Identification and Correlation Analysis of Critical Intersections in Urban Road Networks Based on Vehicle Trajectory Data

Currently, several traffic conflict indicators are used as surrogate safety measures. Each indicator has its own advantages, limitations, and suitability. There are only a few studies focusing on fixed object conflicts of highway safety estimation using traffic conflict technique. This study investigated which conflict indicator was more suitable for traffic safety estimation based on conflict-accident Pearson correlation analysis. First, a high-altitude unmanned aerial vehicle was used to collect multiple continuous high-precision videos of the Jinan-Qingdao highway. The vehicle trajectory data outputted from recognition of the videos were used to acquire conflict data following the procedure for each conflict indicator. Then, an improved indicator Ti was proposed based on the advantages and limitations of the conventional indicators. This indicator contained definitions and calculation for three types of traffic conflicts (rear-end, lane change and with fixed object). Then the conflict-accident correlation analysis of TTC (Time to Collision)/PET (Post Encroachment Time)/DRAC (Deceleration Rate to Avoid Crash)/Ti indicators were carried out. The results show that the average value of the correlation coefficient for each indicator with different thresholds are 0.670 for TTC, 0.669 for PET, and 0.710 for DRAC, and 0.771 for Ti, which Ti indicator is obviously higher than the other three conventional indicators. The findings of this study suggest TTC often fails to identify lane change conflicts, PET indicator easily misjudges some rear-end conflict when the speed of the following vehicle is slower than the leading vehicle, and PET is less informative than other indicators. At the same time, these conventional indicators do not consider the vehicle-fixed objects conflicts. The improved Ti can overcome these shortcomings; thus, Ti has the highest correlation. More data are needed to verify and support the study.

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A Hybrid Approach Based on Variational Mode Decomposition for Analyzing and Predicting Urban Travel Speed

Journal of Advanced Transportation ◽

10.1155/2019/3958127 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12

Author(s):

Eui-Jin Kim ◽

Ho-Chul Park ◽

Seung-Young Kho ◽

Dong-Kyu Kim

Keyword(s):

Correlation Analysis ◽

Hybrid Model ◽

Travel Demand ◽

Hybrid Approach ◽

Road Networks ◽

Travel Speed ◽

Variational Mode Decomposition ◽

Urban Road ◽

Urban Travel ◽

Mode Decomposition

Predicting travel speeds on urban road networks is a challenging subject due to its uncertainty stemming from travel demand, geometric condition, traffic signals, and other exogenous factors. This uncertainty appears as nonlinearity, nonstationarity, and volatility in traffic data, and it also creates a spatiotemporal heterogeneity of link travel speed by interacting with neighbor links. In this study, we propose a hybrid model using variational mode decomposition (VMD) to investigate and mitigate the uncertainty of urban travel speeds. The VMD allows the travel speed data to be divided into orthogonal and oscillatory sub-signals, called modes. The regular components are extracted as the low-frequency modes, and the irregular components presenting uncertainty are transformed into a combination of modes, which is more predictable than the original uncertainty. For the prediction, the VMD decomposes the travel speed data into modes, and these modes are predicted and summed to represent the predicted travel speed. The evaluation results on urban road networks show that, the proposed hybrid model outperforms the benchmark models both in the congested and in the overall conditions. The improvement in performance increases significantly over specific link-days, which generally are hard to predict. To explain the significant variance of the prediction performance according to each link and each day, the correlation analysis between the properties of modes and the performance of the model are conducted. The results on correlation analysis show that the more variance of nondaily pattern is explained through the modes, the easier it was to predict the speed. Based on the results, discussions on the interpretation on the correlation analysis and future research are presented.

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Immediate Velocity Prediction of a Mixed-Traffic Flow in Urban Road Networks with Spatial-Temporal Correlation Analysis

2018 4th International Conference on Green Technology and Sustainable Development (GTSD) ◽

10.1109/gtsd.2018.8595635 ◽

2018 ◽

Cited By ~ 1

Author(s):

Hong Thanh Pham ◽

Tran Van Lang ◽

Van Hoai Tran

Keyword(s):

Correlation Analysis ◽

Traffic Flow ◽

Road Networks ◽

Temporal Correlation ◽

Mixed Traffic ◽

Mixed Traffic Flow ◽

Urban Road ◽

Velocity Prediction

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

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8110473 ◽

2019 ◽

Vol 8 (11) ◽

pp. 473 ◽

Cited By ~ 3

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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