Actual Traffic Conditions Research Articles

Analysis of the Exhaust Emissions of Hybrid Vehicles for the Current and Future RDE Driving Cycle

Hybrid vehicles account for the largest share of new motor vehicle sales in Europe. These are vehicles that are expected to bridge the technological gap between vehicles with internal combustion engines and electric vehicles. Such a solution also makes it possible to meet the limits of motor vehicle emissions, at a time when it is particularly important to test them under actual traffic conditions. This article analyzes the impact of the length of the test routes in relation to current, but also future regulations of approval standards. Three routes of post-phase composition (urban, rural, motorway) with lengths of about 30, 16 and 8 km were selected for the study. Measurements of the main emission components were made using portable emission measurement systems (PEMS), and exhaust emissions were determined using the moving average window (MAW) method. Analysis of the obtained results led to the conclusion that the current requirements for the RDE test (in particular, the duration of the test) enforce a length of each part of 32 km. Reducing the test to 60–90 min causes the individual phases to last 16 km, and the main advantage of such a solution is the very strong influence of the cold start phase on the emission results in the urban phase. Future declarations by lawmakers to drastically reduce the length of the test phases to 8 km will force hybrid vehicles to be tested largely using the internal combustion engine. This will be the right thing to do, especially in the urban phase, as now in addition to a significant reduction in the engine warm-up phase, manufacturers will have to take into account that such an engine thermal condition can also occur in the rural phase.

Research on highway traffic flow prediction model and decision-making method

In order to solve the problem of traffic congestion in a certain area, this paper develops a set of traffic optimization decision system. For analyzing the actual traffic conditions and calculating the traffic volume, density and traffic speed, a traffic prediction model is established and updated iteratively to modify the prediction model parameters. Based on this model, the congestion degree is estimated at the current road section, thus, an intelligent decision-making and the coordinated optimization methods are proposed. Moreover, this paper implements some application experiments on the isometric road of a three-intersection and obtains better prediction results of traffic density and traffic speed on the three-section highway. At the same time, compared with other existing prediction methods, the prediction model presented in this paper not only has higher accuracy, shorter prediction time and stronger anti-interference ability, but also has better effect on vehicle diversion. In addition, it also greatly relieves the traffic pressure on the road, maximizes the complementary advantages between intersections, and balances the good cooperation between each intersection.

Time-varying spatial accessibility of primary healthcare services based on spatiotemporal variations in demand, supply, and traffic conditions: A case study of Seoul, South Korea

IntroductionAlthough demand, supply, and traffic conditions are interdependent, scholars have considered them separately when measuring accessibility of healthcare services. Additionally, the spatiotemporal variations in these three factors are given relatively less attention in healthcare accessibility research, thereby misleading the policy decisions of healthcare planners. This study aims to examine the spatiotemporal variability of accessibility to primary healthcare services and identify areas with a spatial discrepancy between time-varying and static accessibility models in Seoul, South Korea. MethodsUsing the generalized two-step floating catchment area method, we measure time-varying spatial accessibility with de facto population from hourly collected mobile-based data, available primary healthcare facilities, and actual traffic conditions in Seoul. Additionally, the bivariate Local Moran's I allowed for the identification of areas with discrepancies between the two accessibility models. ResultsThe results show that a geographical pattern of time-varying accessibility significantly differs with that of static accessibility. Time-varying accessibility is lower in commercial and business areas and higher in residential areas, compared with static accessibility, which is higher in central Seoul. The result of the bivariate Local Moran's I analysis highlights that ignoring time variations of the three factors will result in overestimation in commercial areas and underestimation in residential areas. Based on the discrepancy between the two models, we identified the overestimated and underestimated areas. ConclusionWe suggest areas to which healthcare policy needs to pay more attention by identifying areas with spatial discrepancies between time-varying accessibility and static accessibility. Considering the time variations will provide a more realistic impression of spatial accessibility to primary healthcare.

