Framework for traffic event detection using Shapelet Transform

Al-Dhanhani, Ahmed; Damiani, Ernesto; Mizouni, Rabeb; Wang, Di

doi:10.1016/j.engappai.2019.04.002

Cited by 13 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are three standard and commonly used performance metrics in the literature; 50‐52 the DR, the FAR and the MTTD. The Detection Rate (DR) is the percentage of the correctly detected congestions, formally:

DR = No.

…”

Section: Traffic Congestion Detection (Tcd) Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Novel traffic congestion detection algorithms for smart city applications

Bawaneh

Simon

2022

Concurrency and Computation

View full text Add to dashboard Cite

Traffic congestion detection (TCD) techniques are becoming a critical component of traffic management systems. They can be considered a pre-step to address traffic jam problems, providing useful input for traffic management systems to predict and avoid traffic congestions' unwanted effects. In this research article, two novel real-time congestion detection algorithms are proposed, the TCD algorithm and the ensemble based traffic congestion detection (EB-TCD) algorithm. TCD can detect the congestion based on one traffic feature (i.e., speed, occupancy, or flow). TCD first cleans and preprocesses the traffic data. After, it computes the absolute of the first derivative of each sample in the traffic feature to determine its anomaly score. Then, the anomaly likelihood of each sample is computed to classify it as an anomaly or normal sample. On the other hand, EB-TCD utilizes the information contained in multiple traffic features in parallel by proposing an ensemble technique to combine the anomaly scores coming out from the different traffic features in one unified score. EB-TCD computes an anomaly score based on each traffic feature separately. Then, it votes for each sample in each traffic feature as an anomaly or not (from the corresponding traffic feature's point of view). After, the weight for each traffic feature is determined by studying the dissimilarity between their anomaly scores. These weights show how much each traffic feature explains the traffic behavior. Finally, the results are combined to form a unified anomaly score for each sample. The proposed statistical-based techniques are able to detect changes in traffic patterns while mitigating the effect of noise in traffic data. Moreover, the proposed algorithms' parameters were tuned and tailored for fast detection, which is a crucial feature for traffic management systems since fast detection accelerates decision-making. The computational complexity analysis has shown the simplicity of the proposed algorithms. Furthermore, the evaluation results have shown that the two algorithms outperform other widespread methods from the literature in terms of false alarm rate and detection time while keeping the detection rate at the same high level.

show abstract

DR = No.

…”

Section: Traffic Congestion Detection (Tcd) Algorithmmentioning

confidence: 99%

“…There are three standard and commonly used performance metrics in the literature; [50][51][52] the DR, the FAR and the MTTD.…”

Section: Traffic Congestion Detection Performance Metricsmentioning

confidence: 99%

Novel traffic congestion detection algorithms for smart city applications

Bawaneh

Simon

2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…); hockey—hit type Slade [ 45 ], Hughes and Franks [ 46 ] Automated detection of events via computer vision or device on athlete/equipment (i.e. ball or stick) Traffic event detection [ 47 ] Task Shot location - Angle/distance of goal face visible Pocock et al [ 48 ], Goldsberry [ 49 ] Player and ball tracking aligned with game logs Task Time in possession - Individual possession length - Length of possession chain - Team split of previous 10 mins Higham et al [ 50 ], Robertson et al [ 32 ] Player and ball tracking aligned with game logs Task/individual Shot trends: ‘hot hand fallacy’ - Team - Individual Skinner [ 51 ], Bar-Eli et al [ 52 ] Player and ball tracking aligned with game logs Individual Disposal efficiency - In game - History Pocock et al [ 48 ], Reich et al [ 53 ] Player and ball tracking aligned with game logs paired with analytics Task Available space - Physical pressure - No. of players between ball and goal - Ratio of attackers to defenders Rein et al [ 54 ], Alexander et al [ 55 ] Player and ball tracking paired with improved analytics Proximity sensor Emotional response in crowds [ 56 ] Task Kick distance Blair et al [ 57 , 58 ] Ball tracking Automated measurement through computer vision Automated detection of distances in cars [ 59 ] Individual/task Physical output - Game time played - Time between efforts - High speed metres Almonroeder et al [ 60 ], Sarmento et al [ 61 ] Player and ball tracking paired wit...…”

Section: Technologymentioning

confidence: 99%

Methodological Considerations for Furthering the Understanding of Constraints in Applied Sports

et al. 2021

View full text Add to dashboard Cite

Commonly classified as individual, task or environmental, constraints are boundaries which shape the emergence of functional movement solutions. In applied sport, an ongoing challenge is to improve the measurement, analysis and understanding of constraints to key stakeholders. Methodological considerations for furthering these pursuits should be centred around an interdisciplinary approach. This integration of methodology and knowledge from different disciplines also encourages the sharing of encompassing principles, concepts, methods and data to generate new solutions to existing problems. This narrative review discusses how a number of rapidly developing fields are positioned to help guide, support and progress an understanding of sport through constraints. It specifically focuses on examples from the fields of technology, analytics and perceptual science. It discusses how technology is generating large quantities of data which can improve our understanding of how constraints shape the movement solutions of performers in training and competition environments. Analytics can facilitate new insights from numerous and complex data through enhanced non-linear and multivariate analysis techniques. The role of the perceptual sciences is discussed with respect to generating outputs from analytics that are more interpretable for the end-user. Together, these three fields of technology, analytics and perceptual science may enable a more comprehensive understanding of constraints in sports performance.

show abstract

“…In fact there can be even an intensity associated to the event presence (a tra c jam has several degrees of severity, going from the slowing down of the tra c to the complete stop for extreme congestion/accidents [2,3]). Furthermore, an event has a space structure (the speed of the tra c ow is space-variant, and timevariant, there might be waves of car density).…”

Section: A Semantic DI Erencementioning

confidence: 99%

Selection of Information Streams in Social Sensing

Gianini

Mio

Viola

et al. 2020

Proceedings of the 12th International Conference on Management of Digital EcoSystems

View full text Add to dashboard Cite

In this work we address the problem of critical source selection in social sensing. We propose an approach to the ranking of information streams, which is aware of the interdependence among streams (redundancy and synergies), of the cost of individual streams, and of the cost related to the integration of multiple streams. The method is based on the use of the Coalitional Game Theory concept of Power Index, and relies on the polynomial-time estimate of the stream sets characteristics. With respect to other works using a power index, the method takes into account that the problem has a non-trivial cost structure.

show abstract

Framework for traffic event detection using Shapelet Transform

Cited by 13 publications

References 7 publications

Novel traffic congestion detection algorithms for smart city applications

Novel traffic congestion detection algorithms for smart city applications

Methodological Considerations for Furthering the Understanding of Constraints in Applied Sports

Selection of Information Streams in Social Sensing

Contact Info

Product

Resources

About