Road Traffic Congestion Research Articles

The validity of a decentralised simulation-based system for the resolution of Road traffic congestion

Intelligent transport systems are cheaper and easier to implement than other forms of solutions to road traffic congestion. Intelligent Transport Systems (ITS) allow information about road traffic to be collected and transmitted to the Traffic Controllers to respond to traffic congestion appropriately. This research study aims to design and test a simulation-based intelligent transport system for predicting traffic flow patterns. The simulation-based system proposed has a decentralised configuration. Road sections are isolated digitally in a decentralised structure and fed relevant information to predict future traffic states and transmit predicted information to other road sections. Two models of control configuration are created based on the same road network, the first is a decentralised model, and the second is a centralised model (no isolated road sections) to be used as a data source for the decentralised model. The simulated system used decentralised model configurations to predict traffic patterns at road intersections. Due to the lack of actual traffic data, a centralised model serves as the digital twin for the designed decentralised model. The two models were simulated using Anylogic Personal Learning Edition 8.5.2. simulation software, and after testing and result extraction, it was discovered that the decentralised model configuration was not valid for representing the non-uniformity of actual-world traffic patterns. The decentralised model had too few similar inflow and outflow rates to the centralised model in both the complex and straightforward road network cases. However, the decentralised model scored 89% in vehicle population density compared to the centralised model in the simple road network case but could not replicate the good result when the road network becomes complex.

Machine Learning Based Misbehaviour Detection in VANET Using Consecutive BSM Approach

Vehicular ad-hoc network (VANET) is an emerging technology for vehicle-to-vehicle communication vital for reducing road accidents and traffic congestion in an Intelligent Transportation System (ITS). VANET communication is vulnerable to various attacks and cryptographic techniques are commonly used for message integrity and authentication of vehicles. However, cryptograhpic techniques alone may not be sufficient to protect against insider attacks. Many VANET safety applications rely on periodic broadcast of basic safety messages (BSMs) from surrounding vehicles that contain important status information about a vehicle such as its position, speed, and heading. If an attacker (misbehaving vehicle) injects false position information in a BSM, it can lead to serious consequences including traffic congestion or even accidents. Therefore, it is imperative to accurately detect and identify such attackers to ensure safety in the network. This paper presents a novel data-centric approach to detect <i>position falsification</i> attacks, using machine learning (ML) algorithms. Unlike existing techniques, the proposed approach combines information from 2 consecutive BSMs for training and testing. Simulations using the Vehicular Reference Misbehavior (VeReMi) dataset demonstrate that the proposed model clearly outperforms existing approaches for identifying a range of different attack types.

A Neuro-Fuzzy Approach to Road Traffic Congestion Prediction

The fast-paced growth of artificial intelligence applications provides unparalleled opportunities to improve the efficiency of various systems. Such as the transportation sector faces many obstacles following the implementation and integration of different vehicular and environmental aspects worldwide. Traffic congestion is among the major issues in this regard which demands serious attention due to the rapid growth in the number of vehicles on the road. To address this overwhelming problem, in this article, a cloud-based intelligent road traffic congestion prediction model is proposed that is empowered with a hybrid Neuro-Fuzzy approach. The aim of the study is to reduce the delay in the queues, the vehicles experience at different road junctions across the city. The proposed model also intended to help the automated traffic control systems by minimizing the congestion particularly in a smart city environment where observational data is obtained from various implanted Internet of Things (IoT) sensors across the road. After due preprocessing over the cloud server, the proposed approach makes use of this data by incorporating the neuro-fuzzy engine. Consequently, it possesses a high level of accuracy by means of intelligent decision making with minimum error rate. Simulation results reveal the accuracy of the proposed model as 98.72% during the validation phase in contrast to the highest accuracies achieved by state-of-the-art techniques in the literature such as 90.6%, 95.84%, 97.56% and 98.03%, respectively. As far as the training phase analysis is concerned, the proposed scheme exhibits 99.214% accuracy. The proposed prediction model is a potential contribution towards smart cities environment.

