Traffic Congestion Analysis Research Articles

Traffic Congestion Analysis of Asaba Road Using Volume to Capacity Ratio and Speed Performance Index

With Nnebisi Road acting as a case study, this study focuses on the investigation of traffic congestion on Asaba Road. The study aims to examine the relationship between vehicle volume and road capacity, enabling transportation authorities to assess congestion levels and make informed decisions to alleviate traffic issues. Peak traffic counts were conducted, revealing the highest traffic flow on specific road sections on different days. The Ibusa – Stadium junction saw the highest volume on Monday, reaching 3,056 vehicles, while Ibusa – Ofili-Ilukwu junction peaked at 3,128 vehicles, also on Monday, and Ibusa – Ogbeogonogo junction hit 3,151 vehicles on Tuesday. Speed performance index varied across junctions and days, generally exceeding 60%. For instance, Ibusa – Stadium Junction ranged from 69.57% to 71.82% from Monday to Friday, Ibusa – Ofili-Ilukwu junction showed values between 63.59% and 68.95%, and Ibusa – Ogbeogonogo Junction ranged from 61.57% to 70.11%. These findings suggest a better road traffic state with higher average speeds. Moreover, the volume to capacity ratio, indicates the level of congestion and delays experienced. The ratio values along the studied road sections ranged from 0.61 to 0.70. These ratios, along with the speed performance index, classify the level of service as class B, indicating moderate congestion and delays with slightly longer travel times, yet still acceptable to users.

Smart City Scenario Editor for General What-If Analysis.

Due to increasing urbanization, nowadays, cities are facing challenges spanning multiple domains such as mobility, energy, environment, etc. For example, to reduce traffic congestion, energy consumption, and excessive pollution, big data gathered from legacy systems (e.g., sensors not conformant with modern standards), geographic information systems, gateways of public administrations, and Internet of Things technologies can be exploited to provide insights to assess the current status of a city. Moreover, the possibility to perform what-if analyses is fundamental to analyzing the impact of possible changes in the urban environment. The few available solutions for scenario definitions and analyses are limited to addressing a single domain and providing proprietary formats and tools, with scarce flexibility. Therefore, in this paper, we present a novel scenario model and editor integrated into the open-source Snap4City.org platform to enable several processing and what-if analyses in multiple domains. Different from state-of-the-art software, the proposed solution responds to a series of identified requirements, implements NGSIv2-compliant data models with formal descriptions of the urban context, and a scenario versioning method. Moreover, it allows us to carry out analyses on different domains, as shown with some examples. As a case study, a traffic congestion analysis is provided, confirming the validity and usefulness of the proposed solution. This work was developed in the context of CN MOST, the National Center on Sustainable Mobility in Italy, and for the Tourismo EC project.

Research on Big Data Traffic Congestion Analysis and Judgement Platform Technology

Due to the lack of peak intersection congestion coordination control strategy, at present, most of the points during the peak period of the urban area only fixed cycle control, can not sense the downstream intersection queuing length in time, resulting in the downstream congestion is still normal release, so that part of the vehicle into the intersection queue, in the cross-direction before the release of the intersection can not be timely driven out of the intersection, affecting the cross-release of the vehicle traffic, resulting in intersection blockage. In the platform to introduce the peak intersection congestion coordination control strategy to reduce the length of vehicle queues to ease peak congestion, the platform is mainly responsible for congestion analysis, indicator calculation, traffic research and judgement can be found in the real-time environment of the congestion and the formulation of countermeasures to support the signal control, traffic organisation, police scheduling and so on.

Machine Learning Models in the large-scale prediction of parking space availability for sustainable cities

