Intelligent Transportation Systems Data Research Articles

Changes to the Transport Behaviour of Inhabitants of a Large City Due the Pandemic

On 11 March 2020, the World Health Organisation (WHO) classified the COVID-19 outbreak as a global pandemic and, in consequence, many countries took steps to prevent the importation and subsequent local transmission of the SARS-CoV-2 virus, resulting in restrictions on economic activity, transport, travel, and daily mobility. Although the COVID-19 pandemic and its impact on daily mobility have been widely addressed in the literature, there is a limited number of studies that indicate to what extent these changes have become permanent. The purpose of this study was to determine the nature and scale of the impact of the COVID-19 pandemic on the local transport system of a large city in Poland (Łódź) and, above all, to identify the permanence of this impact. To accomplish these objectives, a questionnaire survey was conducted using the computer-assisted telephone interviewing (CATI) technique on a sample of 500 residents, which included questions on daily mobility in the period before (autumn 2019) and after (autumn 2022) the pandemic. In addition, the results of the questionnaire survey were juxtaposed with data from intelligent transport systems (ITS) (data from 20 induction loops, and data on the number of tickets validated in public transport vehicles). Not only did the pandemic change the frequency of spatial motivations, but it also affected trip durations and preferred modes of transport. The most unfavourable changes concern the modal division and the increase in the use of private transport at the expense of public transport. Understanding the durability of the impact of the pandemic on changes in the spatial mobility of the population may help to develop transport policies and increase the resilience of transport systems to possible future pandemics.

Meta-Transfer Metric Learning for Time Series Classification in 6G-Supported Intelligent Transportation Systems

Deep learning-based time series classification in 6G-supported Intelligent Transportation Systems (ITS) helps transport decision-making. Deep learning classifier training necessitates a large amount of labeled data for feature extraction. Labeling time series data in 6G-supported ITS is tough. Meta-learning can be used to train deep classifiers with limited data. However, in meta-learning, the tasks are frequently modeled by a low-complexity base learner. It is unable to use more complicate and powerful structures. The meta-learning-pretrained classifier can only perform new classification problems with the same number of classes. Most pre-training strategies do not prioritize enhancing the pre-training phase’s convergence rate and lowering the computational cost. Most research work aims to improve classification performance by increasing the complexity of the classification model. However, this raises computing costs. In this paper, we propose a one-dimensional Multi-Scale Dilated Convolution Neural Network time series classifier (MSDCNN). MSDCNN combines multi-scale CNN and dilated CNN. It can extract multi-scale characteristics from time series and reduce the complexity of the classifier. Furthermore, we propose a pre-training strategy, called Meta-transfer metric Learning using Scale function (MLS). MLS allows the classifier to gain experience from different tasks with various numbers of classes. Experiments show that MLS reduces pre-training computation costs during the pre-training phase. The pre-trained classifier, without using any fine tuning techniques, achieves the highest accuracy by comparing with the state-of-the-art methods. Finally, we present a case study of applying MSDCNN and MLS to detect road accidents in 6G-supported transportation systems.

A novel block-chain based secure cross-domain interaction approach for intelligent transportation systems

In the Intelligent Transportation System (ITS), secure and efficient data communication among vehicles, road testing equipment, computing nodes, and transportation agencies is important for building a smart city integrated transportation system. However, the traditional centralized processing approach may face threats in terms of data leakage and trust. The use of distributed, tamper-proof blockchain technology can improve the decentralized storage and security of data in the ITS network. However, the cross-trust domain devices, terminals, and transportation agencies in the heterogeneous blockchain network of the ITS still face great challenges in trusted data communication and interoperability. In this paper, we propose a heterogeneous cross-chain interaction mechanism based on relay nodes and identity encryption to solve the problem of data cross-domain interaction between devices and agencies in the ITS. First, we propose the ITS cross-chain communication framework and improve the cross-chain interaction model. The relay nodes are interconnected through LibP2P to form a relay node chain, which is used for cross-chain information verification and transmission. Secondly, we propose a relay node secure access scheme based on identity-based encryption (IBE) to provide reliable identity authentication for relay nodes. Finally, we build a standard cross-chain communication protocol and cross-chain transaction lifecycle for this mechanism. We use Hyperledger Fabric and FISCO BCOS blockchain to design and implement this solution, and verify the feasibility of this cross-chain interaction mechanism. The experimental results show that the mechanism can achieve a stable data cross-chain read throughput of 2,000 transactions per second (TPS), which can meet the requirements of secure and efficient cross-chain communication and interaction among heterogeneous blockchains in the ITS, and has high application value.

