Intelligent Transportation Management System Research Articles

Development of an Intelligent Drone Management System for Integration into Smart City Transportation Networks

Drones have experienced rapid technological advancements, leading to the proliferation of small, low-cost, remotely controlled, and autonomous aerial vehicles with diverse applications, from package delivery to personal transportation. However, integrating these drones into the existing air traffic management (ATM) system poses significant challenges. The current ATM infrastructure, designed primarily for traditionally manned aircraft, requires enhanced capacity, workforce, and cost-effectiveness to coordinate the large number of drones expected to operate at low altitudes in complex urban environments. Therefore, this study aims to develop an intelligent, highly automated drone management system for integration into smart city transportation networks. The key objectives include the following: (i) developing a conceptual framework for an intelligent total transportation management system tailored for future smart cities, focusing on incorporating drone operations; (ii) designing an advanced air traffic management and flight control system capable of managing individual drones and drone swarms in complex urban environments; (iii) improving drone management methods by leveraging drone-following models and emerging technologies such as the Internet of Things (IoT) and the Internet of Drones (IoD); and (iv) investigating the landing processes and protocols for unmanned aerial vehicles (UAVs) to enable safe and efficient operations.

Revolutionizing Urban Mobility: Smart Transportation & Vehicle-to-Infrastructure Communication – A Review

The burgeoning popularity of IoT technology has significantly altered transportation, with an extensive variety of repercussions. Intelligent Transportation Systems (ITS) are at the vanguard of this change, which has the potential to fundamentally change urban transportation for both individuals and businesses. ITS has expanded considerably in recent years, owing to global urbanization and industrialization advancements. It is regarded as a game-changing technology that improves road safety, improves traffic flow, and improves the entire driving experience. Despite many research efforts on ITS applications and simulation platforms, formal semantic descriptions for intelligent transportation systems have been noticeably lacking. Rising rates of accidents suggest the necessity for a robust Smart Transport System (STS) that prioritizes improving mobility, and reliability, as well as reducing carbon dioxide and methane emissions from automobiles beyond dispersing gains throughout everywhere. Multiple traffic signal management approaches have been designed to enhance real-time traffic movement at intersections, but none have achieved seamless, congestion-free traffic movements. The creation of an interconnected unified framework that integrates and links vehicles with VANET, IoT, and AI technologies will significantly affect the establishment of an innovative reliable mobility network. Internet of Vehicles (IoV) systems will connect several automobiles to share vital information through an IoT-enabled network. Vehicle-to-infrastructure (V2I) communication is essential in developing intelligent transportation and rail traffic management systems, significantly improving road traffic safety and efficiency. A V2I system integrates transmission and analysis capabilities into vehicles and corresponding infrastructure. Wireless sensors are strategically positioned along highways or driving zones in this system. The significance of safe and effective V2X communication between automobiles and facilities expands substantially as intelligent modes of transport progress.

OSTM-NET: Joint scale variation and occlusion handling deep network for real-time vehicle counting and volume estimation

Vehicle counting and traffic volume estimation using videos are difficult tasks crucial for efficient traffic control in smart cities. Several existing techniques rely in tracking and detecting mechanisms of the vehicles. These methods are ineffective for detecting occluded and small vehicles. Also, contextual information loss occurs in deep counting networks. An innovative Internet-of-Things (IoT)- driven Intelligent Transportation Management (ITM) system is proposed to address these issues. Initially, traffic videos are converted to temporal-spatial images instead of using complex detection and tracking methods. The density map for the temporal spatial image is estimated using an occlusion-aware spatio-temporal multi-scale network (OSTM-Net). It consists of two sub-networks for capturing occluded and small vehicles simultaneously. The scale-aware column network (SCNet) accurately captures small vehicles and preserves contextual information through enhanced scale representation. At the same time, the occlusion management network (OM-Net) uses position-sensitive regions of interest (PSRoI) deformable pooling to address the occlusion issues. Finally, volume estimation and counting are calculated in accordance with the density map obtained from OSTM-Net. Every path in the videos is processed separately using OSTM-Net to calculate the vehicle count in every path for effective traffic control in this proposed approach. Furthermore, the effectiveness of the sub-networks (SCNet and OM-Net) is validated using ablation experiments. The proposed IoT-based ITM achieves high performance in counting vehicles and estimating traffic volume compared to other existing approaches.

