Spatio-Temporal Causal Relations at Urban Road Networks; Granger Causality Based Networks as an Insight to Urban Traffic Dynamics

Urban road traffic spatio-temporal characters reflect how citizens move and how goods are transported, which is crucial for trip planning, traffic management, and urban design. Video surveillance camera plays an important role in intelligent transport systems (ITS) for recognizing license plate numbers. This paper proposes a spatio-temporal visualization method to discover urban road vehicle density, city-wide regional vehicle density, and hot routes using license plate number data recorded by video surveillance cameras. To improve the accuracy of the visualization effect, during data analysis and processing, this paper utilized Internet crawler technology and adopted an outlier detection algorithm based on the Dixon detection method. In the design of the visualization map, this paper established an urban road vehicle traffic index to intuitively and quantitatively reveal the traffic operation situation of the area. To verify the feasibility of the method, an experiment in Guiyang on data from road video surveillance camera system was conducted. Multiple urban traffic spatial and temporal characters are recognized concisely and efficiently from three visualization maps. The results show the satisfactory performance of the proposed framework in terms of visual analysis, which will facilitate traffic management and operation.

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Spatio-temporal clustering for non-recurrent traffic congestion detection on urban road networks

Transportation Research Part C Emerging Technologies ◽

10.1016/j.trc.2014.08.002 ◽

2014 ◽

Vol 48 ◽

pp. 47-65 ◽

Cited By ~ 60

Author(s):

Berk Anbaroglu ◽

Benjamin Heydecker ◽

Tao Cheng

Keyword(s):

Traffic Congestion ◽

Road Networks ◽

Urban Road ◽

Temporal Clustering ◽

Congestion Detection ◽

Spatio Temporal

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Effects of iterative learning based signal control strategies on macroscopic fundamental diagrams of urban road networks

International Journal of Modern Physics C ◽

10.1142/s0129183116500455 ◽

2016 ◽

Vol 27 (04) ◽

pp. 1650045 ◽

Cited By ~ 6

Author(s):

Fei Yan ◽

Fuli Tian ◽

Zhongke Shi

Keyword(s):

Control Strategies ◽

High Capacity ◽

Road Networks ◽

Iterative Learning ◽

Urban Traffic ◽

Signal Control ◽

Traffic Signal Control ◽

Fundamental Diagram ◽

Urban Road ◽

Modeling Uncertainties

Urban traffic flows are inherently repeated on a daily or weekly basis. This repeatability can help improve the traffic conditions if it is used properly by the control system. In this paper, we propose a novel iterative learning control (ILC) strategy for traffic signals of urban road networks using the repeatability feature of traffic flow. To improve the control robustness, the ILC strategy is further integrated with an error feedback control law in a complementary manner. Theoretical analysis indicates that the ILC-based traffic signal control methods can guarantee the asymptotic learning convergence, despite the presence of modeling uncertainties and exogenous disturbances. Finally, the impacts of the ILC-based signal control strategies on the network macroscopic fundamental diagram (MFD) are examined. The results show that the proposed ILC-based control strategies can homogenously distribute the network accumulation by controlling the vehicle numbers in each link to the desired levels under different traffic demands, which can result in the network with high capacity and mobility.

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Mining Topological Dependencies of Recurrent Congestion in Road Networks

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10040248 ◽

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.

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

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9040186 ◽

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.

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IDENTIFICATION OF SIMILARITIES AND PREDICTION OF UNKNOWN FEATURES IN AN URBAN STREET NETWORK

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-185-2018 ◽

2018 ◽

Vol XLII-4 ◽

pp. 185-192 ◽

Cited By ~ 1

Author(s):

U. Feuerhake ◽

O. Wage ◽

M. Sester ◽

N. Tempelmeier ◽

W. Nejdl ◽

...

Keyword(s):

Real World ◽

Public Transportation ◽

Large Scale ◽

Road Networks ◽

Urban Traffic ◽

Construction Sites ◽

Real World Data ◽

Urban Road ◽

The Road ◽

Extensive Evaluation

<p><strong>Abstract.</strong> Accurate predictions of the characteristics of urban streets in particular with respect to the typical traffic situations are crucial for numerous real world applications such as navigation, scheduling of logistic and public transportation services as well as high-level planning of infrastructure which may include planning of construction sites or even changes of the road topology. However, this information may be hard to obtain, especially in complex urban road networks where interdependencies between roads are highly present. In addition, accurate and recent traffic data is not always available, especially for uncommon situations like large-scale public events, traffic accidents or construction sites. This work demonstrates how to employ historical traffic datasets in conjunction with other, infrastructure related data, to derive a deeper understanding of urban traffic behaviour. In particular this paper provides the following contributions: (1) the generation of meaningful features to describe the segments in urban road networks; (2) an unsupervised machine learning approach that identifies similar segments based on those features; (3) a supervised approach to predict unknown features of the segments and, finally, (4) an extensive evaluation of the extracted road characteristics and the proposed methods using real-world data. The resulting clusters reveal the similarities of the street segments and give a different perspective on the road network and the traffic situation, respectively. The experiments on the classification approach demonstrate that unknown features can be predicted with a good quality.</p>

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Application of Chaos Theory in the Prediction of Motorised Traffic Flows on Urban Networks

Mathematical Problems in Engineering ◽

10.1155/2016/5656734 ◽

2016 ◽

Vol 2016 ◽

pp. 1-15 ◽

Cited By ~ 8

Author(s):

Aderemi Adewumi ◽

Jimmy Kagamba ◽

Alex Alochukwu

Keyword(s):

Traffic Flow ◽

Chaos Theory ◽

Road Networks ◽

Urban Traffic ◽

Largest Lyapunov Exponent ◽

Urban Networks ◽

Urban Road ◽

Traffic Flow Prediction ◽

Lyapunov Time ◽

Congestion Problems

In recent times, urban road networks are faced with severe congestion problems as a result of the accelerating demand for mobility. One of the ways to mitigate the congestion problems on urban traffic road network is by predicting the traffic flow pattern. Accurate prediction of the dynamics of a highly complex system such as traffic flow requires a robust methodology. An approach for predicting Motorised Traffic Flow on Urban Road Networks based on Chaos Theory is presented in this paper. Nonlinear time series modeling techniques were used for the analysis of the traffic flow prediction with emphasis on the technique of computation of the Largest Lyapunov Exponent to aid in the prediction of traffic flow. The study concludes that algorithms based on the computation of the Lyapunov time seem promising as regards facilitating the control of congestion because of the technique’s effectiveness in predicting the dynamics of complex systems especially traffic flow.

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