Intelligent Transportation Applications Research Articles

Vehicle detection and classification with spatio-temporal information obtained from CNN

Vehicle detection and classification with convolutional neural networks (CNN) is encouraging and fascinating for intelligent transportation applications. In general, CNN is trained with huge labeled vehicle images, which are rare to find in developing country scenarios. This work proposes a vehicle detection technique with CNN that does not require any labeled vehicle dataset. A CNN is trained with road marks and these act as the background. When a vehicle occupies a road mark a logic 1 is recorded else 0 is saved in the database. This accumulated occupancy data is spatio-temporal information from which vehicle width and length are approximated and classified. This method does not require any GPU for training as well as for implementation in real-time. The detection accuracy of the proposed method on real-time videos is about 98.5%. The runtime performance of CNN model on NVIDIA Jetson Tx2 is 0.01 s, whereas on RaspberryPi4 is 0.03 s. The given method even works in shadow and low-light conditions.

Infrastructure camera calibration with GNSS for vehicle localisation

Intelligent transportation and smart city applications are currently on the rise. In many applications, diverse and accurate sensor perception of vehicles is crucial. Relevant information could be conveniently acquired with traffic cameras, as there is an abundance of cameras in cities. However, cameras have to be calibrated in order to acquire position data of vehicles. This paper proposes a novel automated calibration approach for partially connected vehicle environments. The approach utilises Global Navigation Satellite System positioning information shared by connected vehicles. Corresponding vehicle Global Navigation Satellite System locations and image coordinates are utilised to fit a direct transformation between image and ground plane coordinates. The proposed approach was validated with a research vehicle equipped with a Real-Time Kinematic-corrected Global Navigation Satellite System receiver driving past three different cameras. On average, the camera estimates contained errors ranging from 1.5 to 2.0 m, when compared to the Global Navigation Satellite System positions of the vehicle. Considering the vast lengths of the overlooked road sections, up to 140 m, the accuracy of the camera-based localisation should be adequate for a number of intelligent transportation applications. In future, the calibration approach should be evaluated with fusion of stand-alone Global Navigation Satellite System positioning and inertial measurements, to validate the calibration methodology with more common vehicle sensor equipment.

Individual Mobility Prediction in Mass Transit Systems Using Smart Card Data: An Interpretable Activity-Based Hidden Markov Approach

Individual mobility is driven by demand for activities with diverse spatiotemporal patterns, but existing methods for mobility prediction often overlook the underlying activity patterns. Knowledge of activity patterns can improve the performance and interpretability of existing individual mobility models, leading to more informed policy design and better user experience in intelligent transportation systems. This study develops an activity-based modeling framework for individual mobility prediction in mass transit systems. Specifically, an input-output hidden Markov model (IOHMM) approach is proposed to simultaneously predict the (continuous) time and (discrete) location of an individual’s next trip using transit smart card data. The prediction task can be transformed into predicting the hidden activity duration and end location. Based on a case study of Hong Kong’s metro system, we show that the proposed model can achieve similar prediction performance as the state-of-the-art long short-term memory (LSTM) model. Unlike LSTM, the proposed IOHMM approach can also be used to analyze hidden activity patterns, which provides meaningful behavioral interpretation for why an individual makes a certain trip. Therefore, the activity-based prediction framework offers a way to preserve the predictive power of advanced machine learning methods while enhancing our ability to generate insightful behavioral explanations, which is useful for user-centric policy design and intelligent transportation applications such as personalized traveler information.