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

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

TRAFFIC CONGESTIONS, TIME SPENT AT THE EXPRESSWAY JUNCTIONS, AND ITS IMPACT ON INDIVIDUAL PRODUCTIVITY: A PERCEPTION STUDY OF ILESA-OWO-BENIN EXPRESSWAY IN AKURE ONDO STATE, NIGERIA

The importance of the free flow of traffic, time spent in traffic at junctions, and individual productivity of road users along the Ilesa-Owo-Benin expressway in Akure Ondo State, Nigeria, cannot be overstated. While extant literature has shown that traffic congestion on roads significantly influences how road users perform their duties, few studies have explored the part played by the length of time they spend at junctions and how it impacts individual productivity. We collected data using a Questionnaire survey, comprising questions associated with traffic congestion at junctions of 203 respondents from across the residents (and travellers through Agbogbo/Irese/Futa junctions along Ilesa-Owo-Benin expressway in Akure). With an analysis of variance (ANOVA), we identified the differences in road users' perception of traffic congestions at junctions. We investigated the impact of traffic congestion on the productivity of road users. Finally, we identified potential solutions to the persistent traffic congestion experienced at the junctions. This paper offers a traffic congestion community with a better understanding of traffic congestions on road networks and aid in developing suitable methods and policies for road traffic congestion management.

Comparative analysis of drive-cycles, speed limit violations, and emissions in two cities: Toronto and Beijing

Driving behavior and speed enforcement are both important to road safety and affect vehicle exhaust emissions. Relationships between driving characteristics and safety or emissions have been assessed in multiple studies. However, there is scant information on whether safe driving also reduces emissions and how this relationship changes across urban areas. This study makes use of two similar GPS datasets collected in the metropolitan areas of Toronto and Beijing to conduct a comparative analysis of driving characteristics, speed limit violations, and emissions. Emissions for all trips were computed using the same emission rate database derived from a Portable Emissions Monitoring System (PEMS). We observe that the average speeds in the two cities are close to 25 km/h. In Toronto, the fraction of time spent at speeds over 80 km/h on expressways is 40% higher than in Beijing. We also note a higher level of accelerations in Toronto. The trips in Beijing have approximately 14%, 57%, 14%, and 21% lower emissions of carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx), and particle number (PN), respectively. Drivers in Toronto violate speed limits in 93% of their trips for 21% of trip travel time while the numbers for Beijing are 43% and 4%. These differences are not necessarily due to driving behavior, but rather to driving characteristics, which encompass the effects of behavior, road network design, traffic congestion, trip patterns, and speed enforcement. A scenario was evaluated by reconstructing drive-cycles to assess the effects of speed limit enforcement for trips where violations were detected. In Toronto, if obeying the speed limit, the mean trip travel time was estimated to increase by 1.8 min. In contrast, trip emissions of CO2, CO, NOx, and PN were found to decrease, on average, by 5.2%, 19.1%, 5.2%, and 2.9%, respectively. Speed limit enforcement can result in lower emissions, by reducing aggressive accelerations.

Exploring the Effects of Urban Built Environment on Road Travel Speed Variability with a Spatial Panel Data Model

Road traffic congestion is a common problem in most large cities, and exploring the root causes is essential to alleviate traffic congestion. Travel behavior is closely related to the built environment, and affects road travel speed. This paper investigated the direct effect of built environment on the average travel speed of road traffic. Taxi trajectories were divided into 30 min time slot (48 time slots throughout the day) and matched to the road network to obtain the average travel speed of road segments. The Points of Interest (POIs) in the buffer zone on both sides of the road segment were used to calculate the built environment indicators corresponding to the road segment, and then a spatial panel data model was proposed to assess the influence of the built environment adjacent to the road segment on the average travel speed of the road segment. The results demonstrated that the bus stop density, healthcare service density, sports and leisure service density, and parking entrance and exit density are the key factors that positively affect the average road travel speed. The residential community density and business building density are the key factors that negatively affect the average travel speed. Built environments have spatial correlation and spatial heterogeneity in their influence on the average travel speed of road segments. Findings of this study may provide useful insights for understanding the correlation between road travel speed and built environment, which would have important implications for urban planning and governance, traffic demand forecasting and traffic system optimization.

