Real-time Traffic Information Research Articles

Smart Slot Assignment for Urban EV Charging Using Folium API Integration

Abstract: Efficient management of electric vehicle (EV) charging distribution in urban areas is crucial amid increasing demand and varying traffic conditions. This study proposes a novel web-based solution that integrates Folium Map API for realtime location tracking and Python Flask backend with MySQL database storage to allocate charging demands. By leveraging real-time traffic information from the Folium Map API, the proposed model optimizes charging resource allocation across multiple stations and road networks. The web interface, built using HTML, CSS, and JavaScript, allows users to interact with the system seamlessly. Charging allocation is dynamically adjusted based on real-time data, prioritizing maxi- mum returns. Additionally, an emergency charge counter facility is introduced to cater to unforeseen charging needs, enhancing user flexibility. The system offers two interfaces and a dashboard for both station administrators and users, en- abling pre-booking services for travelers during their journey, facilitating access to nearby charging stations, and providing a facility for prebooking. These features contribute to improved station revenue and pile utilization, highlighting the efficacy of the proposed web-based approach. By leveraging advanced web technologies and APIs, this comprehensive strategy enhances charging infrastructure efficiency, fosters the widespread adoption of electric vehicles, and contributes to sustainable urban mobility management.

Trust me if you can: Practical challenges affecting the integration of carpooling in Mobility-as-a-Service platforms

App-based Mobility-as-a-Service (MaaS) platforms combining public transport, car- and micro-mobility-shared services with real-time dynamic carpooling are emerging as viable alternatives to solo car use for sub-urban contexts. Insights from real-life implementation are however still limited. Which practical conditions may hinder the effectiveness of MaaS platforms leveraging carpooling? We tackle this question from the perspective of potential users of MixMyRide, a Swiss-based MaaS platform, based on co-design workshop sessions performed in Summer-Autumn 2022. We find four elements of practical interest, resonating with limitations already identified for carpooling. First, integrating carpooling in inter-modal trips increases the number of inter-changes potentially affected by delays. This requires real-time traffic information data, re-scheduling tools, and features for quick interaction between platform users. Second, features to create trust between strangers are needed, which require trade-offs between strict identity check and quick registration. Third, carpooling pick-up/drop-off may either endanger safety (if bus stops are used) or require inconvenient prior agreements, negatively affecting the MaaS platform’s dynamism. Fourth, carpooling offer is not granted. To accept possible discomfort in sharing personal space with strangers and time-effort to enter trip offers, drivers need specific incentives, such as sharing of travel expenses, reward vouchers by public institutions, or virtual gamification and feedback on saved emissions.

Understanding the Utilization of Real-Time Traffic Information during Hurricane Evacuations in Texas

Understanding how evacuees use real-time traffic information is crucial for developing effective emergency evacuation response plans for hurricane-prone areas. This paper investigates how such data were used during past hurricane evacuations and post-evacuation returns in Texas with a survey dataset collected between August 2022 and February 2023. We examined the usage patterns of various platforms, including navigation apps, social media, TV, radio, and information provided by public agencies. We found that a larger household size, longer distance to evacuation destinations, and past experience with hurricane evacuations are associated with greater use of real-time information platforms. Experienced evacuees tend to rely on navigation apps and social media, and those with experience before 2010 are more inclined to use the TV and radio as their primary sources of information. Motivation for using these platforms varies among users of different platforms. Although both navigation app users and social media users value their familiarity with the platform, the former also prioritize the convenience of using it. It was also found that TV users prioritize service accessibility, radio users emphasize service availability, and users of official agency information sources place a high value on data accuracy. These findings have implications for policymakers, emergency planners, and traffic engineers involved with disaster response operations to improve the resilience of transportation systems.