A Geometry-Based Stochastic Model for Truck Communication Channels in Freeway Scenarios

Vehicle-to-vehicle (V2V) wireless communication systems are fundamental in many intelligent transportation applications, e.g., traffic load control, driverless vehicle, and collision avoidance. Hence, developing appropriate V2V communication systems and standardization require realistic V2V propagation channel models. However, most existing V2V channel modeling studies focus on car-to-car channels; only a few investigate truck-to-car (T2C) or truck-to-truck (T2T) channels. In this paper, a hybrid geometry-based stochastic model (GBSM) is proposed for T2X (T2C or T2T) channels in freeway environments. Next, we parameterize this GBSM from the extensive channel measurements. We extract the multipath components (MPCs) by using a joint maximum likelihood estimation (RiMAX) and then determine the cluster types based on their evolution patterns. We classify the determined clusters into line-of-sight, single-bounce reflections from static interaction objects (IOs), single-bounce reflections from mobile IOs, multiple-bounce reflections, and density multipath components (DMCs). Particularly, we model multiple-bounce reflections as double clusters following the COST 273/COST2100 method. This paper presents the complete parameterization of the channel model. We validate this model by comparing the delay spread and the angular spreads of arrival/departure obtained from the proposed model with the measurement data.

Adaptive-Learning-Based Vehicle-to-Vehicle Opportunistic Resource-Sharing Framework

With an ever-increasing number of connected devices on roads, it becomes unsustainable to provide nearby specialized execution resources (compute and storage) for servicing innovative applications. Moreover, the vehicular environment being inherently ad hoc and opportunistic, not to mention highly mobile, makes it unsuitable to use traditional cloud computing due to delayed and interrupted services. Thus, there is a possibility to introduce potential collaboration among nearby connected vehicles. However, the underlying decision model for the selection of the most suitable vehicle for task offloading is challenging in such a dynamic environment. In this study, we propose a collaborative vehicular computing framework that adopts online learning for efficient task assignment between local and neighboring computing resources. The underlying workload adaptive task offloading intends to balance out the workload across neighboring vehicles. The framework is compared against three techniques including two adaptive learning techniques in terms of service delay, efficiency, task delivery rate, task failures, and learning regret. The results demonstrate the effectiveness of the proposed resource-sharing network, improving service quality and throughput for servicing innovative intelligent transportation applications.

Domain adaptation metric learning method embedded with structural information for person re‐identification in internet of autonomous unmanned vehicles

AbstractInternet of autonomous unmanned vehicles (IAUV) is a global network of sensors, robots, and autonomous vehicles. Person re‐identification (Re‐ID) is an important intelligent transportation application in IAUV, which needs to be solved using artificial intelligence algorithms. In this study, a domain adaptation metric learning method embedded with structural information (called DAML‐ESI) is designed for person Re‐ID in IAUV. Due to the lack of labeling information in the target domain, DAML‐ESI realizes person Re‐ID with the help of the discriminative and structural information of pedestrian images of related domains. DAML‐ESI projects pedestrian images selected from different domains into a common metric space and establishes a discriminative metric learning model, which requires that the positive sample pair be mapped to a point, and the distance distribution of the negative sample pair be mapped to a fixed value. The projection matrix learned by DAML‐ESI is used to eliminate the distribution differences between different domains, and the distance metric is used to ensure that the learned metric learning model has strong discriminative ability in the metric space. To verify the effectiveness of DAML‐ESI, experimental comparisons are conducted on three person Re‐ID datasets, and DAML‐ESI achieves satisfactory recognition performance.

A Blockchain-Based Multidomain Authentication Scheme for Conditional Privacy Preserving in Vehicular Ad-Hoc Network

Vehicular ad-hoc network enhances driving safety and enables various intelligent transportation applications by adopting the revolutionary vehicular wireless communication technology. This has attracted a lot of attentions from both academia and industry in recent years. Given the sophistication of vehicular manufacturing and the heterogeneity of intelligent transport terminals, performing vehicular authentication is of great importance. The existing schemes have largely considered vehicle security and authentication within a single administrative domain, which lacks supervision of the authority and entity in the intelligent transportation system. In this article, we propose a multidomain vehicular authentication architecture by introducing blockchain technique to build distributed trust and share cross-domain information among multiple administrative domains. To guarantee the anonymity and traceability, a pseudonym-based privacy-preserving authentication method is proposed. Specifically, considering the supervision of authority and the resilience to key escrow, we design a two-phase pseudonym distribution mechanism with the assistance of a roadside unit (RSU) proxy. We conduct in-depth security analysis by comparing with existing works and deploy experiments to show the efficiency and feasibility of the proposed scheme in the multidomain scenario.