Evaluating the Efficacy of Real-Time Connected Vehicle Basic Safety Messages in Mitigating Aberrant Driving Behaviour and Risk of Vehicle Crashes: Preliminary Insights from Highway Scenarios

Connected vehicle (CV) technology has revolutionised the intelligent transportation management system by providing new perspectives and opportunities. To further improve risk perception and early warning capabilities in intricate traffic scenarios, a comprehensive field test was conducted within a CV framework. Initially, data for basic safety messages (BSM) were systematically gathered within a real-world vehicle test platform. Subsequently, an innovative approach was introduced that combined multimodal interactive filtering with an advanced vehicle dynamics model to integrate BSM vehicle motion data with observations from roadside units. In addition, a driving condition perception methodology was developed, leveraging rough sets and an enhanced support vector machine (SVM), to identify aberrant driver behaviours and potential driving risks effectively. Furthermore, this study integrated BSM data from various scenarios, including car-following, lane changes, and free driving within the CV environment, to formulate multidimensional driving state sequence patterns for short-term predictions (0.5 s) utilising the long short-term memory (LSTM) model framework. The results demonstrated the effectiveness of the proposed approach in accurately identifying potentially hazardous driving conditions and promptly predicting collision risks. The findings from this research hold substantial promise in advancing road traffic safety management.

Investigating machine learning for simulating urban transport patterns: A comparison with traditional macro-models

Predicting passenger flow within a city is crucial for intelligent transportation management systems, especially in the context of urban development, post-pandemic policy changes, and infrastructure improvements. Traditional macro models have limitations in accurately capturing the complex structure of real traffic flows, and recent advancements in machine learning offer promising approaches for improving transportation simulations. This research aims to compare the effectiveness of traditional simulation models with a selective machine learning (ML) model for traffic flow prediction in Oslo, Norway. Sensitivity and scenario analyses are conducted to examine the models’ parameters and derive the city’s characteristics. Results substantiate that the traditional Spatial Interaction model (SIM), although interpretable and requiring fewer parameters, has limitations in accurately capturing real flow structures and exhibits greater variability compared to the ML model. Statistical analyses support these findings and raise questions about the validity of the ML model’s results over the SIM. The research highlights the potential of ML models to identify trends in passenger flows and simulate traffic flows in different scenarios related to city development. Overall, the research presents a decision support system for planners and policymakers to predict traffic flow accurately and efficiently. It highlights the benefits and drawbacks of both the traditional SIM and ML models, contributing to the ongoing discussion of the role of machine learning in transportation modeling.

Dynamic adaptive vehicle re-routing strategy for traffic congestion mitigation of grid network

This paper proposes a possible methodology for detecting and mitigating traffic congestion. This method is carried out using a custom-designed traffic scenario model. The model is fully developed in lieu of abundant data support from actual traffic events, which is applicable to localized traffic surveillance conditions, where massive data collection from surveilling devices is infeasible or unviable. This approach includes two parts: model construction and re-routing strategy. The model construction part focuses on the development of a traffic driving scenario, which takes various criteria such as traffic volume and traffic signal into consideration. The goal of this setup is to create a realistic-possible environment, where the proposed methods can be tested. The re-routing strategy is implemented based on the model simulation result of a medium-scale drive-able road map. The idea of the adaptive vehicle re-routing strategy is inspired by the k-shortest path algorithm, adapted with the dynamic congestion re-routing strategy. It will be shown that the model is able to automatically identify congestion patterns that are happening on any road segments, and then initiates a proper re-routing strategy to alleviate such congestion in a timely manner. Although the methodology is realized and validated within a simulated model, the concept is transparent to any transportation system under study without extra complexity. In addition, the proposed modeling and simulation technique can be used for real-time implementation in intelligent transportation management systems.

