Road Traffic Parameters Research Articles

ANALISA KINERJA LALU LINTAS JALAN JENDERAL AHMAD YANI KOTA PALEMBANG

Traffic performance refers to the evaluation of the service quality of a road segment relative to the traffic flow through it. Road traffic performance assessment is measured by the degree of saturation and travel speed. Road capacity (C) is the ability of a road section to accommodate traffic flow in a certain time unit. Road capacity is influenced by the type of side obstacles and the frequency of occurrence. This research was conducted on Jalan Jenderal Ahmad Yani Campus A, Muhammadiyah University of Palembang to calculate road capacity (C) and traffic performance on Jalan Jenderal Ahmad Yani. Data analysis was carried out using the 2023 Indonesian Road Capacity Guide where road capacity and degree of saturation are road traffic performance parameters so that the value of the level of service is known. Based on the results of research and analysis that has been carried out located on Jalan Jenderal Ahmad Yani, Palembang, the road capacity (C) is 2,924 PCU/hour and the free flow speed (VB) value is 49.59 Km/hour, while the road traffic performance and level of service on the Jalan Simpang 4 Jakabaring-Jalan Jenderal Ahmad Yani section is 0.81 and is classified as category D (Flow is close to stable, speed can still be controlled, traffic volume is approaching capacity), while from the direction of Jalan Jenderal Ahmad Yani -Intersection 4 Jakabaring obtained a service level of 0.94 and was classified as category E (Unstable flow and sometimes stopping, traffic volume approaching capacity, low speed and changing).

Road Traffic Forecasting using AirPollution and Atmospheric Data

In this paper, we propose a novel machine learning approach integrating environmental data into road traffic forecasting models. By leveraging historical data on road traffic, air pollution, and atmospheric parameters, we develop predictive models capable of capturing the intricate interplay between traffic patterns and environmental conditions. Our experimental results demonstrate the effectiveness of our approach in improving traffic forecast accuracy compared to conventional methods. This research contributes to advancing understanding of how environmental factors impact traffic dynamics, offering practical insights for enhancing urban transportation management strategies.

A Hybrid Traffic Forecasting Model for Urban Environments Based on Convolutional and Recurrent Neural Networks

This article presents a hybrid model for the continuous monitoring and prediction of the spatio-temporal dependencies of road traffic. This model is built on the basis of training a YOLOv4 convolutional neural network (CNN) and two-level long-short-term memory (LSTM). The initial module of the hybrid model ensures the continuous collection of data and the filling of the database on road traffic parameters, taking into account transport infrastructure and meteorological factors. Taking the interpreted and aggregated data as input, a recurrent neural network (RNN) generates more accurate predictions. Experimental results show that the proposed model provides prediction accuracy (for a 20-minute time interval) in the range of 82–97 % using a limited number of measurements.

Підвищення ефективності функціонування нерегульованого перехрестя з круговим рухом

The analysis and empirical studies of conditional and unconditional parameters of the traffic model at an unregulated intersection with circular traffic in the city of Ternopil were conducted. The intersection "Zbarazke ring" in the city of Ternopil is an unregulated intersection with a circular traffic pattern and is located at the intersection of Stepan Bandera avenue, April 15 street, T. Protasevich street and Pidvolochyskyi highway. To verify the effectiveness of the proposed solutions, simulation models of the intersection "Zbarazke ring" with the existing and proposed traffic management system were created. The conducted traffic modeling using multi-parameter models made it possible to take into account various options for road infrastructure, restrictions and traffic parameters, which made it possible to justify optimal decisions regarding the improvement of the organization of traffic at the object of study. Five variants of traffic organization at the intersection with circular traffic are proposed, the best of them is modeled, analyzed and substantiated, and ways to improve the quality of functioning of this section of the street-road network are proposed. According to the results of the simulation modeling of the intersection "Zbarazke ring", the implementation of the proposed measures will lead to the following improvements compared to the existing traffic conditions at the intersection: the average speed of vehicles will increase by 30%; the average delay time will decrease by 28%; the average number of road transport stops will decrease by 21%; the average time spent in traffic jams will decrease by 50%.