A Review of Flow Migration Through Mobile Networks

The interesting new sources of data for official statistics are cell phone data. Electronic media has defined the way of research human behavior rapidly over the last decade. As data storage and sensing technology progressed, electronic records now cover a diverse variety of human activities from localized data (phone) to open source contributions on Wikipedia and the Open Area Map. Electronic records now encompass the numerous fields of activity. The ad hoc vehicle network is a research community-based wireless technology for the implementation of intelligent transport applications. It is necessary to estimate migration flows and predict future trends to understand the causes and effects of migration and to enforce policies to deliver certain services. Several studies have exposed in this review with their datasets such as Credit card records (CCRs) provide deep insight into buying behavior; Call Details Records (CDRs) present new possibilities for under-implemented human mobility. Therefore, various forms of transportation and other travel behavior, various travel-related events such as "Home-Tour-Work -Tour-Tour” and the corresponding travel-related motifs have also been distinguished by the inclusion of land use details in the GIS data. This review also investigated migration trips residence between cities and inside a single city. The review concluded clear results for the adoption of the mentioned data, for example, mobile phone data (CDRs), because it is very useful as it provides real big data or real time big data without additional cost and is available in telecommunications companies, from which it is possible to analyze the movement of communities and deduce the activity of a particular city. In the future, there is a tendency to use this type of data from Korek Telecom Company in Iraq to the flow migration of Iraq governorates by using the gravity model. As well as, an attempt to study the conditions of cities and the movement of individuals in urban places to clarify the needs of the city in its need for new improvements.

Vehicle License Plate Recognition Using Shufflenetv2 Dilated Convolution for Intelligent Transportation Applications in Urban Internet of Things

Intelligent transportation applications based on urban Internet of Things can improve the efficiency of government services and promote urban modernization. As smart cameras are more and more widely used in cities, artificial intelligence technology is an important force to achieve license plate recognition. An efficient license plate recognition algorithm not only improves the efficiency of traffic management but also saves management costs. This paper proposes a network based on the shufflenetv2 dilated convolution (SDC) model, which includes two parts: license plate location and license plate recognition. SDC model adopts shufflenetv2 as the backbone network, which combines dilated convolution and global context blocks. Therefore, the receptive field and feature expression ability of the model are enhanced. For license plate location, CIOU loss considers not only the coverage area of the bounding box but also the center distance and aspect ratio. For license plate recognition, CTC loss trains the network based on the sequence and solves the sample alignment problem, which improves the accuracy of license plate recognition. The experiments show that the precision of the SDC model in license plate location is 98.7%, which is 5.2%, 5.5%, and 4.1% higher than the precision of Faster-RCNN, YOLOv3, and SSD, respectively. The precision of the SDC model in license plate recognition is 98.2%, which is 5.3%, 3.7%, and 2.9% higher than the precision of LPRNet, AlexNet, and RPNet, respectively.

Motion detection and classification: ultra-fast road user detection

With the emerge of intelligent and connected transportation systems, driver perception and on-board safety systems could be extended with roadside camera units. Computer vision can be utilised to detect road users, conveying their presence to vehicles that cannot perceive them. However, accurate object detection algorithms are typically computationally heavy, depending on delay-prone cloud computation or expensive local hardware. Similar problems are faced in many intelligent transportation applications, in which road users are detected with a roadside camera. We propose utilising Motion Detection and Classification (MoDeCla) for road user detection. The approach is computationally lightweight and capable of running in real-time on an inexpensive single-board computer. To validate the applicability of MoDeCla in intelligent transportation applications, a detection benchmark was carried out on manually labelled data gathered from surveillance cameras overseeing urban areas in Espoo, Finland. Separate datasets were gathered during winter and summer, enabling comparison of the detectors in significantly different weather conditions. Compared to state-of-the-art object detectors, MoDeCla performed detection an order of magnitude faster, yet achieved similar accuracy. The most impactful deficiency of MoDeCla was errors in bounding box placement. Car headlights and long dark shadows were found especially difficult for the motion detection, which caused incorrect bounding boxes. Future improvements are also required for separately detecting overlapping road users.

