Traffic Congestion In Cities Research Articles

Suggestions for Minimizing Traffic Congestion in Cities

Suggestions for Minimizing Traffic Congestion in Cities

Intelligent Fuzzy Traffic Signal Control System for Complex Intersections Using Fuzzy Rule Base Reduction

In this study, the concept of symmetry is employed to implement an intelligent fuzzy traffic signal control system for complex intersections. This approach suggests that the implementation of reduced fuzzy rules through the reduction method, without compromising the performance of the original fuzzy rule base, constitutes a symmetrical approach. In recent decades, urban and city traffic congestion has become a significant issue because of the time lost as a result of heavy traffic, which negatively affects economic productivity and efficiency and leads to energy loss, and also because of the heavy environmental pollution effect. In addition, traffic congestion prevents an immediate response by the ambulance, police, and fire brigades to urgent events. To mitigate these problems, a three-stage intelligent and flexible fuzzy traffic control system for complex intersections, using a novel hybrid reduction approach was proposed. The three-stage fuzzy traffic control system performs four primary functions. The first stage prioritizes emergency car(s) and identifies the degree of urgency of the traffic conditions in the red-light phase. The second stage guarantees a fair distribution of green-light durations even for periods of extremely unbalanced traffic with long vehicle queues in certain directions and, especially, when heavy traffic is loaded for an extended period in one direction and the short vehicle queues in the conflicting directions require passing in a reasonable time. The third stage adjusts the green-light time to the traffic conditions, to the appearance of one or more emergency car(s), and to the overall waiting times of the other vehicles by using a fuzzy inference engine. The original complete fuzzy rule base set up by listing all possible input combinations was reduced using a novel hybrid reduction algorithm for fuzzy rule bases, which resulted in a significant reduction of the original base, namely, by 72.1%. The proposed novel approach, including the model and the hybrid reduction algorithm, were implemented and simulated using Python 3.9 and SUMO (version 1.14.1). Subsequently, the obtained fuzzy rule system was compared in terms of running time and efficiency with a traffic control system using the original fuzzy rules. The results showed that the reduced fuzzy rule base had better results in terms of the average waiting time, calculated fuel consumption, and CO2 emission. Furthermore, the fuzzy traffic control system with reduced fuzzy rules performed better as it required less execution time and thus lower computational costs. Summarizing the above results, it may be stated that this new approach to intersection traffic light control is a practical solution for managing complex traffic conditions at lower computational costs.

Research on The Application of Interactive Design in Subway Public Art

China's urbanization is developing rapidly, and subway space, as an important part of urban construction, is receiving more and more attention from society and the government. The subway has alleviated the city's traffic congestion problem to the greatest extent, and has made rational use of the city's public space system. Public art in underground space has also become a window for urban cultural propaganda. Underground space is transforming from a functional space to a humanistic art space. In recent years, China's subway public art has developed rapidly and presented a diverse state, but there are still some common problems, such as the passive and single communication between space art and passengers, the lack of a certain degree of artistic publicity, and the failure to better meet people's current needs for humanistic art.

Sustainable spatial strategies for mitigating air pollution in quick commerce environments

This paper reviewed various studies on the impact of quick-commerce distribution services on the environment and local communities, with a particular focus on air pollution and increased packaging waste due to the increase in quick-commerce consumption. A systematic literature review was conducted using PubMed, Medline, ResearchGate, Google Scholar, and Scopus to comprehensively investigate and summarise the characteristics of quick commerce distribution, the impact of frequent logistics transport on air pollution, and the increase in packaging waste due to the increase in online shopping demand. Previous studies have mainly addressed the growth characteristics of quick commerce distribution services with the emergence of quick commerce. The results of this study show that quick-commerce distribution services are indeed associated with increased traffic due to frequent transport, which contributes to greenhouse gas emissions and traffic congestion in cities. In addition, due to the nature of quick-commerce consumption, packaging waste is also increasing due to excessive use of packaging materials for freshness and safe delivery. Therefore, this study suggests sustainable consumption behaviour using local shopping malls and private spaces to minimize environmental pollution in the era of changed distribution services.