Identifying Causes of Traffic Crashes Associated with Driver Behavior Using Supervised Machine Learning Methods: Case of Highway 15 in Saudi Arabia

Identifying the causes of road traffic crashes (RTCs) and contributing factors is of utmost importance for developing sustainable road network plans and urban transport management. Driver-related factors are the leading causes of RTCs, and speed is claimed to be a major contributor to crash occurrences. The results reported in the literature are mixed regarding speed-crash occurrence causality on rural and urban roads. Even though recent studies shed some light on factors and the direction of effects, knowledge is still insufficient to allow for specific quantifications. Thus, this paper aimed to contribute to the analysis of speed-crash occurrence causality by identifying the road features and traffic flow parameters leading to RTCs associated with driver errors along an access-controlled major highway (761.6 km of Highway 15 between Taif and Medina) in Saudi Arabia. Binomial logistic regression (BNLOGREG) was employed to predict the probability of RTCs associated with driver errors (p < 0.001), and its results were compared with other supervised machine learning (ML) models, such as random forest (RF) and k-nearest neighbor (kNN) to search for more accurate predictions. The highest classification accuracy (CA) yielded by RF and BNLOGREG was 0.787, compared to kNN’s 0.750. Moreover, RF resulted in the largest area under the ROC (a receiver operating characteristic) curve (AUC for RF = 0.712, BLOGREG = 0.608, and kNN = 0.643). As a result, increases in the number of lanes (NL) and daily average speed of traffic flow (ASF) decreased the probability of driver error-related crashes. Conversely, an increase in annual average daily traffic (AADT) and the availability of straight and horizontal curve sections increased the probability of driver-related RTCs. The findings support previous studies in similar study contexts that looked at speed dispersion in crash occurrence and severity but disagreed with others that looked at absolute speed at individual vehicle or road segment levels. Thus, the paper contributes to insufficient knowledge of the factors in crash occurrences associated with driver errors on major roads within the context of this case study. Finally, crash prevention and mitigation strategies were recommended regarding the factors involved in RTCs and should be implemented when and where they are needed.

Measurements of traffic parameters and conditions in traffic noise research

Abstract: The level of traffic noise depends mainly on road traffic parameters such as: traffic volume, share of trucks in traffic and vehicles speed. Traffic conditions determined by such parameters as, for example, the average travel speed, average delays at the intersection and the number of vehicles stops are also important. Their particular importance is revealed especially in the case of bad traffic conditions and a significant reduction of traffic flow. The paper presents selected methods of measuring road traffic parameters, and in particular - traffic conditions on the example of research on their impact on the noise level in the vicinity of the street and intersections. The results of these tests are also presented, determining the impact of such parameters on traffic noise as: instantaneous speed, sectional speed, average delays at the intersection entry, the number of vehicle stops, volume to capacity of the entry ratio. Keywords: Traffic noise; Traffic conditions; Traffic parameters; Measurement methods

Spatio-Temporal Constraint-Based Low Rank Matrix Completion Approaches for Road Traffic Networks

Road traffic sensing is pivotal in all the imperative services offered by Intelligent Transportation Systems (ITS). Both static and probe based traffic sensing technologies aid in the acquisition of road traffic parameters, represented as spatio-temporal traffic data matrices. These matrices suffer from inevitable data losses, urging accurate matrix reconstruction. Many Low Rank Matrix Completion (LR-MC) approaches have been formulated in the literature, with varied optimization problems and solution methods. The subsequent approaches have exploited spatio-temporal constraints to further reduce the reconstruction error. In this paper, these formulations are modified to develop two new LR-MC approaches, the Augmented Lagrangian Sparsity Regularized Matrix Factorization (AL-SRMF) and the Constrained Low Rank (C-LR). AL-SRMF replaces the Alternating Least Squares (ALS) solution method of SRMF with an AL based method. Optimization in C-LR inherits its temporal constraint from the Temporal and Adaptive Spatial constrained Low Rank (TAS-LR) approach, while adopting its spatial constraint similar to SRMF. Spatial constraint in both C-LR and AL-SRMF is deduced by computing an adjacency matrix that leverages graph notion of the geographical road traffic network. For experimental validations of the proposed approaches, traffic matrices of Californian road network are obtained from PEMS database. Investigations reveal that for a signal integrity (SI) of 0.5, C-LR demonstrated a Normalized Mean Absolute Error (NMAE) of 3.4% and a Root Mean Square Error (RMSE) of 3.2. With a slight increase in computational time, the AL-SRMF rendered least values of NMAE and RMSE, 2.4% and 2.3 respectively, demonstrating significantly better performance than the state-of-the-art approaches.