Two-Tier Clustering with Routing Protocol for IoT Assisted WSN

In recent times, Internet of Things (IoT) has become a hot research topic and it aims at interlinking several sensor-enabled devices mainly for data gathering and tracking applications. Wireless Sensor Network (WSN) is an important component in IoT paradigm since its inception and has become the most preferred platform to deploy several smart city application areas like home automation, smart buildings, intelligent transportation, disaster management, and other such IoT-based applications. Clustering methods are widely-employed energy efficient techniques with a primary purpose i.e., to balance the energy among sensor nodes. Clustering and routing processes are considered as Non-Polynomial (NP) hard problems whereas bio-inspired techniques have been employed for a known time to resolve such problems. The current research paper designs an Energy Efficient Two-Tier Clustering with Multi-hop Routing Protocol (EETTC-MRP) for IoT networks. The presented EETTC-MRP technique operates on different stages namely, tentative Cluster Head (CH) selection, final CH selection, and routing. In first stage of the proposed EETTC-MRP technique, a type II fuzzy logic-based tentative CH (T2FL-TCH) selection is used. Subsequently, Quantum Group Teaching Optimization Algorithm-based Final CH selection (QGTOA-FCH) technique is deployed to derive an optimum group of CHs in the network. Besides, Political Optimizer based Multihop Routing (PO-MHR) technique is also employed to derive an optimal selection of routes between CHs in the network. In order to validate the efficacy of EETTC-MRP method, a series of experiments was conducted and the outcomes were examined under distinct measures. The experimental analysis infers that the proposed EETTC-MRP technique is superior to other methods under different measures.

Dependence-Aware Edge Intelligent Function Offloading for 6G-Based IoV

Using the increasingly wireless communication capacity of 5G/6G technology, edge intelligence (EI) enables modern vehicles to leverage the powerful computing resources of edge servers scattered aside the roads to implement intelligent transportation applications (ITA). The running of these intelligent applications is always accompanied by the calculating and transmitting of massive amounts of road situation data. For the protection of drivers and passengers, these complex processes should be handled in an ultra-reliable and low latent manner. Intelligent processing function offloading from terminals to edge servers is a promising approach to address these issues. However, the runtime environment specification of each intelligent processing function and the interdependence between two consecutive functions pose a challenge for the assignment of the offloaded functions among edge servers. In this paper, we propose a dependence-aware edge intelligent function offloading scheme for 6G-based Internet of Vehicle (IoV). All traditional ITAs are split into different chains of standard intelligent functions. Each edge server can provide some specific intelligent functional services. These services can receive data from cars and serve as different intelligent functions. Then, an intelligent application offloading scheme is changed into an embedding scheme of a service chain. An NP-hard objective function is constructed using a multi-winner committee selection model for this offloading service chain embedding problem. We design two algorithms to get the optimal assignment of intelligent functions using a greedy strategy and dynamic programming strategy separately. Finally, experiments show that when the proportion of vehicles meeting the constraint conditions is not in [9%, 10%], our algorithms are fast.

Transportation Mode Detection Using an Optimized Long Short-Term Memory Model on Multimodal Sensor Data

The advancement of sensing technologies coupled with the rapid progress in big data analysis has ushered in a new era in intelligent transport and smart city applications. In this context, transportation mode detection (TMD) of mobile users is a field that has gained significant traction in recent years. In this paper, we present a deep learning approach for transportation mode detection using multimodal sensor data elicited from user smartphones. The approach is based on long short-term Memory networks and Bayesian optimization of their parameters. We conducted an extensive experimental evaluation of the proposed approach, which attains very high recognition rates, against a multitude of machine learning approaches, including state-of-the-art methods. We also discuss issues regarding feature correlation and the impact of dimensionality reduction.

