scholarly journals Kalman Filter Applications for Traffic Management

Kalman Filter ◽  
10.5772/9583 ◽  
2010 ◽  
Author(s):  
Constantinos Antoniou ◽  
Moshe Ben-Akiva ◽  
Haris N.
Keyword(s):  
Kalman Filter ◽  
Traffic Management

scholarly journals Detection and Tracking of Pedestrians Using Doppler LiDAR

2021 ◽  
Vol 13 (15) ◽  
pp. 2952
Author(s):  
Xiaoyi Peng ◽  
Jie Shan
Keyword(s):  
Kalman Filter ◽  
San Francisco ◽  
Autonomous Vehicles ◽  
Traffic Management ◽  
Pedestrian Detection ◽  
Supervised Machine Learning ◽  
Data Sets ◽  
Doppler Lidar ◽  
Tracking Accuracy ◽  
Detection And Tracking

Pedestrian detection and tracking is necessary for autonomous vehicles and traffic management. This paper presents a novel solution to pedestrian detection and tracking for urban scenarios based on Doppler LiDAR that records both the position and velocity of the targets. The workflow consists of two stages. In the detection stage, the input point cloud is first segmented to form clusters, frame by frame. A subsequent multiple pedestrian separation process is introduced to further segment pedestrians close to each other. While a simple speed classifier is capable of extracting most of the moving pedestrians, a supervised machine learning-based classifier is adopted to detect pedestrians with insignificant radial velocity. In the tracking stage, the pedestrian’s state is estimated by a Kalman filter, which uses the speed information to estimate the pedestrian’s dynamics. Based on the similarity between the predicted and detected states of pedestrians, a greedy algorithm is adopted to associate the trajectories with the detection results. The presented detection and tracking methods are tested on two data sets collected in San Francisco, California by a mobile Doppler LiDAR system. The results of the pedestrian detection demonstrate that the proposed two-step classifier can improve the detection performance, particularly for detecting pedestrians far from the sensor. For both data sets, the use of Doppler speed information improves the F1-score and the recall by 15% to 20%. The subsequent tracking from the Kalman filter can achieve 83.9–55.3% for the multiple object tracking accuracy (MOTA), where the contribution of the speed measurements is secondary and insignificant.

scholarly journals Assignment Matrix Free Algorithms for On-line Estimation of Dynamic Origin-Destination Matrices

2021 ◽  
Vol 2 ◽  
Author(s):  
Marisdea Castiglione ◽  
Guido Cantelmo ◽  
Moeid Qurashi ◽  
Marialisa Nigro ◽  
Constantinos Antoniou
Keyword(s):  
Kalman Filter ◽  
Traffic Management ◽  
Dynamic Traffic Assignment ◽  
Principal Component ◽  
Heterogeneous Data ◽  
Research Field ◽  
Linear Functions ◽  
Data Sources ◽  
Traffic Data ◽  
Non Linear

Dynamic Traffic Assignment (DTA) models represent fundamental tools to forecast traffic flows on road networks, assessing the effects of traffic management and transport policies. As biased models lead to incorrect predictions, which can cause inaccurate evaluations and huge social costs, the calibration of DTA models is an established and active research field. When it comes to estimating Origin-Destination (OD) demand flows, perhaps the most important input for DTA models, one algorithm suggested to outperform all the others for real-time applications: the Kalman Filter (KF). This paper introduces a non-linear Kalman Filter framework for online dynamic OD estimation that reduces the number of variables and can easily incorporate heterogeneous data sources to better explain the non-linear relationship between traffic data and time-dependent OD-flows. Specifically, we propose a model that takes advantage of Principal Component Analysis (PCA) to capture spatial correlations between variables and better exploit the local nature of a specific KF recently proposed in literature, the Local Ensemble Transformed Kalman filter (LETKF). The main advantage of the LETKF is that the Kalman gain is not explicitly formulated which means that, differently from other approaches proposed in the literature, there is no need to compute the assignment matrix or its approximation. The paper shows that the LETKF can easily incorporate different data sources, such as traffic counts and link speeds. Additionally, thanks to the PCA, the model can identify local patterns within the data and better explain the correlation between variables and data. The effectiveness of the proposed methodology is demonstrated first through synthetic experiments where non-linear functions are used to benchmark the model in different conditions and then on the real-world network of Vitoria, Spain (2,884 nodes, 5,799 links) using the mesoscopic simulator Aimsun. Results show that the proposed method leads to better state estimation performances with respect to other Ensemble-based Kalman filters, providing improvements as high as 64% in terms of traffic data reproduction with a 17-fold problem dimensionality reduction.

scholarly journals Perancangan Sistem Pintar Prediksi Trajektori Pesawat Menggunakan Data ADS-B dengan Metode Kalman Filter untuk Mencegah Collision

WARTA ARDHIA ◽  
2017 ◽  
Vol 43 (2) ◽  
pp. 79
Author(s):  
Andri Bharata
Keyword(s):  
Kalman Filter ◽  
Traffic Management ◽  
Air Transportation ◽  
Air Traffic Management ◽  
Near Miss ◽  
Transportation Industry ◽  
Smart System ◽  
Air Space ◽  
Key Factor ◽  
Positional Data

