Traffic congestion and accidents take a toll on commuters' daily experiences and society. Locating the venues prone to congestion and accidents and capturing their perception by public members is invaluable for transport policy-makers. However, few previous methods consider user perception toward the accidents and congestion in finding and profiling the accident- and congestion-prone areas, leaving decision-makers unaware of the subsequent behavior responses and priorities of retrofitting measures. This study develops a framework to identify and characterize the accident- and congestion-prone areas heatedly discussed on social media. First, we use natural language processing and deep learning to detect the accident- and congestion-relevant Chinese microblogs posted on Sina Weibo, a Chinese social media platform. Then a modified Kernel Density Estimation method considering the sentiment of microblogs is employed to find the accident- and congestion-prone regions. The results show that the 'congestion-prone areas' discussed on social media are mainly distributed throughout the historical urban core and the Northwest of Pudong New Area, in reasonably good agreements with actual congestion records. In contrast, the 'accident-prone areas' are primarily found in locations with severe accidents. Finally, the above venues are characterized in spatio-temporal and semantic aspects to understand the nature of the incidents and assess the priority level for mitigation measures. The outcomes can provide a reference for traffic authorities to inform resource allocation and prioritize mitigation measures in future traffic management.@en

Problem definition: Warranty reserves are funds used to fulfill future warranty obligations for a product. In this paper, we investigate the warranty reserve planning problem faced by a manufacturing firm who manages warranties for multiple products. Academic/practical relevance: It is nontrivial to determine a proper amount of reserves to hold, because warranty expenditures are random in nature and reserving either excess or insufficient cash would incur losses. How can warranty reserve levels be optimized and promptly adjusted is a focal issue, especially for firms selling multiple products. Methodology: Inspired by the general pattern of empirical warranty claims data, we first develop an aggregate warranty cost (AWC) forecasting model for a single product by coupling stochastic product sales and failure processes, which can be used to plan for warranty reserves periodically. The reserve levels are then optimized via a distributionally robust approach, because the exact distribution of AWC is generally unknown. To reduce the losses generated from managing the funds, we further investigate two potential loss-reduction approaches: demand learning and funds pooling. Results: For the demand learning algorithm, we prove that, as the sales period grows, the optimal learning parameter asymptotically converges to a constant in a fairly fast rate; our simulation experiments show that the performance of demand learning is promising and robust under general warranty claim patterns. Moreover, we find that the benefits of funds pooling change over different stages of the warranty life cycle; in particular, the relative pooling benefit in terms of reserve losses is nonincreasing over time. Managerial implications: This study offers guidelines on how manufacturers should adaptively forecast and dynamically plan warranty reserves over the warranty life cycle.@en

En-route congestion causes delays in air traffic networks and will become more prominent as air traffic demand will continue to increase yet airspace volume cannot grow. However, most existing studies on flight delay modeling do not consider en-route congestion explicitly. In this study, we propose a new flight delay model, Multi-layer Air Traffic Network Delay (MATND) model, to capture the impact of en-route congestion on flight delays over an air traffic network. This model is developed by a data-driven approach, taking aircraft tracking data and flight schedules as inputs to characterize a national air traffic network, as well as a system-level model approach, modeling the delay process based on queueing theory. The two approaches combined make the network delay model a close representation of reality and easy-to-implement for what-if scenario analysis. The proposed MATND model includes 1) a data-driven method to learn a network composed of airports, en-route congestion points, and air corridors from aircraft tracking data, 2) a stochastic and dynamic queuing network model to calculate flight delays and track their propagation at both airports and in en-route congestion areas, in which the delays are computed via a space–time decomposition method. Using one month of historical aircraft tracking data over China's air traffic network, MATND is tested and shows to give an accurate quantification of delays of the national air traffic network. “What-if” scenario analyses are conducted to demonstrate how the proposed model can be used for the evaluation of air traffic network improvement strategies, where the manipulation of reality at such a scale is impossible. Results show that MATND is computationally efficient, well suited for evaluating the impact of policy alternatives on system-wide delay at a macroscopic level.@en

