Development Of Intelligent Transportation Systems Research Articles

A low cost and effective multi-instance abnormal driving behavior detection system under edge computing

The rapid development of intelligent transportation system (ITS) has brought great convenience to people's life, and also makes various intelligent vehicle services emerge in an endless stream. However, with more than 1.35 million people killed in traffic accidents every year, the detection of abnormal driving behavior has attracted more and more attention. Although some progress has been made in the research of abnormal driving behavior detection, numerous challenges remain. Firstly, it is difficult to manually analyze whether driving behavior is abnormal, which makes it costly to accurately label them for machine learning. Secondly, few studies have considered the problem of class imbalance in the abnormal driving behavior detection, which is common in driving behavior and has a significant negative impact on the detection model, especially in weakly supervised scenarios. Thirdly, assessing a driver's behavior solely based on real-time performance is insufficient, the historical behavior is equally important. To address these challenges, an abnormal driving behavior detection system based on weakly supervised learning under edge computing architecture is proposed in this paper. This system considers the class imbalance problem and utilizes the historical information of the vehicles to provide a low cost and effective detection service. Firstly, the abnormal driving behavior detection is modeled as a multi-instance learning (MIL) problem, which only needs coarse-grained labels for anomaly detection, so that the labeling work becomes low cost. Secondly, the class imbalance problem under weakly supervised learning is solved by incorporating parameter transfer and pre-estimating abnormal scores. This approach enhances the model's sensitivity to rare classes and significantly improves the performance of weakly supervised detection models. Thirdly, in order to learn the historical information of the vehicle, a summary mechanism is used for the road side units (RSUs), which can provide different historical summary of the vehicles for other detection nodes, so as to make the model more effective for different abnormal events. Several experiments are carried out on both simulation dataset and real-world dataset to verify the performance of the proposed method. The results demonstrate that compared with the traditional supervised learning methods, the proposed method can still achieve near-optimal performance when only using coarse-grained labels, and all the strategies proposed have improved the detection performance obviously.

Wi-CL: Low-Cost WiFi-Based Detection System for Nonmotorized Traffic Travel Mode Classification

Traffic travel mode identification and classification are crucial for the development of intelligent transportation systems (ITSs). At present, scholars have investigated the classification of motorized and nonmotorized traffic travel in various road environments; however, the classification of walking and bicycle modes in nonmotorized travel has been largely ignored. Therefore, in this paper, we investigate nonmotorized traffic travel and propose a new low-cost nonmotorized traffic travel mode classification system, known as the Wi-Fi classification (Wi-CL) system that uses Wi-Fi signal detectors and the refined characteristics of nonmotorized travel modes. The Wi-CL system includes four modules: data acquisition module, data processing module, feature extraction module, and mode classification module. In the data acquisition module, the proposed system detects the Wi-Fi signals of traffic participants in road environments. In addition, we propose a received signal strength indicator (RSSI) filtering algorithm for hybrid traffic networks that effectively addresses surrounding obstacles and environmental noise. In the feature extraction module, we extract relevant traffic features to construct a mode classification model. Finally, a recurrent neural network (RNN) framework based on the long short-term memory (LSTM) algorithm is successfully implemented in the mode classification module for traffic travel mode identification. To validate the effectiveness of the Wi-CL system, extensive experiments were conducted using field data collected by Wi-Fi detectors installed at the South China University of Technology (SCUT). The experimental results show that the proposed RSSI filtering algorithm achieves excellent signal filtering results in real road traffic environments. In addition, the constructed travel speed estimation algorithm outperforms other baseline models in four different scenarios (flat-peak walking, midday peak walking, flat-peak cycling, and midday peak cycling), achieving an overall classification accuracy of 97.92%. In summary, our Wi-CL system is a feasible approach for nonmotorized traffic travel mode classification.

