Real Traffic Conditions Research Articles

Towards Simpler Approaches for Assessing Fuel Efficiency and CO2 Emissions of Vehicle Engines in Real Traffic Conditions Using On-Board Diagnostic Data

Towards Simpler Approaches for Assessing Fuel Efficiency and CO2 Emissions of Vehicle Engines in Real Traffic Conditions Using On-Board Diagnostic Data

Дослідження режимів розгону автомобіля Skoda Octavia на роликовому стенді

As you know, the main characteristics that determine the state of the power unit are the power level and torque. These parameters affect the dynamic qualities of the car and can also play a role in fuel consumption and emission levels. To check these characteristics, traction roller stands of the inertial type are used. These stands allow you to simulate real traffic conditions and loads. Usually, on such stands, the traction force on the driving wheels is measured, which makes it easy to calculate the power in certain driving modes. However, creating a full-fledged load on the driving wheels of the car being diagnosed requires the use of powerful loading and driving devices. This significantly increases the cost of the stand and its metal content. An alternative to this method is the compressed acceleration method. In this method, the load is created by a relatively small inertial mass together with a loading and driving device of moderate power. The diagnostic parameter in this method can be traction force on the wheels, acceleration, time or path of acceleration of the wheels in the selected speed range. For measurement accuracy, it is better to use acceleration time. However, all the advantages of the method can be manifested only with the correct choice of test modes. During testing, results depend on the accuracy and stability of maintaining the test mode. Acceleration time will be measured more accurately, the slower the wheels and rollers of the stand accelerate. During the tests, it is necessary to choose the high-speed measurement mode to ensure a sufficiently stable torque value. The article analyzes the acceleration modes of a car on a roller stand and their influence on the accuracy of diagnostics. The smallest error is achieved when measuring acceleration time from 50 to 70 km/h in IV gear with additional load or in V gear without load.

Pedestrian detection using a MEMS acoustic array mounted on a moving vehicle

Automatic pedestrian detection in a vehicle is of vital importance in Advanced Driver Assistance Systems (ADAS). Sensors currently used are based on cameras, radars and lidars, whose performance is degraded in environments with reduced visibility: night, smoke, fog, etc. This paper has validated the use of an active array of 150 MEMS (Micro-Electro Mechanical Systems) microphones incorporated into a conventional car moving in real urban traffic conditions, with the system working in real time, at a rate of 8 detections per second. Together with beamforming, Constant False Alarm Rate (CFAR) detection and lane detection algorithms, a crucial algorithm has been incorporated for the proper performance of the system that discriminates the detections of objects or pedestrians generated by the system from the false alarms generated by road imperfections, such as bumps, cracks, etc. Based on 6000 captures, performed with the car moving at 30 km/h, which is the typical speed limit in urban environments, it has been possible to detect pedestrians positioned at a distance from the car varying between 5 and 20 m, with a detection probability of 0.91 and a false alarm probability of 0.01. The results obtained have validated the effectiveness of using active acoustic arrays in the field of pedestrian detection and position estimation from moving cars in urban environments. The fusion of the presented system with the systems currently used for this purpose, would significantly improve the performance of pedestrian detection systems interacting with AEB (Automatic Emergency Breaking) systems, extending their operability to environments with reduced visibility, and resulting in the reduction in the number of possible collisions with pedestrians, thus increasing their safety.

