With the development of society and economy, the metro has become one of the essential components of the urban transportation system. Commuting passengers prefer the metro due to its punctual, high ...speeds and uncongested characteristics compared to private cars, taxis, bus, etc., especially in morning and evening rush hour. So, identifying metro commuters and mining its commuting mobility patterns play an essential role in improving service quality, promoting public transit use, and optimizing operational scheduling. We develop a method to mine metro commuting mobility patterns using massive smart card data. Firstly, we extracted individual daily regular OD (origin and destination) based on spatio-temporal similarity measurement from massive smart card data. The information entropy gain algorithm is used to further identify commuters from individual regular OD. Secondly, the station-oriented commute space model is built from space views. Metro stations are divided into employment, residential, and balanced type according to job-housing function pattern. They are divided into high efficiency, general, and low efficiency type according to commute efficiency pattern. Function pattern refers to the proportional relationship between the residence and employment land use around the rail station. Efficiency pattern is a comprehensive index to measure the commute time and distance. Finally, stations are clustered by the K-means method to determine what type they are. The experiment found that metro commuters accounted for 41% of the morning peak traffic using smart card data in Chongqing, China. Three typical job-housing function patterns and three commute efficiency patterns are discovered, respectively, and the characteristics of each are mined.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Unlicensed taxis are private vehicles that are not duly licensed or permitted by the jurisdiction in which they operate. Trajectory data contain rich behavior features of mobile objects, which are ...valuable for unlicensed taxi detection. (Electronic Registration Identification) ERI of the motor vehicle is a way to collect trajectory data. ERI’s advantage is that it can record all kinds of vehicles traveling in the city, including taxis, private vehicles. As a pilot city of ERI in China, Chongqing has formed a massive ERI trajectory dataset of motor vehicles. This dataset provides us with an opportunity to detect and analyze unlicensed taxis from a data-driven aspect. In this paper, we complete two main works: detecting city-wide unlicensed taxis and analyzing them. Firstly, we build an unlicensed taxis detection model based on an ensemble learning approach, random forest(RF). The goal of ensemble learning is to improve prediction, generalizability, and robustness over a single classifier. We employ taxis and commuting private vehicles as training samples. The core idea is that unlicensed taxis and taxis are similar in many aspects. We also innovatively utilize the POI information as an input feature to the unlicensed taxi detection model. With the comparison of some baseline models, we have proved our model’s superiority on the ERI dataset. So, we apply the detection model on a real-world dataset and detect the city-wide potential unlicensed taxis. Secondly, we conduct some statistical analysis with the detected potential unlicensed taxis. We find that unlicensed taxis do behave very much like taxis. The hot areas of taxis and unlicensed taxis are not the same, which provides vital information for further traffic management.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
The quick access recorder (QAR), as an airborne device used to monitor and record flight parameters, has been widely installed on various types of aircraft. Based on QAR data, research on runway ...overrun, a typical flight safety incident, has attracted widespread attention in recent years. However, existing runway overrun risk models generally suffer from oversimplified risk metrics or insufficient consideration of risk dynamics. In this paper, we propose a new dynamic runway overrun risk assessment model based on QAR data. We first consider the noise of aircraft trajectory data in the QAR parameters and present a landing trajectory correction method combining ground speed and runway position information. Second, to improve the accuracy of the risk assessment model, we design an algorithm to automatically recognize the aircraft autobrake level during the landing phase, based on which a new dynamic risk assessment model is developed. Finally, feature engineering is performed to extract the relevant contributing factors of runway overrun risk, based on which classification and regression models are applied to identify risky flights and predict the risk values. The proposed risk assessment model was evaluated using QAR data from an airline in China. The results show that the automatic deceleration rate, the way that the aircraft approaches the runway, the touchdown distance, and the kinetic energy at 50 ft are key factors in the risk of runway overrun during the landing phase.
