly available local renewable resources. However, EV charging may also impose significant investment requirement for the power system that needs to be analyzed carefully including the capacity of the existing distribution network system, investments needed in solar PV together with battery storage and additional diesel capacity to meet the incremental demand from EVs. We explore an EV adoption scenario for Maldives for 2030 with 30% of all vehicles including two-wheelers that dominate the transport on the island under two different charging regimes: uncoordinated and optimized coordinated mode. The latter is achieved through a system wide optimization using a modified version of the World Bank Electricity Planning Model (EPM) that optimizes charging load subject to a range of constraints on allowable timing for different categories of vehicles. If charging from the fleet is uncoordinated, a relatively small increase in energy requirement of 3.1% due to EV may lead to a 26.1% increase in generation capacity requirement and hence 15.7% additional investment. While the optimized charging regime helps to drastically cut down on generation capacity requirements to just 1.8% increase and also considerably eases feeder loading, it may also lead to higher emissions as more EV load during off-peak hours lead to an increase in diesel-based generation. We have therefore explored an additional scenario wherein the annual emissions from the power sector are constrained to the baseline (“No EV”) scenario. The analysis shows the importance of focused modeling analysis to understand the ramifications of EV load impact on the power system including significant increase in generation capacity and potential increase in power sector emissions in a fossil-fuel dominated system.

Interpreting rescue vehicle patterns using geovisual analytics for spatiotemporal resource allocation

This article presents a geovisual analytics approach to explore hidden travel patterns of emergency response vehicles in the city of Lahore, Pakistan. The Rescue 1122 is a department that provides emergency response services in Pakistan. These services are like the 911 working in the USA or the 999 emergency services in the UK. The Rescue 1122 emergency vehicles are categorized as fire, rescue, and ambulance services. Different categories of vehicles handle various types of requests depending upon the nature of the emergency. All vehicles are equipped with a Global Positioning System (GPS) tracker. Data obtained from these vehicles generate spatiotemporal sequences. These sequences—also known as trajectories—form the core component of moving object analysis. The aim of this study is to use visualization techniques and to assist government officials for efficient spatiotemporal allocation of the emergency vehicles. Several visualization techniques are applied in this study to get useful insight including clustering, mean time gaps in visits, hotspots, spatiotemporal aggregation and sub-setting, and caller data analysis. The results demonstrate some interesting patterns and highlight the areas that require allocation pre-planning. The findings are beneficial for effective resource planning and for understanding the complexities of a highly urbanized city of Lahore.

Clean vehicles, polluted waters: empirical estimates of water consumption and pollution loads of the carwash industry

Carwash stations use large volumes of water and release harmful chemicals into the environment through their operations. While a significant body of literature has focused on exploring water use in the carwash industry, none has provided comprehensive information on both the pollution loads of the wastewater emanating from this industry and water consumption. Understanding how much water is used and the pollution loads of wastewater from this industry is useful to ensure adoption of water conservation measures and design wastewater recycling systems given the dwindling freshwater resources globally. This study estimated the freshwater quantities used to wash different vehicle types and the pollution loads of the resulting wastewater in the Kumasi Metropolis. Seven proxy carwash stations were purposively selected and monitored to estimate the water used to wash six different categories of vehicles. Composite wastewater samples from three carwash stations were analysed for concentrations of different contaminants which were used to compute pollution loads. Using R software, one-way ANOVA with Tukey's (HSD) post-hoc testing and 2-sample t-test at 95% confidence interval were employed to test statistical differences. After an 8-week monitoring campaign involving 3,667 vehicles, the study showed that average water used for each vehicle type were in the order: Motorbike - 97L (95% CI: 90–103L); Salon car - 158L (95% CI: 154–161L); SUV - 197L (95% CI:191–203L); Buses/Coaches - 370L (95%CI:351–381L); Articulated truck 1,139L (95% CI:916–1,363L); Graders/Loaders - 1405L (95% CI:327–2,483L). Overall, the carwash industry in the Metropolis uses about 1000m3 of freshwater daily and discharges the resulting wastewater into waterways untreated. The wastewater has a low Biodegradability Index (0.3–0.4) and is characterized by a mildly alkali pH (7.6–8.6) with high levels of Sulphates (40.8–69.8 mg/L), COD (990–1413 mg/L), TSS (1260–3417 mg/L) and E. coli (2.3–4.7 × 103 CFU/100mL). Pollution loads of BOD and COD were up to 2tons/year and 6tons/year respectively. Stipulated effluent discharge guideline values were mostly exceeded – in some cases by up to 68 times. To avert the unbridled wastage of freshwater, the study recommends enforcement of wastewater recycling for all carwash stations and promulgation of a tax system that rewards stations that recycle wastewater and surchages those wasting freshwater.