A visual-based toolkit to support mobility data analytics

The Knowledge Discovery from Data (KDD) process is widely used across various domains to get valuable insights from data. Many platforms, like KNIME or RapidMiner, offer effective tools for KDD analysts, allowing them to perform data analytics tasks in a visual fashion, without writing code. In recent years, the increasing availability of mobility data has led to a surge in KDD-based initiatives from both industry and academia in the Intelligent Transportation Systems (ITS) domain. Still, KDD platforms lack comprehensive support for some typical mobility data manipulation tasks. As a result, mobility data analysis still requires a significant coding phase, with reduced productivity and hindered replicability of results.To address this gap, this paper presents a novel solution aimed at supporting ITS data analysts in defining KDD processes more efficiently. More in detail, we extended the KNIME platform by introducing a collection of new components explicitly tailored to facilitate some peculiar KDD tasks from mobility data. These components encompass critical functionalities such as map coverage analysis, trajectory partitioning and map-matching.To showcase the effectiveness of the proposed solution, we used it to replicate a study published in the ITS data analytics domain. Thanks to our proposal, such replication can be accomplished in a few minutes and with just a few clicks, without any manual coding, resulting in a pipeline that is easier to understand, distribute and re-execute, also for domain experts with no programming experience.Our solution is open-source and freely downloadable from the Knime Hub. In this way, we aim to foster data-driven research and practice in the ITS field, by providing researchers and practitioners with more effective analytics tools to handle mobility data.

Vehicle Trajectory Completion for Automatic Number Plate Recognition Data: A Temporal Knowledge Graph-Based Method

Vehicle trajectories represent an essential information source in intelligent transportation systems. Prior trajectory completion models based on Automatic Number Plate Recognition (ANPR) data have typically depended on vehicle re-identification results or road network information or have employed static knowledge graphs to integrate the two information sources. However, these methods have not taken into account the implicit temporal characteristics of trajectories in ANPR data and have neglected individual vehicle preferences. To address this void, this study proposes a Temporal Knowledge Graph-based Vehicle Trajectory Completion Model (TKG-VTC). The model implementation comprises three stages: first, ANPR data are converted into a temporal trajectory knowledge graph; second, knowledge representation learning is conducted using nontemporal relations, a biased temporal regularizer and multivector embeddings to embed the knowledge on the graph; and finally, the embedded results are employed to perform link prediction for incomplete trajectories, thereby restoring vehicle trajectories in ANPR data. Through model evaluation metrics and dimensionality reduction experiments, TKG-VTC is observed to demonstrate the best performance in completing trajectories when compared to TComplEx, TNTComplEx, and TeLM. This research introduces an innovative application of employing temporal knowledge graphs for trajectory reconstruction, which eliminates dependence on vehicle re-identification and road network information in previous methodologies. This is advantageous for enhancing the performance and dependability of vehicle trajectory data in intelligent transportation systems, as well as facilitating the implementation of trajectory prediction, demand analysis, and accident warning applications.

