Combining Map-Based Inference and Crowd-Sensing for Detecting Traffic Regulators

Saremi, Fatemeh; Abdelzaher, Tarek

doi:10.1109/mass.2015.18

Cited by 14 publications

(24 citation statements)

References 11 publications

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“…It is still not clear whether a trained classifier could be applied across different cities or even countries and under which possible conditions, if any. Saremi and Abdelzahe [76] note that they are not convinced of generalising their promising cross-country results and suggest further experimentation on that topic. The second future research direction regards the regulator classification performance under highly diverse regulator categories.…”

Section: Future Directionsmentioning

confidence: 95%

“…Obviously, even using open data sources like OSM, unless details are given regarding the data acquisition, one cannot get access to the exact dataset that other studies had previously used. In one case [76] where data was used from both sources, features were derived from OSM and combined in classifier's feature vector along with features computed from own collected data. In another case [52], open data were used only for testing algorithms but without verifying their results, most probably due to ground truth map unavailability.…”

Section: Dataset Typementioning

confidence: 99%

“…In both studies [52,73], testing for cross-city applicability is not explicitly declared. Only Saremi et al [76] provide special analysis for this aspect of the problem. They trained a random forest classifier in city A and tested it in a city B (A and B belong to the same state).…”

Section: Cross-city Testingmentioning

confidence: 99%

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

Zourlidou¹,

Sester²

2019

IJGI

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Mapping with surveying equipment is a time-consuming and cost-intensive procedure that makes the frequent map updating unaffordable. In the last few years, much research has focused on eliminating such problems by counting on crowdsourced data, such as GPS traces. An important source 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) and this article is motivated by this fact. The topic of this systematic literature review (SLR) is the detection and recognition of traffic regulators such as traffic lights (signals), stop-, yield-, priority-signs, right of way priority rules and turning restrictions at intersections, by leveraging non imagery crowdsourced data. More particularly, the aim of this study is (1) to identify the range of detected and recognised regulatory types by crowdsensing means, (2) to indicate the different classification techniques that can 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 of peer-reviewed literature were used to locate relevant research studies and after different screening steps eleven articles were selected for review. Two major findings were concluded—(a) most regulator types can be identified with over 80% accuracy, even using heuristic-driven approaches and (b) under the current progress on the field, no study can be reproduced for comparative purposes nor can solely rely on open data sources due to lack of publicly available datasets and ground truth maps. Future research directions are highlighted as possible extensions of the reviewed studies.

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Section: Future Directionsmentioning

confidence: 95%

Section: Dataset Typementioning

confidence: 99%

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

Zourlidou¹,

Sester²

2019

IJGI

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“…They report accuracy of 90%. Moreover, Saremi and Abdelzaher (2015) exploit map-based features derived from OSM, such as the speed rating of road segments, distance of one intersection to the next closest one, end-to-end distance, and category of street segments such as motorway, trunk, primary, secondary, tertiary, motorway link, primary link, unclassified, road, residential, or service. They use a Random Forest classifier to predict traffic regulators, examining two different feature-vector settings: using only map-based features and a combination of map-based features with trace-derived attributes (number of stops, traverse speed and stop duration).…”

Section: Related Workmentioning

confidence: 99%

Traffic Regulator Detection Using GPS Trajectories

Golze

Zourlidou

Sester

2020

KN J. Cartogr. Geogr. Inf.

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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.

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“…Such intersectionrelated features can be derived from open maps, e.g., OSM. The study by Saremi and Abdelzaher is unique in this regard [33]. They export features such as speed rating of road segments, distance of one junction to the closest one, end-to-end distance of the road that a junction belongs to, semi distances of a junction to the two ends of the road that belongs to and category of the street segments.…”

Section: Related Workmentioning

confidence: 99%

Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach

Cheng¹,

Zourlidou²,

Sester³

2020

IJGI

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Accurate information of traffic regulators at junctions is important for navigating and driving in cities. However, such information is often missing, incomplete or not up-to-date in digital maps due to the high cost, e.g., time and money, for data acquisition and updating. In this study we propose a crowdsourced method that harnesses the light-weight GPS tracks from commuting vehicles as Volunteered Geographic Information (VGI) for traffic regulator detection. We explore the novel idea of detecting traffic regulators by learning the movement patterns of vehicles at regulated locations. Vehicles’ movement behavior was encoded in the form of speed-profiles, where both speed values and their sequential order during movement development were used as features in a three-class classification problem for the most common traffic regulators: traffic-lights, priority-signs and uncontrolled junctions. The method provides an average weighting function and a majority voting scheme to tolerate the errors in the VGI data. The sequence-to-sequence framework requires no extra overhead for data processing, which makes the method applicable for real-world traffic regulator detection tasks. The results showed that the deep-learning classifier Conditional Variational Autoencoder can predict regulators with 90% accuracy, outperforming a random forest classifier (88% accuracy) that uses the summarized statistics of movement as features. In our future work images and augmentation techniques can be leveraged to generalize the method’s ability for classifying a greater variety of traffic regulator classes.

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Combining Map-Based Inference and Crowd-Sensing for Detecting Traffic Regulators

Cited by 14 publications

References 11 publications

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

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

Traffic Regulator Detection Using GPS Trajectories

Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach

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