An efficient method for extraction and recognition of bangla characters from vehicle license plates

Recognition of characters from vehicle license plate plays a vital role in vehicle tracking, controlling, and maintaining traffic on the roads and high ways. This paper presents an automatic system to detect plate area and recognize characters. The research is carried out on Bangladeshi vehicle license plates because this is a demandable research area in the present age. The method has been implemented using the five consecutive steps such as license plate detection, extraction, character localization, extraction of characters, and recognition of extracted characters. We have introduced an algorithm to detect the Region of Interest (ROI) from the input image. It is performed by applying morphological operation and histogram analysis of vertical and horizontal projection profiles. A dynamic threshold is used to filter out both horizontal and vertical histogram values and ROI is extracted from the license plate image using different geometric properties such as area, bounding box, and aspect ratio. Character segmentation is performed by applying the method of connected component analysis and bounding box technology. Finally, character recognition is carried out using Support Vector Machine (SVM) classifier where extracted Histogram of Oriented Gradient (HOG) features are used as input. The proposed algorithm is applied to 630 images of license plates of different categories of vehicles and achieved 91% accuracy in the extraction of ROI and 94.6% accuracy in the extraction of characters. We have also achieved 100% accuracy in recognition of Bangla characters from a total of 4725 Bangla characters extracted from 630 images of license plates.

Effect of operational attributes on lateral merging position characteristics at mid-block median opening

ABSTRACT The paper proposes a parameter named as lateral merging position (LMP) to understand the placement characterises of U-turning vehicle at mid-block median opening (MBMO) in divided urban roads (DiUR). In this regard, videography data were collected from 14 MBMOs. The LMP was estimated for different categories of U-turning vehicles and the statistical distributions to describe the LMP in 6-lane and 4-lane DiUR has been proposed, separately. Furthermore, the effect of various operational attributes, namely; traffic condition, driver characteristics and vehicle characteristics on LMP are explored. Subsequently, mathematical models have been developed using regression analysis to estimate the LMP for different categories of vehicle with varying operational attributes. The developed models will not only be beneficial for better estimation of conflicting traffic volume but will also be helpful to propose geometrical augmentation at MBMO to reduce traffic congestion and to enhance quality of traffic operation and safety at MBMO.

Augmentation of charging infrastructure for electric vehicles growth in India

India, one of the fastest growing economies, has been a front-runner in fulfilling its commitments towards a cleaner Earth. In this pursuit, it intends to adopt electric vehicles (EVs) to the tune of 30% by 2030. Central and state governments of India have launched several financial incentives to encourage adoption of EVs. While considering various adoption rates of EVs from 5% to 30%, the requirements of additional amounts of electricity and various types of charging stations to feed power to EVs are estimated. At 30% adoption rate, about 140 million EVs (include different categories of vehicles studied in this paper) may become the part of transportation system and correspondingly need about 24,600 MWh of electricity daily. In the high density areas of EVs, such as bus depots, residential complexes, malls etc., large power may be needed at a single point. Thus, it may require sufficient receiving substation capacity and an equivalent high-voltage distribution network.