A novel deep deterministic policy gradient model applied to intelligent transportation system security problems in 5G and 6G network scenarios

Traffic congestion has been an actual problem in large cities, causing personal inconvenience and environmental pollution. To solve this problem, new applications for Intelligent Transportation System (ITS) have been created, to monitor actual traffic conditions. Therefore, fast, reliable and safe systems are desirable for creating a real intelligent transportation environment. Deep learning algorithms have been proposed for a better understanding of traffic behavior from a security-related perspective. Thus, we aim to maximize the safety problems using a deep learning algorithm, where a novel policy gradient model is presented for detecting vehicular misuse. The proposed model uses a triple network replay algorithm, maximizing the network convergence speed. Three networks are selected to optimize the policy network variables. Finally, the replay algorithm is partitioned with the aim of obtaining a faster model. Simulations on a real urban map are performed in a scenario with the integration of 5G or 6G networks. An architectural model for the integration of a Vehicular Ad-hoc Network (VANET) and cellular networks is determined in software-defined networking (SDN). The results show that the accuracy prediction of the proposed system presents better performance compared to related studies, where the proposed model increases its convergence speed and cumulative reward. Thus, the ITS improvement by the proposed deep learning algorithm increases the prediction accuracy, and reduces the transmission delay, treating the traffic path according to the congestion.

Erroneous Vehicle Velocity Estimation Correction Using Anisotropic Magnetoresistive (AMR) Sensors.

Magnetic field sensors installed in the road infrastructure can be used for autonomous traffic flow parametrization. Although the main goal of such a measuring system is the recognition of the class of vehicle and classification, velocity is the essential parameter for further calculation and it must be estimated with high reliability. In-field test campaigns, during actual traffic conditions, showed that commonly accepted velocity estimation methods occasionally produce highly erroneous results. For anomaly detection, we propose a criterion and two different correction algorithms. Non-linear signal rescaling and time-based segmentation algorithms are presented and compared for faulty result mitigation. The first one consists of suppressing the highly distorted signal peaks and looking for the best match with cross-correlation. The second approach relies on signals segmentation according to the feature points and multiple cross-correlation comparisons. The proposed two algorithms are evaluated with a dataset of over 300 magnetic signatures of a vehicle from unconstraint traffic conditions. Results show that the proposed criteria highlight all greatly faulty results and that the correction algorithms reduce the maximum error by twofold, but due to the increased mean error, mitigation technics shall be used explicitly with distorted signals.

An Origin-Destination Demands-Based Multipath-Band Approach to Time-Varying Arterial Coordination

With the development of intelligent transportation technology, more and more traffic information can be obtained to enhance arterial coordination efficiency. A time-varying arterial coordination control program is proposed in this paper. By extracting Origin-Destination (OD) information from Automatic number plate recognition (ANPR) data, an OD-based Multipath-Band method (MP-BAND) is developed to achieve the function of the program. The proposed method breaks vehicle routing at intersections, and treats one link as an analysis unit. The method can automatically select link paths for coordination, and multiple paths can be synchronized simultaneously according to the actual traffic condition. The overlap bandwidth is introduced to capture the connectivity between paths of adjacent intersections. The PM-BAND is formulated as a mixed-integer linear program, which can be solved by the standard branch-and-bound technique. Numerical tests are conducted to evaluate the performance of MP-BAND under different traffic conditions. The results have demonstrated that MP-BAND outperformed MULTIBAND and AM-BAND in various aspects. MP-BAND can improve network performance significantly almost in all scenarios. Moreover, the intersection efficiency was improved significantly, especially the efficiency of left-turning vehicles. Also, MP-BAND can well recognize major routes and improve traffic efficiency. Compared with Synchro, MP-BAND also had superiority when flow rate is not too high. Thus, MP-BAND has a much wider applicability, and can be treated as an optimization core to achieve time-varying arterial coordination.

Resilience Analysis of New York City Transportation Network After Snow Storms

This study presents a quantitative analysis of the snow storm resilience of New York City (NYC) by utilizing network science-based system performance functions and publicly available datasets, that is, New York Department of Sanitation’s snow removal operation and NYC traffic speed data. Several graph theory based metrics and some heuristic metrics are utilized to calculate the temporal changes in transportation network functionality along eight snow events. The NYC transportation network is updated based on snowplow movements, and the performance indexes (PIs) for all metrics are calculated throughout the snow storm timeline. Since PI graphs rely on network topology but not the actual traffic conditions, the times that the system bounces back to “regular” conditions (i.e., time-to-recovery/resilience) are calculated based on the similarity between hourly speed distributions on NYC roads. Bhattacharyya distance and Kolmogorov–Smirnov test are used as measures for distribution similarity. Accordingly, the PI values that correspond to the recovery times are also identified. Within the limitations of the size of the snow storm sample, the findings show that less data-intensive graph theory metrics can be used to estimate the transportation network performance—an estimation that would require extensive and detailed data otherwise. Accordingly, these metrics can be used to make resilience predictions for future events through simulations on modified network topology, and help make recovery forecasts to inform local governments and businesses on when to resume regular operations.