Traffic Signal Controller System and Vehicle Count-Based Road Congestion Detection

Abstract: Traffic congestion is one of the major modern-day crisis in every big city in the world. Recent study of World Bank has shown that average vehicle speed has been reduced from 21 km to 7 km per hour in the last 10 years in Dhaka. Inter metropolitan area studies suggest that traffic congestion reduces regional competitiveness and redistributes economic activity by slowing growth in county gross output or slowing metropolitan area employment growth. As more and more vehicles are commissioning in an already congested traffic system, there is an urgent need for a whole new traffic control system using advanced technologies to utilize the already existent infrastructures to its full extent. Since building new roads, flyovers, elevated expressway etc. needs extensive planning, huge capital and lots of time; focus should be directed upon availing existing infrastructures more efficiently and diligently. Previously different techniques had been proposed, such as infra-red light sensor, induction loop etc. to acquire traffic date which had their fair share of demerits. In recent years, image processing has shown promising outcomes in acquiring real time traffic information using CCTV footage installed along the traffic light. Different approaches have been proposed to glean traffic data. Some of them count total number of pixels , some of the work calculate number of vehicles .These methods have shown promising results in collecting traffic data.

Development of Real-Time Unmanned Aerial Vehicle Urban Object Detection System with Federated Learning

In this paper, an urban object detection system via unmanned aerial vehicles (UAVs) is developed to collect real-time traffic information, which can be further utilized in many applications such as traffic monitoring and urban traffic management. The system includes an object detection algorithm, deep learning model training, and deployment on a real UAV. For the object detection algorithm, the Mobilenet-SSD model is applied owing to its lightweight and efficiency, which make it suitable for real-time applications on an onboard microprocessor. For model training, federated learning (FL) is used to protect privacy and increase efficiency with parallel computing. Last, the FL-trained object detection model is deployed on a real UAV for real-time performance testing. The experimental results show that the object detection algorithm can reach a speed of 18 frames per second with good detection performance, which shows the real-time computation ability of a resource-limited edge device and also validates the effectiveness of the developed system.

CITY TRAFFIC SIMULATION ON MICROSERVICES

Population growth and urbanization in Kazakhstan have led to significant traffic management problems in large urban areas. Initiatives such as intelligent road systems, real-time traffic information, and traffic management have been implemented to address these issues. However, congestion and delays remain as major problems. Effective traffic modeling is necessary to understand traffic patterns, identify bottlenecks, and implement effective solutions for reducing congestion. Microservice architecture has recently become a trend owing to its advantages, and the authors of this paper propose the use of microservices for urban traffic modeling to solve these problems. In this study, any moving object between two points: a car, a bicyclist, or a pedestrian – was presented as an actor. Each actor is represented as a microservice. They communicate their movements through city microservice. After sending their movements, a red line is displayed on the map, indicating their travel paths. This solution helps to simulate movement more realistically because each microservice is independent and can move randomly.

ENHANCING TRAFFIC SAFETY: A COMPREHENSIVE APPROACH THROUGH REAL-TIME DATA AND INTELLIGENT TRANSPORTATION SYSTEMS

Today, traffic accidents are still a difficult and urgent problem for many countries around the world. Traffic accidents on highways are often more serious than accidents on urban roads. Therefore, disseminating emergency information and creating immediate connections with road users is key to rescuing passengers and reducing congestion. Thus, this study applies data fusion and data mining techniques to analyze travel time and valuable information about traffic accidents based on the real-time data collected from On-Board Unit installed in vehicles. The results show that this important information is the vital database to analyze traffic conditions and safety factors, thereby developing a smart traffic information platform. This result enables traffic managers to provide real-time traffic information or forecasts of congestion and traffic accidents to road users. This helps limit congestion and serious accidents on the Highway.

An Economical Velocity Planning Algorithm for Intelligent Connected Electric Vehicles Based on Real-Time Traffic Information

An Economical Velocity Planning Algorithm for Intelligent Connected Electric Vehicles Based on Real-Time Traffic Information

A Spatiotemporal Graph Neural Network with Graph Adaptive and Attention Mechanisms for Traffic Flow Prediction