Braking with a Trailer and Mountain Pass Descent

<div class="section abstract"><div class="htmlview paragraph">A truly strange - but very interesting - juxtaposition of thought occurs when considering customer’s deceleration needs for towing heavy trailers in mountainous regions, and the seemingly very different area of sizing brakes for Battery Electric Vehicles (BEV) and other regenerative braking-intensive vehicle applications, versus brakes for heavy-duty trucks and other vehicles rated to tow heavy trailers. The common threads between these two very different categories of vehicles include (a) heavy dependence on the powertrain and other non-brake sources of energy loss to control the speed of the vehicle on the grade and ensure adequate capacity of the brake system, (b) a need to consider descent conditions where towing a heavy trailer is feasible (in the case of heavy trailer towing) or initiating a descent with a full state of charge is realistic (in the case of BEVs), which forces consideration of different descents versus the typical (for brake engineers) mountain peak descent. In this paper, a vehicle operator-based requirement is derived that integrates engine (or motor), transmission (or gearbox), driveline, brake system, brake cooling, trailer brakes, and for BEVs, battery and regenerative brake capacity, and influences these parameters to enable combination vehicles (tow vehicle plus trailer) of any propulsion type to meet customer expectations for deceleration performance. Case studies are used throughout to illustrate the methodology, and physical (vehicle and chassis dyno) tests are shown to validate it. Additionally, industry standards (such as SAE J2807) that affect straight-line braking of combination vehicles are reviewed for what they really mean to the tow vehicle design. Enhancements to these standards are proposed, and when merged with the mountain pass descent case, form a comprehensive means of specifying brake systems and influencing supporting systems to ensure brake performance for a diverse range of vehicles.</div></div>

Bumper to Bumper: Will the CUSMA Rules of Origin Make America’s Auto Industry Great Again?

In the negotiations for a new NAFTA, the Trump administration targeted the North American auto industry for major change. The Trump administration objected to the NAFTA rules of origin – with which a good must conform to be eligible for NAFTA duty-free treatment – as permitting too much non-NAFTA content in North American automobiles, and was fixated on the US’s significant balance-of-trade deficit with Mexico, most of which is accounted for by trade in automotive goods. For Canada’s auto industry, there is much at stake. In 2017, Canada exported automotive goods to the United States valued at close to $62 billion, and the US market is overwhelmingly the destination for vehicles produced in Canada. Therefore, it is important that the rules of origin with which the Canadian automotive industry must work be transparent and administratively workable. Under the new deal, the Canada-United States-Mexico- Agreement (CUSMA), the rules of origin that will apply to motor vehicles and their parts are considerably more stringent than their NAFTA counterparts. This Commentary analyzes the CUSMA rules of origin for automotive goods, identifies ambiguities and areas of uncertainty, and makes suggestions for clarifications through the adoption of the Uniform Regulations – for which CUSMA fortunately provides – that will provide greater certainty to both businesses and administrators in how to apply the rules. Adapting to the CUSMA rules will require major adjustments in supply chains. This is particularly the case with the substantially higher Regional Value Content (RVC) thresholds required for most automotive goods. While some relief is possible through limited alternative staging alternatives, the staging to the higher RVC thresholds under CUSMA is only three years for passenger vehicles and light trucks, as compared with eight years under NAFTA. Further, the CUSMA rules of origin are needlessly complex. There are multiple categories of parts for different categories of vehicles with varying RVC requirements that depend on the end use of the part. Complexity increases compliance costs, which are burdensome for all producers, but particularly for smaller producers that are less able to afford investment in expensive compliance systems. While the CUSMA rules mostly eliminate NAFTA tracing requirements, the CUSMA text inexplicably retains a form of tracing for several narrow categories of vehicles. The CUSMA rules of origin regime contains two requirements that are unprecedented in such regimes; namely a steel and aluminum purchase requirement and a labour value content requirement. These are performance requirements that are consistent with a managed trade regime (where rules are designed to achieve certain economic outcomes) and not with a free trade regime (which seeks to remove barriers to trade so that economic results are dictated by market forces). Two reports cast doubt on the benefits of the deal for the US. A US International Trade Commission report found, overall, the new rules should lead to a modest increase in employment in the US automotive sector, but the costs of vehicles produced in the United States will increase and production of vehicles there will decline. An International Monetary Fund working paper states quite bluntly that the tighter auto rules of origin will not achieve their desired outcomes by reason of higher costs, increased consumer prices and reduced demand.