Air travel demand has continued to increase rapidly over the past decade, causing severe flight delays. To reduce such delays, Air Navigation Service Providers need to first understand the operational capacity and congestion risks associated with a network, and then develop strategies accordingly. However, limited studies have been conducted due to lack of data. New opportunities have arisen given the availability of large-scale aircraft tracking data and many other digitalized records of operations. In response, we develop a novel data-driven framework that characterizes the operational structure and dynamics of an air traffic network using actual tracking data. The framework includes several new statistical measures and data analytic techniques to summarize airspace availability, network structure, and utilization patterns. We then apply the framework to analyze the air traffic networks in China and the US. The results reveal distinctive characteristics of these two networks. Airspace availability for commercial flights is much more restricted in China than the US. The network in China has a clear structure with distinct utilization patterns, while the network in the US has a more flexible structure featuring complex dynamics. These operational differences indicate that China faces a greater chance of en-route congestion when compared with the US. The results also demonstrate that the data-driven approach is effective to identify the actual behavior and complexity of an air traffic network, which are not captured by existing methods.@en

Future Air Traffic Management systems can benefit from innovative approaches that leverage the increasing availability of operational data to facilitate the development of new performance assessment and decision-support capabilities. This paper presents a data analytics framework for high-fidelity characterization of air traffic flows from large-scale flight tracking data. Machine learning methods are used to exploit spatiotemporal patterns in aircraft movement towards the identification of trajectory patterns and traffic flow patterns. The outcomes and potential impacts of this framework are demonstrated with a comparative analysis of terminal area operations in three representative multi-airport (metroplex) systems of the global air transportation system: New York, Hong Kong and Sao Paulo. As a descriptive tool for systematic analysis of the flow behavior, the framework allows for cross-metroplex comparisons of terminal airspace design, utilization and traffic performance. Novel quantitative metrics are created to summarize metroplex efficiency, capacity and predictability. The results reveal several structural, operational and performance differences between the multi-airport systems analyzed. Our findings show that New York presents the most complex airspace design, with considerably higher number of routes and interactions between them, as well as more dynamic changes in the terminal area flow structure during the day, in part driven by the presence of flow dependencies. Interestingly, it exhibits the best levels of traffic flow efficiency on average, both spatially and temporally, yet the highest variability in metroplex configuration performance, with more pronounced performance degradation during inclement weather.@en

Future Air Traffic Management systems can benefit from innovative approaches that leverage the increasing availability of operational data to facilitate the development of new performance assessment and decision-support capabilities. This paper presents a data analytics framework for high-fidelity characterization of air traffic flows from large-scale flight tracking data. Machine learning methods are used to exploit spatiotemporal patterns in aircraft movement towards the identification of trajectory patterns and traffic flow patterns. The outcomes and potential impacts of this framework are demonstrated with a comparative analysis of terminal area operations in three representative multi-airport (metroplex) systems of the global air transportation system: New York, Hong Kong and Sao Paulo. As a descriptive tool for systematic analysis of the flow behavior, the framework allows for cross-metroplex comparisons of terminal airspace design, utilization and traffic performance. Novel quantitative metrics are created to summarize metroplex efficiency, capacity and predictability. The results reveal several structural, operational and performance differences between the multi-airport systems analyzed. Our findings show that New York presents the most complex airspace design, with considerably higher number of routes and interactions between them, as well as more dynamic changes in the terminal area flow structure during the day, in part driven by the presence of flow dependencies. Interestingly, it exhibits the best levels of traffic flow efficiency on average, both spatially and temporally, yet the highest variability in metroplex configuration performance, with more pronounced performance degradation during inclement weather.@en