Integration of Image-Based Fog Detection with Autonomous Decision System for Intelligent Road Sign

Abstract The paper presents the description of the decision system implemented for Intelligent Road Signs. It focuses on the implementation of the novel air transparency analysis system and its integration with the rule system and the speed control infrastructure. Moreover, there are presented issues of making decisions about the content displayed in the case of autonomous and cooperating signs. To reflect more closely on real-life situations, it is assumed that the content presented by the IRS changes dynamically, depending on the road traffic and weather parameters. The IRS system operation was presented using fog detection as an example.

The impacts of road traffic on urban air quality in Jinan based GWR and remote sensing

Traffic congestion and smog are hot topics in recent years. This study analyzes the impacts of road traffic characteristic parameters on urban air quality quantitatively based on aerosol optical thickness (AOD) and geographical weighted regression (GWR) models, including the road network density, road area occupancy, intersection number, and bus network density as main factors. There are some major research findings. Firstly, there exists a strong positive correlation between the peak congestion delay index (PCDI) and air quality, the correlation has R2 values of up to 0.4962 (R 0.70). Secondly, GWR refines the local spatial changes in the AOD and the road parameters, and the correlation R2 based GWR model all above 0.6. The correlation between AOD and the road area occupancy was the highest, and the correlations with the bus network density and the intersections number were higher than that with the road network density. Thus, bus route planning, bus emission reduction, road network planning, and signal timing (at intersections) have a greater impact on air quality than other policy, especially in areas with traffic jams. The results of this study could provide theoretical support for traffic planning and traffic control, and is promising in practice.

Novel Road Traffic Management Strategy for Rapid Clarification of the Emergency Vehicle Route Based on V2V Communications.

Vehicle-to-vehicle communication is a promising paradigm that enables all vehicles in the traffic road to communicate with each other to enhance traffic performance and increase road safety. Through vehicle-to-vehicle (V2V) communication, vehicles can understand the traffic conditions based on the information sent among vehicles on the road. Due to the potential delay caused by traffic jams, emergency vehicles may not be able to reach their destination in the required time, leading to severe losses. The case is more severe especially in developing countries where no emergency-vehicle-dedicated lanes are allocated. In this study, a new emergency vehicle route-clarifying strategy is proposed. The new clarifying strategy is based on vehicular traffic management in different interference medium scenarios. The proposed model aims, through V2V communication, to find the nearest vehicle with which to communicate. This vehicle plays an important role in reducing the travel time: as the emergency message is received, this vehicle will immediately communicate with all the neighboring vehicles on the road. Based on V2V communications, all the vehicles in the road will clear from the lane in the road for the emergency vehicle can safely reach its destination with the minimum possible travel time. The maximum distance between the emergency vehicle and the nearest vehicle was determined under different channel conditions. The proposed strategy applied an optimization technique to find the varied road traffic parameters. The proposed traffic management strategy was evaluated and examined through different assumptions and several simulation scenarios. The obtained results validated the effectiveness and the accuracy of the proposed model, and also indicated significant improvement in the network’s performance in terms of packet delivery ratio (PDR) and average end-to-end delay (E2E).

