End-to-End Joint Multi-Object Detection and Tracking for Intelligent Transportation Systems

Environment perception is one of the most critical technology of intelligent transportation systems(ITS).Motion interaction between multiple vehicles in ITS makes it important to perform multi-object tracking(MOT).However,most existing MOT algorithms follow the tracking-by-detection framework,which separates detection and tracking into two independent segments and limit the global efciency.Recently,a few algorithms have combined feature extraction into one network;however,the tracking portion continues to rely on data association,and requires com‑plex post-processing for life cycle management.Those methods do not combine detection and tracking efciently.This paper presents a novel network to realize joint multi-object detection and tracking in an end-to-end manner for ITS,named as global correlation network(GCNet).Unlike most object detection methods,GCNet introduces a global correlation layer for regression of absolute size and coordinates of bounding boxes,instead of ofsetting predictions.The pipeline of detection and tracking in GCNet is conceptually simple,and does not require compli‑cated tracking strategies such as non-maximum suppression and data association.GCNet was evaluated on a multivehicle tracking dataset,UA-DETRAC,demonstrating promising performance compared to state-of-the-art detectors and trackers.

A deep learning based misbehavior classification scheme for intrusion detection in cooperative intelligent transportation systems

With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation Systems(C-ITSs)have become an important area of research.As the number of Vehicle to Vehicle(V2V)and Vehicle to Interface(V2I)communication links increases,the amount of data received and processed in the network also increases.In addition,networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be sufficient.Thus,there is a need to augment them with intelligent network intrusion detection techniques.Some machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent times.However,given the expected large network size,there is a necessity for extensive data processing for use in such anomaly detection methods.Deep learning solutions are lucrative options as they remove the necessity for feature selection.Therefore,with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario,the need for deep learning-based techniques is all the more heightened.This work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory(LSTM)and Convolutional Neural Networks(CNNs).The proposed Deep Learning Classification Engines(DCLE)comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge servers.Vehicular data received by the Road Side Units(RSUs)is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this paper.The proposed classifiers identify 18 different vehicular behavior types,the F1-scores ranging from 95.58%to 96.75%,much higher than the existing works.By running the classifiers on testbeds emulating edge servers,the prediction performance and prediction time comparison of the proposed scheme is compared with those of the existing studies.

A Nationwide Evaluation of the State of Practice of Performance Measurements for Intelligent Transportation Systems

State departments of transportation’s (DOTs) decisions to invest resources to expand or implement intelligent transportation systems (ITS) programs or even retire existing infrastructure need to be based on performance evaluations. Nonetheless, an apparent gap exists between the need for ITS performance measurements and the actual implementation. The evidence available points to challenges in the ITS performance measurement processes. This paper evaluated the state of practice of performance measurement for ITS across the US and provided insights. A comprehensive literature review assessed the use of performance measures by DOTs for monitoring implemented ITS programs. Based on the gaps identified through the literature review, a nationwide qualitative survey was used to gather insights from key stakeholders on the subject matter and presented in this paper. From the data gathered, performance measurement of ITS is fairly integrated into ITS programs by DOTs, with most agencies considering the process beneficial. There, however, exist reasons that prevent agencies from measuring ITS performance to greater detail and quality. These include lack of data, fragmented or incomparable data formats, the complexity of the endeavor, lack of data scientists, and difficulty assigning responsibilities when inter-agency collaboration is required. Additionally, DOTs do not benchmark or compare their ITS performance with others for reasons that include lack of data, lack of guidance or best practices, and incomparable data formats. This paper is relevant as it provides insights expected to guide DOTs and other agencies in developing or reevaluating their ITS performance measurement processes.