A High-Precision Vehicle Detection and Tracking Method Based on the Attention Mechanism

Vehicle detection and tracking technology plays an important role in intelligent transportation management and control systems. This paper proposes a novel vehicle detection and tracking method for small target vehicles to achieve high detection and tracking accuracy based on the attention mechanism. We first develop a new vehicle detection model (YOLOv5-NAM) by adding the normalization-based attention module (NAM) to the classical YOLOv5s model. By exploiting the YOLOv5-NAM model as the vehicle detector, we then propose a real-time small target vehicle tracking method (JDE-YN), where the feature extraction process is embedded in the prediction head for joint training. Finally, we present extensive experimental results to verify our method on the UA-DETRAC dataset and to demonstrate that the method can effectively detect small target vehicles in real time. It is shown that compared with the original YOLOv5s model, the mAP value of the YOLOv5-NAM vehicle detection model is improved by 1.6%, while the MOTA value of the JDE-YN method improved by 0.9% compared with the original JDE method.

Blockchain-Enabled VANET for Smart Solid Waste Management

Internet of things (IoT) is being applied in every aspect of daily living to improve the quality of life. Waste management is a critical issue, especially for developing countries. The purpose of this study is to propose an IoT-driven state-of-the-art system for solid waste management for developing countries and urban areas. The lack of planning and availability of resources in urban areas have made a living in densely populated surroundings even more difficult. This research work provides a framework based on blockchain-enabled vehicular ad-hoc networks (VANETs) in the realm of IoT. The step-by-step methods are proposed for the decentralized solution for solid waste management using blockchain-enabled VANET. Advanced ultra-high frequency (UHF) technology is used along with IoT devices for the real-time location of waste vehicles and the detection of waste bins. Geo-fencing techniques are also applied for the monitoring and timely collection of waste from the dump spots. Lastly, blockchain technology is applied in the proposed solution to make machine-to-machine (M2M) communication more secure, reliable and trustworthy across IoT devices. The experimental results are also obtained by deploying a pilot project in Karachi, Pakistan. The real-time dashboard is also obtained to demonstrate the daily statistics of the waste collection and performance of the waste vehicles. The results indicate the successful implementation of SSWMS to achieve real-time tracking, intelligent identification of waste bins, trash weighing, and keeping tabs on waste collection from dump spots using geofencing. In future, the blockchain-enabled VANETs can be applied for route management, intelligent transportation and fleet management systems (FMS) due to the inherent characteristics of blockchain and IoT.

Multi-featured spatial-temporal and dynamic multi-graph convolutional network for metro passenger flow prediction

Metro passenger flow prediction is an essential part of crowd flow forecasting and intelligent transportation management systems. However, two challenges still need to be addressed to achieve a more accurate prediction: (1) accounting for featural dependence instead of considering only the temporal connection and spatial relations; (2) utilising graph structures to address non-European relationships of spatial and featural dependence. To address these challenges, we developed a novel model called the multi-featured spatial-temporal (MFST) and dynamic multi-graph convolutional network (DMGCN) model. Temporal connections are learned from both the local and global information in a time-series sequence using the combination of a time-trend feature mapping block and a gated recurrent unit block. Spatial relation and featural dependence are separately captured by two DMGCN blocks. Each DMGCN block encodes various relationships by constructing multiple graphs consisting of predefined and non-defined topologies. The results of evaluations conducted of the MFST tensor and the DMGCN on the real-world Beijing subway dataset indicate that the prediction performance of the proposed model is superior to that of the existing baselines. The proposed model thus contributes significantly to the improvement of public safety by providing early warnings of large passenger flow and enabling the smart scheduling of resources.

Design and development of an intelligent transportation management system using blockchain and smart contracts

Design and development of an intelligent transportation management system using blockchain and smart contracts

Intelligent ecofriendly transport management system based on IoT in urban areas

Intelligent ecofriendly transport management system based on IoT in urban areas

Smart Bus System for Specially Challenged Individuals

This paper introduces a system design about bus management system based on Wi-Fi module which implemented the basic functions of the intelligent public transport management system, such as monitoring the time of bus arrival, departing from the bus station and reporting stations name automatically. This system can ensure the punctuality of vehicles to run, improve the automation level of reporting stations, and quality of public transport service. The management system has low cost and thus it is more feasible. This System can be a brilliant helping hand for the individuals who are mentally and physically challenged and help them in reaching their respective destinations on time because, in our respective transportation system, the priority will be to drop the physically and mentally impaired individuals as well as senior citizens safely on time.

