Urban Traffic Management System Research Articles

Evaluation of a Probabilistic Framework for Traffic Volume Forecasting Using Deep Learning and Traditional Models

Forecasting is a very important issue in the present times to optimize urban traffic management systems and their proper planning. It is often found that traditional forecasting models cannot fully approximate the non-linear and dynamic nature of traffic patterns. The presented paper proposes a framework that integrates multiple traditional models such as Autoregression (AR), Support Vector Regression (SVR), Exponential Smoothing, Historical Average, Random Walk and Artificial Neural Network (ANN) with deep learning models, especially Stacked Autoencoders, and enhances their predictive efficiency phenomenally. The forecast accuracy is then further enhanced through a dynamic probabilistic model integration strategy that adapts to real-time traffic conditions by dynamically weighting the models based on their performance. It is found that the hybrid framework proposed in the present paper performs much better than the traditional models in predicting traffic volume. Deep learning models with RMSE of 26.9976, AR (RMSE: 22.6679) and SVR (RMSE: 28.2221) provided remarkable prediction accuracy. Based on the results, the authors are confident that the integrated models are useful for real-time traffic management applications. However, they still have great potential for further refinement and optimization.

Short Term Prediction of Urban Traffic Flow based on Machine Learning Algorithms

With the increasing per capita car ownership worldwide, existing transportation facilities are unable to fully meet people's travel needs. The massive traffic flow can lead to traffic congestion, so predicting traffic flow can play a crucial role in urban traffic management. This study predicts the westbound traffic flow data of the Minnesota Department of Transportation (MN DoT) ATR 301 station on Interstate 94 in the United States for 28 consecutive days using a Long Short-Term Memory Network (LSTM) data prediction model. The Random Forest is utilized to analyze the association between attributes and the historical data has been separated into 1-hour intervals. The highway displays a bimodal pattern on weekdays and a unimodal pattern on holidays, according to the final forecast results. Furthermore, after making the necessary adjustments to the parameters, the simulated trend closely resembles the real value, proving that LSTM can be used to model short-term traffic flow. Accurately predicting urban traffic flow can make the management of urban traffic management systems more comprehensive and provide citizens with higher quality travel planning references.

Self-Powered Traffic Lights Through Wind Energy Harvesting Based on High-Performance Fur-Brush Dish Triboelectric Nanogenerators.

Traffic lights play vital roles in urban traffic management systems, providing clear directional guidance for vehicles and pedestrians while ensuring traffic safety. However, the vast quantity of traffic lights widely distributed in the transportation system aggravates energy consumption. Here, a self-powered traffic light system is proposed through wind energy harvesting based on a high-performance fur-brush dish triboelectric nanogenerator (FD-TENG). The FD-TENG harvests wind energy to power the traffic light system continuously without needing an external power supply. Natural rabbit furs are applied to dish structures, due to their outstanding characteristics of shallow wear, high performance, and resistance to humidity. Also, the grid pattern of the dish structure significantly impacts the TENG outputs. Additionally, the internal electric field and the influences of mechanical and structural parameters on the outputs are analyzed by finite element simulations. After optimization, the FD-TENG can achieve a peak power density of 3.275Wm-3. The portable and miniature features of FD-TENG make it suitable for other natural environment systems such as forests, oceans, and mountains, besides the traffic light systems. This study presents a viable strategy for self-powered traffic lights, establishing a basis for efficient environmental energy harvesting toward big data and Internet of Things applications.

Predictive analytics for traffic flow optimization in urban logistics: A transformer-based time series approach.