The search for effective solutions to address traffic congestion presents a significant challenge for large urban cities. Analysis of urban traffic congestion has revealed that more than 70% of it can be attributed to prolonged searches for parking spaces. Consequently, accurate prediction of parking space availability in advance can play a vital role in assisting drivers to find vacant parking spaces quickly. Such solutions hold the potential to reduce traffic congestion and mitigate its detrimental impacts on the environment, economy, and public health. Machine learning algorithms have emerged as promising approaches for predicting parking space availability. However, comparative studies on those machine learning models to evaluate the best suited for a large-scale prediction and within a given prediction time period are missing.In this study, we compared nine machine learning algorithms to assess their efficiency in predicting long-term, large-scale parking space availability. Our comparison was based on two approaches: using on-street parking data alone and 2) incorporating data from external sources (such as weather data). We used automatic machine learning models to compare the performance of different algorithms according to the prediction efficiency and execution time. Our results indicated that the automated machine learning models implemented were well fitted to our data. Notably, the Extra Tree and Random Forest algorithms demonstrated the highest efficiency among the models tested. Moreover, we observed that the Random Forest algorithm exhibited less computational demand than the Extra Tree algorithm, making it particularly advantageous in terms of execution time. Therefore, this work suggests that the Random Forest algorithm is the most suitable machine learning model in terms of efficiency and execution time for accurately predicting large-scale, long-term parking space availability.

Exploring the Application of Big Data Visualization Platform in Urban Traffic Data Analysis

With the rapid development of information technology and the popularity of the Internet, the era of big data has come. The urban traffic system continuously generates a large amount of traffic data, which are so large and diverse that the traditional data processing methods have been unable to effectively process and analyze them. Therefore, it is necessary to process and analyze urban traffic data with the help of emerging big data technologies and methods. Starting from introducing the characteristics of big data visualization platforms and common big data visualization tools and technologies, the article analyzes the scale and characteristics of urban traffic data and the limitations of traditional data analysis methods, discusses the advantages and application prospects of visualization platforms, and focuses on the four perspectives of data cleaning and pre-processing, traffic flow and congestion analysis, traffic behavior and trend analysis, traffic simulation, and planning support. The application of big data visualization platforms in urban traffic data analysis is discussed.

Geographic Analysis of Traffic Congestion in FCT-Abuja, Nigeria

Traffic congestion is responsible for increased urban pollution in urban centers in developing countries. The condition of Abuja traffic congestion is no diverse. The study examines geographic analysis of traffic congestion in FCT-Abuja Nigeria. Efforts were made in this study to accomplish the objectives which are to map traffic congestion areas along selected corridors of Federal Capital Territory (FCT) Abuja using Geographical Information System (GIS) Techniques, Determine the major causes of traffic congestion in the study area and examine the solutions to traffic congestion in the study area. The study adopted random sampling techniques in the administration of well-structured 384 questionnaires to help achieve objective II and III. The data that was collected were analyzed using descriptive statistics. The descriptive analysis that was used includes means, percentage, standard deviation and structured Likert scale. The study revealed that the three major road corridors to the FCT are Abuja- keffi expressway Kubwa –Zuba express and Gwagwalada-Lokoja expressway. The study shows that too many taxis with mean score of 4.14 are the major cause of traffic congestion. While, maintenance of road is the major solution to traffic congestion in the study area with mean score of 4.19. This study recommends the provision of railway service and speedy completion of Apo-Karshi road.

Time delay estimation of traffic congestion propagation due to accidents based on statistical causality

<abstract><p>The accurate estimation of time delays is crucial in traffic congestion analysis, as this information can be used to address fundamental questions regarding the origin and propagation of traffic congestion. However, the exact measurement of time delays during congestion remains a challenge owing to the complex propagation process between roads and high uncertainty regarding future behavior. To overcome this challenge, we propose a novel time delay estimation method for the propagation of traffic congestion due to accidents using lag-specific transfer entropy (TE). The proposed method adopts Markov bootstrap techniques to quantify uncertainty in the time delay estimator. To the best of our knowledge, our proposed method is the first to estimate time delays based on causal relationships between adjacent roads. We validated the method's efficacy using simulated data, as well as real user trajectory data obtained from a major GPS navigation system in South Korea.</p></abstract>

Quantifying the Uncertainty in Long-Term Traffic Prediction Based on PI-ConvLSTM Network