Low-vacuum SEM imaging and viability test of L929 cells exposed to a Euro 6 diesel exhaust gas mixture in a BAT-CELL chamber in comparison with hydrocarbons emission

Exhaust emissions, which count among the most common causes of premature death worldwide, can cause irreversible changes in cells, leading to their damage or degeneration. In this research, L929 line cells were observed after exposure in the BAT-CELL chamber to exhaust gases emitted from a Euro 6 compression-ignition engine. Real road traffic conditions were simulated, taking into account air resistance while driving at speeds of 50 km/h, 120 km/h and idling engine. Morphological analysis of the cells was performed using an environmental scanning electron microscope. It has been observed that diesel exhaust fumes can cause inflammation, which can induce apoptosis or leads to necrotic cell death. The impact of the vehicle exhaust gases can inhibit cell proliferation by almost three times. Moreover, a correlation has been observed between the speed of the inflammatory reaction in cells and the presence of specific hydrocarbon compounds that determine the toxicity of exhaust gases. Research has shown that the toxicity of the emitted exhaust gases has been the highest at the driving speed of 120 km/h. In order to reduce the harmful effects of exhaust emissions, ecological alternatives and the supplementation of legal provisions regarding the compounds subject to limitation are necessary.

Traffic simulation-emission modelling to evaluate impact of signal cycle on automobile emissions

Vehicular traffic comprises different types of vehicles with varying static and dynamic characteristics, under heterogeneous traffic conditions in India. The continuous growth of vehicles results in high congestion and becoming the main source of air pollution. The reduction in emissions is directly linked to the reduction in traffic congestion. At urban corridors, higher concentration of pollutants is observed near the traffic signals because of the influence of traffic signal operations on vehicular speed. The present research is carried out with the principal objective as the study of the effect of intersection control on emissions in the urban intersection of a metropolitan city. Microscopic traffic simulation models have been used to simulate real traffic conditions, and effectively represent the extensive performance of vehicles associated with speed and acceleration states. The simulation model is revised with optimum signal cycle time considering the signal phase system shows a substantial reduction in automobile emissions.

Electromagnetic field exposure monitoring of commercial 28-GHz band 5G base stations in Tokyo, Japan.

Fifth generation (5G) wireless communication is being rolled out around the world. In this work, the latest radio frequency electromagnetic field (EMF) exposure measurement results on commercial 28-GHz band 5G base stations (BSs) deployed in the urban area of Tokyo, Japan, are presented. The measurements were conducted under realistic traffic conditions with a 5G smartphone and using both omnidirectional and horn antennas. First and foremost, in all cases, the electric-field (E-field) intensity is much lower (<-38 dB) than the exposure limits. The E-field intensities for traffic-off cases do not show any significant difference between the two antennas with the maximum being 3.6 dB. For traffic-on cases, the omnidirectional antenna can undesirably capture the radio wave from the smartphone in some cases, resulting in a 7-13 dB higher E-field intensity than that using the horn antenna. We also present comparative results between 4G long term evolution BSs and sub-6-GHz band and 28-GHz band 5G BSs and provide recommendations on acquiring meaningful EMF exposure data. This work is a further step towardthe standardization of the measurement method regarding quasi-millimeter/millimeter wave 5G BSs.

Investigating the feasibility of dynamic speed limit on Indian highways

Speed limits that change as per real time traffic, road, and weather conditions are termed as Dynamic Speed Limits (DSL). Road users are informed of speed limit changes in DSL scheme by electronic signs or Variable Message Signs (VMS) placed above the lanes or side-mounted along highways. In India, most of the highways have static speed limits. The speed limits are kept lower at locations or stretches adjacent to roadside schools, market areas, bus stops and intersections, for enhancing safety of Vulnerable Road Users (VRUs). This causes higher travel time to through traffic while travelling through these stretches. DSL has the potential in reducing the travel time and the variability of speed of vehicles, thus helps in enhancing road safety. The present work investigates the feasibility of DSL on various segments of a typical Indian highway passing through various roadside developments and human activities using cost-benefit analysis. The travel time benefits due to DSL are estimated by comparing the travel times with static and dynamic speed limit conditions. The results indicate that there is a significant amount of saving in aggregate travel time of all vehicles passing through the stretch due to the application of DSL. The benefit cost ratios greater than unity indicated that it is beneficial to deploy DSL on several segments of the highway passing through roadside schools, market areas, bus stops and intersections. The Benefit-Cost Ratio is also used advantageously to prioritize viable locations for deployment of DSL. The findings from the present work are likely to be of interest to policy makers in different developing countries where infrastructure/facilities for VRUs are deficient, road safety is a major concern and speeding is identified as a major problem.

