Traffic Data Collection Methods Research Articles

Data Analysis and Congestion Prediction Model in Intelligent Transportation Systems

The relentless march of urbanization has led to a surge in population density and a dramatic increase in the number of motor vehicles, with traffic congestion emerging as a significant barrier to urban development. Intelligent Transportation Systems (ITS) utilize advanced information technology to monitor and analyze traffic flow in real time, playing a pivotal role in alleviating urban traffic pressures and enhancing road usage efficiency. This paper aims to propose a data analysis method based on machine learning algorithms for real-time traffic monitoring and congestion prediction, thereby providing scientific decision support for urban traffic management. The research encompasses methods of traffic data collection, including data on vehicle location, speed, and flow; preprocessing and feature extraction techniques for traffic data to improve data quality and extract features useful for congestion prediction; and the application of various machine learning algorithms to establish a traffic congestion prediction model. Additionally, this paper assesses and optimizes the model and tests it with actual traffic data to verify its effectiveness and practicality. The research findings indicate that the proposed machine learning-based method can effectively predict traffic congestion, providing a powerful tool for traffic management departments, aiding in preemptive measures to reduce traffic delays and improve the travel experience for citizens. This study not only enriches the field of intelligent transportation systems research but also provides theoretical foundations and technical support for urban traffic management practices.

A Latent-Factor-Model-Based Approach for Traffic Data Imputation with Road Network Information

With the rapid development of the economy, car ownership has grown rapidly, which causes many traffic problems. In recent years, intelligent transportation systems have been used to solve various traffic problems. To achieve effective and efficient traffic management, intelligent transportation systems need a large amount of complete traffic data. However, the current traffic data collection methods result in different forms of missing data. In the last twenty years, although many approaches have been proposed to impute missing data based on different mechanisms, these all have their limitations, which leads to low imputation accuracy, especially when the collected traffic data have a large amount of missing values. To this end, this paper proposes a latent-factor-model-based approach to impute the missing traffic data. In the proposed approach, the spatial information of the road network is first combined with the spatiotemporal matrix of the original traffic data. Then, the latent-factor-model-based algorithm is employed to impute the missing data in the combined matrix of the traffic data. Based on the real traffic data from METR-LA, we found that the imputation accuracy of the proposed approach was better than that of most of the current traffic-data-imputation approaches, especially when the original traffic data are limited.

Evaluating Traffic Operation Conditions during Wildfire Evacuation Using Connected Vehicles Data

With climate change and the resulting rise in temperatures, wildfire risk is increasing all over the world, particularly in the Western United States. Communities in wildland–urban interface (WUI) areas are at the greatest risk of fire. Such fires cause mass evacuations and can result in traffic congestion, endangering the lives of both citizens and first responders. While existing wildfire evacuation research focuses on social science surveys and fire spread modeling, they lack data on traffic operations during such incidents. Additionally, traditional traffic data collection methods are unable to gather large sets of data on historical wildfire events. However, the recent availability of connected vehicle (CV) data containing lane-level precision historical vehicle movement data has enabled researchers to assess traffic operational performance at the region and timeframe of interest. To address this gap, this study utilized a CV dataset to analyze traffic operations during a short-notice evacuation event caused by a wildfire, demonstrating that the CV dataset is an effective tool for accurately assessing traffic delays and overall traffic operation conditions during the selected fire incident. The findings also showed that the selected CV dataset provides high temporal coverage and similar travel time estimates as compared to an alternate method of travel time estimation. The study thus emphasized the importance of utilizing advanced technologies, such as CV data, to develop effective evacuation strategies and improve emergency management.

Traffic Data and Summary Statistics Available through the Long-Term Pavement Performance Program for Pavement Modeling and Beyond

This paper synthesizes traffic data and statistical parameters available through the Long-Term Pavement Performance Program (LTPP), provides recommendations to pavement engineers and researchers about the traffic parameters most suitable for different pavement analyses, and identifies LTPP database tables containing such parameters. The paper also contains information about LTPP traffic data collection methods and recommendations for assessing the rationality of truck weight data collected by weigh-in-motion (WIM) equipment. In summary, this paper was developed to highlight the wealth of truck traffic data available through LTPP and to serve as a resource document for pavement researchers, designers, and transportation planning professionals interested in LTPP truck traffic data and summary parameters characterizing truck traffic.