“The Image of the City” by Kevin Lynch is a landmark planning theory of lasting influence; its scientific rigor and relevance in the digital age were in dispute. The rise of social media and other digital technologies offers new opportunities to study the perception of urban environments. Questions remain as to whether social media analytics can provide a reliable measure of perceived city images? If yes, what implication does it hold for urban planners? This paper describes a study on the perception of city images using a combination of “big data” and “small data” methods in the Tri-City Region in Poland. The aims were to 1) test the hypothesis whether social media analytics can elicit Lynchian elements of city image in consistency with conventional methods, and 2) develop and evaluate social media-based indicators of Imageability for planning practice. Geo-tagged images and texts were collected from Instagram and Twitter, two popular social media platforms in Poland. Text-Mining, Image Processing, Clustering Analysis, Kernel Density Estimation, and Sentiment Analysis were used. Results were compared with benchmarks constructed from official GIS database, questionnaire responses and sketch maps. “District”, “landmark”, and “path” identified on social media were in good agreements with benchmarks, less so for “edge” and “node”. Two social media-based indicators have influenced the perception of a place: Instagramability, the frequency of a place captured on Instagram, was linked to its perception as an architectural landmark and tourist attraction, while Twitterability, the frequency of a place mentioned on Twitter by name, was linked to its perceived niceness and relevance to everyday life of communities. Methods developed in this study have theoretical and practical implications for urban planners.@en

“The Image of the City” by Kevin Lynch is a landmark planning theory of lasting influence; its scientific rigor and relevance in the digital age were in dispute. The rise of social media and other digital technologies offers new opportunities to study the perception of urban environments. Questions remain as to whether social media analytics can provide a reliable measure of perceived city images? If yes, what implication does it hold for urban planners? This paper describes a study on the perception of city images using a combination of “big data” and “small data” methods in the Tri-City Region in Poland. The aims were to 1) test the hypothesis whether social media analytics can elicit Lynchian elements of city image in consistency with conventional methods, and 2) develop and evaluate social media-based indicators of Imageability for planning practice. Geo-tagged images and texts were collected from Instagram and Twitter, two popular social media platforms in Poland. Text-Mining, Image Processing, Clustering Analysis, Kernel Density Estimation, and Sentiment Analysis were used. Results were compared with benchmarks constructed from official GIS database, questionnaire responses and sketch maps. “District”, “landmark”, and “path” identified on social media were in good agreements with benchmarks, less so for “edge” and “node”. Two social media-based indicators have influenced the perception of a place: Instagramability, the frequency of a place captured on Instagram, was linked to its perception as an architectural landmark and tourist attraction, while Twitterability, the frequency of a place mentioned on Twitter by name, was linked to its perceived niceness and relevance to everyday life of communities. Methods developed in this study have theoretical and practical implications for urban planners.@en

“The Image of the City” by Kevin Lynch is a landmark planning theory of lasting influence; its scientific rigor and relevance in the digital age were in dispute. The rise of social media and other digital technologies offers new opportunities to study the perception of urban environments. Questions remain as to whether social media analytics can provide a reliable measure of perceived city images? If yes, what implication does it hold for urban planners? This paper describes a study on the perception of city images using a combination of “big data” and “small data” methods in the Tri-City Region in Poland. The aims were to 1) test the hypothesis whether social media analytics can elicit Lynchian elements of city image in consistency with conventional methods, and 2) develop and evaluate social media-based indicators of Imageability for planning practice. Geo-tagged images and texts were collected from Instagram and Twitter, two popular social media platforms in Poland. Text-Mining, Image Processing, Clustering Analysis, Kernel Density Estimation, and Sentiment Analysis were used. Results were compared with benchmarks constructed from official GIS database, questionnaire responses and sketch maps. “District”, “landmark”, and “path” identified on social media were in good agreements with benchmarks, less so for “edge” and “node”. Two social media-based indicators have influenced the perception of a place: Instagramability, the frequency of a place captured on Instagram, was linked to its perception as an architectural landmark and tourist attraction, while Twitterability, the frequency of a place mentioned on Twitter by name, was linked to its perceived niceness and relevance to everyday life of communities. Methods developed in this study have theoretical and practical implications for urban planners.@en