A reinforcement learning framework for improving parking decisions in last-mile delivery

This study leverages simulation-optimisation with a Reinforcement Learning (RL) model to analyse the routing behaviour of delivery vehicles (DVs). We conceptualise the system as a stochastic k-armed bandit problem, representing a sequential interaction between a learner (the DV) and its surrounding environment. Each DV is assigned a random number of customers and an initial delivery route. If a loading zone is unavailable, the RL model is used to select a delivery strategy, thereby modifying its route accordingly. The penalty is gauged by the additional trucking and walking time incurred compared to the originally planned route. Our methodology is tested on a simulated network featuring realistic traffic conditions and a fleet of DVs employing four distinct lastmile delivery strategies. The results of our numerical experiments underscore the advantages of providing DVs with an RL-based decision support system for en-route decision-making, yielding benefits to the overall efficiency of the transport network. Highlights Combining simulation and optimisation algorithms with reinforcement learning Model DVs en-route parking decisions with a k-armed bandit algorithm Evaluating the impacts of delivery strategies on traffic congestion and in last-mile delivery efficiency

ВОССТАНОВЛЕНИЕ ДВИЖЕНИЯ ПОСЛЕ ОТКАЗОВ С ПРИМЕНЕНИЕМ ВИРТУАЛЬНОЙ СЦЕПКИ ПОЕЗДОВ

The article examines the influence of group automatic train operation technology (virtual coupling) on accelerating the process of restoring traffic after a failure with a break in traffic on a railway section. Possible train-to-train intervals have been determined: calculated and statistical estimates taking into account real traffic conditions on the section and using virtual coupling technology. At the same time, the influence of energy supply shortage was not considered; it is assumed that in the process of mass implementation of group automatic train operation technology, the energy supply system will be strengthened to ensure the capabilities of automation systems. Simulation modeling of the operation of a railway section in the IMETRA system (macromodeling system for transport junctions and grounds) was carried out in order to comprehensively assess the impact of the introduction of group automatic train operation on the performance of the section in normal operation and after restoration of traffic in the case of a long-term (8 hours) failure of technical means. The possibility of strengthening technical inspection points with additional teams has been considered to speed up the restoration of traffic after failure of technical means.

Decentralized platoon formation for a fleet of connected and autonomous trucks

This research explores energy-saving potential through platooning, a method aimed at reducing energy consumption, especially for connected and autonomous vehicles allowing shorter inter-vehicle distances. In platooning, energy is saved due to reduced drag, but there is increased energy use during acceleration and deceleration while forming and maintaining platoons. A decentralized game theory model is created to assess energy-saving capabilities in typical traffic scenarios. This model is a dynamic game where drivers act independently, deciding whether to join a platoon to maximize individual gains. A simulation model is developed to replicate vehicle behavior in realistic traffic conditions, estimating energy consumption over time. Findings show that decentralized decision-making leads to system-wide energy savings of 2.2% to 2.6%, contrasting with a 3% saving achieved through centrally mandated platoon formation.

Fuzzy Logic Model for Assessing Accident Proneness Based on Passenger Vehicle Speed in Real and Virtual Traffic Conditions

Fuzzy Logic Model for Assessing Accident Proneness Based on Passenger Vehicle Speed in Real and Virtual Traffic Conditions

A hybrid approach for fatigue assessment of reinforced concrete bridge deck considering realistic bridge-traffic interaction