This work proposes a novel uncertainty quantification framework for long-term traffic flow prediction (TFP) based on a sequential deep learning model. Quantifying the uncertainty of TFP is crucial for intelligent transportation system (ITS) to make robust traffic congestion analysis and efficient traffic management due to the inherent uncertain and fluctuating nature of traffic flow. However, the performance (e.g., reliability and sharpness) of uncertainty quantification is hard to guarantee, particularly for long-term traffic flow (e.g., one week or two weeks in advance). To this end, this work develops a nonparametric performance-oriented prediction interval (PI) construction approach based on an enhanced sequential convolutional long short-term memory units (ConvLSTM) model, which is named as PI-ConvLSTM. This model can well learn the temporal correlations involved in the multivariate explanatory samples. Specifically, a periodic pattern learning strategy and a performance-oriented loss function are developed to ensure the quality of the derived PIs. Through validating on the real-life England freeway traffic flow dataset, the proposed PI-ConvLSTM proves to be capable of producing the skillful PIs for long-term TFP. For instance, the performance of derived PIs for two-week ahead is 0.175%, 0.198 and 1957.127 in average in terms of reliability, average width and sharpness, respectively. As compared to the benchmark models the proposed model shows at least 68.1% improvement on reliability, 3.4% on average width and 1.7% on sharpness.

Mechanism analysis of traffic congestion at sag section of the tunnel

Mechanism analysis of traffic congestion at sag section of the tunnel

ANALISIS KEMACETAN LALU LINTAS DI RUAS JALAN PROF. DR. H. ALOE SABOE KOTA GORONTALO (STUDI KASUS RUAS JALAN PASAR MOODU)

The city of Gorontalo is an area that has the potential to develop and compete with other regions, one of which is in terms of development in the field of transportation, especially regarding land transportation. The road is a means of transportation that is very important for human survival. On several roads in Gorontalo the ability to walk is still very limited, one of which is on Jalan Prof. Dr. H. Aloe Saboe Gorontalo City (precisely on the Moodu market road section where every Monday there is always a weekly market which causes congestion on the road, considering that Monday is a busy day. That's why the author took the research title "Traffic Congestion Analysis in Jalan Prof. Dr. H. Aloe Saboe, City of Gorontalo (Case study of the Pasar Moodu road section). To find out the side barriers and the level of road service on Jalan Prof. Dr. H. Aloe Saboe, primary data and secondary data are needed. The primary data needed are traffic volume data, road geometric data, side friction survey and instantaneous speed survey, while the secondary data needed is population data in the city of Gorontalo. Based on the results of the analysis, the free flow speed is obtained on the Prof. Dr. H road section. .Aloe Saboe worth 29.48 km/hour, rated capacity of 1930.1 pcu/hour, side resistance of 421 events/hour included in the category of sed side resistance ang(M), and the level of road service is in category B, which means that the flow is still stable with moderate traffic volume and speed is starting to be limited by traffic conditions, low density and internal traffic barriers have not affected speed.

Machine-Learning-Based Real-Time Multi-Camera Vehicle Tracking and Travel-Time Estimation.

Travel-time estimation of traffic flow is an important problem with critical implications for traffic congestion analysis. We developed techniques for using intersection videos to identify vehicle trajectories across multiple cameras and analyze corridor travel time. Our approach consists of (1) multi-object single-camera tracking, (2) vehicle re-identification among different cameras, (3) multi-object multi-camera tracking, and (4) travel-time estimation. We evaluated the proposed framework on real intersections in Florida with pan and fisheye cameras. The experimental results demonstrate the viability and effectiveness of our method.

Efficient Multimedia Frame-Skipping Architecture Using Deep Learning in Vehicular Networks

With the development of 5G networks, vehicle-to-vehicle communication is helping to make travel safe. However, vehicle type detection in the multimedia feed remains a problem. This helps reduce processing time and enable more dynamic connectivity between different vehicles. The development of object classes requires more robust computer vision models and algorithms. However, the main difficulty still lies in the image quality, which depends on the lighting conditions, viewing angle, and physical structure of the vehicles. This research mainly focuses on the development and deployment of a deep learning-based system for traffic congestion analysis. The model uses multiple video feeds and vehicle information to detect, classify, and count vehicles in the live traffic feed. The model is trained with a deep learning approach to align the video image and detect the object in top-down multimedia. The dynamic skipping method helps to process a long video feed and accurately compares the video image with the viewer. The standard query for the vehicle can help in recognizing and creating the models in real-time traffic situations. The proposed model is suitable for many applications that require a specific area for monitoring real-time data analysis and multimedia routine tasks.

