Deviation Point Curriculum Learning for Trajectory Outlier Detection in Cooperative Intelligent Transport Systems

Abstract

Cooperative Intelligent Transport Systems (C-ITS) are emerging in the field of transportation systems, which can be used to provide safety, sustainability, efficiency, communication and cooperation between vehicles, roadside units, and traffic command centres. With improved network structure and traffic mobility, a large amount of trajectory-based data is generated. Trajectory-based knowledge graphs help to give semantic and interconnection capabilities for intelligent transport systems. Prior works consider trajectory as the single point of deviation for the individual outliers. However, in real-world transportation systems, trajectory outliers can be seen in the groups, e.g., a group of vehicles that deviates from a single point based on the maintenance of streets in the vicinity of the intelligent transportation system. In this paper, we propose a trajectory deviation point embedding and deep clustering method for outlier detection. We first initiate network structure and nodes' neighbours to construct a structural embedding by preserving nodes relationships. We then implement a method to learn the latent representation of deviation points in road network structures. A hierarchy multilayer graph is designed with a biased random walk to generate a set of sequences. This sequence is implemented to tune the node embeddings. After that, embedding values of the node were averaged to get the trip embedding. Finally, LSTM-based pairwise classification method is initiated to cluster the embedding with similarity-based measures. The results obtained from the experiments indicate that the proposed learning trajectory embedding captured structural identity and increased F-measure by 5.06% and 2.4% while compared with generic Node2Vec and Struct2Vec methods.

IoT Workload Offloading Efficient Intelligent Transport System in Federated ACNN Integrated Cooperated Edge-Cloud Networks

“…However, this work focused on fixed services and scheduling decisions made at the time of application design. The cooperative vehicle and pedestrian-enabled ITS paradigms presented in these studies [2][3][4]. These studies suggested cooperative schemes in which vehicles can offload their workload and communicate with each other to avoid any collisions with pedestrians in smart cities.…”

“…IoT-based applications like traffic prediction, ticketing, and trip planning are widely utilized in public transport. In European countries, it is common for trams and buses to be ridden on the road, while the metro and train are ridden on distinct routes [3,4]. As a result, public transport providers collaborate to offer user-friendly services to passengers.…”

Semantic Trajectory Frequent Pattern Mining Method with Fuzzy Stay Time Constraint

“…(1) for all user U i in original trajectory sequence traj i (2) for all trajectory in U i (3) for all Sampling point C i in traj k (4) Record start time t si (5) Judge the area where C i is located loc i (6) if loc i ≠ loc i−1 (7) Record end time t ei (8) if…”

“…scan fuzzy semantic trajectory database FD (4) calculate the support P of each sequence (5) calculate the fuzzy stay time membership S(Δt) of each sequence accord to the time membership function (6) if P < σ and μ A j (Δt) < ρ (7) the sequence is less than the minimum support threshold and fuzzy stay time membership threshold (8) define the sequence as an infrequent itemset and delete it (9) create a new sequence database Q i and add frequent itemsets with length i to the database (10) for each frequent itemset T in the sequence database Q i (11) construct the projection database C w of frequent itemset T (12) get the frequent itemset T k of C w (13) T and T k are constructed as frequent sequences T i with length i (14) FTFP � FTFP ∪ T i (15) if projection database C w is not empty (16) repeat steps 8-12, i � i + 1 (17) else output frequent sequence FTFP (18) end for (19) end if (20) return FTFP ALGORITHM 2: FST-FPM algorithm.…”

“…e moving track of the target in the protection area can be obtained through RFID positioning technology [2,3]. It is helpful to analyze the target behavior pattern by analyzing the target trajectory and mining the abnormal trajectory [4,5]. Mining the frequent patterns of the moving track of the target is of a great signi cance for analyzing the target intention, identifying hazard sources, and identifying security vulnerabilities.…”