Internet of Things Based Solutions for Transport Network Vulnerability Assessment in Intelligent Transportation Systems

Intelligent Transportation System(ITS)is essential for effective identification of vulnerable units in the transport network and its stable operation.Also,it is necessary to establish an urban transport network vulnerability assessment model with solutions based on Internet of Things(IoT).Previous research on vulnerability has no congestion effect on the peak time of urban road network.The cascading failure of links or nodes is presented by IoT monitoring system,which can collect data from a wireless sensor network in the transport environment.The IoT monitoring system collects wireless data via Vehicle-to-Infrastructure(V2I)channels to simulate key segments and their failure probability.Finally,the topological structure vulnerability index and the traffic function vulnerability index of road network are extracted from the vulnerability factors.The two indices are standardized by calculating the relative change rate,and the comprehensive index of the consequence after road network unit is in a failure state.Therefore,by calculating the failure probability of road network unit and comprehensive index of road network unit in failure state,the comprehensive vulnerability of road network can be evaluated by a risk calculation formula.In short,the IoT-based solutions to the new vulnerability assessment can help road network planning and traffic management departments to achieve the ITS goals.

An Optimal Deep Learning for Cooperative Intelligent Transportation System

Cooperative Intelligent Transport System(C-ITS)plays a vital role in the future road traffic management system.A vital element of C-ITS comprises vehicles,road side units,and traffic command centers,which produce a massive quantity of data comprising both mobility and service-related data.For the extraction of meaningful and related details out of the generated data,data science acts as an essential part of the upcoming C-ITS applications.At the same time,prediction of short-term traffic flow is highly essential to manage the traffic accurately.Due to the rapid increase in the amount of traffic data,deep learning(DL)models are widely employed,which uses a non-parametric approach for dealing with traffic flow forecasting.This paper focuses on the design of intelligent deep learning based short-termtraffic flow prediction(IDL-STFLP)model for C-ITS that assists the people in various ways,namely optimization of signal timing by traffic signal controllers,travelers being able to adapt and alter their routes,and so on.The presented IDLSTFLP model operates on two main stages namely vehicle counting and traffic flow prediction.The IDL-STFLP model employs the Fully Convolutional Redundant Counting(FCRC)based vehicle count process.In addition,deep belief network(DBN)model is applied for the prediction of short-term traffic flow.To further improve the performance of the DBN in traffic flow prediction,it will be optimized by Quantum-behaved bat algorithm(QBA)which optimizes the tunable parameters of DBN.Experimental results based on benchmark dataset show that the presented method can count vehicles and predict traffic flowin real-time with amaximumperformance under dissimilar environmental situations.

Optimal control and energy storage for DC electric train systems using evolutionary algorithms

Electrified railways are becoming a popular transport medium and these consume a large amount of electrical energy.Environmental concerns demand reduction in energy use and peak power demand of railway systems.Furthermore,high transmission losses in DC railway systems make local storage of energy an increasingly attractive option.An optimisation framework based on genetic algorithms is developed to optimise a DC electric rail network in terms of a comprehensive set of decision variables including storage size,charge/discharge power limits,timetable and train driving style/trajectory to maximise benefits of energy storage in reducing railway peak power and energy consumption.Experimental results for the considered real-world networks show a reduction of energy consumption in the range 15%–30%depending on the train driving style,and reduced power peaks.

Continual driver behaviour learning for connected vehicles and intelligent transportation systems: Framework, survey and challenges

Modelling,predicting and analysing driver behaviours are essential to advanced driver assistance systems(ADAS)and the comprehensive understanding of complex driving scenarios.Recently,with the development of deep learning(DL),numerous driver behaviour learning(DBL)methods have been proposed and applied in connected vehicles(CV)and intelligent transportation systems(ITS).This study provides a review of DBL,which mainly focuses on typical applications in CV and ITS.First,a comprehensive review of the state-of-the-art DBL is presented.Next,Given the constantly changing nature of real driving scenarios,most existing learning-based models may suffer from the so-called“catastrophic forgetting,”which refers to their inability to perform well in previously learned scenarios after acquiring new ones.As a solution to the aforementioned issue,this paper presents a framework for continual driver behaviour learning(CDBL)by leveraging continual learning technology.The proposed CDBL framework is demonstrated to outperform existing methods in behaviour prediction through a case study.Finally,future works,potential challenges and emerging trends in this area are highlighted.

