Forest path condition monitoring based on crowd-based trajectory data analysis

2020 ◽  
pp. 1-18
Author(s):  
Francisco Arcas-Tunez ◽  
Fernando Terroso-Saenz
Keyword(s):  
Information Acquisition ◽  
Road Network ◽  
Road Networks ◽  
Trajectory Data ◽  
Country Road ◽  
Wide Range ◽  
Sport Activities ◽  
Path Condition ◽  
Spatio Temporal ◽  
The City

The development of Road Information Acquisition Systems (RIASs) based on the Mobile Crowdsensing (MCS) paradigm has been widely studied for the last years. In that sense, most of the existing MCS-based RIASs focus on urban road networks and assume a car-based scenario. However, there exist a scarcity of approaches that pay attention to rural and country road networks. In that sense, forest paths are used for a wide range of recreational and sport activities by many different people and they can be also affected by different problems or obstacles blocking them. As a result, this work introduces SAMARITAN, a framework for rural-road network monitoring based on MCS. SAMARITAN analyzes the spatio-temporal trajectories from cyclists extracted from the fitness application Strava so as to uncover potential obstacles in a target road network. The framework has been evaluated in a real-world network of forest paths in the city of Cieza (Spain) showing quite promising results.

scholarly journals RealTE: Real-time Trajectory Estimation Over Large-Scale Road Networks

2022 ◽  
Author(s):  
Qibin Zhou ◽  
Qingang Su ◽  
Dingyu Yang
Keyword(s):  
Travel Time ◽  
Real Time ◽  
High Speed ◽  
Road Network ◽  
Large Scale ◽  
Road Networks ◽  
Trajectory Data ◽  
Real Time Traffic ◽  
Histogram Estimation ◽  
Traffic Estimation

Real-time traffic estimation focuses on predicting the travel time of one travel path, which is capable of helping drivers selecting an appropriate or favor path. Statistical analysis or neural network approaches have been explored to predict the travel time on a massive volume of traffic data. These methods need to be updated when the traffic varies frequently, which incurs tremendous overhead. We build a system RealTER⁢e⁢a⁢l⁢T⁢E, implemented on a popular and open source streaming system StormS⁢t⁢o⁢r⁢m to quickly deal with high speed trajectory data. In RealTER⁢e⁢a⁢l⁢T⁢E, we propose a locality-sensitive partition and deployment algorithm for a large road network. A histogram estimation approach is adopted to predict the traffic. This approach is general and able to be incremental updated in parallel. Extensive experiments are conducted on six real road networks and the results illustrate RealTE achieves higher throughput and lower prediction error than existing methods. The runtime of a traffic estimation is less than 11 seconds over a large road network and it takes only 619619 microseconds for model updates.

scholarly journals Mining Topological Dependencies of Recurrent Congestion in Road Networks

2021 ◽  
Vol 10 (4) ◽  
pp. 248
Author(s):  
Nicolas Tempelmeier ◽  
Udo Feuerhake ◽  
Oskar Wage ◽  
Elena Demidova
Keyword(s):  
Network Topology ◽  
Real World ◽  
Public Transportation ◽  
Road Network ◽  
Road Networks ◽  
Traffic Load ◽  
Data Mining Algorithm ◽  
Urban Road ◽  
The Road ◽  
Spatio Temporal

The discovery of spatio-temporal dependencies within urban road networks that cause Recurrent Congestion (RC) patterns is crucial for numerous real-world applications, including urban planning and the scheduling of public transportation services. While most existing studies investigate temporal patterns of RC phenomena, the influence of the road network topology on RC is often overlooked. This article proposes the ST-Discovery algorithm, a novel unsupervised spatio-temporal data mining algorithm that facilitates effective data-driven discovery of RC dependencies induced by the road network topology using real-world traffic data. We factor out regularly reoccurring traffic phenomena, such as rush hours, mainly induced by the daytime, by modelling and systematically exploiting temporal traffic load outliers. We present an algorithm that first constructs connected subgraphs of the road network based on the traffic speed outliers. Second, the algorithm identifies pairs of subgraphs that indicate spatio-temporal correlations in their traffic load behaviour to identify topological dependencies within the road network. Finally, we rank the identified subgraph pairs based on the dependency score determined by our algorithm. Our experimental results demonstrate that ST-Discovery can effectively reveal topological dependencies in urban road networks.