In this study, we focus on the analysis and prediction of urban logistics traffic flow, a field that is gaining increasing attention due to the acceleration of global urbanization and heightened environmental awareness. Existing forecasting methods face challenges in processing large and complex datasets, particularly when extracting and analyzing valid information from these data, often hindered by noise and outliers. In this context, time series analysis, as a key technique for predicting future trends, becomes crucial for supporting real-time traffic management and long-term traffic planning. To this end, we propose a composite network model that integrates gated recurrent unit (GRU), autoregressive integrated moving average (ARIMA), and temporal fusion transformer (TFT), namely the GRU-ARIMA-TFT network model, to enhance prediction accuracy and efficiency. Through the analysis of experimental results on different datasets, we demonstrate the significant advantages of this model in improving prediction accuracy and understanding complex traffic patterns. This research not only theoretically expands the boundaries of urban logistics traffic flow prediction but also holds substantial practical significance in real-world applications, especially in optimizing urban traffic planning and logistics distribution strategies during peak periods and under complex traffic conditions. Our study provides a robust tool for addressing real-world issues in the urban logistics domain and offers new perspectives and methodologies for future urban traffic management and logistics system planning.

Shaping the Future of Transportation with Automation

This study explores the advancements and challenges of AI-powered self-driving cars, specifically in the context of urban planning, traffic management, and transportation systems. It investigates the technological components of autonomous vehicles, including computer vision, machine learning algorithms, sensor fusion, and real-time decision-making systems. The research further delves into the training and learning procedures, focusing on the use of large datasets, deep neural networks, and reinforcement learning to continuously enhance driving capabilities through interaction with the environment. The goal is to assess the potential of AI to improve road safety, transit efficiency, and individual mobility, while acknowledging the obstacles that need to be overcome for widespread adoption and societal trust.

Optimizing passenger vehicle travel time with Model Predictive Control in multi-region traffic networks

Abstract This study investigates the impact of truck traffic on passenger vehicles in an urban network. Utilizing the Macroscopic Fundamental Diagram (MFD), a methodology to calculate the travel time spent (TTS) by passenger vehicles has been developed. To address this issue, an optimal control problem was formulated and solved using a Model Predictive Control (MPC) approach. The MPC framework has been applied in a centralized manner, to manage accumulation for various modes. To explore different traffic management strategies, the centralized MPC technique was implemented in two distinct configurations: region-based and vehicle-based approaches. It has been tested for various vehicle mixes and multiple control scenarios to assess the effectiveness in reducing passenger travel time spent (PTTS) and vehicle accumulation. The results demonstrate that the vehicle-based MPC approach tends to minimize the number of vehicles more effectively compared to the region-based approach. However, in terms of reducing passenger travel time, the region-based approach outperforms the vehicle-based strategy. This is attributed to enhanced coordination among traffic flow controllers, highlighting the importance of strategic controller interactions in urban traffic management systems. This research enhances both the theoretical framework for optimizing traffic flow and provides valuable practical insights for city planners and engineers aiming to deploy advanced traffic management strategies. Future studies could explore the scalability of these control systems and their capability to integrate real-time traffic data.

Generalising cases of insensitive queuing city traffic management systems and their impact on the delivery of goods by air and road

It is established that the urban traffic management system, as well as the aviation traffic management system, has a significant impact on the multimodal delivery of goods in the road-air transport, which requires the creation of tools for theoretical substantiation and practical implementation. The authors� method of determination the occurrence of vehicles queues in cities using mathematical modelling tools for three scenarios of crossing intersections was developed. The conceptual scheme for calculating the occurrence of the queue of vehicles was proposed. It was noted that vehicles in the queue often arrive randomly, the duration of service is constant, and the probability that before the queue disappears for the first time a certain number of cars cross the intersection has a Borel-Tanner distribution. The given mathematical modelling should become a part of tools for the implementation of the integrated intelligent information system of cities as a cluster of the city traffic management system.

Research on Optimization of Traffic Management in Smart Tourism Cities for Sustainable Development

Abstract In order to accelerate the efficiency of urban traffic operation in tourism and maximize the efficiency of urban traffic operation. This paper conducts systematic research on urban traffic conditions and proposes the construction of an intelligent urban traffic management system. First of all, the system structure is designed from the whole, and the system is divided into the information collection stage, the information processing stage, and the information release stage to clarify the functions that the system needs to realize. The design of the system includes queries for road conditions, bus transfer, walking navigation, and background management of each functional module. By building a good system development environment, system functions can be realized in code. Visitor satisfaction was evaluated using the fuzzy comprehensive evaluation method, and simulation experiments were carried out to evaluate the system’s optimization effect. The satisfaction of tourists in terms of convenience and comfort is improved compared with that before the use of the system. After the implementation of the intelligent traffic management system, the delay time of the intersection with the most complex road conditions in peak hour E is reduced by 19.8 s. The delay time in the peak hour is reduced by 4 s. It shows that the system improves the satisfaction of tourists in terms of convenience and comfort reduces the possibility of congestion for the tourists and the residents to travel, and improves the utilization rate of the road. The utilization rate of the road.