An Architecture and Review of Intelligence Based Traffic Control System for Smart Cities

City traffic congestion can be reduced with the help of adaptable traffic signal control system. The technique improves the efficiency of traffic operations on urban road networks by quickly adjusting the timing of signal values to account for seasonal variations and brief turns in traffic demand. This study looks into how adaptive signal control systems have evolved over time, their technical features, the state of adaptive control research today, and Control solutions for diverse traffic flows composed of linked and autonomous vehicles. This paper finally came to the conclusion that the ability of smart cities to generate vast volumes of information, Artificial Intelligence (AI) approaches that have recently been developed are of interest because they have the power to transform unstructured data into meaningful information to support decision-making (For instance, using current traffic information to adjust traffic lights based on actual traffic circumstances). It will demand a lot of processing power and is not easy to construct these AI applications. Unique computer hardware/technologies are required since some smart city applications require quick responses. In order to achieve the greatest energy savings and QoS, it focuses on the deployment of virtual machines in software-defined data centers. Review of the accuracy vs. latency trade-off for deep learning-based service decisions regarding offloading while providing the best QoS at the edge using compression techniques. During the past, computationally demanding tasks have been handled by cloud computing infrastructures. A promising computer infrastructure is already available and thanks to the new edge computing advancement, which is capable of meeting the needs of tomorrow's smart cities.

Urban Traffic Congestion Prediction: A Multi-Step Approach Utilizing Sensor Data and Weather Information

The continuous growth of urban populations has led to the persistent problem of traffic congestion, which imposes adverse effects on quality of life, such as commute times, road safety, and the local air quality. Advancements in Internet of Things (IoT) sensor technology have contributed to a plethora of new data streams regarding traffic conditions. Therefore, the recognition and prediction of traffic congestion patterns utilizing such data have become crucial. To that end, the integration of Machine Learning (ML) algorithms can further enhance Intelligent Transportation Systems (ITS), contributing to the smart management of transportation systems and effectively tackling traffic congestion in cities. This study seeks to assess a wide range of models as potential solutions for an ML-based multi-step forecasting approach intended to improve traffic congestion prediction, particularly in areas with limited historical data. Various interpretable predictive algorithms, suitable for handling the complexity and spatiotemporal characteristics of urban traffic flow, were tested and eventually shortlisted based on their predictive performance. The forecasting approach selects the optimal model in each step to maximize the accuracy. The findings demonstrate that, in a 24 h step prediction, variating Ensemble Tree-Based (ETB) regressors like the Light Gradient Boosting Machine (LGBM) exhibit superior performances compared to traditional Deep Learning (DL) methods. Our work provides a valuable contribution to short-term traffic congestion predictions and can enable more efficient scheduling of daily urban transportation.

Впровадження концепції перехоплюючих паркінгів в міське середовище

The implementation of intercept parking concepts in urban environments is crucial for improving transportation infrastructure and enhancing residents' quality of life. The city of Rivne, while compact, faces challenges of traffic congestion and air pollution due to its limited area and high population density. One of the key measures to address these issues is the implementation of parking systems, specifically intercept parking. Intercept parking involves creating parking zones outside the city center to reduce the flow of cars in central areas. This can significantly reduce traffic congestion and promote the use of public transportation, thereby improving the city's transportation system. For example, research has shown that effectively utilizing intercept parking can reduce the number of cars entering the city center and encourage the use of public transport. However, successful implementation of intercept parking requires a comprehensive approach and consideration of various aspects, including the needs of the local population, characteristics of the transportation infrastructure, and potential implications for city development and road safety. The article presents research and data collection on the capacity of inbound car flows in various parts of the intercept parking network aimed at evaluating the load on the road network of the city of Rivne during peak periods, taking into account the city's level of motorization. The study analyzed inbound traffic flows at six key points on the main arterial streets of the city. The research results identified a significant load on Rivne's road network from inbound traffic flows, negatively impacting the city's mobility. The only viable solution to this problem is the implementation of an automated intercept parking network both in the suburbs and in the central part of the city. Intercept parking represents a crucial step towards optimizing urban space and improving residents' quality of life by reducing traffic congestion in cities and fostering the development of sustainable urban environments. Implementing intercept parking concepts proves to be a significant step in urban space improvement and enhancing residents' quality of life. The practical application of this concept can contribute to further development and optimization of urban agglomerations, as it promotes efficient use of parking spaces and the development of safe and balanced urban environments.

