Traffic Regulation Recognition using Crowd-Sensed GPS and Map Data: a Hybrid Approach

Zourlidou, Stefania; Golze, Jens; Sester, Monika

doi:10.5194/agile-giss-3-22-2022

Cited by 3 publications

(5 citation statements)

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“…The Chicago groundtruth map is available here [34]. The Hanover dataset (trajectories and groundtruth traffic regulations) is available here [31,32]. Moreover, the Champaign dataset has an average sampling rate of 1 Hz, Hanover of 0.59 Hz (1 sample every 1.7 s), and Chicago has, on average, 0.28 Hz.…”

Section: Datasets For Testing the Proposed Methodsmentioning

confidence: 99%

“…Most open trajectory datasets have a low sampling rate (e.g., 1 sample every 15 s or per minute) and cannot be used to extract features, such as stopping or deceleration events, because between two GPS samples taken e.g., every 15 s, one or more stopping/deceleration events could occur and would not be detected. Therefore, having to deal with these challenges of the datasets, we were able to access three suitable datasets in total: one recorded by the first author of this article and now available as open dataset [31,32], one shared by the first author of the journal article [24], and one open trajectory dataset [33] for which the groundtruth map had to be manually conducted by the first author of this article and is now available as an open dataset too [34]. The three datasets are described in the next section.…”

Section: Dataset Requirements and Limitationsmentioning

confidence: 99%

“…The Chicago trajectory dataset, as already mentioned, is publicly available in [33]. The Hannover dataset (trajectories and groundtruth regulations) is also available in [31,32].…”

Section: Data Availability Statementmentioning

confidence: 99%

See 2 more Smart Citations

Recognition of Intersection Traffic Regulations from Crowdsourced Data

Zourlidou

Sester

2022

IJGI

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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%.

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Section: Datasets For Testing the Proposed Methodsmentioning

confidence: 99%

Section: Dataset Requirements and Limitationsmentioning

confidence: 99%

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Recognition of Intersection Traffic Regulations from Crowdsourced Data

Zourlidou

Sester

2022

IJGI

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“…Congestion is a problem that is highly related to traffic lights, thus, many recent papers try to model the transportation network and observe the effect of congestion (Ben-Dor et al, 2022). There is also existing research on traffic control, which deals with traffic control recognition at junctions (traffic lights, stop, priority, and right of way rules) using crowd-sensed GPS data (vehicle trajectories), as well as features extracted from OpenStreetMap (Zourlidou et al, 2022).…”

Section: Related Workmentioning

confidence: 99%

The Impact of Traffic Lights on Modal Split and Route Choice: A use-case in Vienna

Gogousou,

Canestrini,

Alinaghi

et al. 2024

AGILE GIScience Ser.

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Abstract. The transportation dynamics within a European city, Vienna, are examined using a multi-graph representation of the city’s network. The focus is on time-optimized routing algorithms and the effects of altering the average waiting penalty at traffic lights. The impact of these modifications, whether an increase to 60, 90, or even 150 seconds or a decrease to 10 seconds, is observed in the selection of transportation modes and routes for identical origin and destination pairs. The investigation also extends to whether routes shift towards secondary street networks to avoid traffic lights as the waiting penalty increases. Experimental variations in average waiting time for cars aim to uncover detailed effects on transportation mode choices, route length and time changes, and variations in human energy expenditure. These findings could provide valuable insights into the transportation network and its possibilities and help in urban planning and policy development. The results indicate a shift in transportation mode as the waiting penalty for cars at traffic lights increases, and in some instances, routes are redirected to roads of lower importance such as residential or service roads.

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“…This can be, for example, the detection of changes in road networks or general road network updates (Gao et al, 2021;Tang et al, 2019). Beyond that, the detection and classification of road intersection regulator rules (Zourlidou et al, 2022a;Golze et al, 2020). Additional types of information can be, for example, the detection of road roughness (Hiremath et al, 2021;Wage and Sester, 2021) or the determination of the traffic flow (Tu et al, 2021;Li et al, 2021) or the quantification of traffic flow delays caused by traffic accidents (Golze et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Exploring User Semantic Annotation from Trajectories in the Scenario of Shared Locations

Golze,

Sester,

Feuerhake

et al. 2024

AGILE GIScience Ser.

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Abstract. Over the past decade(s), collecting spatiotemporal data has become easier due to technological advancements and more user-friendly collection processes. Additionally, government agencies, companies, and open data projects have made general environmental data, such as points of interest or land use coverage, more freely available. Scientific studies can combine this spatiotemporal and non-spatial data to analyze different types of human mobility data. The results of these studies are relevant to transportation and urban planning, as similar information is typically collected by means of surveys. However, deriving relevant information from Global Navigation Satellite System (GNSS) trajectories remains challenging due to inaccuracies in the positioning and the unavailability of groundtruth information regarding individual user location semantics (e.g. home place, work place, leisure place or others). This work presents a semantic location annotation approach based on a Hidden Markov Model and the Viterbi optimization algorithm. The model includes location emissions to account for the general usage of a particular location. The annotations are applied to the clustered stop points that identify regions of special interest to individual users in a trajectory data set. The proposed approach demonstrates that the adapted Viterbi optimization can assign the most probable and meaningful semantic labels to the user’s sequences and provides insights on the underlying regions of special interest.

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Traffic Regulation Recognition using Crowd-Sensed GPS and Map Data: a Hybrid Approach

Cited by 3 publications

References 0 publications

Recognition of Intersection Traffic Regulations from Crowdsourced Data

Recognition of Intersection Traffic Regulations from Crowdsourced Data

The Impact of Traffic Lights on Modal Split and Route Choice: A use-case in Vienna

Exploring User Semantic Annotation from Trajectories in the Scenario of Shared Locations

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