Lane-Level Heterogeneous Traffic Flow Prediction: A Spatiotemporal Attention-Based Encoder–Decoder Model

Urban road traffic flow prediction is the key basis for the development of intelligent transportation systems. Lane-level heterogeneous traffic flow prediction will become a new and important challenge in the future development of intelligent transportation. In this study, a spatiotemporal attention-based encoder-decoder model is proposed to solve the lane-level heterogeneous traffic flow prediction problem. Specifically, the traffic flows of different locations and different types of vehicles are all regarded as traffic flows running in independent spaces. A spatial attention layer is added to the encoder to capture the spatial characteristics of traffic flows, and a temporal attention layer is added to the decoder to analyze the temporal correlation of each time interval. Finally, we validate the model by using real traffic monitoring data in the Shunyi District, Beijing, China, and prove the superior performance of the model. Meanwhile, the output of attention weights can deepen the further interpretation of the spatiotemporal correlation of heterogeneous traffic flows.

Dynamic Traffic Prediction-Based Energy Management of Connected Plug-In Hybrid Electric Vehicles with Long Short-Term State of Charge Planning

Vehicle electrification, automation, and connectivity in today's transportation require significant efforts in control design to meet conflicting goals of energy efficiency, traffic safety, as well as comfort. The rapid development of intelligent transportation systems (ITS) and the rapid growth of connectivity technologies enable vehicles to receive more information about traffic conditions, which provides a reliable solution for the energy management of plug-in hybrid electric vehicles (PHEVs). This article proposes a predictive energy management strategy (EMS) for connected PHEV based on real-time dynamic traffic prediction. First, the future traffic information is predicted by establishing a wavelet neural network (WNN). Thus, the global driving condition can be predicted. Then, the particle swarm optimization (PSO) algorithm is used to optimize the parameters of WNN to plan a global battery state-of-charge (SOC) reference. Second, a long short-term memory-based velocity predictor is proposed for the predictive EMS, by planning SOC over a prediction horizon based on the global SOC reference. Finally, the performance of the proposed EMS with WNN and PSO-WNN is verified by the actual traffic data. The results show that it can improve the fuel economy by 17.57% and 28.19%, respectively.

Human Mobility Prediction with Region-based Flows and Road Traffic Data

Predicting human mobility is a key element in the development of intelligent transport systems. Current digital technologies enable capturing a wealth of data on mobility flows between geographic areas, which are then used to train machine learning models to predict these flows. However, most works have only considered a single data source for building these models or different sources but covering the same spatial area. In this paper we propose to augment a macro open-data mobility study based on cellular phones with data from a road traffic sensor located within a specific motorway of one of the mobility areas in the study. The results show that models trained with the fusion of both types of data, especially long short-term memory (LSTM) and Gated Recurrent Unit (GRU) neural networks, provide a more reliable prediction than models based only on the open data source. These results show that it is possible to predict the traffic entering a particular city in the next 30 minutes with an absolute error less than 10%. Thus, this work is a further step towards improving the prediction of human mobility in interurban areas by fusing open data with data from IoT systems.

Traffic Speed Sequence Prediction by Adaptive Weighted Long Short-Term Memory With Classification-Type Loss

Accurate traffic speed prediction is necessary to promote the development of intelligent transportation systems. The construction of consummate models is challenging owing to nonlinearity, nonstationarity, and long-term dependence in traffic speed prediction. This study proposed an ensemble long short-term memory (LSTM) model that was based on adaptive weighting, in which ensemble learning was the main solution. First, a data preprocessing model based on a seasonal statistical model was introduced to reconcile the long- and short-term dependence of the data. Second, the LSTM time step was considered during training, and a classification-type loss was designed to calculate the error rate in the ensemble system. Last, an adaptive weighting strategy was constructed to integrate a series of LSTMs generated in the system to obtain an ensemble model for traffic speed prediction. The experimental results showed that the proposed method was more stable and accurate than individual methods and existing ensemble learning methods.