Excavation of the Internet of Things in Urban Areas Based on an Intelligent Transportation Management System

Transportation, as one of the most common aspects people use in their daily lives, has resulted in highly complex traffic in urban areas due to the large number of private vehicles. As some results of the traffic congestion, there is energy consumption, environmental pollution, unplanned accidents, and time is wasted due to congestion and traffic jams. With the aid of the Internet of Things (IoT), which is an excellent computerized technology solution for all field claims, Internet of Things (IoT) technology has recently provided an efficient and effective traffic management system, especially in transportation, due to the combined functions IoT can handle, there are management, monitoring, tracking, identifying, computing, and so on. This article provided a comprehensive overview of a variety of intelligent management systems that have been built using IoT to alleviate traffic congestion.

Issues of building an intelligent water transport management system

The current level of technology in terms of instruments, devices and software enables the construction of local intelligent transport systems that contribute to the prevention of accidents. It has become possible to build crewless and unmanned vessels. Geographical information services such as Google Maps, Mapbox, OpenStreetMap have already shown their effectiveness. The relevance of the study stems from the possibility to partially automate ship’s route planning nowadays. As navigation monitoring is carried out by means of VHF (very high frequency) - Automatic Identification System (AIS) receivers, the authors propose to collect and analyse data. However, for the construction of the geographical information system and data processing, the authors justified the proposal to build the concept, methodological foundations, mathematical models and scenarios, which will serve as the basis for the development of software for the geographical information system. They propose data types for constructing time/speed matrices for planning optimal routes based on the current navigational situation. The data collected will provide a 12-36 hour forecast and allow for the determination of vessel speeds and times, considering vessel specifications, traffic of other vessels, queue at locks, forecast of hydro- and meteorological conditions, estimates of traffic intensity and density. The service is expected to optimise the route in terms of speed and journey time to meet the transport and logistics challenge.

Adaptive multi-phase approach for solving the realistic vehicle routing problems in logistics with innovative comparison method for evaluation based on real GPS data

ABSTRACT Transportation management, as a part of the supply chain management, is a complex process that consists of planning and delivering goods to customers. The paper presents a complete multi-phase intelligent and adaptive transportation management system, which includes data collection, parameter tuning, and the heuristic algorithm based on the Tabu search for vehicle routing. The paper describes the procedure for collecting Global Positioning System (GPS) data and analyzing the compliance with the proposed routes based on the data collected. The described routing algorithm is powerful and supports many real-world limitations. An algorithm for the anomaly detection in the GPS data is presented as well as the usage of collected GPS data to improve the future results of the algorithm. The concept was implemented and tested on real data in some of the largest distribution companies in Bosnia and Herzegovina. The proposed approach resulted with more than satisfactory results in real-world application.

Dependability of Directional Millimeter Wave Vehicle-to-Infrastructure Communications

Vehicle-to-infrastructure (V2I) communication is an important enabler for intelligent transportation and rail traffic management systems, which are expected to provide significant road traffic safety and efficiency enhancements. Such systems require a level of dependability of the wireless communication link that can be hard to support by state-of-the-art technologies. Transmissions in the millimeter wave (mmWave) band have the potential to provide sufficient bandwidth to support not only traffic management services, but additionally also web and entertainment applications for car and train passengers. mmWave transmissions require directional antennas at the transmitter and/or receiver to compensate for the significant pathloss and to achieve a practically acceptable maximum coupling loss. Such directional communication is feasible in V2I scenarios, because the mobility of users is confined to streets and roads. In this article, we discuss the dependability of directional mmWave V2I communications under two-ray fading conditions. We review methods for enhancing the dependability of the communication link by exploiting macroscopic spatial diversity and by mitigating interference from other transmitters. We furthermore provide guidelines for a dependable system design under two-ray fading and identify open research questions in this context.