Privacy-preserving and efficient data sharing for blockchain-based intelligent transportation systems

Recent years have witnessed the development and adoption of blockchain technology in intelligent transportation systems (ITS) because of its authenticity and traceability. However, increasing ITS devices impose grand challenges in privacy-preserving and efficient data sharing. Recent research has demonstrated that integrating searchable symmetric encryption in blockchain enables privacy-preserving data sharing among ITS devices. However, existing solutions focus only on single-keyword searches over encrypted ITS data on the blockchain and suffer from privacy and efficiency issues when extended to multi-keyword scenarios. This work proposes a bloom filter-based multi-keyword search protocol for ITS data with enhanced efficiency and privacy preservation. We design a bloom filter to select a low-frequency keyword from the multiple keywords input by the ITS data owner. The low-frequency keyword can filter out a large portion of the ITS data from the search result, thus significantly reducing the computational cost. Furthermore, each identifier-keyword pair is attached with a pseudorandom tag that enables the completion of a search operation in only one round. In this manner, privacy is preserved because there are no intermediate rounds and results. In addition to the multi-keyword search protocol, we specify the addition and deletion protocols to enable dynamic updates of data records. We conducted a comprehensive performance evaluation of the protocols. The experimental results indicate that the proposed multi-keyword search protocol saves 14.67% query time and 59.96% financial cost.

Missing Traffic Data Imputation for Artificial Intelligence in Intelligent Transportation Systems: Review of Methods, Limitations, and Challenges

Missing data in Intelligent Transportation Systems (ITS) could lead to possible errors in the analyses of traffic data. Applying Artificial Intelligence (AI) in these circumstances can mitigate such problems. Past works focused only on specific data imputation methods, such as tensor factorization or a specific neural network model. While there are review papers covering singular topics regarding missing data, there are none in the field of traffic, to the best of our knowledge, that introduces the process of missing data collection and the viability of the traffic data collected while also broadly covering the popularly used models of recent years. This has led to non-uniformity of the terms used in missing data imputation, limited research in areas where datasets are not available, and a narrowed view of the methods used for data imputation. Hence, this paper aims to standardize the terms used in missing data classifications, look into the limitations of using available public or private datasets for urban traffic research, and discuss popular statistical and data-driven methods used by recent AI and ITS papers. It was found that tensor decomposition-based methods are the most popular for missing data imputation, followed by Generative Adversarial Networks and Graph Neural Networks, all of which rely on a large training dataset. Meanwhile, Probability Principle Component Analysis (PPCA) methods provide valuable insights via traffic analysis and are used for real-time traffic imputation. This paper also highlights the need for more efficient and reliable methods for traffic data collection, such as online APIs.

Optimizing Internet of Things-Based Intelligent Transportation System’s Information Acquisition Using Deep Learning

This work first discusses the Intelligent Transportation System (ITS)-oriented dynamic and static Information Acquisition Models (IAMs) and explains the information collection mechanism of the Internet of Things (IoT)-based ITS. The goal is to improve travel conditions and contribute to a better urban environment. In order to do so, the Faster Region-based Convolutional Neural Network (Faster R-CNN) is introduced to extract the IoT-based ITS’s electronic data features. It is observed that the Faster R-CNN has excellent recall and accuracy in extracting the features from the ITS electronic data sets. Specifically, the Faster R-CNN’s average recall and accuracy reach 83.89% and 86.79%. The accuracy is 6.20% higher than the R-CNN method. Thus, the Faster R-CNN algorithm features more robust and reliable performance for collecting and analyzing ITS data. Overall, this work examines ITS-oriented electronic information collection and automatic detection against the technological background of applying Computer Vision, Deep Learning, and IoT in urban traffic management. In particular, it explains the IoT-based ITS’s electronic information collection mechanism under Deep Learning (Faster R-CNN). The finding offers a theoretical foundation for implementing Deep Learning technologies in collecting ITS-oriented big data and smart city construction.