A Discussion on Finding the Opportunity of Intelligent Transportation Systems (ITS) Implementation in Peru Based on Economics

The means of transport are major points in a country's infrastructure and economic development, so it is important to analyse its methods and obstacles for further interventions. This study aims to discuss the Opportunity of Intelligent Transportation Systems (ITS) implementation in Peru based on the Economy. The study was subdivided into parts. In the first part briefly reviewed the system of transportation in other countries in comparison with Peru. It further discusses the technologies influencing ITS development, the barriers to ITS implementation as well as Strategies for Implementing ITS in Peru. Three research questions guided that study and an expo facto research design was adopted as the study utilizes secondary data. The study does not consider human participation, rather, kinds of literature and reports of financial status and projections were collected, collated, utilized, and analysed for the study. The study revealed that the worldwide ITS issues is more controlled that that witnessed in Peru as the issue fluctuates between 0 and 100%. Peru has a promising economy which ITS will be implemented with ease. It is concluded that a comprehensive analysis taking into account a range of economic, social, and technological factors will be required to fully understand the potential and challenges of ITS implementation in the country.

Towards Next Generation of Pedestrian and Connected Vehicle In-the-Loop Research: A Digital Twin Co-Simulation Framework

Digital Twin is an emerging technology that replicates real-world entities into a digital space. It has attracted increasing attention in the transportation field and many researchers are exploring its future applications in the development of Intelligent Transportation System (ITS) technologies. Connected vehicles (CVs) and pedestrians are among the major traffic participants in ITS. However, the usage of Digital Twin in research involving both CV and pedestrian remains largely unexplored. In this study, a Digital Twin framework for CV and pedestrian in-the-loop simulation is proposed. The proposed framework consists of the physical world, the digital world, and data transmission in between. The features for the entities (CV and pedestrian) that need digital twining are divided into external state and internal state, and the attributes in each state are described. We also demonstrate a sample architecture under the proposed Digital Twin framework, which is based on Carla-Sumo Co-simulation and Cave automatic virtual environment (CAVE). A case study that investigates Vehicle-Pedestrian (V2P) warning system is conducted to validate the effectiveness of the presented architecture. The proposed framework is expected to provide guidance to the future Digital Twin research, and the architecture we build can serve as the testbed for further research and development of ITS applications on CV and pedestrians.

Industry 4.0 technologies as a driver for the development of intelligent transport systems

The technologies of Industry 4.0 are considered as a driver of digitalization of transport and logistics ecosystems and their practical significance for increasing the efficiency of the industry, integrating with intellectual priorities for the development of the digital economy at the national and interstate levels.We used materials from open sources on the Internet, as well as the Transport Strategy of the Russian Federation until 2030, the strategy for the digital transformation of key sectors of the economy. The methodology of theoretical research, comparative and system analysis were applied. The results of the study show that the level of implementation of digital services and the digital connectivity of participants in the transport and logistics market will become determining factors in the process of increasing the competitiveness and efficiency of the transport industry.The results of the current stage of the digital transformation of the industry are characterized, the concept of the development of the logistics sector is presented, taking into account the current geopolitical agenda.Further scaling the use of digital services in transport and logistics ecosystems will solve the problem of ensuring the transport and digital sovereignty of the Russian Federation in the face of modern geopolitical challenges and will form points of growth for the competitiveness of the Russian transport system for Industry 4.0.In the context of the fourth industrial revolution, in order to increase the competitiveness of both the Russian economy as a whole and the transport and logistics industry, Russia will have to participate more actively in shaping the landscape of high-tech production, creating and implementing the main digital technologies that ensure its functioning.

THUẬT TOÁN SAI PHÂN HIỆU CHỈNH LAX FRIEDRICHS CHO BÀI TOÁN DỰ BÁO MẬT ĐỘ GIAO THÔNG CÓ TRỄ

In recent years, traffic density forecasting has been playing an important role in developing and improving the performance of intelligent traffic systems. Traffic density forecasting to optimize traffic management, urban management of vehicular traffic have the ability to coordinate traffic, optimize traffic light signals and apply intelligent regulation based on forecasts, thereby improving the handling of the road segment and reducing travel time. Therefore, the construction of predictive algorithms along with integration into traffic management systems is essential to promote the sustainable development of intelligent transportation systems in the future. In this research, an algorithm has been developed to predict traffic density on the delayed Lighthill - Whitham - Richards (LWR) model, in which a regularization difference method has been proposed as the basis for the algorithm. This article mainly focus on building an algorithm and performing experimental calculations to verify the correctness of the algorithm on a mathematical model.

