Analysis Of Traffic Conditions Research Articles

ОЦЕНКА ЗОН ТРАНСПОРТНЫХ ЗАТОРОВ НА ОСНОВЕ ДАННЫХ ОБ УСЛОВИЯХ ДОРОЖНОГО ДВИЖЕНИЯ «ПЛАВАЮЩИХ» АВТОМОБИЛЕЙ

Effective forecasting of traffic congestion on the road network benefits various road users. The article provides an analysis of traffic conditions based on data received from “floating” cars in order to assess traffic congestion zones

A Study of Subic Bay Maritime Traffic Condition and Risk Analysis

This study focused on the maritime traffic condition at the Subic Bay Metropolitan Authority, Philippines. It is based on a one-year survey conducted in 24-hour monitoring of shipping traffic movements. Every month, the set is rigged in proximity to the Bay where the movements of ships are targeted. The data collected by the equipment are the ship’s tracks, the time of passage; the sizes and types of ships captured according to their LOA (length overall). These surveys were carried out in a one-day duration, each month, for a period of one (1) year to realize the pattern of movements of each ship captured around the Bay. The one-year survey conducted had shown that vessels continue to visit the Bay in various conducts of commerce. The tracks showed that the existing wharves and piers can handle medium-sized ships at present. The tracks, likewise, demonstrated that the entry/exit of vessels was scattered implying a random route chosen by vessels on a single area of the Bay. Based on the analysis of traffic conditions, it was identified that the bay lacks a traffic management system, like monitoring of movements of sea-crafts while plying in the bay, especially in the Subic Bay Metropolitan Authority area. It is then recommended to continue improving the infrastructure, systems, and redevelopment of the Bay, and to conduct a follow-up/continuum study to analyze the overall safety conditions of the Bay including the off- Subic Bay Metropolitan Authority area.

Traffic arrival pattern estimation at urban intersection using license plate recognition data

Traffic arrival pattern is an indispensable part of the traffic state analysis and optimization of signal control schemes at the urban intersection. In some previous research relevant to the traffic parameters at the intersection, such as the queue length estimation, delay estimation, and vehicle trajectory reconstruction, the vehicle arrival patterns are assumed to obey a Normal or Poisson distribution. However, the actual traffic arrival patterns at the intersection are dynamic and more complex. License plate recognition (LPR) data containing rich vehicle trajectory information are promising in data-driven applications of traffic control and management. We propose a framework based on the LPR data, to infer the dynamic traffic arrival pattern at the intersection. The proposed integrative data-driven framework consists of three main parts, a method for inferring traffic signal cycle parameters, deriving the cycle start time and cycle length from time headway sequences; a link travel time estimation method, extracting the valid travel time at the selected time interval, and an arrival pattern estimation model based on the above modules to perform the second-level estimation at the downstream intersection. The unique characteristics of LPR data are exploited and utilized in this study. To validate the proposed method, the LPR data from two adjacent intersections in the city of Changsha in China are used. Numerical results reveal that the proposed framework can achieve satisfactory estimation under different traffic scenarios. The findings in this study can be extended for supporting efficient traffic control applications.

Identification of Location and Camera Parameters for Public Live Streaming Web Cameras

Public live streaming web cameras are quite common now and widely used by drivers for qualitative analysis of traffic conditions. At the same time, they can be a valuable source of quantitative information on transport flows and speed for the development of urban traffic models. However, to obtain reliable data from raw video streams, it is necessary to preprocess them, considering the camera location and parameters without direct access to the camera. Here we suggest a procedure for estimating camera parameters, which allows us to determine pixel coordinates for a point cloud in the camera’s view field and transform them into metric data. They are used with advanced moving object detection and tracking for measurements.