Intelligent DoS Attack Detection with Congestion Control Technique for VANETs

VehicularAd hoc Network(VANET)has become an integral part of Intelligent Transportation Systems(ITS)in today’s life.VANET is a network that can be heavily scaled up with a number of vehicles and road side units that keep fluctuating in real world.VANET is susceptible to security issues,particularly DoS attacks,owing to maximum unpredictability in location.So,effective identification and the classification of attacks have become the major requirements for secure data transmission in VANET.At the same time,congestion control is also one of the key research problems in VANET which aims at minimizing the time expended on roads and calculating travel time as well as waiting time at intersections,for a traveler.With this motivation,the current research paper presents an intelligent DoS attack detection with Congestion Control(IDoS-CC)technique for VANET.The presented IDoSCC technique involves two-stage processes namely,Teaching and Learning Based Optimization(TLBO)-based Congestion Control(TLBO-CC)and Gated Recurrent Unit(GRU)-based DoS detection(GRU-DoSD).The goal of IDoS-CC technique is to reduce the level of congestion and detect the attacks that exist in the network.TLBO algorithm is also involved in IDoS-CC technique for optimization of the routes taken by vehicles via traffic signals and to minimize the congestion on a particular route instantaneously so as to assure minimal fuel utilization.TLBO is applied to avoid congestion on roadways.Besides,GRU-DoSD model is employed as a classification model to effectively discriminate the compromised and genuine vehicles in the network.The outcomes from a series of simulation analyses highlight the supremacy of the proposed IDoS-CC technique as it reduced the congestion and successfully identified the DoS attacks in network.

Parameter Tuned Deep Learning Based Traffic Critical Prediction Model on Remote Sensing Imaging

Remote sensing(RS)presents laser scanning measurements,aerial photos,and high-resolution satellite images,which are utilized for extracting a range of traffic-related and road-related features.RS has a weakness,such as traffic fluctuations on small time scales that could distort the accuracy of predicted road and traffic features.This article introduces an Optimal Deep Learning for Traffic Critical Prediction Model on High-Resolution Remote Sensing Images(ODLTCP-HRRSI)to resolve these issues.The presented ODLTCP-HRRSI technique majorly aims to forecast the critical traffic in smart cities.To attain this,the presented ODLTCP-HRRSI model performs two major processes.At the initial stage,the ODLTCP-HRRSI technique employs a convolutional neural network with an auto-encoder(CNN-AE)model for productive and accurate traffic flow.Next,the hyperparameter adjustment of the CNN-AE model is performed via the Bayesian adaptive direct search optimization(BADSO)algorithm.The experimental outcomes demonstrate the enhanced performance of the ODLTCP-HRRSI technique over recent approaches with maximum accuracy of 98.23%.

A Nonlinear Spatiotemporal Optimization Method of Hypergraph Convolution Networks for Traffic Prediction

Traffic prediction is a necessary function in intelligent transporta-tion systems to alleviate traffic congestion.Graph learning methods mainly focus on the spatiotemporal dimension,but ignore the nonlinear movement of traffic prediction and the high-order relationships among various kinds of road segments.There exist two issues:1)deep integration of the spatiotempo-ral information and 2)global spatial dependencies for structural properties.To address these issues,we propose a nonlinear spatiotemporal optimization method,which introduces hypergraph convolution networks(HGCN).The method utilizes the higher-order spatial features of the road network captured by HGCN,and dynamically integrates them with the historical data to weigh the influence of spatiotemporal dependencies.On this basis,an extended Kalman filter is used to improve the accuracy of traffic prediction.In this study,a set of experiments were conducted on the real-world dataset in Chengdu,China.The result showed that the proposed method is feasible and accurate by two different time steps.Especially at the 15-minute time step,compared with the second-best method,the proposed method achieved 3.0%,11.7%,and 9.0%improvements in RMSE,MAE,and MAPE,respectively.

