Traffic Regulator Detection Using GPS Trajectories

This paper explores the idea of enriching maps with features predicted from GPS trajectories. More specifically, it proposes a method of classifying street intersections according to traffic regulators (traffic light, yield/priority-sign and right-of-way rule). Intersections are regulated locations and the observable movement of vehicles is affected by the underlying traffic rules. Movement patterns such as stop events or start-and-stop sequences are commonly observed at those locations due to traffic regulations. In this work, we test the idea of detecting traffic regulators by learning them in a supervised way from features derived from GPS trajectories. We explore and assess different settings of the feature vector being used to train a classifier that categorizes the intersections based on traffic regulators; also, we test several experimental setups. The results show that a Random Forest classifier with oversampling and Bagging booster enabled can predict the intersection regulators with 90.4% accuracy. We discuss future research directions and recommend next steps for improving the results of this research. © 2020, The Author(s)

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.)

Traffic Regulator Detection and Identification from Crowdsourced Data—A Systematic Literature Review

Mapping with surveying equipment is a time-consuming and cost-intensive procedure thatmakes the frequent map updating unaffordable. In the last few years, much research has focused oneliminating such problems by counting on crowdsourced data, such as GPS traces. An importantsource of information in maps, especially under the consideration of forthcoming self-driving vehicles,is the traffic regulators. This information is largely lacking in maps like OpenstreetMap (OSM) andthis article is motivated by this fact. The topic of this systematic literature review (SLR) is the detectionand recognition of traffic regulators such as traffic lights (signals), stop-, yield-, priority-signs, right ofway priority rules and turning restrictions at intersections, by leveraging non imagery crowdsourceddata. More particularly, the aim of this study is (1) to identify the range of detected and recognisedregulatory types bycrowdsensingmeans, (2) to indicate the different classification techniques thatcan be used for these two tasks, (3) to assess the performance of different methods, as well as (4)to identify important aspects of the applicability of these methods. The two largest databases ofpeer-reviewed literature were used to locate relevant research studies and after different screeningsteps eleven articles were selected for review. Two major findings were concluded—(a) most regulatortypes can be identified with over 80% accuracy, even using heuristic-driven approaches and (b) underthe current progress on the field, no study can be reproduced for comparative purposes nor can solelyrely on open data sources due to lack of publicly available datasets and ground truth maps. Futureresearch directions are highlighted as possible extensions of the reviewed studies

Recognition of Intersection Traffic Regulations from Crowdsourced Data

In this paper, a new method is proposed to detect traffic regulations at intersections using GPS traces. The knowledge of traffic rules for regulated locations can help various location-based applications in the context of Smart Cities, such as the accurate estimation of travel time and fuel consumption from a starting point to a destination. Traffic regulations as map features, however, are surprisingly still largely absent from maps, although they do affect traffic flow which, in turn, affects vehicle idling time at intersections, fuel consumption, CO2 emissions, and arrival time. In addition, mapping them using surveying equipment is costly and any update process has severe time constraints. This fact is precisely the motivation for this study. Therefore, its objective is to propose an automatic, fast, scalable, and inexpensive way to identify the type of intersection control (e.g., traffic lights, stop signs). A new method based on summarizing the collective behavior of vehicle crossing intersections is proposed. A modification of a well-known clustering algorithm is used to detect stopping and deceleration episodes. These episodes are then used to categorize vehicle crossing of intersections into four possible traffic categories (p1: free flow, p2: deceleration without stopping events, p3: only one stopping event, p4: more than one stopping event). The percentages of crossings of each class per intersection arm, together with other speed/stop/deceleration features, extracted from trajectories, are then used as features to classify the intersection arms according to their traffic control type (dynamic model). The classification results of the dynamic model are compared with those of the static model, where the classification features are extracted from OpenStreetMap. Finally, a hybrid model is also tested, where a combination of dynamic and static features is used, which outperforms the other two models. For each of the three models, two variants of the feature vector are tested: one where only features associated with a single intersection arm are used (one-arm model) and another where features also from neighboring intersection arms of the same intersection are used to classify an arm (all-arm model). The methodology was tested on three datasets and the results show that all-arm models perform better than single-arm models with an accuracy of 95% to 97%