Dynamic Management of Urban Coastal Traffic and Port Access Control

Urban traffic congestion and vehicle/passenger port recurring delays are major obstacles of coastal urban area sustainability. Most research in coastal urban road management has focused on congestion detection without the effective integration of the dynamic interactions with port queueing systems. For securing coastal city environmental, social and economic efficiency, this paper develops and tests a dynamic urban coastal traffic and port management system. The integrated system controls traffic and port gates’ operations based on ITS/C-ITS methodologies. The system integrates dynamic models for congestion detection, using ANN and a parameterized model, on a coastal urban road network that leads to a city port and identifies optimal solutions for road traffic and port queuing gate control. The system communicates with users via connected vehicles and VMS. The system was tested in a coastal urban road leading to Patras Southern Port, Greece, and at port control gates. Field and simulation data were used to assess system performance and social–environmental impacts. The results reveal that the system’s application offers benefits to the individual driver moving towards the Port to board a ship (gaining at least 7 min and consuming 0.306 L less fuel) as well as to society (39.72% increase in traffic safety) and environment (1,445,132 g CO2 emission reduction).

A Blockchain-Based Shared Bus Service Scheduling and Management System

With the continuous development of urbanization, it has become an important issue to effectively alleviate urban road traffic congestion and improve traffic efficiency. By combining blockchain technology and shared buses, this paper builds an intelligent traffic-service scheduling management system based on blockchain. The system effectively solves the core problems of shared buses, improves data security and privacy protection, realizes intelligent scheduling and route planning, and simplifies cross-organization cooperation and settlement processes. The research shows that the system can reduce the distance and number of buses, and improve the service quality and operation efficiency while ensuring the waiting time of passengers. The results of this paper verify the feasibility and advantages of the system, bring innovation and improvement to the field of traffic management, and promote the sustainable development of urban intelligent traffic management system. Future research could further explore the application of blockchain technology in traffic management to achieve more intelligent and sustainable urban traffic management.

Game theory applications in traffic management: A review of authority-based travel modelling

With the exponential growth in urbanization, urban traffic congestion has become a challenging task with an adverse impact on the economic structure and ecosystem. Managing traffic congestion involves the interaction of multiple decision-makers (i.e., travelers and authorities). Accordingly, these are well-suited to being analyzed under the guise of game theory. Within the transport literature, there are several models that incorporate game theory concepts. While game theory can be very useful for analyzing traffic congestion management, it does have some significant drawbacks that need to be acknowledged. Thus, this manuscript presents a state-of-the-art review of game theory as applied to urban traffic congestion management systems from an authority-based perspective. Authority-based congestion management systems provide a detailed discussion on how game theory models have enhanced traffic signal control efficiency, identified optimal congestion pricing mechanisms and detailed development in mode choice modelling. Apart from the conventional topologies, this manuscript covers the comparative criticism of the various proposed game-theoretic models in terms of mode choice, traffic signal control, and congestion pricing. Further, based on the comparative critiques, this manuscript provides research directions for researchers, engineers, policymakers, and industrial communities.

An Urban Traffic Management System based on Vehicle Cooperation

The next generation of smart cities will rely on Intelligent Transport Systems (ITSs) due to the increased complexity and dynamism of traffic caused by continuous urbanization and population growth. The traditional techniques to deal with these challenges are expensive and have a great impact on people's lives and, in this scenario, the introduction of computational and technological solutions is necessary. In order to minimize the problems caused by congestion in urban centers, we present Let Me Know, a traffic management system inspired by the communication among vehicles that allows the request and availability of information related to vehicular traffic. The vehicles request information in order to update a distributed database containing momentary analyzes of road traffic. Through extensive performance analysis, we show our system's ability to reduce traffic congestion with a low impact on the network.