This study addresses the complex challenges associated with road traffic flow prediction and congestion management through the enhancement of the attention-based spatiotemporal graph convolutional network (ASTGCN) algorithm. Leveraging toll data and real-time traffic flow information from Orange County, California, the algorithm undergoes refinement to adeptly capture abrupt changes in road traffic dynamics and identify instances of acute congestion. The optimization of the graph structure is approached from both macro and micro perspectives, incorporating key factors such as road toll information, node connectivity, and spatial distances. A novel graph self-learning module is introduced to facilitate real-time adjustments, while an attention mechanism is seamlessly integrated into the spatiotemporal graph convolution module. The resultant model, termed AASTGNet, exhibits superior predictive accuracy compared to existing methodologies, with MAE, RMSE, and MAPE values of 8.6204, 14.0779, and 0.2402, respectively. This study emphasizes the importance of incorporating tolling schemes in road traffic flow prediction, addresses static graph structure limitations, and adapts dynamically to temporal variations and unexpected road events. The findings contribute to advancing the field of traffic prediction and congestion management, providing valuable insights for future research and practical applications.

Edge-Enabled Smart Traffic Management System: An IoT Implementation for Urban Mobility

Effective traffic management has emerged as a critical issue in today's rapidly urbanising areas with a rise in the number of cars. By developing an Edge-Enabled Smart Traffic Management System (EESTMS) run on the Internet of Things (IoT), this paper puts forth a novel approach. EESTMS makes use of edge computing and IoT technology's promise to improve urban transportation. An large network of thoughtfully placed sensors and cameras dispersed around the city forms the system's central structure. These gadgets continuously gather data on the volume, speed, and congestion of moving vehicles. This information offers insightful information about traffic trends. We can lessen latency and lighten the load on centralised systems by processing this data at the edge. In order to analyse the data and identify traffic bottlenecks and congestion hotspots, machine learning techniques are used. Real-time analysis allows for dynamic traffic signal adjustments, which optimise traffic flow and shorten commuter travel times. EESTMS also offers a user-friendly interface with real-time traffic information, alternate routes, and tailored navigation advice that is accessible via mobile applications and web platforms. By making wise decisions, commuters can lessen their stress and carbon footprint. EESTMS plays a critical role in advancing sustainability by reducing fuel consumption and greenhouse gas emissions through effective traffic management, in addition to enhancing urban mobility. By giving emergency vehicles priority routing, this system also improves emergency response times. The application of EESTMS has shown promising outcomes in terms of lessened traffic congestion, improved commuter experiences, and lower environmental impact. Innovative solutions like EESTMS can open the door for smarter, more sustainable urban mobility as cities continue to grow, eventually enhancing citizens' quality of life.

Hybrid travel time estimation model for public transit buses using limited datasets

<p>A reliable transit service can motivate commuters to switch their traveling<br />mode from private to public. Providing necessary information to passengers<br />will reduce the uncertainties encountered during their travel and improve<br />service reliability. This article addresses the challenge of predicting dynamic<br />travel times in urban areas where real-time traffic flow information is<br />unavailable. In this perspective, a hybrid travel time estimation model<br />(HTTEM) is proposed to predict the dynamic travel time using the predicted<br />travel times of the machine learning model and the preceding trip details. The<br />proposed model is validated using the location data of public transit buses of,<br />Tumakuru, India. From the numerical results through error metrics, it is found<br />that HTTEM improves the prediction accuracy, finally, it is concluded that the<br />proposed model is suitable for estimating travel time in urban areas with<br />heterogeneous traffic and limited traffic infrastructure.</p>

Real-time traffic monitoring system using IoT-aided robotics and deep learning techniques

The increasing number of vehicles on the road has made traffic regulations challenging to manage, particularly in large and crowded cities. Real-time traffic monitoring systems are one of the most important factors that enable efficient traffic flow and enhanced mobility. Therefore, vehicles and drivers have always needed reliable and accurate real-time traffic information. Recently, various solutions have been proposed to solve the problems and concerns in traffic situations. One alternative solution is vehicular cloud computing (VCC). Additionally, an IoT-aided robotic (IoRT) model has been developed with a modern architecture that integrates IoT sensor nodes and cameras to gather real-time traffic data. The main contributions of this research work are to implement two deep learning techniques based on modified LeNet-5 for real-time traffic sign recognition and the transfer learning-based Inception-V3 model for detecting and recognizing traffic lights. Furthermore, optimal distance was found between the ultrasonic sensors and the obstacles using ultrasonics’ waves time and speed to reduce road accidents. The data, which is collected by sensors and cameras, is processed using various image processing algorithms and it is sent to the cloud to be available for drivers and commuters through a mobile application. Test results indicate that the proposed models have significant improvements in terms of accuracy. The modified LeNet-5 achieved accuracy rates of 99.12% and 99.78% on the German Traffic Sign Recognition Benchmark (GTSRB) and extended GTSRB (EGTSRB) datasets, respectively, whereas the second model, trained on Laboratory for the Intelligent and Safe Automobiles (LISA) dataset, attained a 98.6% accuracy rate. Compared to the related traffic monitoring systems, the findings of this study outperform other works by 3.78% for traffic sign recognition and by 1.02% for traffic light detection and recognition.