Evolution of road traffic congestion control: A survey from perspective of sensing, communication, and computation

Road traffic congestion can inevitably degrade road infrastructure and decrease travel efficiency in urban traffic networks, which can be relieved by employing appropriate congestion control. According to different developmental driving forces, in this paper, the evolution of road traffic congestion control is divided into two stages. The ever-growing number of advanced sensing techniques can be seen as the key driving force of the first stage, called the sensing stage, in which congestion control strategies experienced rapid growth owing to the accessibility of traffic data. At the second stage, i.e., the communication stage, communication and computation capability can be regarded as the identifying symbols for this stage, where the ability of collecting finer-grained insight into transportation and mobility reality improves dramatically with advances in vehicular networks, Big Data, and artificial intelligence. Specifically, as the pre-requisite for congestion control, in this paper, existing congestion detection techniques are first elaborated and classified. Then, a comprehensive survey of the recent advances for current congestion control strategies with a focus on traffic signal control, vehicle route guidance, and their combined techniques is provided. In this regard, the evolution of these strategies with continuous development of sensing, communication, and computation capability are also introduced. Finally, the paper concludes with several research challenges and trends to fully promote the integration of advanced techniques for traffic congestion mitigation in transportation systems.

Investigating the impacts of urban speed limit reduction through microscopic traffic simulation

Road traffic congestion has become an everyday phenomenon in today's cities all around the world. The reason is clear: at peak hours, the road network operates at full capacity. In this way, growing traffic demand cannot be satisfied, not even with traffic-responsive signal plans. The external impacts of traffic congestion come with a serious socio-economic cost: air pollution, increased travel times and fuel consumption, stress, as well as higher risk of accidents. To tackle these problems, a number of European cities have implemented reduced speed limit measures. Similarly, a general urban speed limit measure is in preparatory phase in Budapest, Hungary. In this context, a complex preliminary impact assessment is needed using a simulated environment. Two typical network parts of Budapest were analyzed with microscopic traffic simulations. The results revealed that speed limits can affect traffic differently in diverse network types indicating that thorough examination and preparation works are needed prior to the introduction of speed limit reduction.

Research on Decision Evaluation Model of HOV Lane Setting

In order to achieve the purpose of improving the travel efficiency of commuters in the periphery of the city, expanding the beneficiary groups of urban rail transit, and alleviating urban road traffic congestion, when planning and setting up HOV in the periphery of the city, it is necessary to analyze the feasibility of HOV lane setting from both the demand conditions and the setting conditions. This paper combines machine learning to construct a decision-making evaluation model for HOV lane setting and studies the optimal layout model and algorithm of HOV lanes in service rail transit commuter chain. The setting, planning, and layout of HOV lanes are a two-way interactive process of traveler's path selection and designer's road planning. Finally, after the model is constructed, the performance of the system model is verified. The results show that the system studied in this paper can be used for traffic data and lane planning analysis. Therefore, in the process of urban operation, the HOV model constructed in this paper is mainly used to alleviate urban traffic and improve urban operation efficiency.

Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities.

In an effort to reach accident-free milestones or drastically reduce/eliminate road fatalities rates and traffic congestion and to create disruptive, transformational mobility systems and services, different parties (e.g., automakers, universities, governments, and road traffic regulators) have collaborated to research, develop, and test connected vehicle (CV) technologies. CVs create new data-rich environments and are considered key enablers for many applications and services that will make our roads safer, less congested, and more eco-friendly. A deeper understanding of the CV technologies will pave the way to avoid setbacks and will help in developing more innovative applications and breakthroughs. In the CV paradigm, vehicles become smarter by communicating with nearby vehicles, connected infrastructure, and the surroundings. This connectivity will be substantial to support different features and systems, such as adaptive routing, real-time navigation, and slow and near real-time infrastructure. Further examples include environmental sensing, advanced driver-assistance systems, automated driving systems, mobility on demand, and mobility as a service. This article provides a comprehensive review on CV technologies including fundamental challenges, state-of-the-art enabling technologies, innovative applications, and potential opportunities that can benefit automakers, customers, and businesses. The current standardization efforts of the forefront enabling technologies, such as Wi-Fi 6 and 5G-cellular technologies are also reviewed. Different challenges in terms of cooperative computation, privacy/security, and over-the-air updates are discussed. Safety and non-safety applications are described and possible future opportunities that CV technology brings to our life are also highlighted.