A Method of User Travel Mode Recognition Based on Convolutional Neural Network and Cell Phone Signaling Data

As urbanization accelerates, traffic congestion in cities has become a problem. Therefore, accurately identifying urban residents’ travel patterns is crucial for urban traffic planning and intelligent transportation systems. In this study, a convolutional neural network (CNN) approach based on multichannel feature extraction using mobile phone signaling data to identify user travel modes is proposed. Here, a trajectory generation method was designed for five types of travel modes. By designing a spatiotemporal threshold screening method, anomalies were identified and processed, combined with the feature analysis method, key points in the signaling extracted, the travel trajectory sliced, and travel sub-trajectory data generated. Next, in the travel mode identification stage, road network information was introduced to improve localization accuracy, and the method for calculating feature values improved. A user travel feature dataset was generated by calculating the feature values, and the travel modes represented by each class were classified and recognized based on the CNN method. Satisfactory results were achieved through experiments using mobile phone signaling and field research data in Kunming, China. The experimental results showed that analysis based on mobile phone signaling data could classify, identify, and obtain different travel category modes. This method’s accuracy was 84.7%. The method provided a feasible way of identifying travel patterns in the context of smart cities and big data, providing strong support for urban transport planning and management, and has the potential for wider application.

A heuristic approach for scheduling advanced air mobility aircraft at vertiports

Recent progress in electric vertical take-off and landing (eVTOL) vehicles suggests that soon these vehicles could safely and efficiently transport people and cargo in urban areas. Therefore, advanced air mobility vehicles could become an alternative means of transport to overcome traffic congestion in cities in the upcoming years. There has been enormous interest from companies and governments in recent years in developing such technologies and enabling markets for new air transportation services. Despite the interest in the topic, little research has been done to address the aircraft scheduling problem in advanced air mobility take-off and landing areas (vertiports). The vertiports serve as the airports of eVTOL vehicles and could experience congestion problems similar to those of airports. This work proposes two optimization models for scheduling departing and landing aircraft at the vertiports’ common ground taxi routes (taxiways), gates, and touchdown and lift-off (TLOF) pads. The mathematical models include advanced air mobility features such as separation rules and blocking constraints. As scheduling objectives, the first model maximizes the vertiport throughput, and the second model minimizes the deviation from the expected take-off/landing time. In addition, as a solution methodology, we developed two heuristic algorithms that use scheduling rules to assign and sequence the aircraft to the vertiport components. Computational results show that the optimization models find optimal schedules for small-sized instances of up to 10 aircraft, while the heuristic algorithms provide good results in terms of solution quality and computational time for large instances.

UAV-Assisted Traffic Speed Prediction via Gray Relational Analysis and Deep Learning

Accurate traffic prediction is crucial to alleviating traffic congestion in cities. Existing physical sensor-based traffic data acquisition methods have high transmission costs, serious traffic information redundancy, and large calculation volumes for spatiotemporal data processing, thus making it difficult to ensure accuracy and real-time traffic prediction. With the increasing resolution of UAV imagery, the use of unmanned aerial vehicles (UAV) imagery to obtain traffic information has become a hot spot. Still, analyzing and predicting traffic status after extracting traffic information is neglected. We develop a framework for traffic speed extraction and prediction based on UAV imagery processing, which consists of two parts: a traffic information extraction module based on UAV imagery recognition and a traffic speed prediction module based on deep learning. First, we use deep learning methods to automate the extraction of road information, implement vehicle recognition using convolutional neural networks and calculate the average speed of road sections based on panchromatic and multispectral image matching to construct a traffic prediction dataset. Then, we propose an attention-enhanced traffic speed prediction module that considers the spatiotemporal characteristics of traffic data and increases the weights of key roads by extracting important fine-grained spatiotemporal features twice to improve the prediction accuracy of the target roads. Finally, we validate the effectiveness of the proposed method on real data. Compared with the baseline algorithm, our algorithm achieves the best prediction performance regarding accuracy and stability.