THE PERFORMANCE OF QUEUE LENGTH OF VEHICLE ON THE ROUNDABOUT AT SELAMAT DATANG MONUMENT USING PTV VISSIM

Traffic density or congestion in DKI Jakarta is an issue that difficult to resolved. In this study, an analysis of traffic congestion was carried out by looking at the length of the vehicle queue that occurred at the roundabout. PTV VISSIM performs simulations by entering vehicle volume data and others. The program also shows the results of projections for the next 5 years to find out the impact of traffic at the roundabout. The value of traffic growth per year is 1%. In analyzing the existing conditions using PTV VISSIM, calibration and validation are necessary. On the Jl. Thamrin N-S obtained a queue length of 461.69 m. For sections on Jl. Sudirman S-N is 482.13 m. For the queue length in the existing condition, projections are made for the next 5 years. After the projection, simulation comes with 2 alternatives, road widening and underpass. Based on the projections and alternatives made, the queue length can be obtained. Based on the analysis with these projections and alternatives, the alternative underpass for the queue length on Jl. Thamrin N-S is 0.0 m long and on Jl. Sudirman S-N is 356.20 m. On the Jl. Thamrin N-S is 407.10 m and on Jl. Sudirman S-N is 444.98 m.

Deep Learning-based Detection System for Heavy-Construction Vehicles and Urban Traffic Monitoring

In this intelligent transportation systems era, traffic congestion analysis in terms of vehicle detection followed by tracking their speed is gaining tremendous attention due to its complicated intrinsic ingredients. Specifically, in the existing literature, vehicle detection on highway roads are studied extensively while, to the best of our knowledge the identification and tracking of heavy-construction vehicles such as rollers are not yet fully explored. More specifically, heavy- construction vehicles such as road rollers, trenchers and bulldozers significantly aggravate the congestion in urban roads during peak hours because of their deadly slow movement rates accompanied by their occupation of majority of road portions. Due to these reasons, promising frameworks are very much important, which can identify the heavy-construction vehicles moving in urban traffic-prone roads so that appropriate congestion evaluation strategies can be adopted to monitor traffic situations. To solve these issues, this article proposes a new deep-learning based detection framework, which employs Single Shot Detector (SSD)-based object detection system consisting of CNNs. The experimental evaluations extensively carried out on three different datasets including the benchmark ones MIO-TCD localization dataset, clearly demonstrate the enhanced performance of the proposed detection framework in terms of confidence scores and time efficiency when compared to the existing techniques.

Siting priorities for congestion-reducing projects in Dhaka: a spatiotemporal analysis of traffic congestion, travel times, air pollution, and exposure vulnerability

Traffic congestion increases travel time and is a major source of pollution and health damage in developing-country cities. Data scarcity frequently confines traffic improvement projects to sites where congestion can be easily measured. This article uses spatiotemporal data from new global sources to revisit the siting problem in Dhaka, Bangladesh, where local congestion measures are augmented by estimates of citywide travel time, pollution exposure, and pollution vulnerability. We combine Google Traffic data with an econometric model linking traffic, pollution readings from a local monitoring station, and weather data to estimate the spatial distribution of vehicular pollution. We explore pollution-vulnerability implications by incorporating spatial distributions of poor households, children, and the elderly. Using the Open Source Routing Machine and OpenStreetMaps, we estimate systemwide travel-time gains from reducing congestion at each point in a grid covering the Dhaka metro area. We find a large divergence of siting priorities in single-dimensional exercises that focus exclusively on local congestion, citywide travel time, vehicular pollution, or vulnerable-resident pollution exposure. By implication, optimal siting requires a social objective function with explicit weights assigned to each of the four dimensions. The new global information sources permit extending this multidimensional approach to many cities throughout the developing world.

A prediction model for atmospheric pollution reduction from urban traffic

In order to reduce the atmospheric pollution in urban areas, an enhanced approach is proposed in this paper for the traffic congestion analysis. The approach is formulated as bi-level optimization program considering additional constraints in the traffic assignment problem. To respect the required eco-friendly threshold constraint, the travel demand between several origin–destination pairs was categorized in two classes: old polluting cars and modern (less) nonpolluting cars. The validity of the formulation was verified by optimality conditions. Two network examples are discussed to explain the properties and advantages of the suggested technique. It is found that for the both examples, the proposed optimal solution displays better results as compared to the common user equilibrium route choice policies. As a result, the enhanced approach leads to traffic network congestion relief with minimum air pollution and maximum use of routes network.