ENHANCING THE EFFECTIVE FUNCTIONING OF INTERSECTIONS WITH INTENSIVE TRAFFIC AND PEDESTRIAN FLOWS

Introduction. Problematic sections of the road network are intersections with high traffic intensity of conflicting pedestrian and traffic flow, in particular, right and left turns. At such crossings, there are significant traffic delays. In the case of vehicles turning left or right at an intersection when passing at a traffic light signal that allows traffic, should give way to pedestrians crossing the roadway on the crosswalk. In the case of high intensity of pedestrian traffic, it is impossible to complete the turn of such vehicles, they remain at the intersection, so there are significant traffic delays for returning vehicles.Purpose. Therefore, the work aims to study the interaction of traffic and pedestrian flows with high intensity at regulated intersections to improve their capacity.Methods. To study the parameters of traffic flows, in particular, the length of congestion, we used traffic flow models, which include a psychophysiological model of following a vehicle moving in front, and a model based on traffic rules. To model, pedestrian flows the Wiedemann model was used. The leastsquares method was used to obtain empirical nonlinear dependences of congestion length on the intensities of conflicting pedestrian and traffic flows.Results. The paper uses known methods for solving applied problems in the field of traffic for some unique cases, namely the intersection of intensive -traffic and pedestrian flows. Studies of road and pedestrian traffic parameters at crossings have shown that the length of congestion significantly depends on the intensity of traffic flow, but with increas-ing intensity of pedestrian traffic more than 1000 people/h, the length of congestion changes insignificantly. If there are trams at the intersection, the length of congestion increases by an average of 1.14% and depends significantly on the intensity of traffic and pedestrian flows.It is proposed to introduce three-phase traffic light regulation on such sections when one of the phases allows traffic only for pedestrians. The simulation results showed that the length of congestion in the absence of rail vehicles on the approaches to the intersection decreases by an average of 45.3% and is weakly dependent on the intensity of pedestrian flows. If there are rail vehicles at the crossing, the length of congestion can reduce by an average of 13.7%. But in some cases may increase.Conclusion. The research methodology presented in this paper can be used for intersections with another traffic organization scheme to justify the feasibility of introducing threephase traffic light control when one of the phases allows traffic only to pedestrians.

Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction

A vehicular road traffic noise prediction methodology based on machine learning techniques has been presented. The road traffic parameters that have been considered are traffic volume, percentage of heavy vehicles, honking occurrences and the equivalent continuous sound pressure level. Leq A method to include the honking effect in the traffic noise prediction has been illustrated. The techniques that have been used for the prediction of traffic noise are decision trees, random forests, generalized linear models and artificial neural networks. The results obtained by using these methods have been compared on the basis of mean square error, correlation coefficient, coefficient of determination and accuracy. It has been observed that honking is an important parameter and contributes to the overall traffic noise, especially in congested Indian road traffic conditions. The effects of honking noise on the human health cannot be ignored and it should be included as a parameter in the future traffic noise prediction models.

Macroscopic Traffic Simulation of Autonomous Vehicle Effects

The increasing worldwide demand on urban road transportation systems requires more restrictive measures and policies to reduce congestion, time delay and pollution. Autonomous vehicle mobility services, both shared and private, are possibly a good step towards a better road transportation future. This article aims to study the expected impact of private autonomous vehicles on road traffic parameters from a macroscopic level. The proposed methodology focuses on finding the different effects of different combinations of autonomous vehicle penetration and Passenger Car Units (PCU) on the chosen road traffic model. Four parameters are studied: traveled daily kilometers, daily hours, total daily delay and average network speed. The analysis improves the four parameters differently by implementing autonomous vehicles. The parameter total delay has the most significant reduction. Finally, several mathematical models are developed for the percentage of improvement for each chosen parameter.

Real-time monitoring of traffic parameters

This study deals with the problem of rea-time obtaining quality data on the road traffic parameters based on the static street video surveillance camera data. The existing road traffic monitoring solutions are based on the use of traffic cameras located directly above the carriageways, which allows one to obtain fragmentary data on the speed and movement pattern of vehicles. The purpose of the study is to develop a system of high-quality and complete collection of real-time data, such as traffic flow intensity, driving directions, and average vehicle speed. At the same time, the data is collected within the entire functional area of intersections and adjacent road sections, which fall within the street video surveillance camera angle. Our solution is based on the use of the YOLOv3 neural network architecture and SORT open-source tracker. To train the neural network, we marked 6000 images and performed augmentation, which allowed us to form a dataset of 4.3 million vehicles. The basic performance of YOLO was improved using an additional mask branch and optimizing the shape of anchors. To determine the vehicle speed, we used a method of perspective transformation of coordinates from the original image to geographical coordinates. Testing of the system at night and in the daytime at six intersections showed the absolute percentage accuracy of vehicle counting, of no less than 92%. The error in determining the vehicle speed by the projection method, taking into account the camera calibration, did not exceed 1.5 km/h.

Estimating Parameters for Traffic Flow Using Navigation Data on Vehicles

The article describes the method for estimating transport flow parameters using the two-fluid Herman-Prigogine mathematical model developed considering the proposed method of estimating parameters for the system based on the passive processing of navigation data on the movement of vehicles. The efficiency of the suggested algorithms and mathematical models for estimating road traffic flow parameters and the system as a whole was confirmed performing tests using a set of tracks on the main highways of Belarus.

