Extracting Stops from Spatio-Temporal Trajectories within Dynamic Contextual Features

Wu, Tao; Shen, Huiqing; Qin, Jianxin; Xiang, Longgang

doi:10.3390/su13020690

Cited by 8 publications

(3 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…), we can detect locations that are interesting to the observed object, given that it stayed there for a relatively long time. The problem of partitioning trajectories into sequences of stops and moves is a well-studied topic [31] and there are many different algorithms that provide solutions (e.g., [32], [33], [34]).…”

Section: Methodsmentioning

confidence: 99%

Recognition of Intersection Traffic Regulations From Crowdsourced Data

Zourlidou¹,

Sester²,

Hu³

2022

Preprint

View full text Add to dashboard Cite

In this paper, a new method is proposed to detect traffic regulations at intersections using GPS traces. The knowledge of traffic rules of 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, CO_2 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 vehicles crossing intersections is proposed. A modification of a well-known clustering algorithm for detecting stopping and decelerating events is presented. These detected events 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 junction arm, together with other speed/stop/deceleration features, extracted from trajectories, are then used as features to classify the junction 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 junction arm are used (one-arm model) and another where features also from neighbouring junction arms of the same junction 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 94\% to 97\%.

show abstract

Section: Methodsmentioning

confidence: 99%

Recognition of Intersection Traffic Regulations From Crowdsourced Data

Zourlidou¹,

Sester²,

Hu³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…), we can detect locations that are interesting to the observed object, given that it stayed there for a relatively long time. The problem of partitioning trajectories into sequences of stops and moves is a well-studied topic [40] and there are many different algorithms that provide solutions (e.g., [41][42][43]).…”

Section: Clustering-based Stop and Deceleration Event Detection In Tr...mentioning

confidence: 99%

Recognition of Intersection Traffic Regulations from Crowdsourced Data

Zourlidou

Sester

2022

IJGI

View full text Add to dashboard Cite

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%.

show abstract

“…Furthermore, we developed prototype equipment for experiments. The prototype uploads the observation results of roughness and the Spatiotemporal trajectory information [20] to the city service center via IoT. Based on the Web-GIS service, a map of urban potholes with the high spatial and temporal resolution is easily generated.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Road Pothole Mapping Based on Vibration Analysis in Smart City

Dong

Chen

Zhang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Vehicle-road collaboration is inseparable for smart cities, and the potholes detection is an essential direction in vehicleroad collaboration. With the development of artificial surface observation technology and surveying technology, road potholes detection accuracy has improved in recent years. However, the legacy detection methods do not have the ease of service and realtime observation capability, and thus the potholes in the road cannot be mapped in time. To solve this key issue, we proposed a reflectometry method to realize real-time potholes observation with vibration signals analysis and Spatio-temporal trajectory fusion. We further developed several prototype devices for validation. These prototype devices measure the acceleration signal mounted on the wheel steering lever and implement edge signal processing and Spatio-temporal information fusion on the prototype. Observation results and Spatio-temporal information are been rapidly transmitted to the sensing server via NB-IoT. Results and analyses demonstrated that this method successfully enabled the potholes observation in real-time through light and a rapidly deployable platform that relies on repeated trajectory data from the vehicle. Results also demonstrated that this method was not limited by vehicle type, speed, or engine operating condition. In the road experiment, the proposed method provided stable, efficient, and real-time potholes observation results. Compared with traditional methods, the method reduces costs and improves sensing efficiency based on Spatio-temporal trajectory fusion, and potholes information can be prompted in real-time. This innovation provides a strategic exploration and thinking to deal with road potholes in real-time sensing.

show abstract

Extracting Stops from Spatio-Temporal Trajectories within Dynamic Contextual Features

Cited by 8 publications

References 56 publications

Recognition of Intersection Traffic Regulations From Crowdsourced Data

Recognition of Intersection Traffic Regulations From Crowdsourced Data

Recognition of Intersection Traffic Regulations from Crowdsourced Data

Real-Time Road Pothole Mapping Based on Vibration Analysis in Smart City

Contact Info

Product

Resources

About