Zone-based stable and secure clustering technique for VANETs

Vehicular Ad-hoc Network (VANET) is a pivotal element of the Intelligent Transportation System (ITS) and has gained significant attention in both academia as well as in the industry. The driving experience has been improved with the deployment of VANETs in the transportation system. In this scenario, accurate real-time traffic information is required to be forwarded to the correlated vehicles using Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. The V2V system is prone to the issue of communication overhead and security, whereas the V2I-based model for data dissemination faces the issue of scalability. In addition to this, wireless communication among vehicles is affected by potential attackers, that not only threaten the vehicle's security but also the commuter's life. An impersonation attack is considered the most common and dangerous attack in VANETs. In this paper, we propose a hybrid system that utilizes both V2V and V2I communications for secure data dissemination in urban scenarios. We use a zone-based stable and secure clustering mechanism to identify Cluster Heads (CH) as a message forwarding node to and from the Road Side Unit (RSU), which reduces the communication overhead and latency of the network. We utilize the concept of Internet Control Message Protocol (ICMP) and the monitoring mode of RSU for the detection and mitigation of impersonation attacks in VANETs. Finally, we validate our results using a network simulator (NS2).

Task Scheduling of Real-Time Traffic Information Processing Based on Digital Twins

The Intelligent Transportation System under Digital Twins can provide accurate data sources for traffic control. The present work focuses on the real-time information processing and task scheduling problems of the Internet of Vehicles (IoV) system based on Virtual Reality. They are the Quality/Distance Algorithm (QDA), Task Density Algorithm, Distance Balance Algorithm (DBA), and Bionic-DBA (B-DBA). The simulation experiment analysis suggests that the DBA algorithm takes the balance of travel distance into account and effectively improves task quality. The Utility Function in B-DBA and the Biological Heuristic Search Algorithm in Pareto Ant Colony Optimization play a critically important role in enhancing the overall task quality. In addition, a Transmission based on Privacy Protection (TPP) algorithm is designed to protect the attribute-based privacy information in the traffic information transmission system. This algorithm ensures that the real-time traffic information processing system resists various attacks from malicious nodes. It has been verified that when the number of selfish nodes accounts for 30%, the transmission efficiency of the TPP algorithm reaches 0.77. The research content has a practical reference value for providing users with continuous and high-quality IoV network services.

Vehicle Trajectory Data Mining for Artificial Intelligence and Real-Time Traffic Information Extraction

It aims to improve the efficiency of information collection and extraction in the current intelligent transportation system, and accurately mine the vehicle trajectory data By using Artificial Intelligence (AI) and Deep Learning methods, the trajectory data generated during vehicle driving are deeply mined and analyzed, and the characteristics of driving behavior of vehicle drivers are modeled and analyzed in detail. Then, a method of mining driving behavior characteristics based on Convolutional Neural Network (CNN) and vehicle trajectory is proposed. Based on the mathematical principle of wavelet packet and Least Square Support Vector Machine (LSSVM), a combined model of trajectory mining is constructed and applied to the short-term prediction of traffic flow. The traffic flow of Binjiang Road and Renmin Road in Guangzhou, Guangdong Province from August 19 to August 21, 2021 is predicted to verify the accuracy of the trajectory mining combined model. The results show that the combination model of data mining has good fitting effect, and the average accuracy is above 0.8. Besides, the effectiveness of the Deep Learning model in driver behavior classification is verified. The accuracy of the classification model is 75.2% for trajectory, and that is 76.8% for driver behavior characteristics. It is of great significance to effectively utilize the knowledge data in Intelligent Transportation System (ITS) and extract valuable information from it, which has certain reference value for the subsequent refined prediction of vehicle behavior.