ПЛАНИРОВАНИЕ ДОРОЖНОГО ТРАФИКА В КОНТЕКСТЕ СИСТЕМЫ «УСТОЙЧИВЫЙ ГОРОДСКОЙ ТРАНСПОРТ»

One of the factors affecting the intersection capacity of nodes in the street-road network are cargo transport vehicles in the traffic flows. The existing methods for assessing the impact of cargo transport vehicles on the road traffic parameters are based on statistical data. The research is based on the use of neural networks to process big data (BIGDATA) from CCTV cameras in real time mode. As a result of the interpretation and analysis of big data, the patterns of changes in cargo transport vehicles during the day and its impact on the intersection capacity of nodes in the street-road network were established. The presented study allows to improve the decision-making efficiency while optimizing the road traffic planning.

RESEARCH ON APPLICATION OF TRAFFIC INFORMATION COLLECTION BASED ON MILLIMETER WAVE RADAR

This paper proposes a new type of traffic information collection equipment—millimeter wave radar based on ground sensing coils, video detection, microwave detection and other traffic collection equipment; the purpose of the research is to collect road traffic information parameters at intersections through millimeter wave radar. , Such as traffic volume, traffic flow density, time average speed, interval average speed, headway time, vehicle queue length, etc.; introduced the working principle of the frequency modulated continuous wave (FMCW) millimeter wave radar traffic monitoring system modulated by triangle wave, using the Doppler effect to get The speed of the vehicle; the speed-distance formula and the combination of numbers and shapes are used to obtain the queue length of the vehicle; the triangular wave radar can detect the position information of multiple vehicles at the same time, and can obtain real-time traffic volume and traffic flow density; use the speed of a single vehicle and The position information of multiple vehicles can obtain time average speed, interval average speed, headway time and other parameters; compared with existing research, the results obtained in this paper have certain practical significance.

Vehicle Trajectory Reconstruction for Signalized Intersections with Low-Frequency Floating Car Data

Floating car data are beneficial in estimating traffic conditions in wide areas and are playing an increasing role in traffic surveillance. However, widespread application is limited by low-sample frequency which makes it hard to get a complete picture of a vehicle’s motion. An accurate and reliable reconstruction of a vehicle’s trajectory could effectively result in a higher sampling frequency enabling a more accurate estimation of road traffic parameters. Existing methods require additional information such as nearby vehicles, signal timing strategies, and queue patterns which are not always available. To address this problem, this paper presents a method used with low-sample frequency data to reconstruct vehicle trajectories through intersections, without the need for extra information. Furthermore, the additional parameters for the speed-time curve distributions for deceleration rate and acceleration rate are generated. A piecewise deceleration and acceleration model is developed to calculate the acceleration rate for different travel modes in the trajectory. The distribution parameters of the acceleration data for each travel mode are then estimated using a new Expectation Maximization (EM) algorithm. The acceleration statistics are then used to reconstruct the corresponding parts of the trajectory. Compared to the reference trajectories (truth), the test results show that the method developed in this paper achieves improvement in accuracy ranging from 16 to 67% over the commonly used linear interpolation method. In addition, the proposed method is not very sensitive to the sampling interval of the floating car data, unlike the linear interpolation method where the error grows rapidly with increasing sampling interval.

Estimation of the change in the parameters of traffic in the organization of the bus lane

Abstract The article deals with the issues of improving the quality of public transport services through implementation of the concept of public transport priority. In many cities, when this concept is implemented, insufficient attention is paid to change of traffic for individual transport. Choice of optimal traffic pattern allows to ensure high speed of public transport traffic and reduce negative effect for individual transport when introducing a bus lane without expanding the roadway. The purpose of this research is to determine traffic parameters in introducing a bus lane and minimize deterioration of road traffic for individual transport. The results of simulation of road traffic in a street and road network section with a bus lane have been obtained. Based on the simulation results, the total delay time was calculated for all passengers of both individual and public transport. The results confirm the hypothesis that changing the bus lane parameters affects the road traffic parameters. Dependencies of the total delay time on the road traffic intensity of individual transport and the distance from the bus lane end to the stop line of an intersection.

Clarification on Requirements of Travel Speed Management for Vehicles in Winter Period

Abstract The article develops the requirements for managing travel speeds vs. weather, road conditions and traffic flow parameters in Tyumen region as well as studies the applicability and law enforcement of the existing procedures for establishing the travel speed limits.