scholarly journals Generating Road Networks for Old Downtown Areas Based on Crowd-Sourced Vehicle Trajectories

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 235
Author(s):  
Caili Zhang ◽  
Yali Li ◽  
Longgang Xiang ◽  
Fengwei Jiao ◽  
Chenhao Wu ◽  
...  
Keyword(s):  
Road Network ◽  
Large Scale ◽  
Road Networks ◽  
Low Frequency ◽  
Trajectory Data ◽  
Vehicle Trajectories ◽  
Intersection Detection ◽  
Density Peaks ◽  
Road Network Extraction ◽  
Representative Points

With the popularity of portable positioning devices, crowd-sourced trajectory data have attracted widespread attention, and led to many research breakthroughs in the field of road network extraction. However, it is still a challenging task to detect the road networks of old downtown areas with complex network layouts from high noise, low frequency, and uneven distribution trajectories. Therefore, this paper focuses on the old downtown area and provides a novel intersection-first approach to generate road networks based on low quality, crowd-sourced vehicle trajectories. For intersection detection, virtual representative points with distance constraints are detected, and the clustering by fast search and find of density peaks (CFDP) algorithm is introduced to overcome low frequency features of trajectories, and improve the positioning accuracy of intersections. For link extraction, an identification strategy based on the Delaunay triangulation network is developed to quickly filter out false links between large-scale intersections. In order to alleviate the curse of sparse and uneven data distribution, an adaptive link-fitting scheme, considering feature differences, is further designed to derive link centerlines. The experiment results show that the method proposed in this paper preforms remarkably better in both intersection detection and road network generation for old downtown areas.

scholarly journals Exploring the Spatial Distribution Characteristics and Correlation Factors of Wayfinding Performance on City-Scale Road Networks Based on Massive Trajectory Data

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Jun Li ◽  
Yan Zhu ◽  
Zhenwei Li ◽  
Wenle Lu ◽  
Yang Ji ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Road Network ◽  
Evaluation Method ◽  
Road Networks ◽  
Small Samples ◽  
Trajectory Data ◽  
Index Set ◽  
Taxi Drivers ◽  
Path Distance ◽  
Correlation Factors

Understanding how urban residents process road network information and conduct wayfinding is important for both individual travel and intelligent transportation. However, most existing research is limited to the heterogeneity of individuals’ expression and perception abilities, and the results based on small samples are weakly representative. This paper proposes a quantitative and population-based evaluation method of wayfinding performance on city-scale road networks based on massive trajectory data. It can accurately compute and visualize the magnitude and spatial distribution differences of drivers’ wayfinding performance levels, which is not achieved by conventional methods based on small samples. In addition, a systematic index set of road network features are constructed for correlation analysis. This is an improvement on the current research, which focuses on the influence of single factors. Finally, taking 20,000 taxi drivers in Beijing as a case study, experimental results show the following: (1) Taxi drivers’ wayfinding performances show a spatial pattern of a high level on arterial road networks and a low level on secondary networks, and they are spatially autocorrelated. (2) The correlation factors of taxi drivers’ wayfinding performances mainly include anchor point, road grade, road importance, road complexity, origin-destination length, and complexity, and each factor has a different influence. (3) The path complexity has a higher correlation with the wayfinding performance level than with the path distance. (4) There is a critical point in the taxi drivers’ wayfinding performances in terms of path distance. When the critical value is exceeded, it is difficult for a driver to find a good route based on personal cognition. This research can provide theoretical and technical support for intelligent driving and wayfinding research.