Calculation of the overturning wind speed of large road vehicles at exposed sites

High-sided vehicles are particularly vulnerable to high wind conditions and at sites that are regarded as vulnerable, a range of vehicle restrictions are imposed in high winds. These may include vehicle speed reductions or complete restrictions on the movement of different categories of vehicle at different wind gust speeds. This paper builds on earlier work that has been carried out and seeks to develop a simple but conservative method that can be used to specify vehicle restriction strategies. This is based on a collation of a wide range of data for aerodynamic rolling moment coefficients that allows a simple parameterisation to be developed. This is then used in an overturning model to develop a non-dimensional relationship between overturning gust speed and vehicle speed. The parameter used in the non-dimensionalisation is a characteristic wind speed that is a function of vehicle weight and geometry, and effectively specifies the vulnerability of the vehicle to overturning in high winds. Dimensional relationships between overturning gust velocities and vehicle velocities can thus be derived for different vehicle types and used to develop site-specific vehicle restriction methods.

Modelling saturation flow rate and right turn adjustment factor using area occupancy concept

Saturation flow rate is the most important parameter required for the capacity analysis of signalised intersections. Under homogeneous traffic conditions, the concept of constant queue discharge headway is adopted for the estimation of saturation flow. But that concept could not be adopted under heterogeneous traffic conditions due to absence of lane discipline and wide variation in static and dynamic characteristics of vehicles. The variability in vehicle mix under heterogeneous traffic is usually accounted for in the estimation of saturation flow by using the widely accepted measure, Passenger Car Unit (PCU). But estimation of individual PCU values for different categories of vehicles under heterogeneous traffic conditions is a complex task due to its dependency on multitude of site specific factors. Hence in this study saturation flow in PCU/h is estimated with out resorting to individual PCU factors. Area occupancy, a surrogate measure of density and a measure of proportion of time vehicle area is present in observation region, is taken as the basis for the estimation of saturation flow rate in PCU/h. Area occupancy of the heterogeneous traffic stream during saturated green time is equated to standard car occupancy and the number of standard car spaces are determined. Saturation flow in PCU/h is then estimated based on number of standard car spaces. A mathematical model is developed for the prediction of saturation flow for approaches that satisfy the base conditions set in the present study. Effect of right turn movements on saturation flow rate is also explored. Turning vehicles tend to reduce the departure efficiency of through moving vehicles due to the turning manoeuvre in combined movements. Adjustment factor on saturation flow rate is proposed to account for the influence of right turn traffic that is given right of way in same phase along with through traffic.

Probabilistic Gross Vehicle Weights and Associated Axle Loads for Military Vehicles in Bridge Evaluation and Code Calibration

Military vehicles frequently use civilian bridges. The loading effects of military vehicles, both wheeled and tracked, are specific and different than those of civilian vehicles in normal traffic. Calibration to determine appropriate load factors for military loading of civilian bridges has not been fully performed and the corresponding levels of safety have not been quantified. This is partially due to the lack of probabilistic information of the gross vehicle weights and corresponding axle loads of military vehicles while operating in real-world conditions. This paper quantifies probabilistically the gross vehicle weight and axle loads for three military vehicles in use by NATO, each of which is representative of: military transport vehicles; armoured personnel carriers; and main battle tanks. A general means are proposed to quantify the probabilistic gross vehicle weight of military vehicles on the basis of maximum nominal payload as a proportion of the total nominal vehicle weight. Based on observed probabilistic gross vehicle weight of military vehicles, it is recommended to differentiate between military transport and military fighting vehicles as different categories of vehicles in bridge evaluation.