Trajectory analysis at intersections for traffic rule identification

In this paper, we focus on trajectories at intersections regulated by various regulation types such as traffic lights, priority/yield signs, and right-of-way rules. We test some methods to detect and recognize movement patterns from GPS trajectories, in terms of their geometrical and spatio-temporal components. In particular, we first find out the main paths that vehicles follow at such locations. We then investigate the way that vehicles follow these geometric paths (how do they move along them). For these scopes, machine learning methods are used and the performance of some known methods for trajectory similarity measurement (DTW, Hausdorff, and Fréchet distance) and clustering (Affinity propagation and Agglomerative clustering) are compared based on clustering accuracy. Afterward, the movement behavior observed at six different intersections is analyzed by identifying certain movement patterns in the speed- and time-profiles of trajectories. We show that depending on the regulation type, different movement patterns are observed at intersections. This finding can be useful for intersection categorization according to traffic regulations. The practicality of automatically identifying traffic rules from GPS tracks is the enrichment of modern maps with additional navigation-related information (traffic signs, traffic lights, etc.)

Placement and Movement Episodes Detection using Mobile Trajectories Data

Teostatud töö eesmärgiks on tuvastada asukohaandmetest seisu- ning liikumisepisoode kasutades selleks trajektoori ülekattuvusmaatriksit. Antud töös kasutatud andmed on väga hajusad nii ajalises kui ka geograafilises mõttes. Seetõttu on antud ülesanne suur väljakutse. Välja pakutud lahenduse raames teostati andmeanalüüs mille raames tuvastati kasutajatele tähtsad asukohad ning pakuti välja algoritm, mille abil tuvastda seisu- ning liikumisepisoodid. Andmete analüüsimiseks ning visualiseerimiseks kasutati R-i.This thesis presents a trajectory episode matrix to enable the detection of placement and movement episodes from mobile location data. The data used in this work is very sparse in time and space. Therefore, the estimation of user’s placement and movement patterns poses a big challenge. The presented approach performs data analysis to find meaningful locations and introduces an algorithm to detect movement and placement episodes. To perform the analysis and visualize the results a statistical analysis tool was developed with R. The work done as a result of this thesis can be used to improve the identification of the meaningful locations and to help predicting the semantic meanings of mobile user’s patterns

Survey of maps of dynamics for mobile robots

Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area

Towards Efficient and Secure Intelligent Transportation Services: AI-driven Traffic Light Controller and Privacy-Preserving Mobility Data Generation

The widespread adoption of artificial intelligence (AI) and Intelligent Transportation Systems (ITS) technologies has led to the increasing application of AI-based ITS controllers, with the Traffic Signal Controller (TSC) being a prominent example. Reinforcement learning (RL) models have shown promising results for adaptively adjusting traffic light schedules in urban environments through RL-based TSCs (RL-TSCs). The real-world deployment of RL-TSCs involves three key aspects: performance, security, and data privacy. In terms of performance, RL-TSC models need to be designed with consideration for various metrics, such as fair traffic scheduling and air quality impact. To address this, our approach takes into account a multi-objective constrained learning formulation to optimize performance. However, the use of RL-TSCs for automation, by leveraging external inputs, introduces security concerns that require active research to mitigate. We address these security challenges by introducing an innovative defense mechanism. Additionally, the training of RL-TSCs relies on real-world mobility datasets, necessitating the protection of data privacy at different levels of granularity. To minimize the constraints associated with limited real data availability or privacy concerns, we introduce two distinct directions: synthetic trajectory data generation using recent generative AI methods, and location privacy models for raw mobility datasets based on differential privacy, which safeguard individual trajectories and aggregated mobility datasets.This research provides a valuable tool for evaluating the practical deployment of RL-TSCs, particularly in real-world settings where the last mile of implementation and security is paramount. By addressing the key challenges of performance, security, and data privacy, this work aims to facilitate the successful real-world deployment of AI-powered ITS controllers

Examining the Active Transportation - Built Environment Relationship in London, Ontario

Research on the relationship between the built environment and active transportation has accelerated and expanded over the past 20 years. This growth is in large part due to continuing evidence of rising rates in obesity and Type-2 diabetes that coincides with decreasing rates of physical activity across all ages in the post-industrial world. Walking more is a simple solution to increasing rates of physical activity. While for most people walking is possible throughout the day, there has been a decrease in the use of walking as a means of transportation. This study examines environmental determinants of active transportation from two perspectives: 1) working adults and 2) elementary school children. It adopts multiple methodologies for identifying travel corridors in geographic information systems (GIS) analysis and tests a novel technique by applying a hexagonal grid to extract built environment measures. Results from this research suggest global positioning system (GPS) tracking is a viable method to capture built environment measures, especially for children. As in previous studies, this study found distance between origin and destination to be the most important determinant to active travel with socio-economic status also playing a key role for adults and children. Results from this research are concurrent with previous literature while employing hexagons as a geographic unit. Examining the active transportation/built environment relationship through the use of GPS and a hexagonal areal unit is a new approach that deserves serious consideration for further research