Research on Modeling and Indexing of Trajectories of Moving Objects in Road Networks

2013 ◽  
Vol 756-759 ◽  
pp. 1234-1239
Author(s):  
Yan Ling Zheng
Keyword(s):  
Road Network ◽  
Moving Objects ◽  
Road Networks ◽  
Experimental Results ◽  
Index Structure ◽  
Index Structures ◽  
The Road ◽  
The Past ◽  
Spatio Temporal ◽  
Temporal Trajectory

Proposed a new index structure, named MG2R*, can efficiently store and retrieve the past, present and future positions of network-constrained moving objects. It is a two-tier structure. The upper is a MultiGrid-R*-Tree (MGRT for short) that is used to index the road network. The lower is a group of independent R*-Tree. Each R*-Tree is relative to a route in the road network, can index the spatiotemporal trajectory of the moving objects in the road. Moreover, moving objects query is implemented based on this index structure. It compared to other index structures for road-network-based moving objects, such as MON-Tree, the experimental results shown that the MG2R* can effectively improve the query performance of the spatio-temporal trajectory of network-constrained moving objects.

scholarly journals Bottleneck Based Gridlock Prediction in an Urban Road Network Using Long Short-Term Memory

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1412
Author(s):  
Ei Ei Mon ◽  
Hideya Ochiai ◽  
Chaiyachet Saivichit ◽  
Chaodit Aswakul
Keyword(s):  
Road Network ◽  
Short Term Memory ◽  
Road Networks ◽  
Sample Sizes ◽  
Short Term ◽  
Urban Road Network ◽  
Term Memory ◽  
Urban Road ◽  
Wide Range ◽  
Long Short Term Memory

The traffic bottlenecks in urban road networks are more challenging to investigate and discover than in freeways or simple arterial networks. A bottleneck indicates the congestion evolution and queue formation, which consequently disturb travel delay and degrade the urban traffic environment and safety. For urban road networks, sensors are needed to cover a wide range of areas, especially for bottleneck and gridlock analysis, requiring high installation and maintenance costs. The emerging widespread availability of GPS vehicles significantly helps to overcome the geographic coverage and spacing limitations of traditional fixed-location detector data. Therefore, this study investigated GPS vehicles that have passed through the links in the simulated gridlock-looped intersection area. The sample size estimation is fundamental to any traffic engineering analysis. Therefore, this study tried a different number of sample sizes to analyze the severe congestion state of gridlock. Traffic condition prediction is one of the primary components of intelligent transportation systems. In this study, the Long Short-Term Memory (LSTM) neural network was applied to predict gridlock based on bottleneck states of intersections in the simulated urban road network. This study chose to work on the Chula-Sathorn SUMO Simulator (Chula-SSS) dataset. It was calibrated with the past actual traffic data collection by using the Simulation of Urban MObility (SUMO) software. The experiments show that LSTM provides satisfactory results for gridlock prediction with temporal dependencies. The reported prediction error is based on long-range time dependencies on the respective sample sizes using the calibrated Chula-SSS dataset. On the other hand, the low sampling rate of GPS trajectories gives high RMSE and MAE error, but with reduced computation time. Analyzing the percentage of simulated GPS data with different random seed numbers suggests the possibility of gridlock identification and reports satisfying prediction errors.

scholarly journals A Hybrid Method to Incrementally Extract Road Networks Using Spatio-Temporal Trajectory Data

2020 ◽  
Vol 9 (4) ◽  
pp. 186
Author(s):  
Yunfei Zhang ◽  
Zexu Zhang ◽  
Jincai Huang ◽  
Tingting She ◽  
Min Deng ◽  
...  
Keyword(s):  
Hybrid Method ◽  
Human Life ◽  
Rapid Development ◽  
Road Networks ◽  
Urban Traffic ◽  
Force Model ◽  
Trajectory Data ◽  
Incremental Change ◽  
Spatio Temporal ◽  
Temporal Trajectory