The Transit-Oriented Development (TOD) Improvement Towards a Sustainable Development

Transit-Oriented Development (TOD) is gaining popularity as a tool to achieve sustainabledevelopment. It consists of a mixed-use development, which can cater to urban sprawl, road traffic congestion and an increase in carbon gas emission. There are efforts and incentives by the Malaysian policies related to TOD constructed under the Eleventh Malaysian Plan (RMK-11) to encourage public transportation, walking, and cycling as an alternative to the main modes. However, the efforts seem in vain as the public are not concerned regarding this issue. This study examined TOD's implementation towards sustainable community with the following objectives: to determine thecriteria and concept of TOD, investigate the issues and challenges in implementing TOD towards sustainable development, and recommend TOD improvement towards a sustainable development. The scope of this study is to identifythe TOD implementation around Subang Jaya station towards sustainable development. A mixed-methodof qualitative and quantitative research is used in a semi-structured interview, questionnaire distribution, observation, and document analysis. The quantitative data collected was analyzed using Statistical Package for Social Science (SPSS) 22, while the qualitative data was analyzed using content analysis. The results show that the implementation of TOD still immature and in the developing phase. The top three criteria were identified; reduce private vehicle and road traffic congestion, reduce pollution and carbon emission, and connectivity to other spaces or area. Three significant issues and challenges identified; a limited number of park-and-ride facilities, housing price around transit station and quality service of the public transportation served by related agencies. A workable recommendation for TOD improvement towards a sustainable community developed after industry practitioners tested it. Thesesuggestions and guidance can help industry players in making sound decisions when undertaking TOD projects

Literature Study: Highway Traffic Management with Sentiment Analysis and Data Mining

In today's era, technology is growing rapidly, many of the latest technologies are in great demand by the Indonesian people, one of which is social media. Various social media such as Facebook, Twitter, Instagram, have become very popular applications for various ages, including teenagers, adults, and the elderly. Social media has a positive impact that can help people convey the latest information through posts on their respective accounts. Social media can disseminate information in a short time, this is why social media is an interesting application to research. The problem of road traffic congestion is strongly influenced by the number of vehicles that pass every day. A large number of private vehicles and public vehicles that pass greatly confuses the atmosphere of highway traffic. Congestion often occurs during working hours. Road congestion also often occurs when an unwanted incident occurs. Sentiment analysis algorithms and data mining algorithms can be combined to find information on traffic jams through social media such as Facebook, Twitter, Instagram, and other social media. The results show that sentiment analysis methods and data mining algorithms can be used to find information about current traffic jams through social media. The conclusion from this literature study can be seen that the K-Nearest Neighbor data mining algorithm is the best choice to overcome road traffic congestion, which will then be further developed in the form of highway traffic management modeling.

Multi-Class Freeway Congestion and Emission Based on Robust Dynamic Multi-Objective Optimization

In order to solve the problem of traffic congestion and emission optimization of urban multi-class expressways, a robust dynamic nondominated sorting multi-objective genetic algorithm DFCM-RDNSGA-III based on density fuzzy c-means clustering method is proposed in this paper. Considering the three performance indicators of travel time, ramp queue and traffic emissions, the ramp metering and variable speed limit control schemes of an expressway are optimized to improve the main road and ramp traffic congestion, therefore achieving energy conservation and emission reduction. In the VISSIM simulation environment, a multi-on-ramp and multi-off-ramp road network is built to verify the performance of the algorithm. The results show that, compared with the existing algorithm NSGA-III, the DFCM-RDNSGA-III algorithm proposed in this paper can provide better ramp metering and variable speed limit control schemes in the process of road network peak formation and dissipation. In addition, the traffic congestion of expressways can be improved and energy conservation as well as emission reduction can also be realized.