Reinforced concrete (RC) bridge decks are directly exposed to daily traffic loads and often experience surface cracking caused by excessive stress or fatigue accumulation. The bridge deck fatigue performance over traffic loads needs to be assessed based on accurate dynamic interaction analysis of the bridge and realistic moving vehicles. Most of existing studies on fatigue assessment focus on either the bridge global response without sufficient details about the bridge deck, or refined bridge deck modeling without considering the full dynamic interaction between moving traffic and the bridge structure realistically. A hybrid fatigue assessment approach is developed by combining mode-based global bridge-traffic dynamic interaction analysis, finite-element (FE)-based refined bridge deck model, and fatigue assessment method directly based on the vehicle loads and shear strength of the bridge deck. The proposed approach is demonstrated with a typical 3-span concrete bridge under realistic traffic and road surface conditions. Based on the dynamic interaction and stress analysis results, the fatigue damage factor is further investigated with different road surface roughness levels and heavy truck proportions. It is found that the proposed analytical approach provides a useful tool to predict the bridge deck response and potential fatigue damage under realistic traffic flow.

Developing Fuel Efficiency and CO2 Emission Maps of a Vehicle Engine Based on the On-Board Diagnostic (OBD) Approach

A vehicle interacts with the road, other vehicles, and traffic control devices in real traffic conditions. The level of traffic influences driving patterns and, consequently, this can affect the vehicle´s fuel efficiency and emissions. This study aims to develop engine maps of fuel consumption and CO2 emissions for a light vehicle operating under real traffic conditions. A representative passenger vehicle of the Ecuadorian vehicle fleet, powered by gasoline, was selected for the experimental campaign that was developed on a test route designed according to real driving emission (RDE) regulation. An on-board diagnostic (OBD) device was used for recording in real-time engine and vehicle operating parameters. Moreover, CO2 emissions were estimated using the fuel rate registered from the OBD system of the vehicle This study proposed a novel methodology for developing two-dimensional contour engine maps based on OBD data. The result showed that the vehicle engine operated in real traffic conditions with a brake thermal efficiency (BTE) of 27%, a brake-specific fuel consumption (BSFC) of 275 g/kWh, and a carbon dioxide (CO2) energy-emission factor of 716 g/kWh. In terms of distance, the CO2 emission factor for the tested vehicle was approximately 190 g/km. Overall, this study demonstrates that the OBD approach is a potential method to be used to assess the fuel consumption and emissions of a vehicle operating under real-world traffic conditions, especially in Latin American countries, where portable emission measurement systems (PEMS) are not readily available.

Performance Assessment of an ITU-T Compliant Machine Learning Enhancements for 5G RAN Network Slicing

Network slicing is a technique introduced by 3GPP to enable multi-tenant operation in 5 G systems. However, the support of slicing at the air interface requires not only efficient optimization algorithms operating in real time but also its tight integration into the 5 G control plane. In this paper, we first present a priority-based mechanism enabling defined performance isolation among slices competing for resources. Then, to speed up the resource arbitration process, we propose and compare several supervised machine learning (ML) techniques. We show how to embed the proposed approach into the ITU-T standardized ML architecture. The proposed ML enhancement is evaluated under realistic traffic conditions with respect to the performance criteria defined by GSMA while explicitly accounting for 5 G millimeter wave channel conditions. Our results show that ML techniques are able to provide suitable approximations for the resource allocation process ensuring slice performance isolation, efficient resource use, and fairness. Among the considered algorithms, polynomial regressions show the best results outperforming the exact solution algorithm by 5–6 orders of magnitude in terms of execution time and both neural network and random forest algorithms in terms of accuracy (by 20–40 %), sensitiveness to workload variations and training sample size. Finally, ML algorithms are generally prone to service level agreements (SLA) violation under high load and time-varying channel conditions, implying that an SLA enforcement system is needed in ITU-T's 5 G ML framework.