A Novel Framework for Detecting Slow Moving Vehicles in Urban Traffic Using Robust Visual Features

In the era of Intelligent transportation systems, traffic congestion analysis based on moving vehicles is gaining tremendous importance due to its significance as well as complex intrinsic ingredients. However, in the existing literature, traffic congestion analysis for urban roads are mainly carried out based on vehicle speed, which may drastically vary depending upon the type of vehicle. On the other hand, in the current urban traffic scenarios, one of the main factor for heavily congested traffic is, the presence of slow moving vehicles(such as Bulldozers and Excavators), which occupy a huge portion of road and also fail to coop with average speed of other moving vehicles. In this way, the slow moving vehicles can affect the traffic flow characteristics to a greater extent, due to which their detection as well as tracking in traffic-prone roads are very much essential in order to design efficient traffic facilities in urban regions. In this paper, a new visual features based framework is presented, which can detect the presence of slow moving vehicles by means of employing SURF-based features. The experimental results carried out on training as well as testing datasets demonstrate the reasonable performance of the proposed framework, which can be further incorporated in congestion evaluation systems.

Traffic Congestion Analysis Based on a Web-GIS and Data Mining of Traffic Events from Twitter.

Smart cities are characterized by the use of massive information and digital communication technologies as well as sensor networks where the Internet and smart data are the core. This paper proposes a methodology to geocode traffic-related events that are collected from Twitter and how to use geocoded information to gather a training dataset, apply a Support Vector Machine method, and build a prediction model. This model produces spatiotemporal information regarding traffic congestions with a spatiotemporal analysis. Furthermore, a spatial distribution represented by heat maps is proposed to describe the traffic behavior of specific and sensed areas of Mexico City in a Web-GIS application. This work demonstrates that social media are a good alternative that can be leveraged to gather collaboratively Volunteered Geographic Information for sensing the dynamic of a city in which citizens act as sensors.

Performance Analysis of Traffic Congestion Using Designated Neural Network Training Algorithms

A lot of neural network training algorithms on prediction exist and these algorithms are being used by researchers to solve evaluation, forecasting, clustering, function approximation etc. problems in traffic volume congestion. This study is aimed at analysing the performance of traffic congestion using some designated neural network training algorithms on traffic flow in some selected corridors within Akure, Ondo state, Nigeria. The selected corridors were Oba Adesida road, Oyemekun road and Oke Ijebu road all in Akure. The traffic flow data were collected manually with the help of field observers who monitored and record traffic movement along the corridors. To accomplish this, three common training algorithms were selected to train the traffic flow data. The data were trained using Bayesian Regularization (BR), Scaled Conjugate Gradient (SCG) and Levenberg-Marquardt (LM) training algorithms. The outputs/performances of these training functions were evaluated by using the Mean Square Error (MSE) and Coefficient of Regression (R) to find the best training algorithms. The results show that, the Bayesian regularization algorithm, performs better with MSE of 2.37e-13 and R of 0.9999 than SCG and LM algorithms.

Urban road congestion patterns under the COVID-19 pandemic: A case study in Shanghai

The novel coronavirus (COVID-19) pandemic has had a significant impact on human mobility around the world. Many cities issued “stay-at-home” orders during the outbreak of COVID-19, and many commuters have also changed their travel modes in the post pandemic period; e.g., transit/bus passengers have switched to driving or car-sharing. Urban road traffic congestion patterns are significantly different than they were pre-pandemic, and understanding such changes can be an opportunity to improve future emergency traffic management and control. Previous studies on this topic have focused on natural disasters or major accidents/incidents. However, very few studies have analyzed the empirical traffic congestion patterns that have occurred during a pandemic. This study takes Shanghai as an example, and conducts a retrospective analysis of empirical spatio-temporal road traffic congestion during the COVID-19 pandemic. The three-month road traffic speed data in the 446 Traffic Analysis Zones (TAZs) collected from Baidu Maps was used in this study. The algorithm of Singular Value Decomposition (SVD) was employed to investigate the inherent composition of the spatio-temporal variation simultaneously influenced by several factors. Three principal components were identified from the spatio-temporal variation, including the stable, main part of variation; the part of the variation that is affected by commuting; and the part of the variation that is affected by migrant populations and the pandemic. The results may suggest ways to improve the emergency management and control of urban roadways in other metropolitan areas worldwide during and after the COVID-19 pandemic period.