Modelling road traffic congestion at urban merge section under mixed traffic conditions

Identification and evaluation of traffic congestion are critical for better management of urban traffic, especially at merge sections where two different traffic streams converge and generate shockwaves. This study evaluates the travel time reliability measure (travel time index (TTI)) as a reliable congestion measure by analysing the impact of traffic flow parameters such as traffic volume, capacity, road geometrics and conflicting traffic flows at merge sections on TTI under mixed traffic conditions. The data required for congestion analysis were collected through video recording. Statistical models were developed to: (a) evaluate the relationship between road geometric characteristics and capacity of merge sections; (b) estimate traffic density and its evolution with time and compare it against occupancy values; and (c) correlate the relationship between TTI and traffic behaviour at merge sections and determine the variations in TTI for different levels of service of merge sections. The models developed are statistically validated using secondary data sets from the same study section, as well as from data collected from other multi-lane merge sections. The developed model successfully estimated recurring congestion by considering both space and time. This study highlights the potential usage of TTIs in capturing travel time variations more accurately at merge sections under mixed traffic conditions.

Cellular automata-based modeling and simulation of the mixed traffic flow of vehicle platoon and normal vehicles

The development of autonomous driving technology and communication technology has made it possible for the realization of vehicle platoon. In the future, for a long time, there will be a mixture of platoons and normal vehicles on the road. The platoon will have a greater impact on traffic flow. Therefore, it is necessary to study the characteristics of the traffic mixed by platoons and normal vehicles. In this paper, we propose a traffic flow model for the traffic mixed by platoons and normal vehicles based on cellular automata and analyze the influence of platoon market penetration and platoon size on the road capacity, traffic congestion, and lane change frequency. The paper draws the following main conclusions. Under a given traffic environment, there is an optimal platoon penetration and platoon size that can maximize the capacity. Platoons can effectively alleviate traffic congestion, while the congestion ratio approaches zero when the proportion and size of the platoon reach a certain threshold. When the platoon market penetration is below 40%, the lane change frequency of the platoon gradually increases with the increment of the platoon penetration and size, while the platoon penetration is above 60%, the lane change frequency of the platoon decreases with that. When the platoon size is large enough, the lane change frequency will reduce to zero.

RETRACTED ARTICLE: IoT perception and public transportation network optimization based on big data algorithms

Big data uses network information technology in an extremely large number of information systems to quickly process and analyze this huge data information. In this way, the Internet of things system has the characteristics of rapidness, high efficiency, low density, and strong authenticity. Cohesion analysis technology refers to the process of effectively combining data mining and machine learning algorithms, a technology for fast and complete processing. The main working mechanism of cluster analysis is to classify similar objects according to the correlation of different data in the objects. Big data and Internet of things technology methods and traffic data have applicability, providing new ways to use big data in traffic and Internet of things. Through the analysis of various road traffic problems, the possibility of optimizing traffic through big data and the Internet of things is created, which will bring very good ideas and references for the development of intelligent transportation. Today, with the rapid development of the Internet of things, with the emergence and popularization of new hardware and the popularization and development of smart electronic terminals and mobile devices, traffic information–related data has also doubled. It is an inevitable requirement of development to use big data system for scientific and standardized management of traffic management departments, so that the traffic management system can be more optimized and reasonable, so as to completely solve the current phenomenon of road traffic congestion. To improve the transportation management of the latest information in the transportation system requires the use of information technology and data management systems. The big data system in the transportation field is an effective way to improve intelligent transportation management and transportation.