Flight safety is a hot topic in the aviation industry. Statistics show that safety incidents during landing are closely related to the flare phase because this critical period requires extensive ...pilot operations. Many airlines require that pilots should avoid performing any forward stick inputs during the flare. However, our statistical results from about 86,504 flights show that this unsafe pilot operation occasionally happens. Although several case studies were conducted previously, systematic research, especially based on a large volume of flight data, is still missing. This paper aims to fill this gap and provide more insights into the issue of pilots’ unsafe stick operations during the flare phase. Specifically, our work is based on the Quick Access Recorder (QAR) data, which consist of multivariate time-series data from various flight parameters. The raw data were carefully preprocessed, then key features were extracted based on flight expert experience, and a K-means clustering algorithm was utilized to divide the unsafe pilot operations into four categories. Based on the clustering results, we conducted an in-depth analysis to uncover the reasons for different types of unsafe pilot stick operations. In addition, extensive experiments were conducted to further investigate how these unsafe operations are correlated with different factors, including airlines, airports, and pilots. To the best of our knowledge, this is the first systematic study analyzing pilots’ unsafe forward stick operations based on a large volume of flight data. The findings can be used by airlines to design more targeted pilot training programs in the future.
In civil aviation industry, runway overrun is a typical landing safety incident concerned by both airlines and authorities. Among various contributing factors to the runway overrun incident, long ...landing plays an important role. However, existing studies for long landing prediction mainly depend on classic machine learning methods and handcrafted features. As a result, they usually require much expert knowledge and provide unsatisfactory results. To address these problems, this paper proposes an innovative deep sequence‐to‐sequence model which utilizes QAR (Quick Access Recorder) data for accurate long landing pre‐ diction. Specifically, to cope with the high heterogeneity of QAR dataset, a data pre‐processing procedure is first proposed which includes data cleaning, interpolation and normalization steps. Second, to avoid the noises incurred by too many QAR parameters and relieve the reliance on expert experience, the GBDT (gradient boosting decision trees) model is employed to choose the most relevant parameters as features. Then a CNN‐LSTM and TG‐attention encoder‐decoder architecture is proposed to accurately predict future aircraft ground speed and radio height sequences, based on which the touchdown distance can be finally calculated. Experimental results on a large QAR dataset with 44,176 A321 flights validate effectiveness of the proposed method.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Some researchers treat traffic flow as an entirety while predicting short-term traffic flow. Through analyzing real-world traffic flow, we have found that urban traffic shows a stable changing ...process along with random disturbs. An alternative way is to decompose traffic flow into two components: periodicity and volatility. We propose a hybrid method named Time-Series Analysis and Supervised-Learning (TSA-SL) for short-term traffic flow prediction from the perspective of traffic flow decomposition. In the method, period traffic flow is modeled with a typical TSA method called Fourier Transform (FT), where periodic behaviors are described as the combination of sines and cosines. The volatility of the current location is determined by its surroundings, so spatial–temporal correlations are extracted as input features of SL. Then, three hybrid prediction models, including FT-SVR, FT-GBRT and FT-LSTM, are built with proposed TSA-SL. In the experiment, an Electronic Registration Identification (ERI) dataset including massive real-world individual trajectories is employed. Comparing with classical baseline models, our proposed TSA-SL method has certain superiority. Furthermore, we decompose traffic flow into different components in terms of traveling purposes and vehicle types. The experimental results show that our method performs better in predicting partial traffic flow than predicting all traffic flow.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The massive uncertain data generated by network applications has potential value, and it is of great significance to carry out clustering analysis of uncertain data. However, the uncertainty of data ...brings a serious challenge to traditional clustering algorithms. There are some problems in the existing clustering algorithms for uncertain data. (1) Some algorithms have a lot of meaningless distance calculations when calculating the distance of uncertain objects, and then the calculation complexity of algorithms increases. (2) The data model in clustering algorithms results in the loss of data distribution information, and then the accuracy of the algorithms decreases. In this paper, we propose an uncertain tuple density clustering (UTDC) algorithm for uncertain data. Firstly, we extract the data distribution features of uncertain instances and construct uncertain tuples by introducing the cloud model, which realizes the pruning of clustering objects. Secondly, we apply the EW distance to the traditional density clustering algorithm DBSCAN, which completes the density clustering for uncertain data. The experimental results show that comparing with UK-Means and FDBSCAN, UTDC algorithm effectively reduces the computational complexity and improves the accuracy.