Remote sensing traffic scene retrieval based on learning control algorithm for robot multimodal sensing information fusion and human-machine interaction and collaboration.

In light of advancing socio-economic development and urban infrastructure, urban traffic congestion and accidents have become pressing issues. High-resolution remote sensing images are crucial for supporting urban geographic information systems (GIS), road planning, and vehicle navigation. Additionally, the emergence of robotics presents new possibilities for traffic management and road safety. This study introduces an innovative approach that combines attention mechanisms and robotic multimodal information fusion for retrieving traffic scenes from remote sensing images. Attention mechanisms focus on specific road and traffic features, reducing computation and enhancing detail capture. Graph neural algorithms improve scene retrieval accuracy. To achieve efficient traffic scene retrieval, a robot equipped with advanced sensing technology autonomously navigates urban environments, capturing high-accuracy, wide-coverage images. This facilitates comprehensive traffic databases and real-time traffic information retrieval for precise traffic management. Extensive experiments on large-scale remote sensing datasets demonstrate the feasibility and effectiveness of this approach. The integration of attention mechanisms, graph neural algorithms, and robotic multimodal information fusion enhances traffic scene retrieval, promising improved information extraction accuracy for more effective traffic management, road safety, and intelligent transportation systems. In conclusion, this interdisciplinary approach, combining attention mechanisms, graph neural algorithms, and robotic technology, represents significant progress in traffic scene retrieval from remote sensing images, with potential applications in traffic management, road safety, and urban planning.

Managing mixed traffic at signalized intersections: An adaptive signal control and CAV coordination system based on deep reinforcement learning

Managing the mixed traffic involving connected and autonomous vehicles (CAVs) and human-driven vehicles (HVs) at a signalized intersection has become a concern of researchers. However, the performances of most existing control methods are limited, especially when CAV penetration rate is low, since they fail to make a better trade-off between safety and operational efficiency for both CAVs and HVs. To this end, this study proposes a deep reinforcement learning (DRL) powered control system for the mixed traffic at signalized intersections, which aims to optimize operational efficiency of both CAVs and HVs while assuring safety and reducing interference on HVs’ driving habits. The system adopts an adaptive traffic signal control strategy and an efficient CAV control policy with a passing rule proposed as a link in between. The traffic signal control strategy allows traffic light to adaptively adjust its phase and duration based on real-time traffic information, while the CAV control policy permits the CAVs meeting certain safety constraints to form platoons and pass through the intersection in a coordinated manner regardless of traffic signals. As an efficient DRL algorithm, Deep Q-Network (DQN) is adopted to adaptively control the signals and implement CAV coordination. The proposed system is examined on Simulation of Urban Mobility (SUMO), given different CAV penetration rates and traffic conditions. It is found that the proposed system not only outperforms the state-of-the-art control methods on reducing travel time and fuel consumption under low CAV penetration rate, but also enlarges its advantages with the increase of CAV penetration rate. In certain traffic scenarios, the proposed system can even achieve a maximum reduction of travel time by 37.33% and fuel consumption by 15.95%, in comparison to the existing method with the best performance. Besides, to some extent, the comparisons between the performances of CAVs and HVs demonstrate certain benefits of introducing CAVs into the mixed traffic.