Deep learning‐influenced joint vehicle‐to‐infrastructure and vehicle‐to‐vehicle communication approach for internet of vehicles

AbstractThe internet of vehicle (IoV) orchestration is an emerging technology in heterogeneous vehicles to contrivance diverse intelligent transportation applications. The roadside unit (RSU) plays a vital role during service provisioning. Vehicle‐to‐vehicle and vehicle‐to‐infrastructure communications have consistently accomplished the services in a vehicular network. However, persisting the increased vehicles' quality of experience and network vendors' utilities and which RSUs have to select for effective, reliable service are critical open research challenges to consolidate RSU services to enhance network service utility rate. In this article, we design a deep learning‐inspired RSU Service Consolidation Approach based on two‐models to enhance the service reliability by formulating the RSU coverage issue with the RSU Migration model and content delivery issue with Linear Programming‐based Multicast model. Adaptive Packet‐Error measurement system to optimize service reliability rate at the edge of cooperative vehicular network based on content correlation. The performance and efficiency are examined based on MATLAB. The simulation outcome shows RSC approach has low execution cost by 39%, service reliability rate by 71% than the state‐of‐art approaches.

Towards small target recognition with photonics-based high resolution radar range profiles.

Photonics-based radar expands the bandwidth of traditional radars and enhances the radar range resolution. This makes it possible to recognize small-size targets using the high resolution range profiles (HRRPs) acquired by a photonics-based broadband radar. In this paper, we investigate the performance of small target recognition using HRRPs of a photonics-based radar with a bandwidth of 8 GHz (28-36 GHz), which is built based on photonic frequency multiplication and frequency mixing. A convolutional neural network (CNN) is used to extract features of the HRRPs and classify the targets. In the experiment, recognition of four types of small-size targets is demonstrated with an accuracy of 97.16%, which is higher than target recognition using a 77-GHz electronic radar by 31.57% (2-GHz bandwidth) and 8.37% (4 GHz-bandwidth), respectively. Besides the accuracy, target recognition with photonics-based radar HRRPs is proved to have good generalization capability and stable performance. Therefore, photonics-based radar provides an efficient solution to small target recognition with one-dimension HRRPs, which is expected to find import applications in air defense, security check, and intelligent transportation.

Highway alignments extraction and 3D modeling from airborne laser scanning point clouds

Accurate highway alignments and three-dimensional (3D) models are essential for various intelligent transportation applications. Airborne laser scanning (ALS) provides a desirable means of data collection, which increases data quality and collection efficiency. However, automatic alignments extraction and 3D modeling remain open problems. Therefore, this paper proposes an effective framework to extract highway alignments by minimizing an elaborate energy function and reconstruct highway 3D models with the restrictions of alignments. Specifically, the proposed method contains the following steps: (1) Adopt an adaptive method based on spatially smooth and interconnected grid cells to recognize highway pavement points from ALS data. (2) Extract pavement boundaries and lane markings from the pavement areas using the α-shape algorithm and a marking tracking strategy. (3) Extract highway alignments by minimizing an energy function and reconstruct highway 3D models with the restrictions of alignments. The method was validated in scenes of various highways, where the point density is 10–25 pts/m2. The extracted alignments respectively achieved the correctness of 90.67% and 99.25% and the completeness of 87.60% and 99.55% within 10 cm and 15 cm errors. The root mean square error (RMSE) of the generated 3D model is 2.4 cm on pavement and 5.8 cm on hills and slopes.

Integration of 4G LTE and DSRC (IEEE 802.11p) for Enhancing Vehicular Network Performance in IoV Using Optimal Cluster-Based Data Forwarding (OCDF) Protocol