With the rapid development of urban traffic, accurate and up-to-date road maps are in crucial demand for daily human life and urban traffic control. Recently, with the emergence of crowdsourced mapping, a surge in academic attention has been paid to generating road networks from spatio-temporal trajectory data. However, most existing methods do not explore changing road patterns contained in multi-temporal trajectory data and it is still difficult to satisfy the precision and efficiency demands of road information extraction. Hence, in this paper, we propose a hybrid method to incrementally extract urban road networks from spatio-temporal trajectory data. First, raw trajectory data were partitioned into K time slices and were used to initialize K-temporal road networks by a mathematical morphology method. Then, the K-temporal road networks were adjusted according to a gravitation force model so as to amend their geometric inconsistencies. Finally, road networks were geometrically delineated using the k-segment fitting algorithm, and the associated road attributes (e.g., road width and driving rule) were inferred. Several case studies were examined to demonstrate that our method can effectively improve the efficiency and precision of road extraction and can make a significant attempt to mine the incremental change patterns in road networks from spatio-temporal trajectory data to help with road map renewal.

scholarly journals Critical Segments Identification for Link Travel Speed Prediction in Urban Road Network

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xiaolei Ru ◽  
Xiangdong Xu ◽  
Yang Zhou ◽  
Chao Yang
Keyword(s):  
Information Acquisition ◽  
Prediction Accuracy ◽  
Road Network ◽  
Large Scale ◽  
Road Networks ◽  
Route Planning ◽  
Travel Speed ◽  
Traffic Information ◽  
Operational Conditions ◽  
The Road

Predicting traffic operational condition is crucial to urban transportation planning and management. A large variety of algorithms were proposed to improve the prediction accuracy. However, these studies were mainly based on complete data and did not discuss the vulnerability of massive data missing. And applications of these algorithms were in high-cost under the constraints of high quality of traffic data collecting in real-time on the large-scale road networks. This paper aims to deduce the traffic operational conditions of the road network with a small number of critical segments based on taxi GPS data in Xi’an city of China. To identify these critical segments, we assume that the states of floating cars within different road segments are correlative and mutually representative and design a heuristic algorithm utilizing the attention mechanism embedding in the graph neural network (GNN). The results show that the designed model achieves a high accuracy compared to the conventional method using only two critical segments which account for 2.7% in the road networks. The proposed method is cost-efficient which generates the critical segments scheme that reduces the cost of traffic information collection greatly and is more sensible without the demand for extremely high prediction accuracy. Our research has a guiding significance on cost saving of various information acquisition techniques such as route planning of floating car or sensors layout.

scholarly journals An Intersection-First Approach for Road Network Generation from Crowd-Sourced Vehicle Trajectories

2019 ◽  
Vol 8 (11) ◽  
pp. 473 ◽  
Author(s):  
Caili Zhang ◽  
Longgang Xiang ◽  
Siyu Li ◽  
Dehao Wang
Keyword(s):  
Road Network ◽  
Rapid Development ◽  
Road Networks ◽  
Uneven Distribution ◽  
Trajectory Data ◽  
Network Generation ◽  
The Road ◽  
Lower Accuracy ◽  
Road Intersections ◽  
Vehicle Trajectory

Extracting highly detailed and accurate road network information from crowd-sourced vehicle trajectory data, which has the advantages of being low cost and able to update fast, is a hot topic. With the rapid development of wireless transmission technology, spatial positioning technology, and the improvement of software and hardware computing ability, more and more researchers are focusing on the analysis of Global Positioning System (GPS) trajectories and the extraction of road information. Road intersections are an important component of roads, as they play a significant role in navigation and urban planning. Even though there have been many studies on this subject, it remains challenging to determine road intersections, especially for crowd-sourced vehicle trajectory data with lower accuracy, lower sampling frequency, and uneven distribution. Therefore, we provided a new intersection-first approach for road network generation based on low-frequency taxi trajectories. Firstly, road intersections from vector space and raster space were extracted respectively via using different methods; then, we presented an integrated identification strategy to fuse the intersection extraction results from different schemes to overcome the sparseness of vehicle trajectory sampling and its uneven distribution; finally, we adjusted road information, repaired fractured segments, and extracted the single/double direction information and the turning relationships of the road network based on the intersection results, to guarantee precise geometry and correct topology for the road networks. Compared with other methods, this method shows better results, both in terms of their visual inspections and quantitative comparisons. This approach can solve the problems mentioned above and ensure the integrity and accuracy of road intersections and road networks. Therefore, the proposed method provides a promising solution for enriching and updating navigable road networks and can be applied in intelligent transportation systems.

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