An Intelligent Transportation System Application for Smartphones Based on Vehicle Position Advertising and Route Sharing in Vehicular Ad-Hoc Networks

Alerting drivers about incoming emergency vehicles and their routes can greatly improve their travel time in congested cities, while reducing the risk of accidents due to distractions. This paper contributes to this goal by proposing Messiah, an Android application capable of informing regular vehicles about incoming emergency vehicles like ambulances, police cars and fire brigades. This is made possible by creating a network of vehicles capable of directly communicating between them. The user can, therefore, take driving decisions in a timely manner by considering incoming alerts. Using the support of our GRCBox hardware, the application can rely on vehicular ad-hoc network communications in the 5 GHz band, being V2V （vehicle-to-vehicle） communication provided through a combination of Android-based smartphone and our GRCBox device. The application was tested in three different scenarios with different levels of obstruction, showing that it is capable of providing alerts up to 300 meters, and notifying vehicles within less than one second.

Multi-Vehicle Cooperative Positioning Based on Edge-Computed Multidimensional Scaling

As the basis of location-based services(LBS),positioning is one of the most essential parts in intelligent transportation systems(ITS).Although global positioning system(GPS)has been widely used in vehicle positioning,it can not achieve lane level positioning accuracy.Motivated by the mature ranging technologies such as radar and ultra-wideband(UWB),several cooperative positioning(CP)methods have been proposed to enhance the accuracy and robustness of GPS.In this paper,we proposed a twostage CP algorithm that combines multidimensional scaling(MDS)and Procrustes analysis for vehicles with GPS information.Specifically,the optimized MDS based on the scaling by majorizing a complicated function(SMACOF)algorithm is first proposed to get the relative coordinates of vehicles which can tackle measurements of different error distributions,then Procrustes analysis is carried out to transform the relative coordinates of vehicles to their absolute coordinates based on GPS information.All the computations are performed at the mobile edge computing node(MECN)for the request of ultra-reliable and low latency communications(URLLC).Simulation results validate that the proposed algorithm can greatly improve the positioning accuracy and robustness for vehicles.

Sustainable transit vehicle tracking service,using intelligent transportation system services and emerging communication technologies:A review

The most salient problems of transit vehicle service in Latin American intermediate cities include:the high number of passengers involved in traffic accidents;traffic congestion caused by transit vehicles,and pollution generated by these vehicles,which increases in high congestion scenarios.To improve upon this situation,a research was conducted on the transit vehicle tracking service,which is a basic service for implementing mobility solutions for the aforementioned problems,the most relevant characteristics of this service for the context of Latin American intermediate cities were identified,and an implementation was proposed.This paper presents the four stages of the study:(a)a review of the state-of-the-art of services or systems related to vehicle tracking,including wireless communications technologies,implemented sustainability approaches,usage of special algorithms for efficiency improvement,and the intelligent transportation system(ITS)architecture used as a basis;(b)the process of identifying relevant characteristics of the service for a given context;(c)proposal of an ITS architecture for this service in an intermediate city,its requirements and the suggested technologies;and(d)development of experiments for validating usage of the key suggested technologies.The review allowed to identify the main service characteristics,with regard to vehicle positioning technologies,the recommended wireless communication technology(long range,LoRa),energy consumption considerations,and use of artificial intelligence(AI)to calculate waiting time of users at bus stops.Finally,an ITS architecture for the city of Popayan(Colombian city)considering the aforementioned characteristics is proposed,and the experiments related to the use of these technologies are described in detail.