Open-sourced real-time visualization platform for traffic simulation

To solve the city's traffic congestion problem, micro-traffic simulation is widely used to analyze the current situation and implement a plan to solve the problem. With the nature of the simulation, it is possible to design multiple scenarios and predict future traffic conditions. The state of the network is usually represented visually through 3D graphics and numbers such as speed, delay time, etc. However, analysis results are generally not intuitive, making it difficult for non-experts to judge traffic indicators. This study presents the possibility of utilizing an open-sourced real-time web-based platform linking simulation results to dashboards. Here, the micro traffic simulation, VISSIM is used. Traffic flow at intersections and location information of individual vehicles are stored in the cloud from VISSIM at analysis intervals. In real-time, the saved results are loaded into Elasticsearch, an open-source visualization tool. Data are visualized according to temporal and spatial characteristics and constitute the dashboard layout. Users intuitively analyze and predict traffic conditions through this visualization platform. In addition, traffic conditions can be easily communicated to general users who use web-based information.

An adaptive timing mechanism for urban traffic pre-signal based on hybrid exploration strategy to improve double deep Q network

With the increasing traffic congestion in cities, the priority of public transit has become a consensus for the development and management of urban transportation. The traffic pre-signal mechanism, which gives priority in time and space to buses by optimizing road right-of-way allocation, has gained wide attention and application. In order to broaden the action exploration range of the agent and avoid the pre-signal decision from falling into suboptimal strategy or local optimal strategy. For the exploration strategy of the DDQN algorithm, this paper reduces the probability of directly selecting the local optimal action and increases the probability of selecting non-greedy actions based on the principle that “the action with a larger value function is more likely to be selected.” This paper addresses the problem that the existing urban traffic pre-signal mechanism cannot adaptively adjust the advance time, and proposes a traffic pre-signal adaptive timing mechanism based on a Hybrid Exploration Strategy Double Deep Q Network (HES-DDQN) by combining the epsilon -greedy strategy and Boltzmann strategy. We have used the traffic simulation software VISSIM to conduct simulation experiments on an intersection. The experimental results show that, compared with the method of setting no pre-signal and the formula method of setting pre-signal, the HES-DDQN pre-signal mechanism can significantly reduce the average delay of buses, the waiting queue length, and the number of stops of social vehicles.

Smart Traffic Management System for Metropolitan Cities of Kingdom Using Cutting Edge Technologies

The expansion of technology in metropolitan centers draws people to cities, which causes excessive traffic on the roadways during peak hours. This exacerbated the traffic situation, resulting in a delay, a loss of resources, and a waste of time. Like any other metropolitan metropolis, Riyadh, Saudi Arabia, has everyday traffic congestion during business hours. The current traffic management has made many attempts to ease traffic congestion in cities; despite these measures, the problem has not been solved adequately. To handle this road congestion, there is a need to appropriately store the big data collected by traffic sensors and utilize it for efficient traffic management employing cutting-edge technology. This study provides an architecture for a smart traffic management system that uses modern technologies such as the Internet of Things (IoT), cloud computing, 5G, and big data to aid conventional traffic management systems and efficiently handle the stated problem. The proposed technique has the potential to reduce traffic congestion significantly. Our proposed solution encourages mobility by using roadside messaging agents to offer real-time traffic information on traffic congestion and unexpected traffic incidents. Citizens will save time by getting these early warning messages, particularly during peak hours. As part of the suggested method, each signal dashboard gets traffic information. A case study is used in the research to evaluate alternative solutions to traffic congestion. The case study results show that the proposed strategy outperforms the present options.

Airport location for smart and sustainable living: A model and a case study of rural Missouri, U.S.

• Airports can be relocated to reduce carbon footprint of driving to airport location. • A carbon-footprint formula helps compare emissions from transportation. • Reducing transportation's carbon footprint enables sustainable and smart living. • Real-world results from Missouri, U.S., for smart and sustainable airport location. We consider the problem of determining the location of a regional airport with connections to major hubs within an airline network for the objective of minimizing the carbon footprint of travelers, road congestion, and accident risk. This is aligned with an overarching goal of smart and sustainable living. The overall travel journey is usually composed of a trip from home to the airport, called the connecting trip , and a trip in the air, which is composed of one or more flights. Currently, in the United States, millions of travelers drive in excess of 100 miles to reach overcrowded airports. The connecting trip not only magnifies the driver's carbon footprint, but also creates traffic congestion in cities that have airports—further increasing CO 2 generation in areas surrounding the airports and worsening road safety. The model proposed here identifies a suitable airport location to directly reduce total driving miles. Indirectly, congestion on roads connecting to the airport and the traffic intensity at the airports in the hub-and-spoke network will also be reduced and passengers’ comfort levels will be improved. We will employ a case study from the state of Missouri, United States, to illustrate our model and its concomitant analysis.