Safety is the main aspect that the most concerned in the air transportation industry. Two of the elements that play important role in order to maintain aviation safety are the ability of ATC personnel in navigating the aircrafts over the Indonesia’s air space and the availability of air navigation facilities so that an effective and safe air traffic management can be achieved. The ability of ATC is absolutely a key factor in aviation business. The competent one, not only required to have decent knowledge and able to navigate and guide the aircrafts, but also have to be supported by adequate navigation system and facilities. As for enhancing the level of safety, smart system is offered as a tool to aid the ATC in making a decision to prevent the collision in the air. Automatic Dependent Surveillance Broadcast (ADS-B) is one of the air navigation instruments which have high accuracy in surveilling the aircrafts movement. The information that are retrieved from ADS-B, such as positional data (latitude, longitude and latitude) and speed, can assist the ATC to analyze the level of safety as well as the level of density of aircrafts in certain area. Dalam mendukung keselamatan penerbangan, ATC yang bertugas di darat, belum dilengkapi sebuah sistem peringatan bahaya seperti halnya TCAS yang digunakan di pesawat. Sama halnya seperti TCAS, sistem pintar ini akan memberi peringatan dini untuk mencegah terjadinya tabrakan. Selain itu, sistem ini akan memberi peringatan berdasarkan hasil prediksi data live pesawat, bukan ketika sudah pada posisi kemungkinan tabrakan seperti halnya TCAS. Sistem pintar prediksi trajektori merupakan sebuah sistem yang berfungsi sebagai sistem bantuan yang dapat digunakan oleh ATC ketika sedang mengawasi lalu lintas udara. Sistem pintar ini memprediksi posisi pesawat selama beberapa waktu ke depan. Dengan hasil prediksi posisi oleh sistem ini, akan diberikan early warning kepada ATC, jika prediksi posisi dua pesawat atau lebih memungkinkan untuk terjadi tabrakan (collision) atau near miss. Dengan adanya peringatan prediksi, ATC dapat mencegah kemungkinan tabrakan dengan lebih cepat. Sistem pintar menggunakan data ADS-B, yang merupakan salah satu fasilitas navigasi penerbangan karena memiliki tingkat akurasi data yang tinggi. Data ADS-B tersebut, diolah menggunakan metode Kalman Filter untuk menghasilkan prediksi trajektori dengan tingkat error yang kecil. Kalman filter sendiri banyak digunakan untuk mengolah dan memprediksi data-data pergerakan yang linear, seperti pergerakan pesawat, pergerakan manusia, pergerakan angin dan yang lainnya.

scholarly journals Real-time vehicle detection and tracking using improved histogram of gradient features and Kalman filters

2018 ◽  
Vol 15 (1) ◽  
pp. 172988141774994 ◽  
Author(s):  
Xinyu Zhang ◽  
Hongbo Gao ◽  
Chong Xue ◽  
Jianhui Zhao ◽  
Yuchao Liu
Keyword(s):  
Kalman Filter ◽  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
Vehicle Detection ◽  
Transportation Systems ◽  
Driver Assistance Systems ◽  
Filter Model ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Subsequent Motion

Intelligent transportation systems and safety driver-assistance systems are important research topics in the field of transportation and traffic management. This study investigates the key problems in front vehicle detection and tracking based on computer vision. A video of a driven vehicle on an urban structured road is used to predict the subsequent motion of the front vehicle. This study provides the following contributions. (1) A new adaptive threshold segmentation algorithm is presented in the image preprocessing phase. This algorithm is resistant to interference from complex environments. (2) Symmetric computation based on a traditional histogram of gradient (HOG) feature vector is added in the vehicle detection phase. Symmetric HOG feature with AdaBoost classification improves the detection rate of the target vehicle. (3) A motion model based on adaptive Kalman filter is established. Experiments show that the prediction of Kalman filter model provides a reliable region for eliminating the interference of shadows and sharply decreasing the missed rate.

Safety Culture and Safety-Relevant Behavior in Air Traffic Management

2015 ◽  
Vol 5 (1) ◽  
pp. 3-17 ◽  
Author(s):  
Michaela Schwarz ◽  
K. Wolfgang Kallus
Keyword(s):  
Safety Culture ◽  
Traffic Management ◽  
Service Providers ◽  
Five Factor Model ◽  
Principal Component ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Psychometric Validation ◽  
Safety Attitudes ◽  
Culture Development

Since 2010, air navigation service providers have been mandated to implement a positive and proactive safety culture based on shared beliefs, assumptions, and values regarding safety. This mandate raised the need to develop and validate a concept and tools to assess the level of safety culture in organizations. An initial set of 40 safety culture questions based on eight themes underwent psychometric validation. Principal component analysis was applied to data from 282 air traffic management staff, producing a five-factor model of informed culture, reporting and learning culture, just culture, and flexible culture, as well as management’s safety attitudes. This five-factor solution was validated across two different occupational groups and assessment dates (construct validity). Criterion validity was partly achieved by predicting safety-relevant behavior on the job through three out of five safety culture scores. Results indicated a nonlinear relationship with safety culture scales. Overall the proposed concept proved reliable and valid with respect to safety culture development, providing a robust foundation for managers, safety experts, and operational and safety researchers to measure and further improve the level of safety culture within the air traffic management context.

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