Travel time forecasting on a freeway corridor: a dynamic information fusion model based on the random forests approach

Purpose Metropolitan areas suffer from frequent road traffic congestion not only during peak hours but also during off-peak periods. Different machine learning methods have been used in travel time prediction, however, such machine learning methods practically face the problem of overfitting. Tree-based ensembles have been applied in various prediction fields, and such approaches usually produce high prediction accuracy by aggregating and averaging individual decision trees. The inherent advantages of these approaches not only get better prediction results but also have a good bias-variance trade-off which can help to avoid overfitting. However, the reality is that the application of tree-based integration algorithms in traffic prediction is still limited. This study aims to improve the accuracy and interpretability of the models by using random forest (RF) to analyze and model the travel time on freeways. Design/methodology/approach As the traffic conditions often greatly change, the prediction results are often unsatisfactory. To improve the accuracy of short-term travel time prediction in the freeway network, a practically feasible and computationally efficient RF prediction method for real-world freeways by using probe traffic data was generated. In addition, the variables’ relative importance was ranked, which provides an investigation platform to gain a better understanding of how different contributing factors might affect travel time on freeways. Findings The parameters of the RF model were estimated by using the training sample set. After the parameter tuning process was completed, the proposed RF model was developed. The features’ relative importance showed that the variables (travel time 15 min before) and time of day (TOD) contribute the most to the predicted travel time result. The model performance was also evaluated and compared against the extreme gradient boosting method and the results indicated that the RF always produces more accurate travel time predictions. Originality/value This research developed an RF method to predict the freeway travel time by using the probe vehicle-based traffic data and weather data. Detailed information about the input variables and data pre-processing were presented. To measure the effectiveness of proposed travel time prediction algorithms, the mean absolute percentage errors were computed for different observation segments combined with different prediction horizons ranging from 15 to 60 min.

Development of Joint Stated-Preference-off-Revealed-Preference Model for Shanghai Commute Mode Choices in Response to Parking Fee Management

Facing challenges in parking demand-and-supply imbalance and severe road traffic congestion during peak periods in Shanghai, in this paper we develop an SP-off-RP (stated-preference-off-revealed-preference) choice model to analyze relations between parking fee and commute mode choices based on survey data collected there. The survey questionnaire collects information about travelers’ daily commute, travel choices in the SP context, and personal socioeconomic and demographic attributes. The road network and public transportation network data are also used for model development. The model includes three main travel modes: car, public transit, and non-motorized mode. Variables that significantly influence mode choice and the reasons behind it are discussed, including the parking fee, the level-of-service (LOS) of the three modes, and socioeconomic and demographic variables. In the process of model development, a random sample of full-mode commute trips in Shanghai is integrated to improve model precision. The study reveals that the new random disturbance in the SP context is relatively large. The direct elasticity of the parking fee is estimated at −0.85, which means that when the parking fee increases by 10%, the average probability of choosing a private car for the commute will decrease by 8.5%. It is also found that transit LOS improvements have potential to reduce auto use in Shanghai. The study provides references on parking pricing as an alternative policy for travel demand management in Shanghai.

RETRACTED ARTICLE: Digital forensics system based on dynamic path prediction and the competitiveness of cross-border e-commerce SMEs

With the continuous increase in the number of urban cars, the problem of vehicle road traffic congestion has become more and more serious. The traffic path dynamic system plays a vital role in alleviating road traffic congestion. By predicting the traffic flow of different sections of the city, the route can be optimized, so as to optimize the traffic route plan and effectively solve the traffic congestion problem. In order to accurately predict the traffic dynamics in each part, this article mainly studies the traffic flow forecasting technology and route dynamic selection technology from three aspects: basic principles, research status at home and abroad, and practical applications. Due to the rapid development of communication technology, smart mobile devices have become one of the mainstream communication tools in today’s society. It is gradually replacing computers as an important tool for information processing, storage, and dissemination. Therefore, the rapid development of forensic technology using mobile digital has become a new hot research issue. In the past 2 years, with the rapid development of the digital and information age, the development of cross-border e-commerce businesses based on the Internet has also shown an unstoppable trend. At present, China’s foreign trade is not optimistic. Cross-border e-commerce uses the Internet to break the limitation of information asymmetry between merchants and customers. Compared with traditional foreign trade, it has the advantages of low cost, high profit, and short order cycle. It is a small- and medium-sized enterprise in China. The development of international trade provides convenience and enhances the competitiveness of enterprises.