Applications of Big Data technology in Intelligent Transportation System

With the continuous development of Intelligent Transportation System (ITS), amount of data can be collected, generated and used by ITS, and the application of big data technology in Intelligent Transportation System is also applied in different kinds of fields. This paper first explains the definition of big data and intelligent transportation systems. Second, the framework of big data technology in the Intelligent Transport System (ITS) is introduced, which summarizes the data collection methods in specific scenarios of ITS, the application methods of data analysis in ITS, and the application of big data technology in ITS. Furthermore, this paper introduces several case studies on the application of big data technology in ITS, including public transport service planning, road traffic flow prediction, road traffic accident analysis, personal travel route planning, rail traffic management and control, and data analysis and prediction of bridge safety. Finally, this paper discusses some challenges of using big data technology in ITS.

Attention-Based Multiple Graph Convolutional Recurrent Network for Traffic Forecasting

Traffic forecasting is essential in the development of intelligent transportation systems, as it enables the formulation of effective traffic dispatching strategies and contributes to the reduction of traffic congestion. The abundance of research focused on modeling complex spatiotemporal correlations for accurate traffic prediction, however many of these prior works perform feature extraction based solely on prior graph structures, thereby overlooking the latent graph connectivity inherent in the data and degrading a decline in prediction accuracy. In this study, we present a novel Attention-based Multiple Graph Convolutional Recurrent Network (AMGCRN) to capture dynamic and latent spatiotemporal correlations in traffic data. The proposed model comprises two spatial feature extraction modules. Firstly, a dot product attention mechanism is utilized to construct an adaptive graph to extract the similarity of road structure. Secondly, the graph attention mechanism is leveraged to enhance the extraction of local traffic flow features. The outputs of these two spatial feature extraction modules are integrated through a gating mechanism and fed into a Gated Recurrent Unit (GRU) to make spatiotemporal interaction predictions. Experimental results on two real-world traffic datasets demonstrate the superiority of the proposed AMGCRN over state-of-the-art baselines. The results suggest that the proposed model is effective in capturing complex spatiotemporal correlations and achieving about 1% improvements in traffic forecasting.

Break the Data Barriers While Keeping Privacy: A Graph Differential Privacy Method

The booming development of Internet of Vehicles (IoV) has brought new vitality to the construction of intelligent transportation systems (ITS). At the same time, a huge amount of data has been generated due to the gradual development of IoV toward large scale, complex, and diversified. These data are owned by the companies that vehicles belonging to or service providers, such as taxi companies own taxi data. Due to interest and privacy considerations, data owners are not willing to share data, thus a serious data isolated island problem is created, which is detrimental to the development of ITS. Therefore, this article focuses on how to prevent privacy disclosure of vehicles while sharing vehicle data to improve the service. Considering the amount of interactive data and privacy disclosure during data release, vehicle data are abstracted from text form into a graph-structured data form. At the same time, graph differential privacy (DP) together with anonymity protection is proposed innovatively to firmly protect vehicle privacy. Moreover, to solve the high complexity of big data graph-structure transformation, an accelerated nodes and edges combined graph DP (ACGDP) algorithm is proposed. Based on the simulations of real-world data that combine electric and nonelectric taxies, it is verified that our proposed scheme has a tradeoff between information availability and privacy protection. With the graph DP processed data, our proposed scheme reduces the average wasted mileage for charging by 3.87% and achieves a 44.28% increase in drivers’ income. Drivers’ satisfaction of receiving orders and charging preference reaches 68% after the graph-structured data reuse.