Trajectory prediction for heterogeneous traffic-agents using knowledge correction data-driven model

There is a dilemma regarding the accuracy and reality of vehicle trajectory prediction. Balancing and predicting the effective trajectory is a topic of debate in autonomous driving. We investigated this issue using knowledge-driven and data-driven methods to estimate the performance of the two most common methods and found that improving the accuracy, in reality, is challenging. Therefore, we propose a novel trajectory prediction framework for heterogeneous traffic agents, where knowledge residuals are associated with data-driven methods and correct the results to make them more consistent with actual traffic conditions on the premise of high accuracy. Experiments on six public datasets showed that the proposed framework outperforms benchmarks. With an ablation study, we further verified that our method has a good generalisability for new scenarios and high generality in data-driven model selection.

Stabilization Strategy of a Novel Car-Following Model with Time Delay and Memory Effect of the Driver

In this paper, a novel car-following model is established integrating the drivers’ memory of previous information. The drivers’ memory of the vehicle ahead is introduced as an influencing factor on the drivers’ expected behavior. The time delay feedback control term is added to the model to increase the stability interval of the system. By comparing the stability intervals of the controlled and uncontrolled models, the necessity of adding a delay feedback control item is demonstrated. The validity and feasibility of the time delay feedback control strategy are proved by numerical simulations. In this paper, the stability interval of the system is determined by the definite integral stability method (DISM) and the Hopf bifurcation analysis method. According to the number of unstable eigenvalues derived from the system eigenvalue equation, the appropriate time delay feedback control parameters are set. By choosing the optimal parameters, the new model can optimize the traffic flow to the maximum extent, eliminate the stop-and-go of vehicles, and make the traffic stable. Numerical examples close to actual traffic conditions are given to verify the feasibility of the control strategy using the verified design steps. Next generation simulation (NGSIM) measurements are used to conduct parameter calibration of the new model.

Train Classification Using a Weigh-in-Motion System and Associated Algorithms to Determine Fatigue Loads.

This paper presents a methodology for classifying train passages into different types with a weigh-in-motion (WIM) system to allow the calibration of railway fatigue load models and identify individual vehicles from the measurements for the continuous calibration of railway WIM stations from in-service trains. The quality assurance of the measured responses is demonstrated using statistical methods. This paper discusses the measurement station, the method used for processing the raw data, the algorithm used to identify the train types and vehicles automatically, and the limits of the obtained load spectra. The measurement errors are demonstrated to be satisfying for use in fatigue load model calibration. Furthermore, this paper proposes actions for accurately obtaining the actual traffic conditions and describes the future work required in this area.

Development of Online Vissim Traffic Microscopic Calibration Framework Using Artificial Intelligence for Cairo CBD Area

This paper makes a notable contribution to Transportation Planning in urban areas by considering the application of a key transportation Planning software package for the traffic conditions in the Central Business District of Cairo. For urban areas with heterogeneous and very congested traffic conditions and uneven driving behavior such development can be very useful. The paper shows how a microscopic simulation model using a Multilayer Feedforward neural network (MFNN) to calibrate online the VISSIM package driving parameters’ values based on predictions of travel time and traffic flow on the network elements. The two-step calibration procedure is faster and more applicable for on-line models than the approaches followed in current literature that require time-consuming iterations for model calibration. Also, this research uses combined Artificial Intelligence models (Long-Short Term Memory based Recurrent Neural Networks - LSTM-RNNs) and Multilayer Feedforward neural network (MFNNs) and calibrates them based on the driving behavior and traffic condition on successive time intervals. In this way, the prediction of the future traffic condition is based on actual traffic conditions on the past intervals and the actual driving behavior.

Efficiency and Reliability Analysis of Self-Adaptive Two-Stage Fuzzy Control System in Complex Traffic Environment

In this paper, a self-adaptive, two-stage fuzzy controller is established to realize the real-time online optimization of traffic signal timing plan, which takes multimodels of transportation as the research object to analyze the reliability of the control system at the isolated urban intersection. In this system, the first stage calculates traffic urgency degree for all red phases and selects the red phase with maximum traffic urgency degree as the next green phase. The second stage determines whether to extend or terminate the current signal phase. Aiming at the problems of the parameters of membership functions empirical settings and insufficient response to the real-time fluctuation in traffic flow, the controller introduces an improved hybrid genetic algorithm to solve it and enable the controller to self-learn. Finally, a microsimulation platform is constructed based on the VISSIM and Python language to evaluate the efficiency and reliability of the controller under complex actual traffic conditions. Results showed that the average delay time per vehicle is reduced by 14.59%, while the average number of stops per vehicle is reduced by 0.71% compared with the traditional control method. Results indicate that the traffic signal timing plan generated by the controller can efficiently improve the intersection traffic capacity and has good efficiency and reliability under the condition of medium saturation and unsteady flow.