Hybrid Deep Learning Enabled Air Pollution Monitoring in ITS Environment

Intelligent Transportation Systems(ITS)have become a vital part in improving human lives and modern economy.It aims at enhancing road safety and environmental quality.There is a tremendous increase observed in the number of vehicles in recent years,owing to increasing population.Each vehicle has its own individual emission rate;however,the issue arises when the emission rate crosses a standard value.Owing to the technological advances made in Artificial Intelligence(AI)techniques,it is easy to leverage it to develop prediction approaches so as to monitor and control air pollution.The current research paper presents Oppositional Shark Shell Optimization with Hybrid Deep Learning Model for Air Pollution Monitoring(OSSOHDLAPM)in ITS environment.The proposed OSSO-HDLAPM technique includes a set of sensors embedded in vehicles to measure the level of pollutants.In addition,hybridized Convolution Neural Network with Long Short-Term Memory(HCNN-LSTM)model is used to predict pollutant level based on the data attained earlier by the sensors.In HCNN-LSTM model,the hyperparameters are selected and optimized using OSSO algorithm.In order to validate the performance of the proposed OSSO-HDLAPM technique,a series of experiments was conducted and the obtained results showcase the superior performance of OSSO-HDLAPM technique under different evaluation parameters.

Reference Modelof Desired Yaw Anglefor Automated Lane Changing Behavior of Vehicle

In this paper,it studies the problem of trajectory planning and tracking for lane changing behavior of vehicle in automatic highway systems. Based on the model of yaw angle acceleration with positive and negative trapezoid constraint,by analyzing the variation laws of yaw motion of vehicle during a lane changing maneuver,the reference model of desired yaw angle and yaw rate for lane changing is generated. According to the yaw angle model,the vertical and horizontal coordinates of trajectory for vehicle lane change are calculated. Assuming that the road curvature is a constant,the difference and associations between two scenarios are analyzed,the lane changing maneuvers occurred on curve road and straight road,respectively. On this basis,it deduces the calculation method of desired yaw angle for lane changing on circular road. Simulation result shows that,it is different from traditional lateral acceleration planning method with the trapezoid constraint,by applying the trapezoidal yaw acceleration reference model proposed in this paper, the resulting expected yaw angular acceleration is continuous,and the step tracking for steering angle is not needed to implement. Due to the desired yaw model is direct designed based on the variation laws of raw movement of vehicle during a lane changing maneuver, rather than indirectly calculated from the trajectory model for lane changing, the calculation steps are simplified.

Paper the Resilience of ITS: Variable Message Signs Contribution to Road Network Resilience under Incident and High Congestion Conditions: A State of the Art Review

Any failure or disruption in traffic flow can propagate through the road network. However, the server of such disruption and its consequences depends on the robustness and resiliency of transportation systems. In this context, traffic management (TM) measures will help the traffic stream to prevent the occurrence of such conditions or recover faster after experiencing the disruption. The main objective of this paper was to elaborate the contribution of TM measures to the resiliency of transportation systems, as well as, their vulnerability against external threats. Furthermore, a concept design for variable message signs (VMS) is developed and evaluated in terms of contribution to the resiliency of road networks. As well, new vulnerabilities associated with the implementation of VMS are investigated. The result of this study pointed out that ramp-metering, variable message signs, variable speed limits, and autonomous vehicles are valuable tools to mitigate the severity of traffic disruptions. VMS is one of the most effective approaches that enhance traffic resiliency by reducing traffic inflow to congested areas. However, these measures have opened new vulnerabilities to threats, especially cyber-attacks. Several cases of VMS hacks have occurred in the world and provided false messages to road users. It gets even worse with using an integrated wireless communication interface. Therefore, it is necessary to consider the security of such systems in advance, before practical application.

Blockchain-enabled decentralized identity management:The case of self-sovereign identity in public transportation

Identity management has been ripe for disruption over the past few years due to recurring incidents of data breaches that have led to personal information leaks and identity theft.The rise of blockchain technology has paved the way for the development of self-sovereign identity(SSI)—a new class of user-controlled resilient identity management systems that are enabled by distributed ledger technology.This paper examines how SSI management can be used in a public transportation sector that spans different operators in multiple countries.Specifically,the paper explores how a blockchain-based decentralized identity management system can draw on the SSI framework to provide high-level security and transparency for all involved parties in public transportation ecosystems.Accordingly,building on analyses of the existing public transportation ticketing solutions,we elicited requirements of a comparable system based on the SSI principles.Next,we developed a low-fidelity prototype to showcase how passengers can utilize standardized travel credentials that are valid across different transportation networks in Europe.The proposed system eliminates the need for multiple travel cards(i.e.,one for each transportation provider)and empowers individuals to have better control over the use of their identities while they utilize interoperable ticketing systems across Europe.Overall,building on the public transportation case,we offer a proof-of-concept that shows how individuals can better manage their identity credentials via the SSI framework.