Joint offloading decision and resource allocation in vehicular edge computing networks

With the rapid development of Intelligent Transportation Systems (ITS), many new applications for Intelligent Connected Vehicles (ICVs) have sprung up. In order to tackle the conflict between delay-sensitive applications and resource-constrained vehicles, computation offloading paradigm that transfers computation tasks from ICVs to edge computing nodes has received extensive attention. However, the dynamic network conditions caused by the mobility of vehicles and the unbalanced computing load of edge nodes make ITS face challenges. In this paper, we propose a heterogeneous Vehicular Edge Computing (VEC) architecture with Task Vehicles (TaVs), Service Vehicles (SeVs) and Roadside Units (RSUs), and propose a distributed algorithm, namely PG-MRL, which jointly optimizes offloading decision and resource allocation. In the first stage, the offloading decisions of TaVs are obtained through a potential game. In the second stage, a multi-agent Deep Deterministic Policy Gradient (DDPG), one of deep reinforcement learning algorithms, with centralized training and distributed execution is proposed to optimize the real-time transmission power and subchannel selection. The simulation results show that the proposed PG-MRL algorithm has significant improvements over baseline algorithms in terms of system delay.

Bidirectional spatial–temporal traffic data imputation via graph attention recurrent neural network

Spatiotemporal traffic data is increasingly important in transportation services with the development of intelligent transportation system (ITS). However, due to various unpredictable disruptions in the data collection and storage process, traffic data is often incomplete which will seriously hinder downstream tasks if not handled properly. Most existing methods for traffic data imputation either impose too strong assumptions on the data distribution or almost ignore the interdependencies across time steps and the information expressed by missingness. In this article, we propose a graph attention recurrent neural network (GARNN) for traffic data imputation. In our model, we impute data from both temporal and spatial perspectives. First, we model the observations and missingness separately via two LSTMs to treat the missingness of data as another special information distinct from observations. Then, a decay mechanism and graph attention network (GAT) are applied to learn the interdependencies across time steps and capture the spatial correlations respectively to generate temporal estimation and spatial estimation. Finally, those two estimations are integrated into the ultimate imputation. The whole process is in a bidirection. The proposed method is evaluated on two public datasets under three different missing scenarios. Experimental results show the effectiveness of the proposed model compared with other baselines.

Development of intelligent transport systems of Ukraine

The article determines that the possibilities of the extensive direction of meeting the needs of society in increasing the volume of passenger transportation by increasing the number of vehicles are largely exhausted, especially in large cities. A generalization of the modern development of intelligent transport systems (ITS) in Ukraine was carried out in two directions. The direction of introduction of advanced driver assistance systems (ADAS) into vehicles is highlighted. Reasonable expediency of the MaaS (Mobility as a service) direction of regulating passenger flows. The importance of ITS implementation in Ukraine is highlighted. The goals of ITS development and the possibility of their application are considered. The main traffic management systems are outlined using the example of GPS tracking in the city of Lviv. Important aspects of the development of ITS in Ukraine are disclosed and the development of ITS of Ukraine is presented, in order to increase road safety. It has been proven that under the conditions of a well-developed intelligent transport system, non-stop synchronized traffic on streets and roads is ensured.

Cooperative Control of Traffic Signals and Vehicle Trajectories

The transportation system is one of the most important parts of the country's economy. At the same time, the growth in road traffic has a significant negative impact on the economic performance of the industry. One of the ways to increase the efficiency of using the transportation infrastructure is to manage traffic flows, incl. by controlling traffic signals at signalized intersections. One of the trends in the development of intelligent transportation systems is the creation of vehicular ad hoc networks that allow the exchange of information between vehicles and infrastructure, as well as the development of autonomous vehicles. As a result, it becomes possible to formulate the problem of cooperative control of vehicle trajectories and traffic signals to increase the capacity of intersections and reduce fuel consumption and travel time. This paper presents a method for managing traffic flow at an intersection, which consists of the cooperative control of traffic signals and trajectories of connected/autonomous vehicles. The developed method combines an algorithm for the adaptive control of traffic signals based on a deterministic model for predicting the movement of vehicles and a two-stage algorithm for constructing the trajectory of vehicles. The objective optimization function used to construct the optimal trajectories takes into account fuel consumption, travel time on the road lane, and waiting time at the intersection. Experimental studies of the developed method were carried out in the microscopic traffic simulation package SUMO using three simulation scenarios, including two synthetic scenarios and a scenario in a real urban environment. The results of experimental studies confirm the effectiveness of the developed method in terms of fuel consumption, travel time, and waiting time in comparison with the adaptive traffic signal control algorithm.