Convolutional Neural Network-Based Travel Mode Recognition Based on Multiple Smartphone Sensors

Nowadays, large-scale human mobility has led to increasingly severe traffic congestion in cities, how to accurately identify people’s travel mode has become particularly important for urban traffic planning and management. However, traditional methods are based on telephone interviews or questionnaires, which makes it difficult to obtain accurate and effective data. Nowadays, numbers of smartphones are equipped with various sensors, including accelerometers, gyroscopes, and GPS, providing a novel social sensing data source to detect people’s travel modes. The fusion of multiple sensor data is a promising way for travel mode detection. However, how to use these sensor data to accurately detect travel mode is still a challenging task. In this paper, we presented a light-weight method for travel mode detection based on four types of smartphone sensor data collected from an accelerometer, gyroscope, magnetometer, and barometer, and a prototype application was developed. Then, a novel convolutional neural network (CNN) was designed to identify five representative travel modes (walk, bicycle, bus, car, and metro). We compared the overall performance of the proposed method via different hyperparameters, and the experimental results show that the F value of the proposed method reaches 97%, which verified the effectiveness of the proposed method for travel mode classification.

Campus: University or a Modern Urban Structure?

Modern cities face a range of problems, such as the need to redevelop inefficiently used former industrial territories, traffic congestion in cities, air, water and soil pollution and the disappearance of cities' last remaining natural areas. These issues require rethinking methods for the redevelopment and renovation of city districts. The campus model for the formation of sustainable territorial units in a city is set to replace conventional zoning (residential area, industrial area, city centre, recreational area). In campus structures, like in university campuses, everything is grouped together: residential and recreational facilities, schools and workplaces. Meanwhile, the environment, with elements of an individually-branded design code, is an area for likeminded people (residents of the campus) to interact. New social and economic models, as well as new global ideas, contribute to the appearance of new campus city blocks and micro- and macro-structures that are comparable to smart cities – the showcases of contemporary intellectual communities. In such urban areas, nature also plays a different role: it ceases to be a passive background, a means of featureless landscaping based on standard principles. The present research offers a description, analysis and classification of campus urban structures, based on the preliminary study of historical types of landscape organization in university campuses. Based on comparative historical analysis and experimental modeling, it distinguishes five types of the architectural and landscape organization of campuses: the enclosed model, the communicative model, the podium model, the nature-oriented model and the nature-equivalent model. In nature-equivalent campuses, nature becomes the main participant in the environment. Such urban structures become natural elements themselves, as they become parts of the ecosystem: environmentally safe, sustainable and self-regulating components of the natural and anthropogenic global landscape. The material laid out in the present research is of practical importance for students of architecture and campus designers.

Contemporary Australian socio-cultural factors and their influence on medical student rural career intent.

ObjectiveTo understand how contemporary socio‐cultural factors may impact medical students' rural career intent.DesignCross‐sectional study using data from the national Federation of Rural Australian Medical Educators survey.Participants/settingMedical students across 18 Australian universities who completed a full academic year of clinical training in rural areas in 2019.Main outcome measure(s)Preferred location of practice post‐training reported to be either: (i) a major city; (ii) a regional area or large town; or (iii) a small rural location.ResultsIn total, 626 students completed the survey (70.1% response rate). A small rural location was the most preferred location of practice after graduation for 28.3% of the students (95% CI 21.6–36.0). Four socio‐cultural factors were positively associated with a preference for a rural career location: poor health status of rural people, motor vehicle traffic congestion in cities, rural generalist training opportunities in the state and the Royal Flying Doctor Service. Other socio‐cultural factors, including specialists’ under‐employment, Medicare freeze effect on doctors' income, bullying/sexual harassment in hospitals, climate change/natural disasters or recognised rural health personalities did not influence the investigated outcome.ConclusionsOur findings indicate a novel association between contemporary socio‐cultural factors and rural career intention in a cohort of Australian rural clinical school students. These findings advocate for further consideration of research exploring socio‐cultural factors shaping rural career intent and workforce outcomes.