Change of drivers’ functional condition during dangerous goods transportation

The study of the human factors in traffic safety is a relatively young scientific field that began to develop several decades before the mass car advent. The origins of transport psychology and psychophysiology were mostly aimed at creating more comfortable and safe driving conditions due to the design of highways. Over time, there was a need to take into account drivers working conditions, namely the ergonomics of vehicles. The latest works have already taken into account previous experience and considered the issues of freight and passenger transportation technological processes, including the human factor. Among the objects and subjects of research in previous works were considered the processes of driving vehicles with different ergonomic characteristics and buses of different capacities, which created a kind of case matrix for planning experiments. When it comes to trucks, the matrix of cases became much larger, as the set of ergonomic and dynamic features of vehicles adds a much wider set of specialization of cargo and truck types. In this case, the most important issue is to determine the road conditions impact the driver's functional condition, engaged in the transport of dangerous goods, which is the purpose of this article. The research used: methods of field survey to establish the values of traffic flow on highways; methods of in-house research to determine the value of road capacity; electrophysiological methods to determine changes in the driver's functional condition. The article describes the typing of road conditions according to their complexity, as well as the differentiation of drivers according to their psychological and professional qualities. Experimental studies of changes in driver's functional state of different age and socionic groups with different road conditions complexity, during dangerous goods transportation (second class of risk). The main results indicate that the analysis of traffic conditions' impact on the driver's functional state shows different dynamics of psycho-emotional stress change for people characterized by certain types of the nervous system and the ratio of their age to work experience.

THE IMPACT OF THE PCE MC CORRECTION VALUE USING MODIFIED AVERAGE TIME HEADWAY METHOD TO ROAD PERFORMANCE (CASE STUDY: VETERAN STREET, MALANG CITY)


 
 
 
 Passenger Car Equivalent (PCE) is a conversion factor of various types of vehicles compared to passenger cars or other light vehicles concerning their impact on traffic behavior. The PCE value used in calculating the performance of urban roads refers to Manual Kapasitas Jalan Indonesia (MKJI 1997) as an Indonesian Highway Capacity Manual. There have been many changes in traffic behavior nowadays compared to in 1997. The calculation of the PCE MC value using the average time headway method does not match the actual conditions. Therefore, modifications are needed by calculating the vehicle dimension factors to obtain the PCE value. The result of the PCE MC calculation using the modified average time headway method shows 0.17. This value is smaller than the value of the PCE MC set by the MKJI 1997, which shows 0.25. Based on the analysis of traffic conditions for ten years, the DS value using the PCE MC according to the MKJI 1997 obtains DS>1. Meanwhile, the DS value calculated based on the corrected PCE MC has DS<1. The difference in the value of PCE MC has implications for road performance. As a result, it affects traffic maintenance management in the form of maintenance costs and decision-making.
 
 
 

Learning a robust classifier for short-term traffic state prediction

Accurate and real-time prediction of short-term traffic states is a crucial research topic in modern intelligent transportation systems. However, effectively modeling traffic state predictions is difficult due to the complicated characteristics of stochastic and dynamic traffic processes. In addition, collected traffic data are typically influenced by external factors (e.g., weather, traffic jams and accidents), leading to errors and missing data. This increases the difficulty in selecting an effective method for predicting traffic conditions over time. To improve the traffic state prediction performance and alleviate the negative effect of traffic data with outliers, a novel multiclass classification least squares twin support vector machine model based on the robust L2,p-norm (0<p≤2) distance, known as PLSTSVM, was proposed. We adjusted the PLSTSVM parameters to balance the prediction accuracy and training time. To solve the optimization problem of the PLSTSVM, an iterative algorithm was developed, which has great potential for solving other optimization problems. In addition, an integrated classification indicator system based on speed, traffic volume, occupancy rate and ample degree was used, increasing the feasibility of the traffic state analysis. To improve the learning and generalization abilities of the nonlinear PLSTSVM, we combined the polynomial kernel function and the radial basis function to construct a hybrid kernel function. The results on two real traffic datasets demonstrate that our model yields better prediction performance and robustness than other competitors, which make unsatisfactory predictions.