Distributed Acoustic Sensor Systems for Vehicle Detection and Classification

Intelligent transport systems (ITS) are pivotal in the development of sustainable and green urban living. ITS is data-driven and enabled by the profusion of sensors ranging from pneumatic tubes to smart cameras which are used to detect and categorise passing vehicles. Simple sensors, such as pneumatic tubes, are successfully deployed for counting passing vehicles but are not useful for vehicle tracking or re-identification. Smart cameras, on the other hand, collect comprehensive information but suffer from occlusion, patchy coverage, and compromised vision in adverse weather and visibility. This work explores a novel ITS data source based on optical fibre which acts as uninterrupted length of virtual sensors using a distributed acoustic sensor (DAS) system. Based on real DAS data collected in the field, we first present a study of latent DAS features that uniquely identify a given vehicle, otherwise referred to as vehicle signature. We formulate a classification problem that examines incoming DAS data to extract vehicle signatures and identify the different types of vehicle. To this end, we implement different classification methods and present a comparative performance analysis that reveals novel insights into the potential role of DAS in ITS applications. This work is a pilot study of DAS for vehicle classification that is driven by real-DAS data and validated by promising results where a vehicle type is correctly identified with 94% accuracy and the size of a vehicle with 95% accuracy.

Harmonization of National Access Points to Intelligent Transport Systems data: A data content and added value perspective

Intelligent Transport Systems (ITS) constitute a key driver for the adaptation of transport sector to the concurrent digital era. Therefore, the necessity to develop a solid framework under which specific norms will be followed by ITS applications, such as being accurate and up to date seems more than important. This is the prime mission that National Access Points (NAPs) aspire to accomplish. However, the architecture and implementation of NAPs varies across Europe. Hence, all Member States have joined their forces, through the project “National Access Point Coordination Organization for Europe (NAPCORE)”, to harmonize their NAPs. This paper aims to describe the scenery of ITS-related data exchange by highlighting the benefits resulting from NAPs and the challenges that need to be overcome. Finally, it aims to present planned activities related to the required content of NAPs.

Missing Traffic Data Imputation with a Linear Generative Model Based on Probabilistic Principal Component Analysis

Even with the ubiquitous sensing data in intelligent transportation systems, such as the mobile sensing of vehicle trajectories, traffic estimation is still faced with the data missing problem due to the detector faults or limited number of probe vehicles as mobile sensors. Such data missing issue poses an obstacle for many further explorations, e.g., the link-based traffic status modeling. Although many studies have focused on tackling this kind of problem, existing studies mainly focus on the situation in which data are missing at random and ignore the distinction between links of missing data. In the practical scenario, traffic speed data are always missing not at random (MNAR). The distinction for recovering missing data on different links has not been studied yet. In this paper, we propose a general linear model based on probabilistic principal component analysis (PPCA) for solving MNAR traffic speed data imputation. Furthermore, we propose a metric, i.e., Pearson score (p-score), for distinguishing links and investigate how the model performs on links with different p-score values. Experimental results show that the new model outperforms the typically used PPCA model, and missing data on links with higher p-score values can be better recovered.

Creation of a Mock-up Bridge Digital Twin by Fusing Intelligent Transportation Systems (ITS) Data into Bridge Information Model (BrIM)

Passage of overweighted commercial vehicles is one of the significant causes of damage to the pavement and structural components of bridges. Weigh-in-motion (WIM) systems can currently detect real-time traffic data; however, these data are stored in standalone databases. Building information modeling (BIM) has transformed the construction industry by injecting “information” into the building model and integrating different databases. BIM capabilities for bilateral exchange of data led to the inception of digital twin. This research investigates the feasibility of developing a digital twin of a mock-up bridge by integrating WIM data into a bridge information model (BrIM). The system was validated by first creating a mock-up bridge with affixed weight sensors attached to microcomputers and then developing a BrIM model and passing scaled vehicles over in real time with varying weight capacities. This study showed the feasibility of creating digital twins, ultimately enabling future research.