“The Image of the City” by Kevin Lynch is a landmark planning theory of lasting influence; its scientific rigor and relevance in the digital age were in dispute. The rise of social media and other digital technologies offers new opportunities to study the perception of urban environments. Questions remain as to whether social media analytics can provide a reliable measure of perceived city images? If yes, what implication does it hold for urban planners? This paper describes a study on the perception of city images using a combination of “big data” and “small data” methods in the Tri-City Region in Poland. The aims were to 1) test the hypothesis whether social media analytics can elicit Lynchian elements of city image in consistency with conventional methods, and 2) develop and evaluate social media-based indicators of Imageability for planning practice. Geo-tagged images and texts were collected from Instagram and Twitter, two popular social media platforms in Poland. Text-Mining, Image Processing, Clustering Analysis, Kernel Density Estimation, and Sentiment Analysis were used. Results were compared with benchmarks constructed from official GIS database, questionnaire responses and sketch maps. “District”, “landmark”, and “path” identified on social media were in good agreements with benchmarks, less so for “edge” and “node”. Two social media-based indicators have influenced the perception of a place: Instagramability, the frequency of a place captured on Instagram, was linked to its perception as an architectural landmark and tourist attraction, while Twitterability, the frequency of a place mentioned on Twitter by name, was linked to its perceived niceness and relevance to everyday life of communities. Methods developed in this study have theoretical and practical implications for urban planners.@en

This installment of Trends & Controversies provides an array of perspectives on the latest research in system informatics. Kang Zhao, Yao Xie, and Kwok-Leung Tsui introduce the work in 'System Informatics: From Methodology to Applications.' On the methodology side, in 'Projection-Based Process Monitoring and Empirical Divergence,' Wenpo Huang, Wei Jiang, Qingming Wei, and Yanting Li propose a framework of projection-based methods, and in 'One-Class Classification Methods for Process Monitoring and Diagnosis,' Sugon Cho and Seoung Bum Kim discuss how a data analytics algorithm can be used as a control chart. On the application side, 'IoT-Enabled System Informatics for Service Decision Making,' by Kaibo Liu and Jianjun Shi, reviews current trends and future opportunities for IoT, with a special focus on issues related to the big data collected by multiple sensors. 'Quantifying the Risk Level of Functional Chips in DRAM Wafers,' by Young-Seon Jeong, Byunghoon Kimb, Seung Hoon Tong, In-Kap Chang, and Myong K. Jeong, not only identifies research challenges and opportunities for decision making with massive data in the process of semiconductor manufacturing but also quantifies the risk level of functional chips in DRAM wafers. Finally, 'Flight Operations Monitoring through Cluster Analysis: A Case Study,' by Florent Charruaud and Lishuai Li, describes a new method called cluster-based anomaly detection to help airline safety experts monitor daily flights and detect anomalies.@en

This installment of Trends & Controversies provides an array of perspectives on the latest research in system informatics. Kang Zhao, Yao Xie, and Kwok-Leung Tsui introduce the work in 'System Informatics: From Methodology to Applications.' On the methodology side, in 'Projection-Based Process Monitoring and Empirical Divergence,' Wenpo Huang, Wei Jiang, Qingming Wei, and Yanting Li propose a framework of projection-based methods, and in 'One-Class Classification Methods for Process Monitoring and Diagnosis,' Sugon Cho and Seoung Bum Kim discuss how a data analytics algorithm can be used as a control chart. On the application side, 'IoT-Enabled System Informatics for Service Decision Making,' by Kaibo Liu and Jianjun Shi, reviews current trends and future opportunities for IoT, with a special focus on issues related to the big data collected by multiple sensors. 'Quantifying the Risk Level of Functional Chips in DRAM Wafers,' by Young-Seon Jeong, Byunghoon Kimb, Seung Hoon Tong, In-Kap Chang, and Myong K. Jeong, not only identifies research challenges and opportunities for decision making with massive data in the process of semiconductor manufacturing but also quantifies the risk level of functional chips in DRAM wafers. Finally, 'Flight Operations Monitoring through Cluster Analysis: A Case Study,' by Florent Charruaud and Lishuai Li, describes a new method called cluster-based anomaly detection to help airline safety experts monitor daily flights and detect anomalies.@en