<p>IoV is a known platform for exchanging data between vehicles and distinct networks through diverse communication media. Embedded technologies like IoT and Intelligent Transportation, are aimed to build smart networks for IoV to support diverse automated applications such as smart vehicle control, intelligent traffic control, and dynamic data services. However, in the smart domain, implementation of IoV has unresolved challenges. Synchronization of vehicles and humans is a crucial issue in making decisions. Therefore, proper understanding of the pertinent issues about IoV implementation that can improve the VNs performance are essential. DSRC and cellular networks are considered as potential alternatives for endorsing V2X communications. DSRC is employed for intelligent and automotive transportation applications through short-range data exchange between DSRC-components. Although spectrum assigned to DSRC alone will not be appropriate to satisfy huge information traffic needs for internet access in vehicles. Cellular networks offer potential solutions, attributing extensive range of cell coverage, broadly deployed infrastructure and greater capacity. Nevertheless, centralized characteristics of these networks limit the ability to handle low-latency communications that can challenge the efficacy of several safety applications. This paper reviews potential DSRC and wireless integrated solutions for efficient vehicular communications. In methodology, first we reviewed existing technologies that integrate DSRC with other wireless technologies and secondly, the study is carried out to highlight the limitations for each supporting vehicular communications. Thereby, the paper embeds a brief comparative analysis. Finally, an algorithm is proposed to integrate DSRC and 4G-LTE with a novel Optimal Clustering based Data Forwarding Protocol for efficient data transmission to attain better performance in IoV. The performance of the proposed algorithm is compared with the existing approaches using the NS-3 simulation tool. The obtained results demonstrate that the proposed algorithm outperforms over the existing approaches in terms of End to End Delay and throughput. </p>

Nanogenerators for smart cities in the era of 5G and Internet of Things

<h2>Summary</h2> 5G has taken off at a brisk speed over the years, bringing significant benefits to the Internet of Things (IoT) devices and wireless sensor nodes. The launching of 5G technology provides an excellent opportunity for the faster development of smart cities. Nanogenerators (NGs) have been widely demonstrated as sustainable power sources and self-powered active sensors. The last 15 years of research on NGs have revealed that it can contribute to the digitalization of smart city services, such as localized renewable energy supplies, intelligent transportation, smart vehicles, and digital healthcare applications. The integration of novel NG technology in smart cities will solve problems pertinent to sustainable power sources for decentralized IoT devices and provide pathways for realizing self-powered active sensing systems. In this review, we will provide a comprehensive review of current research on NGs' applications in different sectors of a smart city. More importantly, we will show how NGs can be a game changer in the development of smart cities under 5G services and how the usage of NGs can boost the convenience of city dwellers. Our aim is to draw more attention to NG applications in the digitalization of smart cities and provide a guideline for applying smart concepts in future urban planning.

RCIVMM: A Route Choice-Based Interactive Voting Map Matching Approach for Complex Urban Road Networks

With the enhancement of location-acquisition technologies, GPS trajectories play an essential role in data-driven intelligent transportation applications, which requires an accurate approach to match raw GPS trajectories to road segments on a digital map. However, for complex urban roads containing elevated roads and surface roads, map matching for low-frequency GPS data is still challenging. This article aims to address the biases and instability problem in existing approaches. To this end, we combine the spatial-temporal characteristics of GPS data in complex roads with driving behaviours and present a novel global map matching method including truncated density clustering algorithm, statistic features based spatial-temporal analysis, and driving-behaviour-based track modification. Additionally, a weighted-matrix based interactive voting algorithm is proposed to select the best results from a global perspective. The experiments are conducted with two real GPS trajectory datasets under three road conditions. The result shows that our approach outperforms state-of-art approaches for urban complex road networks in both accuracy and efficiency.

A survey on attack detection, estimation and control of industrial cyber–physical systems

Cyber–physical systems (CPSs) are complex systems that involve technologies such as control, communication, and computing. Nowadays, CPSs have a wide range of applications in smart cities, smart grids, smart manufacturing and intelligent transportation. However, with integration of industrial control systems with modern communication technologies, CPSs would be inevitably exposed to increasing security threats, which could lead to severe degradation of the system performance and even destruction of CPSs. This paper presents a survey on recent advances on security issues of industrial cyber–physical systems (ICPSs). We specifically discuss two typical kinds of attacks, i.e., Denial-of-Service (DoS) attack and Deception attack, and present recent results in terms of attack detection, estimation, and control of ICPSs. Classifications of current studies are analyzed and summarized based on different system modeling and analysis methods. In addition, advantages and disadvantage of various methodologies are also discussed. Finally, the paper concludes with some potential future research directions on secure ICPSs.