Accurate short-term traffic flow prediction is an important basis of intelligent transportation systems (ITS) such as transportation operations and urban planning applications. However, due to the ...lack of complete directly measured data on urban traffic flow, existing studies cannot adequately mine the dynamic spatial-temporal correlations characterizing traffic flows in urban road networks. Electronic registration identification (ERI), which is an emerging technology for uniquely identifying a vehicle, can help collect the travel records of all vehicles. This inspires us to employ ERI big data for traffic flow prediction. In this paper, we propose a dynamic spatial-temporal feature optimization method with ERI big data for short-term traffic flow prediction based on a gradient–boosted regression tree, called DSTO-GBRT. Firstly, the framework of DSTO-GBRT is built. Secondly, we analyze the dynamic spatial-temporal correlations among the current prediction point and upstream correlative points using the Pearson correlation coefficient (PCC). Thirdly, to eliminate the linear correlations among features, we exploit principal component analysis (PCA) to optimize the original training data and obtain optimized training data. In the experiment, real-world ERI big data from Chongqing are employed for the proposed DSTO-GBRT method. Compared with ST-GBRT, ARIMA, DSTO-BPNN and DSTO-SVM, the results demonstrate that DSTO-GBRT can provide timely and adaptive prediction even in rush hour, when traffic conditions change rapidly. Furthermore, compared with DSO-GBRT and DTO-GBRT, the results show that the proposed DSTO-GBRT method is more accurate.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The simulation of pedestrian evacuation in emergency is of great significance to the public facilities design and emergency plan implementation. For study pedestrian behaviors under emergency, this ...paper first analyzes emergency and pedestrian characteristics. In order to describe the spread effect of emergency on pedestrian evacuation process, the concept of emergency diffusion field is put forward. In addition, the concept of panic coefficient is put forward in order to depict the effect of pedestrian panic psychology on the evacuation process. Then, the floor field model consisted of static floor field, dynamic floor field and emergency diffusion field is established, the corresponding updating rules are defined. Finally, the experiments results reproduce the pedestrian evacuation process under different conditions, and the influence factors in the process of pedestrian evacuation are analyzed, verify the validity of the model
•The concept of emergency diffusion field and panic coefficient are put forward.•The floor field model consisted of static floor field, dynamic floor field and emergency diffusion field is established.•The experimental results verify the validity of the model.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Deep understanding of residents’ travel patterns would provide helpful insights into the mechanisms of many socioeconomic phenomena. With the rapid development of location-aware computing ...technologies, researchers have easy access to large quantities of travel data. As an important data source, taxi trajectory data are featured by their high quality, good continuity and wide distribution, making it suitable for travel pattern mining. In this paper, we use taxi trajectory data to study spatial–temporal characterization of urban residents’ travel patterns from two aspects: attractive areas and hot paths. Firstly, a framework of trajectory preprocessing, including data cleaning and extracting the taxi passenger pick-up/drop-off points, is presented to reduce the noise and redundancy in raw trajectory data. Then, a grid density based clustering algorithm is proposed to discover travel attractive areas in different periods of a day. On this basis, we put forward a spatial–temporal trajectory clustering method to discover hot paths among travel attractive areas. Compared with previous algorithms, which only consider the spatial constraint between trajectories, temporal constraint is also considered in our method. Through the experiments, we discuss how to determine the optimal parameters of the two clustering algorithms and verify the effectiveness of the algorithms using real data. Furthermore, we analyze spatial–temporal characterization of Chongqing residents’ travel pattern.
•We use taxi GPS data to analyze spatial–temporal feature of travel patterns.•A grid clustering algorithm is proposed to discover travel attractive areas.•A method of trajectory clustering is used to discover urban hot paths.•Temporal factor is taken into consideration in spatial-temporal clustering.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
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