Analysis of the Impact of no Passing Zones on Fuel Costs

The primary task of traffic and transportation planning is to improve the conditions of traffic and level of service on the entire road network. On three sections, which were analyzed in this paper, the possibility of reducing the precentage of no passing zones was noticed, by changing the horizontal signalization on the segments of road section where the clear visibility for overtaking was determined for one direction of the two-way highway. In the first part of the paper, the analysis of traffic conditions on the existing and on the newly designed variants of all three observed road sections was performed. Analysis of the level of service on the intersections that are part of this research are done in chapter 2. Increasing the speed on the newly designed section results in a reduced travel time and this leads to savings in fuel costs as shown in chapter 3. Traffic forecast for the planning 2029 is done in chapter 4, the forecasted traffic load implies future traffic volumes for which adequate capacities of the road network need to be provided. Chapter 5 is based on determination of the relationship between the benefits brought by the newly designed variant and the costs that are realized during its construction. The results of this analysis were used as input data for the economic evaluation of the project solutions, where the savings in fuel costs were analyzed.

Visualization and Analysis of Traffic Flow and Congestion in India

The paper takes an analysis of traffic conditions in a developing country, namely, India. India is a country with a rapidly growing economy and a large market, and it has the second largest population in the world, which was 1.3 billion in 2018. India also suffers from environmental problems, such as air pollution and global warming that is contributed by traffic CO2 emissions from transportation. In order to analyze this problem, a particularly challenging issue in developing countries like India, is the collection of traffic data. In general, developing countries do not often have well established infrastructure such as installations of small traffic signals, they lack new road construction and public transportation, etc. This study is the first real traffic congestion analysis in India and introduces unique traffic flow analysis such as: (1) Collecting over a month of recent traffic data in a major city in India, (2) defining traffic congestion from occupancy parameter based on traffic flow theory and observation data, and (3) using geographical special analysis (GIS) for identifying traffic congestion location. These three combination analysis enables one to identify the most congested area in the city with quantitative congestion condition. This study becomes useful to other countries that have similar issues.

Highway Event Detection Algorithm Based on Improved Fast Peak Clustering

Aiming at the mining of traffic events based on large amounts of highway data, this paper proposes an improved fast peak clustering algorithm to process highway toll data. The highway toll data are first analyzed, and a data cleaning method based on the sum of similar coefficients is proposed to process the original data. Next, to avoid the shortcomings of the excessive subjectivity of the original algorithm, an improved fast peak clustering algorithm is proposed. Finally, the improved algorithm is applied to highway traffic condition analysis and abnormal event mining to obtain more accurate and intuitive clustering results. Compared with two classical algorithms, namely, the k-means and density-based spatial clustering of applications with noise (DBSCAN) algorithms, as well as the unimproved original fast peak clustering algorithm, the proposed algorithm is faster and more accurate and can reveal the complex relationships among massive data more efficiently. During the process of reforming the toll system, the algorithm can automatically and more efficiently analyze massive toll data and detect abnormal events, thereby providing a theoretical basis and data support for the operation monitoring and maintenance of highways.

Real-Time Traffic Flow Controls at Signalised Intersections: A Case Study of the Said-Jawda intersection in Karbala

As the problem of urban traffic congestion increases, the need for advanced technology and equipment to be introduced for the improvement of traffic control also increases. Traffic problems have worsened last years due to significant increases in the number of vehicles on the roads and the limited availability of resources in the current infrastructure. The easiest way to control traffic lights is to use timer for each phase; however, another way is to use electronic sensors to detect vehicles to supply the required traffic signals for a given cycle. To enable effective traffic flow through a signalised intersection, this study proposes a system for controlling traffic lights that detects vehicles using videos and imaging techniques instead of electronic sensors embedded in the pavement. Four cameras were installed at a height of three metres on the four approaches to a set of traffic lights. The captured images were then analysed using digital image processing based on Python to facilitate vehicle detection and traffic condition analysis. The work is thus divided into two segments: data collection and traffic control solution generation. The results indicated that using the proposed automated system could produce more effective traffic control as compared to SIDRA fixed-time controls, with delay reduced within the range 40 to 90% and cycle length reduced by 30 to 65%.