NiPA: Nicknames Pool Approach for Protecting Privacy of User Data in Intelligent Transportation Systems

The intelligent transportation system has made a huge leap in the level of human services, which has had a positive impact on the quality of life of users. On the other hand, these services are becoming a new source of risk due to the use of data collected from vehicles, on which intelligent systems rely to create automatic contextual adaptation. Most of the popular privacy protection methods, such as Dummy and obfuscation, cannot be used with many services because of their impact on the accuracy of the service provided itself, they depend on changing the number of vehicles or their physical locations. This research presents a new approach based on the shuffling Nicknames of vehicles. It fully maintains the quality of the service and prevents tracking users permanently, penetrating their privacy, revealing their whereabouts, or discovering additional details about the nature of their behavior and movements. Our approach is based on creating a central Nicknames Pool in the cloud as well as distributed sub-pools in fog nodes to avoid intelligent delays and overloading of the central architecture. Finally, we will prove by simulation and discussion by examples the superiority of the proposed approach and its ability to adapt to new services and provide an effective level of protection. In the comparison, we will rely on the well-known privacy criteria: Entropy, Ubiquity, and Performance.

Special issue on big data in transportation

Optimization and analysis of transportation systems have become considerably more autonomous and smarter by using connectivity and automation to overcome urbanization problems, such as an ever-growing population, increasing personal travel activities, limited budgets for infrastructure deployments and so on. With this in mind, there is an increasing need for high cooperation both strategically and operationally among different types of transportation modes, each with different objectives and policies. More than ever, Intelligent Transportation Systems (ITS) are playing a significant role in providing efficient integration and cooperation among different technologies and services, and complex and diverse multi-source ITS data have increased massively. Those valuable vast amounts of data can be handled by big data analytics in terms of providing efficient, safe and convenient ITS operations. Through big data–oriented emerging technologies, traffic becomes more intelligent, more manageable and safer. This special issue focusses on data science, data systems and transportation science and their applications to deliver effective and efficient solutions to the current challenges of handling big data in real-world ITS applications. From 19 submitted articles to this special issue, 4 articles were selected based on their reviews. Each article was reviewed by at least two reviewers and went through at least two rounds of reviews. The contributions of these articles are discussed in brief below. Claudio Teixeira, Luana Fragoso, Marta Mattoso, Diego Carvalho, Eduardo Bezerra, Jorge Soares, Glauco Amorim and Eduardo Ogasawara, ‘A horizontal partitioning-based method for frequent pattern mining in transport timetable’. A public transportation timetable is a document that provides information about public transportation service times to help passengers plan their trip. The timetable will often include the times when a service is scheduled to arrive and depart from specific places. T-mine, a unique frequent pattern mining (FPM) technique for improving knowledge discovery in timetable datasets, is presented in this study to solve the issues of lowering FPM computational cost and redundancy in association rules (AR). Authors show that the T-mine can generate a greater number of distinct, non-redundant and contextualized ARs than the traditional approach without functional dependency. Zain Ul Abideen, Heli Sun, Zhou Yang and Hamza Fahim, ‘Regional based Multi-Module Spatial-Temporal Networks Predicting City-wide Taxi Pickup/Dropoff demand from Origin to Destination’. Accurate forecasting of taxi demand is essential for drivers, riders, ride-hailing businesses and zonal directors. The goal of this work, in the future time interval between all areas estimating taxi demand, is to handle city-wide taxi demand origin–destination (OD) forecasting by Multi-Module Spatial–Temporal Networks (MMSTNs), which is a subject worth studying. Authors do a lot of tests on a real-world dataset called NYCTaxi to show how they can anticipate taxi demand around the city. They also show that their method surpasses prior strategies in terms of accuracy. Fethullah Gocer and Nazmi Sener, ‘Spherical Fuzzy Extension of AHP-ARAS Methods Integrated with Modified k-Means Clustering for Logistics Hub Location Problem’. A hub location problem (HLP) is concerned with where facilities should be located and how demand nodes should be allocated to hubs in order to route goods flow to o-d lowest cost pathways. Using a modified weighted k-means clustering algorithm and Multi-Criteria Decision-Making (MCDM) tools under a spherical fuzzy set environment, this work suggests a two-layered methodology to address the logistic HLP. An application of the network structure for postal/cargo operations in Turkey demonstrates the validity of the suggested assessment model. Iván García-Aguilar, Rafael Marcos Luque-Baena and Ezequiel López-Rubio, ‘Improved detection of small objects in road network sequences using CNN and super resolution’. The authors provide a new approach that uses deep convolutional neural networks for super-resolution to identify small-scale visual objects (educed size elements) and enhance the accuracy of class prediction. The results demonstrate that using super-resolution techniques improves the detections made on an image from video surveillance systems. The guest editors hope that the research contributions and findings in this special issue will benefit the readers in enhancing their knowledge and encouraging them to work on various aspects of Big Data in transportation. We want to express our sincere thanks to the Editor-in-Chief for and Special Issue Editor of Expert Systems for allowing us to organize this issue. The editorial office staff are excellent, and we thank them for their support. We are also thankful to all the authors who made this special issue possible and to the reviewers for their thoughtful contributions.