Autonomous driving under V2X environment: state-of-the-art survey and challenges

Abstract As Vehicle-to-Anything (V2X) is viewed as emerging technology, which could improve the mobility and efficiency of urban traffic operations, and enhance the safety of drivers. Supported by the construction of vehicular network facilities, V2X has been applied to various urban traffic management systems. However, due to the limitation of wireless communication, outstanding issues on privacy and security in V2X applications are still urgent. The purpose of our studies is to provide a comprehensive review of V2X-aided autonomous driving. Firstly the standards and protocols on V2X technologies for mobile communication and high-definition map are provided. The research scenarios of V2X-aided automobiles on urban links and highways are discussed. Furthermore, the several challenges of legislation in different countries, privacy issues, and security are discussed. Finally, the research directions for V2X-aided autonomous driving are further given.

Fusion of engineering insights and emerging trends: Intelligent urban traffic management system

Traffic congestion is a great concern, especially in urban areas where the vehicles’ number on roads continues to intensify significantly against the slow development of road infrastructure. To resolve this intractable problem, researchers proposed various Traffic Management Systems that rely on the fusion of traffic data sources and emerging technologies to maximize the traffic flow, and thus fluidify the intersections since they are the origin of traffic congestions, and also reroute the vehicles to avoid traffic congestions areas, and thus reduce fuel consumption and air pollution. Although there are many studies on traffic management, there is still a lack of research covering the entire urban road traffic management system. Therefore, this study intends to fill the gap, generating new insight into the state of research related to the entire Traffic Management System. The principal aim of this survey is to provide a complete view of current approaches proposed in the literature to handle the traffic congestion problem. We first supply a review of the body of knowledge in recent years of Traffic Management Systems. Second, we summarize the benefits and drawbacks of the recent literature. Third, we analyze and discuss the three main subsystems of a Traffic Management System and then classify the various contributions according to the subsystem to which they belong. Finally, we present and discuss future research orientations and open issues that need further investigation for a realistic and efficient Traffic Management System.

Data Visualization and Interaction of Urban Traffic Logistics Management System Using WebGIS.

At present, the large amount of data generated by transportation and logistics in cities brings great difficulties to data management and operation. The purpose is to explore the applicability of WebGIS and expand the application of intelligent interactive urban traffic logistics management. An urban traffic logistics management system is designed for urban traffic route assignment and intelligent interaction based on the WebGIS system and the basic principles and related algorithms of urban traffic route assignment. Then, the operation of the system under different shortest path algorithms and different path assignment algorithms is discussed. As a result, the Dijkstra algorithm runs faster than the Floyd algorithm. The comparison of three different road assignment algorithms shows that the no road assignment algorithm has the smallest average time consumption, which is 264.43 ms. The running time of the continuous average algorithm is the same as that of the user balance algorithm. The WebGIS system has practical application value compared with the TransCAD system and Cube system. This article proposes that the interactive function of the system can be used normally. The vehicle information of the test sample points can be displayed, and the function realization effect is good. The research results can provide scientific references for the follow-up research on intelligent transportation.

Coordinated Development of Urban Intelligent Transportation Data System and Supply Chain Management

With the development of the urban economy, the number of people using various means of transportation is also increasing, resulting in a huge workload of the traffic data system and prone to errors. Supply chain management can formulate a reasonable production plan according to the comprehensive information data generated by the supply chain management system, such as market demand analysis, purchasing demand analysis, and supplier assessment and evaluation. Therefore, this paper proposes the coordinated development of urban intelligent transportation data system and supply chain management, so as to improve the overall efficiency of the logistics system and the level of customer service. This paper aims to study the importance and advantages of the coordinated development of urban intelligent transportation data system and supply chain management. As can be seen from the data in Table 2, the percentage of people using a car increased by 18.2% in 2015, and by 2020, the percentage of people using a car increased by 36.9%. As shown in Figure 10, the traditional urban traffic data management system has the disadvantages of large amount of data and various and complex data types. Among them, the percentage of large amount of data is between 70% and 75%, and the percentage of diverse and complex data is between 62% and 68%. It can be seen that the number of people using cars is increasing, resulting in an increasing workload for the transportation system. On this basis, the intelligent traffic data system should be used to solve this problem and the coordinated development of the intelligent traffic data system and supply chain management can achieve a win-win situation.