Fuzzy permutation entropy derived from a novel distance between segments of time series

As effective tools for time series analyzing, a variety of information entropies have been widely applied in engineering, economic, biomedicine and other fields. In this paper, we define a new distance between finite sequence based on inversion and derive a new entropy, Fuzzy Permutation Entropy(FPE). A comparison of recognition performance for WGN, 1/f noise, periodical, or chaotic sequence and sine waves shows that FPE is valid on distinguishing deterministic signals from stochastic signals. Further contrast studying versus FE and PE shows that FPE is more sensitive to the alteration of the complexity of time series and more effective on separating different signals. Moreover, FPE is used to explore the distinction of the complexity of various traffic flows via the time headways which is simulated from the improved brake light rule model. We hope it is conducive to design Intelligent Transportation Management System.

Short-Term Traffic Flow Prediction Using the Modified Elman Recurrent Neural Network Optimized Through a Genetic Algorithm

Traffic stream determining is an essential part of the intelligent transportation management system. Precise prediction of traffic flow provides a basis for other tasks, like forecasting travel time. While traditional methods have some merits for improving traffic prediction precision in some ways, high precision, considering different circumstances, is still difficult to achieve. This article presents a short-term traffic flow prediction model based on the Modified Elman Recurrent Neural Network model (GA-MENN) to deal with this practical problem. In GA-MENN, the algorithm of Elman Recurrent Neural Network is modified, optimized through the Genetic Algorithm (GA) and considered weather conditions, weekday, hour and day’s classification to forecast the vehicle velocity in Tehran streets and highways. The traffic data were collected from the online Google Map API service for 139 routs in 7 districts in Tehran. The method improves prediction precision and also lowers the prediction error rate, according to experimental results. Exploratory outcomes verify the superior performance of the proposed traffic condition prediction model over Regression Multi-layer Perceptron, Linear Regression, Logistic Regression, Probabilistic Neural Network, Regression Generalized Feedforward, Time-lag Recurrent Network, Support Vector Machine model, Elman neural network, K- NN model, ARIMA, Kalman filter model, Convolutional Neural Networks (CNNs), SARIMA, and Long Short-Term Memory (LSTM) model. To the best of our knowledge, this is the first occasion when that traffic stream is gauged in urban roads and avenues in this specific way.

A Low-Cost, Flexible Pressure Capacitor Sensor Using Polyurethane for Wireless Vehicle Detection.

Detection of vehicles on the road can contribute to the establishment of an intelligent transportation management system to allow smooth transportation and the reduction of road accidents. Thus far, an efficient and low-cost polymer flexible pressure sensor for vehicle detection is lacking. This paper presents a flexible sensor for vehicle sensing and demonstrates a wireless system for monitoring vehicles on the road. A vehicle sensor was fabricated by sandwiching a polyurethane material between aluminum top/bottom electrodes. The sensing mechanism was based on changes in capacitance due to variation in the distance between the two electrodes at an applied external pressure. A clear response against a pressure load of 0.65 Mpa was observed, which is the same pressure as that of the car tire area in contact with the road. Significantly, the sensor was easy to embed on the road line due to its mechanical flexibility and large size. A field test was carried out by embedding the sensor on the road and crossing the sensor with a car. Moreover, the signal displayed on the tablet indicated that the sensing system can be used for wireless detection of the axle, speed, or weight of the vehicle on the road. The findings suggest that the flexible pressure sensor is a promising tool for use as a low-cost vehicle detector in future intelligent transportation management.

Management system project design of urban intelligent transportation

Focused on the pressure of transportation resource and the increasing traffic demand, this paper starts from the classification of urban transportation system, making use of communication technology, information technology and computer technology, etc., analyzing the feasibility of the project based on urban intelligent transportation management system, and putting forward the study and design of urban intelligent transportation management system, pointing out the improvements on intelligent processing of transportation information, systematic control theory, application and performance of urban intelligent transportation management system.