A hybrid algorithm for time-dependent vehicle routing problem with soft time windows and stochastic factors

In order to meet the logistics and distribution requirements with poor actual traffic conditions, this paper considers adding soft time windows and stochastic factors (road traffic congestion, weather changes, etc.) based on classic vehicle routing problem. Firstly, we construct a time-dependent vehicle routing problem (TDVRP) with soft time windows model with the objective function of minimizing the total cost of distribution. A Hybrid Algorithm (HA) combining Sweep Algorithm (SA) and Improved Particle Swarm Optimization (IPSO) is developed to solve this model. Secondly, we compare IPSO with other algorithms through benchmark test functions and traditional vehicle routing problem cases. Finally, By applying HA to the modified Solomon benchmark test set, this paper compares HA with the existing solutions in the number of vehicles used, total transportation distance and total waiting time. Then experimental results manifest the practicability of HA.

Fatigue reliability assessment and life-cycle cost analysis of roadway bridges equipped with weigh-in-motion systems

The fatigue safety assessment of critical details in steel bridges is dominated by several uncertainties mainly related to load modelling. The use of integrated weigh-in-motion (WIM) systems in roadway bridges allows to collect all the necessary information for tracking actual traffic conditions. Based on WIM data, the uncertainty associated to the evaluation of the probability of fatigue failure is reduced, allowing the optimal scheduling of repair and maintenance activities and thus improving safety and economic savings. The paper presents a methodology for estimating fatigue life cycle cost (LCC) of roadway bridges equipped with a WIM system that considers uncertainties in measurement and modelling errors. The procedure allows the quantification of the economic gain enabled by WIM systems, useful in assessing the cost/accuracy trade-off for a given WIM system. It is found that the continuous use of a WIM system may improve the rate of decrease of the structural reliability index, compared with temporally limited use of WIM data. The periodic update of the reliability index and of the probability of failure based on WIM measurements results in a rather accurate estimation of the fatigue life cycle cost, which is crucial in identifying the payback time of the WIM system.

An improved car-following model based on multiple preceding vehicles under connected vehicles environment

Connected vehicles can obtain the position and velocity of multiple preceding vehicles. According to the characteristics of connected vehicles, this paper proposes the new control term and improves the original car-following model. The model considers the optimized acceleration and velocity difference information among the preceding vehicles. Meanwhile, linear stability analysis of the proposed model is executed and the stability criterion is given. It shows that the model has better stability compared with the existing car-following models. Three scenarios including car-following evolution, starting process and braking process are simulated, and the response time, space headway and velocity fluctuations are analyzed separately. The numerical simulation results show that the controlling strategy proposed in this paper has a noticeable effect on the stability of the traffic system. In addition, the calibration results based on the measured data show that the proposed model is more consistent with the actual traffic conditions.

In-line measurement of exhaust mercury emissions by an instrumented light-duty vehicle using both on-road and test track experiments

The initial purpose of this study has been to develop an instrumental platform for monitoring mercury (Hg) emissions from vehicle exhausts under actual traffic conditions. The platform was then mounted onto a fully-instrumented passenger car to identify emission proxies and factors governing the emissions of gaseous elemental Hg (GeM) and its complementary fraction (GdPM). Data obtained from the road were complemented by data acquired on a test track at either stabilized speeds or well-characterized speed variations. GeM emissions increased overall with both driving speed and fuel consumption; nonetheless, they were influenced by the sequence of accelerations and duration associated with the preceding idling or low-speed driving situations. GdPM emissions varied considerably over the course of trips or track tests, with medians ranging from 7% to 70% of the total Hg emissions stemming from fuel. Such high percentages could be explained by a series of redox reactions, whose kinetics and yield were influenced as much by exhaust gas temperature as by driving conditions or the exhaust system structural configuration. Lastly, an analysis of the GeM and GdPM signals showed that urban cores constitute emission hotspots during rush hour when handling low-speed driving and stop-and-go traffic.