Exhaust Emissions from Euro 6 Vehicles in WLTC and RDE—Part 1: Methodology and Similarity Conditions Studies

The article is an attempt to perform an ecological assessment of passenger cars with various types of engines in road emission tests. The main research problem and, at the same time, the goal was to develop a method for determining the exhaust emissions from motor vehicles in real traffic conditions based on results obtained in homologation tests. The tests were carried out on vehicles equipped with gasoline, diesel, and hybrid engines, and the obtained results were analyzed. All of the selected vehicles were of the same class—passenger cars, with a similar curb weight, similar maximum engine power, and in the same emission class (Euro 6d). The authors compared the dynamic parameters of vehicle motion in established emission tests: Worldwide harmonized Light vehicles Test Cycles and Real Driving Emissions. Four procedures were used to analyze and compare the operating conditions of the vehicles in the WLTC and RDE tests, differing in how the phases in the tests were divided as well as having a different methodology for determining the road emissions in the tests. The procedures were as follows: WLTC (where the test was divided and the determination of the road emission of exhaust gases was carried out according to the standard WLTP procedure), RDE (the road test was divided into sections and the exhaust emission was determined according to the standard RDE procedure), WLTC1+2 (the test was divided into phases: 1 + 2, 3, and 4; a combination of phases 1 and 2 corresponding to the urban section of the RDE test), WLTCRDE (where drive phases were divided and emissions determined in the same way as in the RDE procedure, which assumes the division of the test into sections based on vehicle speed). The implementation of the research task in the form of an algorithm procedure when comparing the dynamic parameters of the movement in the WLTC and RDE tests is the leading goal presented in this article. The division of the WLTC test into sections (urban, rural, and motorway) according to the RDE procedure and also the calculation of the total emissions in the test according to this procedure resulted in obtaining similar road emission values in the test.

Analysing the Energy Consumption of Urban Electric Vehicles for Real Driving Scenarios

Driving efficiently with an electric vehicle is a key requirement to reduce energy consumption. This article aims to develop a unique two-layer driving pattern recognition approach for the classification of roadway type along with traffic situation for a particular route. The main idea is to use this information to accurately predict the driving pattern and hence analyse the energy consumption of an electric vehicle driving along a specific route under real driving scenarios. The main novelty of the contribution to develop a classification technique based on representative drive cycles, which characterise a roadway type and traffic condition on the basis of certain features. The simulation results indicate a classification accuracy of 85.2–97.7% in driving pattern recognition resulting in prediction of energy consumption under real traffic and road conditions.

Collision avoidance for autonomous surface vessels using novel artificial potential fields

As the demand for transportation through waterways continues to rise, the number of vessels plying the waters has correspondingly increased. This has resulted in a greater number of accidents and collisions between ships, some of which lead to significant loss of life and financial losses. Research has shown that human error is a major factor responsible for such incidents. The maritime industry is constantly exploring newer approaches to autonomy to mitigate this issue. This study presents the use of novel Artificial Potential Fields (APFs) to perform obstacle and collision avoidance in marine environments. This study highlights the advantage of harmonic functions over traditional functions in modeling potential fields. With a modification, the method is extended to effectively avoid dynamic obstacles while adhering to COLREGs. Improved performance is observed as compared to the traditional potential fields and also against the popular velocity obstacle approach. A comprehensive statistical analysis is also performed through Monte Carlo simulations in different congested environments that emulate real traffic conditions to demonstrate robustness of the approach.

Energetic Impacts of Autonomous Vehicles in Real-World Traffic Conditions From Nine Open-Source Datasets