Distributed Intelligent Transportation System Data Management and Processing Platform

In recent years, with the rapid development of China's road construction and the continuous improvement of people's living standards, the number of urban motor vehicles has continued to grow year after year. Motor vehicles have become one of the main means of transportation for urban residents. At the same time, due to the increasing amount of traffic information data, a single server platform can not meet the reception and storage of massive traffic data, nor can it meet the needs of rapid query of massive traffic data. Therefore, this paper will use the distributed system to process data to solve the above problems, so as to ensure the real-time transmission and storage of massive traffic data. This paper discusses and analyzes the characteristics of distributed intelligent transportation system and the design of communication system data management and processing platform. Through the behavior decision algorithm of distributed transportation system, the flow data of distributed intelligent transportation system are tested and analyzed. The experimental results show that in the time period from 6:00 to 8:00, from 12:00 to 14:00 and from 16:00 to 18:00 when the traffic data reaches the peak, the amount of data processed by each communication server is very different, and the difference data amount can reach 60000 traffic data. The statistical results of this traffic data amount can show that the ratio of the data amount sent by each front-end bayonet every moment is not equal to the ratio of the data amount of each front-end bayonet every day, which is enough to show that the data management and processing capacity and efficiency of the distributed intelligent transportation system have been greatly improved.

Hourly Origin-Destination Matrix Estimation Using Intelligent Transportation Systems Data and Deep Learning.

Predicting the travel demand plays an indispensable role in urban transportation planning. Data collection methods for estimating the origin–destination (OD) demand matrix are being extensively shifted from traditional survey techniques to the pre-collected data from intelligent transportation systems (ITSs). This shift is partly due to the high cost of conducting traditional surveys and partly due to the diversity of scattered data produced by ITSs and the opportunity to derive extra benefits out of this big data. This study attempts to predict the OD matrix of Tehran metropolis using a set of ITS data, including the data extracted from automatic number plate recognition (ANPR) cameras, smart fare cards, loop detectors at intersections, global positioning systems (GPS) of navigation software, socio-economic and demographic characteristics as well as land-use features of zones. For this purpose, five models based on machine learning (ML) techniques are developed for training and test. In evaluating the performance of the models, the statistical methods show that the convolutional neural network (CNN) leads to the best performance in terms of accuracy in predicting the OD matrix and has the lowest error in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE). Moreover, the predicted OD matrix was structurally compared with the ground truth matrix, and the CNN model also shows the highest structural similarity with the ground truth OD matrix in the presented case.

Data denoising and compression of intelligent transportation system based on two‐dimensional discrete wavelet transform

SummaryTwo‐dimensional discrete wavelet transform is an important tool for digital image analysis. It is widely used in the field of image editing, such as image coding and compression and digital image processing. The realization of effective two‐dimensional discrete wavelet transform has important realities in data or image processing. This paper mainly introduces the research of intelligent transportation system data denoising and compression based on two‐dimensional discrete wavelet transform and intends to provide solutions to the problem of data overload in the process of intelligent transportation system data collection, transmission, and storage. This paper proposes the construction of two‐dimensional discrete wavelet transform, including separation calculation method and non‐separation calculation method, and proposes the construction of time–space data model of intelligent transportation system, including support vector machine (SVM), K nearest neighbor algorithm, and deep neural network algorithm. It is proposed to construct two‐dimensional wavelet transform to denoise and compress data of intelligent transportation systems. The experimental results in this paper show that the signal after denoising by two‐dimensional discrete wavelet transform is smoother, with a maximum difference of 0.57, and the denoising effect is better.