Research on Traffic Condition Discriminant Technology of Urban Intersections Under Intelligent Lighting System Based on Fuzzy Comprehensive Evaluation

Firstly, the basic traffic data collection methods based on intelligent lighting system are introduced, including RTMS data collection and video data collection. Then, based on the fuzzy comprehensive evaluation, a traffic condition discrimination model of urban intersections under the intelligent lighting system is established. The average speed, stop delay and queue length detected by the intelligent lamp post collection equipment are selected as evaluation factors, and the traffic status of signalized intersections is defined as smooth traffic, normal congestion and severe congestion. The research results have realized the fine discrimination of urban intersection traffic operating condition under the intelligent lighting system.

Edge-Based Traffic Flow Data Collection Method Using Onboard Monocular Camera

AbstractTraffic data collection is the fundamental step in most applications of intelligent transportation systems (ITS). Recently, traffic data collection methods have become more robust and diver...

Integrating Data Mining and Microsimulation Modelling to Reduce Traffic Congestion: A Case Study of Signalized Intersections in Dhaka, Bangladesh

A growing body of research has applied intelligent transportation technologies to reduce traffic congestion at signalized intersections. However, most of these studies have not considered the systematic integration of traffic data collection methods when simulating optimum signal timing. The present study developed a three-part system to create optimized variable signal timing profiles for a congested intersection in Dhaka, regulated by fixed-time traffic signals. Video footage of traffic from the studied intersection was analyzed using a computer vision tool that extracted traffic flow data. The data underwent a further data-mining process, resulting in greater than 90% data accuracy. The final data set was then analyzed by a local traffic expert. Two hybrid scenarios based on the data and the expert’s input were created and simulated at the micro level. The resultant, custom, variable timing profiles for the traffic signals yielded a 40% reduction in vehicle queue length, increases in average travel speed, and a significant overall reduction in traffic congestion.

COMPARATIVE ANALYSIS OF MECHANICAL AND MANUAL MODES OF TRAFFIC SURVEY FOR TRAFFIC LOAD DETERMINATION

Traffic survey was carried out to obtain data that are necessary for traffic study and evaluation. One of the most important characteristics of a traffic stream is its volume which can be defined as the number of vehicles passing through a section of a road per unit time. The road project which is approximately 75 km in length consists of a number of traversed roads, but the traffic study is focused only on the 6 major intersections. The surveys of traffic were conducted using volume count from 7am to 7pm each day because working at night is very unsafe in this area, by using an automatic traffic recorder (Trax 1 Plus) and road tube installation for collecting accurate data with manual counting for comparison. This study aims to analyze the current road traffic data collection methods - comparing both mechanical and manual mode of Traffic Survey simulation - in terms of capabilities and limitations on Nigeria roads using Ogoja (Mbok) Junction and Mfum road in Cross River State as a case study. The tendency of the mechanical counting machine to withstand nonstop24hrs-continuous counting was noted during the exercise compared to manual counting, since security at night cannot be predicted on manual. The design equivalent standard axle load (ESAL) generated from the result shows that Ikom-Cameroon Junction has the highest value with a difference of (0.999x10^ 6 ) between the mechanical and manual, this value shows a downward effect using manual data for the pavement structural thickness evaluation, this can further degenerate to the structural failure at the earlier stage, tensional fatigue, cracks etc. However, traffic study for the period 2015 to 2035 was considered in the analysis, the equivalent standard axle load (ESAL) generated along the section of the corridor and the pavement thickness shows that automatic counter machine is more accurate than the manual. Nonetheless the mechanical counter is more advantageous compared to the manual counter to this reasons the mechanical counter should therefore be employed during the raw data traffic survey. Manual on the other hand less useful for traffic survey in predicting the feature traffic analysis. http://dx.doi.org/10.4314/njt.v35i2.1

Methods of analyzing traffic imagery collected from aerial platforms

A limitation of most traditional methods of traffic data collection is that they rely on techniques that are strictly local in nature. Aerial imagery sensors can provide sufficient resolution to sense vehicle locations and movements across broader spatial and temporal scales. Digital imagery, global positioning systems, and automated image processing can be used to improve the spatial coverage, accuracy and cost-effectiveness of the data collection and reduction. In this paper, an approach for collecting and analyzing aerial imagery is given. To illustrate the value of the imagery, the paper outlines methods to generate estimates of speeds, travel times, densities, and queueing delays.