A Fuzzy Logic-Based Framework for Route Choice in Vehicle Navigation Systems

Vehicle Navigation Systems (VNS) is an important component of Intelligent Transportation Systems (ITS). These Systems are designed to assist drivers in making pre trip and enroute travel choice decisions, and typically, they must provide route choice, route guidance and other related services. Although there have been a lot of existed systems in the market, and most of them used lots of contemporary technologies, they are believed short of ″true intelligence″, because they paid little attention to the subjective issues in driver′s route choice behavior, such as travel objectives and personal preferences, etc. \;However, the VNS is designed for its users, and the successful implementation of VNS is largely dependent on the driver′s acceptance. If the driver feels that the VNS can′t give him (her) a satisfactory choice, he (she) will not use it, then, the marketing value of VNS will decline. And on the whole, the transport benefit that is mainly gained by the wide use of ITS will lost. \;Supported by the research project of ″Beijing Intelligent Urban Transportation Systems″, this paper presents a conceptual model to deal with this problem. We first defined the driver′s objective as a linguistic statement that has a set of attributes. These attributes are then treated as the fuzzy sets on the universal of all the existed routes. By determining each attribute′s membership function and assign driver dependent perception to these attributes, we can change the multi criteria route choice problem into a fuzzy logic based decision making problem. Then, to meet the demands of dynamic real time route selection, we use a limited routes set for choice and can swiftly get a satisfactory solution that we think is the driver′s actually needs.

Efficient transport pricing-why,what,and when?

Striking a balance between societal benefits and costs of transportation lies at the heart of transport planning and transport systems analysis.Increased transport and urbanization enable the many benefits of modern socieities through specialization of labour,production and lifestyles-but these trends simultaneously increase the drawbacks of transportation,such as carbon emissions,congestion,noise and air quality problems.Technical developments and improved infrastrastructure can help reduce these drawbacks,but they do not solve the fundamental problem that those reaping the benefits of transport-travellers,firms,customers-do not perceive the full social costs of transportation.To balance transport costs and benefits,efficient pricing is necessary.Despite a wealth of theoretical arguments,technical developments and substantial practical experience,efficient transport pricing is still rare.Focusing on the example of urban congestion pricing,this paper summarizes why transport pricing is needed,lessons learnt from practical experience,and what the main obstacles are.The two most important obstacles seem to be political power struggles between different levels of governments,and that even if total social gains vastly exceed total social losses,the losses tend to be more salient;losers tend to be easy to identify,while winners tend to be more dispersed and perhaps only exist in the future.

An analysis on possibility of intelligent speed adaptation in terms of drivers' consciousness

Intelligent speed adaptation （ISA） is considered as an effective measure to reduce number of traffic accidents in the field of intelligent transportation systems （ITS）. On the other hand, its effects for traffic safety are still doubted by many people. To make the possibility analysis, an experiment is conducted by using driving simulator. Regarding ISA ap- proaches, there are three modes： mandatory, voluntary and advisory. Among them, the advisory type seems to be the easiest one to introduce. Therefore, we focus on the advisory mode in this study by considering ISA just at the beginning stage in Japan. The experiment consists of four steps： without ISA, ISA using pictures, ISA using voices and again without ISA. The outputs obtained from the driving simulator are analyzed combined with the consciousness of the participants. The experiment shows that the ISA can improve recognition of speed limitation especially for people who have random rambling or looking aside tendency. Furthermore, the ISA especially when using voices can contribute in changing the consciousness of people who are aggressive in driving. Their driving speeds can reduce so that positive effects on traffic safety can be concluded.