Alternative approach to traffic state analysis on Indian roads using image processing

In India, video monitoring of urban roads in order to estimate traffic condition parameters (volume, speed and density) is difficult due to the heterogeneous nature of the traffic and poor lane discipline. As an alternative, a methodology to estimate levels of traffic congestion on urban roads using image processing of monitored video recordings was developed. Four levels of congestion, from completely congested to free-flow conditions, were identified utilising surrogates such as area occupancy or average velocity along with additional attributes such as the number of corner features extracted using image processing. The algorithm was adjusted for changes in lighting and camera angles. The performance of the framework was evaluated on videos recorded from a major road in Chennai, India. The findings indicated that the proposed framework is well-suited for real-time identification of traffic states and has potential as a cost-effective tool for updating traveller information systems in similar road transport networks.

Network-wide identification of turn-level intersection congestion using only low-frequency probe vehicle data

Locating the bottlenecks in cities where traffic congestion usually occurs is essential prior to solving congestion problems. Therefore, this paper proposes a low-frequency probe vehicle data (PVD)-based method to identify turn-level intersection traffic congestion in an urban road network. This method initially divides an urban area into meter-scale square cells and maps PVD into those cells and then identifies the cells that correspond to road intersections by taking advantage of the fixed-location stop-and-go characteristics of traffic passing through intersections. With those rasterized road intersections, the proposed method recognizes probe vehicles’ turning directions and provides preliminary analysis of traffic conditions at all turning directions. The proposed method is map-independent (i.e., no digital map is needed) and computationally efficient and is able to rapidly screen most of the intersections for turn-level congestion in a road network. Thereby, this method is expected to greatly decrease traffic engineers’ workloads by providing information regarding where and when to investigate and solve traffic congestion problems.

Evaluation of the Quality of Service of Pedestrian Flows on a Signalized Intersections

In 5th edition of Highway Capacity Manual (2010) the methodology for evaluating the quality of service provided to pedestrians traveling through a signalized intersection first time was shown. The same methodology has been retained in the latest edition of the aforementioned manual. Provided methodology include determination of the key parameters for evaluation the quality of service of pedestrian flows on signalized intersections. In this paper mentioned methodology was shown through the short procedural steps. In order to verify the possibility of applying this procedure in local conditions, an analysis of traffic conditions at an intersection in Novi Sad was performed as an example.

Kernel PCA for road traffic data non‐linear feature extraction

Road traffic data bring great challenges for data processing and traffic-state analysis. The feature extraction is an effective way to make full use of road traffic data. Here, the authors explore the temporal correlation of road traffic data in time series and a new non-linear feature extraction method for road traffic data, namely, kernel principal component analysis (PCA). The low-dimensional road traffic data are mapped into the high-dimensional space based on a kernel function, and then the PCA method is applied to conduct feature extraction in the high-dimensional space. To have a clear comparison, the authors compare the kernel PCA method with a linear PCA-based feature extraction method. The results show that the extracted features under kernel PCA are more useful than PCA and the kernel PCA method has a better compression effect than PCA under the same condition.

Analysis of the main bases of the coastal zone, as a pathway for the movement of special vehicle and monitoring complexes

The key point of the article, developed in Nizhny Novgorod State Technical University n.a. R.E. Alekseev is Autonomous mobile robotic complex for monitoring of coastal zones. The classification of coastal zones as the pathway for special vehicle and mobile complexes of coastal zones monitoring is considered. The analysis of traffic conditions is carried out. It is concluded that the dominant in the coastal zones, which account for most of the supporting surfaces, can be considered sand-gravel and sand areas, as well as snow pathway in the winter.