Research on Exhaust Emissions in Dynamic Operating States of a Combustion Engine in a Real Driving Emissions Test

The paper describes the method of investigations of exhaust emissions from a combustion engine under operation classified in terms of its dynamic states. In this paper, the engine operating states are determined through the vehicle driving under actual traffic conditions in the RDE (Real Driving Emissions) test. Based on the recorded tracings of the vehicle velocity, the engine states were classified as static for the acceleration of the absolute value lower than the adopted classification limit. Besides, the authors analyzed the engine operating states for the positive as well as negative acceleration. For the adopted engine operating states, zero-dimensional characteristics of the emission intensity for individual exhaust components were determined (average value, coefficient of variation). The influence of the analyzed operating states on the emission of individual exhaust components was assessed. The greatest increase in the intensity of the emission of nitrogen oxides was observed for the positive vehicle acceleration model and the lowest (also for the nitrogen oxides) for the negative vehicle acceleration. On average, the greatest increase in the emission intensity of pollutants and the intensity of particle number occurred for the dynamic states of the engine corresponding to positive acceleration. The conclusions from the performed investigations entitle the authors to propose a greater allowance for the exhaust emission-related criteria in the engine control algorithms.

Calculation method for air resistance coefficient of vehicles in tunnel with different traffic conditions

Extra-long tunnels are developing rapidly as an option for transportation systems. However, owing to their complex configurations, harmful pollutants can easily accumulate, leading to increased reliance on mechanical ventilation systems. Traffic wind can also be used to supplement ventilation. Nevertheless, current tunnel ventilation guidelines cannot accurately reflect the traffic wind. Moreover, wind tunnel tests and numerical methods are time consuming, and do not generally incorporate different traffic conditions. In this study, traffic conditions in a tunnel are quantified into five traffic condition parameters: distribution of lanes, vehicle distance, blockage ratio, vehicle speed, and proportion of large-scale vehicles. Using a computational fluid dynamics (CFD) method, the influences of the five parameters on the air resistance coefficients of vehicles in the tunnel are investigated. The results show that the distribution of lanes, blockage ratio, vehicle distance, and proportion of large-scale vehicles have significant influences on the air resistance coefficients of the vehicles; however, the impact of the vehicle speed is small. Based on the results, a general calculation method for the air resistance coefficient of vehicles in a tunnel is developed, considering the blockage ratio, vehicle distance, and proportion of large-scale vehicles in actual traffic conditions. The method shows acceptable accuracy (with relative errors of less than 10%), and is convenient for use in the design and operation of tunnel ventilation.

Development of E-rickshaw driving cycle (ERDC) based on micro-trip segments using random selection and K-means clustering techniques

In India, auto rickshaws are the most convenient and cheapest mode of near-to-door transport in both rural and urban areas. Such vehicles powered with internal combustion engines (ICEs) are one of the main sources of pollutants on urban corridors. One way to minimize tail-pipe emissions is to use electric motors in place of ICE. To evaluate the vehicle performance, energy consumption, driving behavior, optimal design and management of such electric vehicles, driving cycle is an important tool. So far, only limited studies exist on the development of a driving cycle for e-rickshaw. Moreover, these studies are concentrated in urban traffic environment and research accounting rural and urban environment together remain unexplored. In this study, real world driving data for 100 trips of e-rickshaw are collected on a road stretch passing through rural and urban setting. A high-end GPS data logger was used to collect vehicle kinematics such as continuous speed profile, acceleration/deceleration, heading, and vehicle position coordinates. Nine different driving characteristics representing actual traffic conditions are identified and used for developing e-rickshaw driving cycle (ERDC). Two approaches, random selection and k-means clustering are explored to arrive at best representative ERDC using micro-trips technique. The analysis results revealed that k-means clustering outperforms the random selection method with additional benefit of accounting traffic conditions systematically. The insights from this study can be used to understand and model the performance of e-rickshaw, in terms of energy consumption and driving characteristics, compared to other fossil-fuel driven automobiles.

Optimization of an Energy Storage System for Electric Bus Fast-Charging Station

To relieve the peak operating power of the electric grid for an electric bus fast-charging station, this paper proposes to install a stationary energy storage system and introduces an optimization problem for obtaining the optimal sizes of an energy buffer. The charging power demands of the fast-charging station are uncertain due to arrival time of the electric bus and returned state of charge of the onboard energy storage system can be affected by actual traffic conditions, ambient temperature and other factors. The introduced optimization is formulated as a stochastic program, where the power matching equality of the total charging demands of connected electric buses is described as a chance constraint by denoting a satisfaction probability, then a stochastic supremum for the operating power of the electric grid is defined by actual data and the problem finally can be solved by convex programming. A case study for an existing electric bus fast-charging station in Beijing, China was utilized to verify the optimization method. The result shows that the operation capacity cost and electricity cost of the electric grid can be decreased significantly by installing a 325 kWh energy storage system in the case of a 99% satisfaction probability.