Potential increases or decreases in energy consumption for various traffic operation modes is a research focus in light-duty autonomous vehicle (AV) studies. However, AVs have unclear energy consumption patterns compared with human-driving vehicles (HV), especially under diverse real-world traffic conditions. Considering nine open-source AV activity datasets from six nations, three nontrivial characteristics of light-duty AVs under real traffic conditions were found. 1) The energy consumption of AVs was similar to that of HVs in the low-speed range and was lower than HVs in the high-speed range. 2) The turning point between low-speed and high-speed was a variable parameter for different data sources. 3) The energy consumption of AVs may be higher than that of HVs under complex traffic conditions in the high-speed range. Based on a vehicle-specific power distribution model, this study developed an AV energy consumption model under real traffic conditions at average speeds of 10 to 100 km/h. Potential energy consumption savings and losses were determined using two parameters: the turning point between low-speed and high-speed and the rate of change in vehicle-specific power standard deviation in the high-speed range. Under real traffic conditions, the results implied that light-duty AVs had the potential to reduce energy consumption by 3.78% to 26.47% under stable conditions but could also increase energy consumption by 3.25% to 48.93% compared with light-duty HVs under unstable conditions.

Traffic Flow Pattern Based Approach to Predict Real Driving Emission Test Routes

Due to the poor representation of NEDC emission test, the European Commission put into the force a new procedure, which contains both a laboratory test and a real word driving test. In this new test procedure, even though WLTC replaces NEDC, it is required to test the vehicle under real traffic conditions, which contains urban, rural and highway segments. Due to the new emission regulation, considerable number of academic work focus on the creation of RDE (Real Driving Emission) routes and RDE tests. In this study, it is introduced a new methodology for creating potential RDE routes. The routes, created in MATLAB code by using Istanbul road data containing a half an hour average trip records were used to predict a probable RDE test road. This model creates a number of RDE routes starting from defined coordinates and then analyses all alternative routes with respect to traffic flow rate and RDE boundary conditions identified by the European Commission. The routes obtained using the methodology developed are tested in real life conditions and evaluated according to actual existing traffic conditions. The agreement between the results of the method and the results of the actual driving test is limited in urban road section. However, for rural and highway road sections, there is a significant agreement between the predictions and actual test results.

A Novel Way of Optimizing Headlight Distributions Based on Real Life Traffic and Eye Tracking Data Part 1: Idealized Baseline Distribution

In order to find optimized headlight distributions based on real traffic data, a three-step approach is chosen. Since the complete investigations are too extensive to fit into a single publication, this paper is the first in a series of three publications. Over three papers, a novel way to optimize automotive headlight distributions based on real-life traffic and eye-tracking data is presented, based on 119 test subjects who participated in over 15,000 km of driving, including recordings of gaze behavior, light data, detection distances, and other objects in traffic. In the present paper, a baseline headlight distribution is derived from a series of detection tests conducted under ideal conditions, with a total of three tests, each with 19–30 subjects, conducted within the same test environment. In the first test, the influence of low beam intensity on the detection of pedestrians on the sidewalk (5.0 m from the center of the driving lane) is investigated. In the second test, the influence of different high beam intensities was investigated for the same detection task. In the third test, the headlight distribution and intensity are kept constant at a representative high beam level, but the detection task is changed. In this test, the pedestrian detection target is placed along different detection angles, ranging from immediately adjacent to the road (2.5°) to 15.5 m away from the center of the driving lane (8.0°). As mentioned, all of these tests were conducted under ideal conditions, with the studies taking place on an airfield with a 1.2 km long straight road and normal road markings, but without oncoming traffic, tasks other than keeping the vehicle with cruise control within its lane, or other distracting objects present. The tests yielded two sets of data; the first is the intensity, based on the first two studies, needed to ensure sufficient intensity to detect objects under ideal conditions at distances needed for different driving speeds. The last test then uses these intensities and necessary variations in the required intensity to create an idealized, symmetric headlight distribution as a baseline for subsequent publications. Although the distribution applies only to passenger vehicles like the one used in the test, the same approach could be applied to other vehicle types. The second paper of this series will focus on real traffic objects and their distributions within the traffic space in order to identify relevant areas in headlight distribution when driving under real traffic conditions. The third paper of this series will analyze driver gaze distributions during real driving scenarios. The data from all three papers are used to create optimized headlight distributions, thereby showing how such an optimized distribution relates to current headlight distributions in terms of luminous flux, intensity, and overall distribution.