Chinese microblog users’ sentiment-based traffic condition analysis

With the increasing number of vehicles in China, traffic condition analysis is of great significance to urban planning and public administration. However, the state-of-the-art traffic condition analysis approaches mainly rely on sensors, which are high cost and limited coverage. To solve these problems, we propose a semi-supervised learning method which uses the social network data instead and analyzes the traffic condition based on users’ sentiment in Chinese Microblog. This approach is a low-cost crowdsourcing solution. Firstly, we train the gated recurrent unit (GRU) model and generative adversarial networks to estimate the sentiment of Microblog with traffic information. Secondly, we calculate the Traffic Sentiment Index to predict whether traffic jams happen or not. In order to reduce the data annotated by manpower, we propose a new idea to employ the conditional generative adversarial networks to generate robust features which are used as a supplement to the training set of GRU. Finally compared with the GRU model trained by solely the manual annotation data, our method improves the classification accuracy by 3.79%. Furthermore, by using the Traffic Sentiment Index, we build a traffic condition analysis system and predict the time and roads of traffic jams in 4 Chinese cities. The predication experiment shows similar results with Baidu map which uses a lot of mobile phones as sensors, and proves the low-cost characteristic and performance of our approach.

Analysis Of Traffic Conditions Based On The Percentage Of Drivers Using The Instructions Displayed On VMS Boards

Abstract The theme of the publication is to show the influence of human factor on traffic conditions during the traffic incident. The publication also depicts the functionality of the model at which the simulation was performed. The model was constructed in the VISSIM and VISUM software also using Visual Basic for Applications – Excel, [8,9]. By coordinating programs VBA and VISSIM was automated turned on or off the incident as well as turned on or off the VMS with information about the proposed of the alternative route. The additional differentiation of the percentage of drivers using the information displayed enabled to compare the data with identical external conditions influencing at traffic. For statistical analysis of data was used statistical program Statgraphics Centurion which made possible to build a model describing the impact of the behavior of drivers on traffic conditions. It is an innovative approach to modeling the impact on traffic conditions accepted by drivers information transmitted on the boards.

Analysis of Traffic Conditions in Urban Region Based on Data from Fixed Detectors

To evaluate traffic conditions in an urban region, the average travel speed in the street network and the average traffic flow per lane are selected as indexes. The results for identifying traffic conditions are obtained using macroscopic fundamental diagram. Because of the difficulty of collecting the traffic parameter of travel speed from widely distributed fixed detectors directly, in this paper, the relationship between average travel speed and queue lengths at signalized intersections within the urban area is established based on an equivalent assumption. Finally, the average travel speed estimation model proposed in this paper is tested with microscopic simulation platform, and the results showed that the average travel speed estimation model performed well with satisfactory accuracy. In addition, the best performance of the network efficiency can be identified based on the reflections of macroscopic fundamental diagram, and the evaluation system is simple enough for application in engineering. Therefore, the result is instructive both for generating traffic control strategies and for making route choices.

Modeling Impact of Traffic Conditions on Variability of Midblock Roadside Fine Particulate Matter

The objective of this study was to examine the concentration variation of midblock roadside particulate matter less than 2.5 &amp;Mu;m (PM2.5) as a function of very high resolution meteorological and traffic data. Morning peak period measurements were taken at a midblock roadside location on an urban arterial commuter roadway. For the impact of dynamic traffic conditions to be captured, data were analyzed at 10-s intervals, a substantially higher resolution than that used in typical roadside air quality study designs. Particular attention was paid to changes in traffic conditions, including fleet mix, queuing, and vehicle platooning over the course of the study period, and the effect of these changes on PM2.5. Significant correlations were observed between vehicle platoons and increases in PM2.5 concentrations. Traffic state analysis was employed to determine median PM2.5 levels before and after the onset of congestion. A multivariate regression model was estimated to determine significant PM2.5 predictors while controlling for autocorrelation. Significance was found not only in the simultaneous traffic variables but also in lagged traffic variables; in addition, the effects of vehicle types and wind direction were quantified. Modeling results indicated that traffic state (e.g., congestion) and vehicle type had a significant impact on roadside PM2.5 concentrations. This study serves as a demonstration of the abilities of very-high-resolution data to identify the effects of relatively minute changes in traffic conditions on air pollutant concentrations.