Computer Vision Application to Automatic Number Plate Recognition

Automatic number plate recognition, using image processing techniques, is indicating new ways of capturing traffic data. Such systems could play an important role in automatic toll collection. The requirement is to develop a practical system able to work under a variety of number plate geometries, sizes, and different fonts. The target of this research is to use neural network techniques to recognize characters in a number plate, so as to achieve an acceptable level of accuracy. In order to depend on such systems to replace the conventional traffic data collection methods, their accuracy level must match conventional methods. An advantage of using automatic systems is that it does not depend on human factors and it can work under difficult circumstances.

SEMI‐AUTOMATIC ANALYSIS OF SMALL FORMAT PHOTOGRAPHY FOR TRAFFIC STUDIES AT COMPLEX INTERSECTIONS

AbstractIt has been recognised for many years that photographic methods of traffic data collection can overcome many of the problems associated with more traditional methods, particularly with regard to the substantial reduction in manpower requirements achieved, the comprehensive nature of the data which may be obtained and the permanent record provided of these data. Against these advantages, the major drawbacks are the expense involved in obtaining suitable photographic coverage and the time consuming operation of collecting the data from the photography.The problems of obtaining photographic coverage have been considered in an earlier paper (Garner and Mountain, 1978a) and recommendations made regarding the most suitable type of photography for analysis of traffic flow data at complex urban and suburban intersections. This paper concentrates, therefore, on the second problem, namely that of extracting the required data from the photographs. The technique described involves the use of a co‐ordinate reader to obtain the two dimensional co‐ordinates of vehicles on successive frames, a coordinate transformation system to convert photographic co‐ordinates to ground coordinates and, finally, a computer based co‐ordinate matching technique.RésuméOn sait depuis plusieurs années que l'application de la photogrammétrie aux études sur la circulation urbaine, peut être fort avantageuse: réduction de personnel, diversité des données obtenues, actualisation facile des fichiers. Un obstacle à leur diffusion cependant: le coût de la prise de vue et la lenteur de l'extraction des données.Le problème de la prise de vue a déjà donné lieu à un article (Garner et Mountain, 1978a). Celui‐ci décrit des méthodes d'exploitation: on use d'un lecteur de coordonnées pour mesurer les coordonnées bi‐dimensionnelles des véhicules sur les clichés et d'une transformation menant à un système cartographique.ZusammenfassungSeit vielen Jahren ist bekannt, dassfotografische Verfahren zur Erfassung von Verkehrsdaten rationeller sind als traditionelle Methoden, insbesondere was die Einsparung an Arbeitszeit, die Datendichte und die kontinuierliche Aufzeichnung betrifft. Dagegen sprechen die erhöhten Kostenfür die fotografische Aufnahme und die zeitaufwendige Datenentanahme aus Bildern.Die Probleme der fotografischen Aufnahme wurden bereits früher erörtert (Garner und Mountain, 1978a), und es wurden Empfehlungen für den günstigsten Aufnahmefall zur Analyse des Verkehrsflusses an komplexen Kreuzungen in Städten bzw. Vorortengegeben. Die vorliegende Arbeit konzentriert sich daher auf das 2. Problem, die Gewinnung der geforderten Daten aus Fotos. Die beschriebene Technologie umfasst die Verwendung einer Koordinatenablesevonichtung zur Ermittlung der zweidimensionalen Koordinaten von Fahrzeugen auf aufeinanderfolgenden Bildern, ein Transformationssystem zur Umwandlung von Bild‐ in Geländekoordinaten und ein rechnergestütztes Aufzeichnungsverfahren.