In the above article [1], Table I, III, and IV should show 'N/m' instead of 'kN/m' and they should also show 'Ns/m' instead of 'kNs/m.' The revised tables are shown below. Also, in (1), “kpw ” should be changed to “kpw.” And on page 2952, first line, in the left column, “They also found…” should be changed to “We also found…”.@en

Condition monitoring, as part of the intelligent infrastructure concept, can significantly improve the reliability, safety and efficiency of rail operations. Degradation in infrastructure can be detected before a problem occurs, without interrupting normal operations. This paper contributes to the development of analytics tools and methods for condition monitoring using sensor data. A data-driven method is proposed to monitor the wheel wear of high-speed trains using onboard vibration sensors. In this method, a number of signal processing techniques and statistical methods are applied and extended to extract useful information from multi-location vibration data and estimate the wheel wear. We test the method using real operational data collected from high-speed trains in China over half a year. Preliminary results show that the method is accurate and can be easily applied to real world operations. The method can be extended to provide fault or degradation predictions, which reduces the in-service failures, and enables predictive maintenance.@en

High-tech giants and start-ups are investing in drone technologies to provide urban air delivery service, which is expected to solve the last-mile problem and mitigate road traffic congestion. However, air delivery service will not scale up without proper traffic management for drones in dense urban environment. Currently, a range of Concepts of Operations (ConOps) for unmanned aircraft system traffic management (UTM) are being proposed and evaluated by researchers, operators, and regulators. Among these, the tube-based (or corridor-based) ConOps has emerged in operations in some regions of the world for drone deliveries and is expected to continue serving certain scenarios that with dense and complex airspace and requires centralized control in the future. Towards the tube-based ConOps, we develop a route network planning method to design routes (tubes) in a complex urban environment in this paper. In this method, we propose a priority structure to decouple the network planning problem, which is NP-hard, into single-path planning problems. We also introduce a novel space cost function to enable the design of dense and aligned routes in a network. The proposed method is tested on various scenarios and compared with other state-of-the-art methods. Results show that our method can generate near-optimal route networks with significant computational time-savings.@en

A multiple-airport system (MAS) consists of more than two airports in a metropolitan area under a large block of terminal airspace that is managed by one or two air traffic control units. When the capacity of an airport or of the terminal airspace drops, flight delays occur in the MAS system. A quick estimation and predication of traffic congestion in the MAS is important yet challenging. This paper aims to develop a queuing network model of MAS using point-wise stationary queues. The model analyzes the changes of non-stationary queues under the principle of flow conservation to capture flight delay propagation in the system. Regression analyses are performed to examine the relationship between the arrival and departure efficiencies of different airports. The model is validated with the data of Guangdong–Hong Kong–Macao Greater Bay Area airports. Simulation results show that the model can effectively estimate flight delays in the MAS.@en