Analysis of Mobility Changes Caused by COVID-19 in a Context of Moderate Restrictions Using Data Collected by Mobile Devices

Since its inception, COVID-19 has changed several dynamics in society, both on a personal and professional level. Mobility was one of the most affected aspects, as a result of the necessary social distancing and preventive measures that had to be enacted by the various countries and which restricted, at various times, freedom of movement. The impact that COVID-19 had, and still has, on mobility is important to be understood so that the necessary measures can be taken in order to return to normality and, for example, not regress in the steps that were being taken in encouraging the use of public transport as a measure to combat the carbon footprint as well as traffic congestion in cities. This paper intends to analyze the reality of Spain and Portugal, in the period between May 10th and July 2nd, 2021, in which both countries had already finished restricting mobility measures. The study used data from Google Community Mobility Reports and was done by regions, taking into account the average age of inhabitants and the number of inhabitants in each region. The analysis focused on different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential. One of the main conclusions of this study is the lower use of public transports for fear of a greater risk of infection. This could be a problem in the medium term if this trend continues. On the other hand, it is important to highlight a greater presence in parks after the end of the lockdown, which promotes healthy habits that combat problems such as overweight or obesity. COVID-19 can be seen as an opportunity to promote more active mobility through the creation of infrastructure, such as for bicycles.

Exploring challenges of engaging in socio-economic activities due to traffic congestion in Sekondi-Takoradi

Undoubtedly traffic congestion continues to be a challenge that negatively impacts socio-economic activities in most cities in the world. It does not only hinder the smooth movement of people but also freight and services. Admittedly, there have been a number of studies that have examined the causes and effects of traffic congestion in Ghanaian cities; nonetheless, these studies have largely concentrated on the larger metropolis which includes Accra and Kumasi with little attention given to mid-sized emerging urban centers. Using data collected from field survey, in-depth interviews and field observations in Sekondi-Takoradi, this study examines the effects of traffic congestion on socio-economic activities along five major road corridors in the study area. The survey data were analyzed using descriptive and inferential statistics while in-depth interviews were integrated with the results using quotes from informants. The findings from the study revealed a spatial variation of the effects of traffic congestion along the five corridors studied. In particular, it was observed that increased traffic congestion led to the reduction in sales made by traders and commercial drivers, while students and workers who used these roads also asserted they often get to school and workplaces very late. The study therefore recommends road expansion along these routes and use of road tolls to regulate the activities of ‘trotro’ (public buses) and taxis in the city in order to minimize traffic congestion in the city. Key words: Traffic congestion, socio-economic activities, spatial variation, corridors, Takorad, Metropolis.

Flow-based unit is better: exploring factors affecting mid-term OD demand of station-based one-way electric carsharing

Station-based one-way carsharing (OWC) serves as a flexible method to enjoy the benefits of car travel, while also demonstrating the potential to mitigate environmental challenges and traffic congestion in cities. In the previous studies on OWC, location-based units such as a single station, station cluster, and land parcel were generally used as the basic analysis units for demand; however, these studies failed to consider the association among the operating areas. This led to results that were pertinent to a specific OWC system and cross-section of the development process. The objective of this study is to explore the significant factors related to flow-based demand (i.e., four-weekly bookings from the origin spatial unit to the destination spatial unit (OD bookings)). Four groups of explanatory variables are adopted: carsharing spatial unit attributes, built environment, transportation facilities, and OD trip attributes (such as public transportation and car travel distance between the OD). A combination model integrating machine learning and a generalized linear model is also developed to address the zero-inflation issue of the data. Moreover, an approach of Shapley additive explanations is implemented to determine the considerable effects of the factors. The results show that (1) the OD trip attributes play an important role in estimating the carsharing OD demand; (2) taxi and carsharing demands exhibit a non-significant partial overlap, and carsharing may compete with buses and supplement to the metro; and (3) the travel purpose in carsharing is diverse for any land use over a four-week period.