Spatial–temporal dependence and similarity aware traffic flow forecasting

Traffic flow forecasting is the cornerstone of the development of intelligent transportation systems. Accurate forecasting is conducive to the control and management of urban traffic. However, it is still a challenge to extract the complex spatial–temporal relationships in the traffic flow. The existing methods based on Graph Convolutional Network (GCN) can effectively extract spatial–temporal dependence, but it is difficult to extract spatial–temporal similarity because of the limitation of the graph structure. In this paper, we propose a spatial–temporal dependence and similarity-aware method called Dependence and Similarity aware Temporal Graph Convolutional Network (DS-TGCN), which combines two modules to extract the complex spatial–temporal relationships and make traffic flow forecasting. One module is our Spatial–Temporal Similarity Feature module (STSF) newly designed for extracting the spatial–temporal similarity directly. Another module is the spatial–temporal convolution module with an attention mechanism, which can extract spatial–temporal dependence dynamically. The experiments on two types of datasets demonstrate that our proposed method outperforms the existing methods in terms of the effectiveness of traffic flow forecasting.

Перспективи впровадження блокчейн-технології в транспортну логістику та управління ланцюгами поставок

The article, based on the account of features, advantages, varieties of blockchain, highlights its weaknesses and strengths in terms of implementation in transport logistics and supply chain management. The study of the external environment of the introduction of blockchain in the logistics industry has revealed the opportunities and threats that accompany this process in the modern economy of countries and may arise in the future. Based on structuring the research results on the strengths and weaknesses of blockchain technology, opportunities and threats of its implementation in the logistics industry, a SWOT analysis of this technology was carried out in terms of its use in transport logistics and supply chain management. As strategies that allow taking advantage of blockchain and taking into account the possibilities of the external environment, the development of intelligent transport systems (ITS) using blockchain together with its use in rail, sea and air international cargo transportation has been determined. Nine main strategies for the use of blockchain were allocated, which determine long-term benchmarks of activity at different institutional levels of the economy: micro, meso and macro levels. The proposed strategies are relevant in the context of using blockchain in the areas of urban and humanitarian logistics; logistics of meta- and meso-logistic systems of supply chains formed in the process of international trade; logistics of macro-logistic systems of countries and sectors of their economies; information logistics; financial and smart logistics and related to sectors of economies B2B, B2C, C2C, G2B, G2C, B2G. An integrated approach to the implementation of blockchain in the transport industry and supply chain management based on certain strategic benchmarks will allow the most measured implementation of the basic principles of the logistics management concept, such as transparency, efficiency, reduction of overall costs, confidentiality, trust, authentication, cooperation, integration, and optimization.

Fuzzy model of transport demand

The development of transport networks is costly and cannot solve all transport problems. In this regard, scientifically based planning of the development of the transport complex acquires an important role to improve the quality of the functioning of the transport system, such as improving the organization of traffic in various parts of the network, optimizing the system of public transport routes, creating convenient transfer hubs, etc. To solve such problems, the paper considers the development of intelligent transport systems and mathematical modeling of transport demand. The central task of developing a transport model is to determine the equilibrium state of the transport system, i.e., the problem of the distribution of flows in the network, which involves two main stages. In the first stage, one or several correspondence matrices are evaluated based on the traffic flow's initial data and behavior. The second stage consists of the distribution of traffic matrices on the graph of the transport network, i.e., in solving the problem of finding traffic flows or the problem of transport equilibrium. The problem of transport equilibrium is reduced to an optimization problem in which restrictions are imposed depending on the values of the elements of the traffic matrix. The search for new methods for solving optimization problems of weakly formalized processes is caused by the presence of processes and phenomena, which is difficult to describe by methods of classical mathematics due to their indeterminacy and unreliability, incompleteness, and fuzziness of the initial information. The mathematical model of the optimization problem is described by the objective function and constraints. Classical mathematical programming and its variations are largely a normative methodology for effective choice. Fuzzy programming highlights the natural multiplicity of inaccurately defined goals, restrictions, and their meanings.