A Novel Traffic Surveillance System Using an Uncalibrated Camera

The objective of this paper is to present an effective and reliable method for the traffic surveillance using the concepts of digital image processing. The paper proposes a system that can detect, track, and estimate velocity of vehicles using an uncalibrated camera and also detect and recognize their registration number plate. This approach provides a cost-effective alternative for traffic flow monitoring and surveillance. This robust system finds its applications in urban traffic management systems, military installation, and research facility security systems. This approach is a computationally efficient approach for detecting and tracking moving cars on the road utilizing uncalibrated cameras mounted on the road. It is also helpful for military installation because all the security issues have been detected by using this approach.

Smart big data framework for insight discovery

Big Data deal with new challenges such as data variety, data veracity (correct, incorrect, misleading, etc.) and data completeness (provide a single part of the overall information.). In fact, the knowledge discovered from a single source that can offer incorrect or incomplete data, may have a negative impact on the quality of decisions based on it. Therefore, integrating data coming from multiple sources allows verifying the veracity and ensuring the completeness of the results and thus improving the quality of analysis and enhancing business decisions. In this paper, we present a smart framework that falls within the Big Data value chain process and aims to improve the quality of analytical results by focusing two main concerns regarding Big Data Integration; data completeness and data veracity. The framework integrates Big Data in order to build a complete global and correct insight from heterogeneous sources. The paper presents two implementations of the framework in the context of urban and highway traffic management systems.

A GIS and Fuzzy-based Model for Identification and Analysis of Accident Vulnerable Locations in Urban Traffic Management System: A Case Study of Bhubaneswar

The world has seen road accident and its related societal and economical impact as one of the live, persisting problem in the last 2-3 decades and its prominence has been observed in the developing countries of the Asian subcontinent. With no exception, all major cities in India are facing the various challenges related to road accidents, mostly due to the large population density. Among the major cities in India, Bhubaneswar is a very fast growing city with aim to be the most livable city in Asia in the coming few years. With the city of Bhubaneswar as our study area, we address the issues related to road safety by determining the degree of severity of road accidents. We study the accident related data collected for the last decade using the spatial tools of Geographical Information System (GIS). Then using a GIS-map based analysis and a fuzzy-based model, we have found the spatio-temporal distribution of accident vulnerable locations with their degree of severity. Our experimental results show the accident hot-spots with values of selected contributing parameters such as timing, traffic density, vehicle speed, road intersections.

Link Travel Time Estimation for Arterial Networks Based on Sparse GPS Data and Considering Progressive Correlations

Understanding complicated city traffic patterns has been recognized as a critical goal by twenty-first-century urban planners and traffic management systems, resulting in a significant rise in the quantity and variety of traffic data gathered. For example, in a growing number of large cities, taxi firms have begun collecting metadata for each vehicle trip, such as origin, destination, and travel duration. Taxi data offer information on traffic patterns, allowing the study of urban flow – what will traffic look like between two sites on a particular day and time in the future? This paper proposes a method based on sparse GPS probe data, that focuses on allocating travel time data to the different links traveled between GPS observations. This model incorporates the progressive spatial correlations between the links in a network. The main goal of this work is to show how we can consider progressive spatial correlations and improve our results more realistically with a simple adjustment in the previously known parametric methods. For estimating arterial travel time, the methodology is applied to a case study for the partial network of New York City-based on the data collected from the taxicabs in New York City, providing the locations of origins, destinations, and travel times. The model estimates quarter-hourly averages of urban link travel times using OD trip data. This study proposes a more accurate approach for estimating link travel times, that fully utilizes the partial information received from taxi data in cities.