Safety is a top priority for civil aviation. Data mining in digital Flight Data Recorder (FDR) or Quick Access Recorder (QAR) data, commonly referred to as black box data on aircraft, has gained interest for proactive safety management. New anomaly detection methods, primarily clustering methods, have been developed to monitor pilot operations and detect any risks from such flight data. However, all existing anomaly detection methods are offline learning — the models are trained once using historical data and used for all future predictions. In practice, new flight data are accumulated continuously and analyzed every month at airlines. Clustering such dynamically growing data is challenging for an offline method because it is memory and time intensive to re-train the model every time new data come in. If the model is not re-trained, false alarms or missed detections may increase since the model cannot reflect changes in data patterns. To address this problem, we propose a novel incremental anomaly detection method based on Gaussian Mixture Model (GMM) to identify common patterns and detect outliers in flight operations from digital flight data. It is a probabilistic clustering model of flight operations that can incrementally update its clusters based on new data rather than to re-cluster all data from scratch. It trains an initial GMM model based on historical offline data. Then, it continuously adapts to new incoming data points via an expectation–maximization (EM) algorithm. To track changes in flight operation patterns, only model parameters need to be saved, not the raw flight data. The proposed method was tested on three sets of simulation data and two sets of real-world flight data. Compared with the traditional offline GMM method, the proposed method can generate similar clustering results with significantly reduced processing time (57 %–99 % time reduction in testing sets) and memory usage (91 %–95 % memory usage reduction in testing sets). Preliminary results indicate that the incremental learning scheme is effective in dealing with dynamically growing data in flight data analytics.@en

Short-term forecasting of passenger flow is critical for transit management and crowd regulation. Spatial dependencies, temporal dependencies, inter-station correlations driven by other latent factors, and exogenous factors bring challenges to the short-term forecasts of passenger flow of urban rail transit networks. An innovative deep learning approach, Multi-Graph Convolutional-Recurrent Neural Network (MGC-RNN) is proposed to forecast passenger flow in urban rail transit systems to incorporate these complex factors. We propose to use multiple graphs to encode the spatial and other heterogenous inter-station correlations. The temporal dynamics of the inter-station correlations are also modeled via the proposed multi-graph convolutional-recurrent neural network structure. Inflow and outflow of all stations can be collectively predicted with multiple time steps ahead via a sequence to sequence(seq2seq) architecture. The proposed method is applied to the short-term forecasts of passenger flow in Shenzhen Metro, China. The experimental results show that MGC-RNN outperforms the benchmark algorithms in terms of forecasting accuracy. Besides, it is found that the inter-station driven by network distance, network structure, and recent flow patterns are significant factors for passenger flow forecasting. Moreover, the architecture of LSTM-encoder-decoder can capture the temporal dependencies well. In general, the proposed framework could provide multiple views of passenger flow dynamics for fine prediction and exhibit a possibility for multi-source heterogeneous data fusion in the spatiotemporal forecast tasks.@en

Predicting Estimated Time of Arrival (ETA) for a Multi-Airport System (MAS) is much more challenging than for a single airport system because of complex air route structure, dense air traffic volume and vagaries of traffic conditions in an MAS. In this work, we propose a novel “Bubble” mechanism to accurately predict medium-term ETA for a Multi-Airport System (MAS), in which the prediction of travel time of an origin–destination (OD) pair is decomposed into two stages, termed as out-MAS and in-MAS stages. For the out-MAS stage, Auto-Regressive Integrated Moving Average (ARIMA) is used to predict the travel time of a flight to reach the MAS boundary. For the in-MAS stage, we construct new spatio-temporal features based on clustering analysis of trajectory patterns facilitated by a novel data-driven hybrid polar sampling method. A sequence-to-sequence prediction model, Multi-variate Stacked Fully connected Bidirectional Long–Short Term Memory, is further developed to achieve multi-step-ahead predictions of in-MAS travel time for each trajectory pattern using the spatio-temporal features as input. Finally, the medium-term ETA prediction for an MAS is achieved by integrating the out-MAS and in-MAS prediction with the help of trajectory pattern prediction via random forest. A case study of predicting medium-term ETA for a typical MAS in China, Guangdong–Hong Kong–Macao Greater Bay Area, is conducted to demonstrate the usage and promising performance of the proposed method in comparison to several commonly used end-to-end learning methods.@en