Application of Intelligent Transportation Systems in Analyses of Human Spatial Mobility in Cities

This article contains results of studies on the applicability of data from Intelligent Transportation Systems (ITS) for the purposes of geographical studies regarding the spatial mobility of inhabitants within a big city. The article focuses on the option of applying two types of sub-systems – induction loops and automatic number-plate recognition (ANPR) – and includes examples of analyses based on the resulting data, which can serve as a basis for mobility studies. The area on the example of which the capabilities of application of ITS data have been presented is Lodz – a large city in central Poland. The conducted research shows that ITS systems offer an enormous potential in providing data for spatial mobility studies. In order to fully exploit its worth, however, it is imperative to expand the research procedure by including, for instance, the results of qualitative research. Also, the interpretation of results obtained on the basis of ITS data ought to be performed with an awareness of numerous significant preliminary and simplifying assumptions.

Privacy and Utility Preserving Trajectory Data Publishing for Intelligent Transportation Systems

Nowadays, the extensive collection and storage of massive personal GPS data in intelligent transportation systems every day provide great convenience for trajectory data analysis and mining research, thus bringing valuable information for real-life applications. Yet, protecting personal privacy is also more challenging in the smart environment. When trajectories of individuals are published together with their sensitive attributes such as disease, income etc., one can use partial trajectory knowledge for identity, sensitive locations, and sensitive values of target individuals. We present $(\alpha, K)_{L}$ -privacy model and an anonymization scheme aimed at ${I}$ dentifying and ${E}$ liminating ${V}$ iolating privacy ${S}$ ubtrajectories (IEVS), to prevent privacy disclosure while preserving the accuracy and high quality of published trajectories. In particular, IEVS employs three anonymization techniques, i.e., trajectory splitting, location suppression, and sensitive value generalization to eliminate all subtrajectories violating $(\alpha, K)_{L}$ -privacy principle. Experiments show our scheme is effective to improve the data utility of anonymized trajectories when compared with previous work.

Mapping dynamic road emissions for a megacity by using open-access traffic congestion index data

The high populations of vehicles in global megacities have raised serious mobility and sustainability challenges, such as traffic congestion, air pollution deterioration and energy consumption issues. Detailed emission inventories at the link level are a prerequisite to accurately quantifying ambient pollution concentrations and identifying hotspots of human exposure within urban areas. The increasing adoption of intelligent transportation system data in smart-city initiatives worldwide has offered unprecedented opportunities for improving transportation air quality management. Based on the open-access traffic congestion index, we construct a high-resolution emission inventory of hourly fluxes of air pollutants and carbon dioxide from on-road vehicles over the whole road network in Shenzhen, China. Fine-grained quantification of ‘excess’ emissions from rush-hour traffic is explored, finding varied emission enhancement (14.3–30.4%) for different pollutants, as well as 24.3–26.8% and 19.6–22.0% ‘excess’ consumption for gasoline and diesel, respectively, in a central business district of Shenzhen during the rush hour periods. Also, we quantified the impacts of freight activities on pollutant emissions that freight activities can largely increase the street-level emission rates of nitrogen oxides, fine particulate matter and carbon dioxide, in which heavy-duty trucks share up to 50% of the total emissions of these species. This study provides a novel approach of high-resolution traffic emission inventory construction that can be potentially utilized in many types of cities, particularly for cities suffering from data-sparse situations, and further strengthen the intelligence and accuracy of vehicle emission management.