A novel fuzzy inference method for urban incomplete road weight assignment

ABSTRACT One of the keys in time-dependent routing is determining the weight of each road network link based on traffic information. To facilitate the estimation of the road’s weight, Global Position System (GPS) data are commonly used in obtaining real-time traffic information. However, the information obtained by taxi-GPS does not cover the entire road network. Aiming at incomplete traffic information on urban roads, this paper proposes a novel fuzzy inference method. It considers the combined effect of road grade, traffic information, and other spatial factors. Taking the third law of geography as the basic premise, that is, the more similar the geographical environment, the more similar the characteristics of the geographical target will be. This method uses a Typical Link Pattern (TLP) model to describe the geographical environment. The TLP represents typical road sections with complete information. Then, it determines the relationship between roads lacking traffic information and the TLPs according to their related factors. After obtaining the TLPs, this method ascertains the weight of road links by calculating their similarities with TLPs based on the theory of fuzzy inference. Aiming at road links at different places, the dividing – conquering strategy and globe algorithm are also introduced to calculate the weight. These two strategies are used to address the excessively fragmented or lengthy links. The experimental results with the case of Newcastle show robustness in that the average Root Mean Square Error (RMSE) is 1.430 mph, and the bias is 0.2%; the overall RMSE is 11.067 mph, and the bias is 0.6%. This article is the first to combine the third law of geography with fuzzy inference, which significantly improves the estimation accuracy of road weights with incomplete information. Empirical application and validation show that the method can accurately predict vehicle speed under incomplete information.

Hotspots-based patrol route optimization algorithm for smart policing

Smart policing based on the analysis of big data ensures the development and sustainability of police policy. However, it is difficult to find instances in which the results of data analysis have been applied to actual policy in the field of crime prevention. The South Korean police force recognizes the need for smart policing and is engaged in various research and field support activities. Some examples that are especially relevant for crime investigation include analyzing the connections between cases and predicting the location of the next crime in a series of crimes and the location of suspects. However, it is difficult to find examples of police policy that use big data. Therefore, this study aims to suggest a model that uses big data to respond to emergency calls efficiently. First, we extract hotspots that are predicted to be locations of criminal activity based on an analysis of the association between community environment data and crime data. Second, we create a route having the shortest travel time to the crime location by developing a route optimization algorithm. Lastly, we assess the performance of the patrol routes in reflecting real-time traffic information. If the data application model suggested in this study could be adjusted and applied to the current police patrol system, the model could be used by each police department effectively.

스마트도시의 안전성 향상을 위한 스마트기술 적용방안에 관한 연구

In order to propose ways to apply smart technology to improve the safety of smart cities, this study aims to propose smart technologies that are highly related to safety management in the areas of transportation, medical care, environment, nature, and crime prevention in smart cities, such as AR, BIM, big data, and cloud. , IoT, VR, robot automation, drones, and 3D printing were analyzed. For the study, the applicability of smart technology in each field was investigated by safety management experts using a matrix evaluation method, and the results can be summarized as follows. In the transportation field, big data technology for collecting and processing traffic information through GPS, GIS and smart phone apps, drone technology that monitors real-time traffic information and utilizes sensors such as vehicles, roads and signal devices, and various collected information The safety of smart cities can be secured by applying IoT technology that builds prediction models based on traffic patterns. In the medical field, robot automation technology that sets up predictive models for telemedicine, health management, drug processing and infectious diseases, drone technology that utilizes the movement of patients and medical staff, attachment of monitoring devices, and securing public health response data, etc. It is predicted that it will be possible to secure the safety of smart cities through 3D printing technology that can manufacture medical instruments, auxiliary devices, and various artificial bodies, as well as various medical models and educational tools. In the environmental field, drone technology enables real-time monitoring of water quality, air, and various environmental factors, robotic automation technology that uses robots in places to collect environmental data within the city, and environmental infrastructure that installs and collects buildings, vehicles, and various infrastructure facilities. IoT technology can be applied to improve the safety of smart cities. In the natural field, drone technology is capable of collecting and utilizing various data by attaching video, thermal images, LiDAR scans, radars and sensors, and integrated management linked to a central system focused on the installation of houses, buildings and various infrastructure facilities and environmental changes. The safety of smart cities can be improved by utilizing possible IoT technology, BIM technology that can establish response plans for storm and flood damage, cold waves, heatstroke, and earthquakes through 3D models, and big data technology that collects, integrates, and builds platforms for safety-related data. It can be promoted. In the crime prevention field, drone technology that can detect the occurrence of abnormal signals such as sound, heat, and optical sensors and link with the local police station, IoT technology that can collect and analyze voice recognition technology, and AI that strengthens facial and vehicle recognition in intelligent CCTV. The safety of smart cities can be secured through robot automation technology that combines the deployment of basic robots and collaboration with drones and IoT technologies.