Key feature selection and risk prediction for lane-changing behaviors based on vehicles’ trajectory data

2019 ◽  
Vol 129 ◽  
pp. 156-169 ◽  
Author(s):  
Tianyi Chen ◽  
Xiupeng Shi ◽  
Yiik Diew Wong
Keyword(s):  
Feature Selection ◽  
Risk Prediction ◽  
Trajectory Data ◽  
Lane Changing

scholarly journals Crash Risk Prediction Model of Lane-Change Behavior on Approaching Intersections

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Yingshuai Li ◽  
Jian Lu ◽  
Kuisheng Xu
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Risk Index ◽  
Video Recording ◽  
Risk Prediction Model ◽  
Risk Level ◽  
Lane Change ◽  
Trajectory Data ◽  
Lane Changing ◽  
Conflict Risk

The driving tendency of drivers is one of the most important factors in lane-changing maneuvers. However, the heterogeneity of the characteristics of drivers’ lane-changing behaviors has not been adequately considered. The primary objective of the present study is to explore the risk level of the lane-changing implementation process under different driving tendencies upon approaching signalized intersections in an urban area. This paper defines the Integrated Conflict Risk Index (ICRI), which takes into account the probability and severity of risk. Using the index as the dependent variable, the risk prediction model of implementing the lane-change process is established. A series of experiments, which included a questionnaire, a number of tests, and on-road experiments, was conducted to identify the driving tendencies of the participants. A combination of video recording and instrumented vehicles was used to collect lane-changing trajectory data of different driving tendencies. The parameters of the model were calibrated, and the results indicate that driving tendency has a significant effect on the risk level of lane-changing execution. More specifically, the more aggressive the driving tendency, the higher the risk level. The quantitative results of the study can provide the basis for conflict risk assessment in the existing lane-changing models.

Using vehicular trajectory data to explore risky factors and unobserved heterogeneity during lane-changing

2021 ◽  
Vol 151 ◽  
pp. 105871
Author(s):  
Qinghong Chen ◽  
Ruifeng Gu ◽  
Helai Huang ◽  
Jaeyoung Lee ◽  
Xiaoqi Zhai ◽  
...  
Keyword(s):  
Unobserved Heterogeneity ◽  
Trajectory Data ◽  
Lane Changing

scholarly journals ANALYSIS OF DRIVER BEHAVIOR IN VEHICLE FOLLOWING AND LANE CHANGING PARAMETERS ON URBAN MID BLOCK SECTIONS

2021 ◽  
Vol 9 (2) ◽  
pp. 1169-1177
Author(s):  
Sowjanya, Et. al.
Keyword(s):  
Road Traffic ◽  
Driver Behavior ◽  
Video Data ◽  
Heterogeneous Traffic ◽  
Heterogeneous Environments ◽  
Trajectory Data ◽  
Lateral Direction ◽  
Lane Changing ◽  
Traffic Conditions ◽  
Road Section

In mixed traffic situations, there is weak or no lane behavior of the driver much more complicated where vehicle and driver behavior show a huge difference between them. Road traffic driving behavior on urban midblock sections is one of the most complex phenomena to be examined particularly in heterogeneous traffic conditions. This is often attributed to the capacity of the road section and the traffic flow features at the macroscopic and microscopic level of a road section. Very few researchers have attempted to investigate these features in heterogeneous environments because of the lack of adequate information gathering methods and the amount of complexity involved. In this background, an access controlled mid block road section was selected for video data collection. The main objectives of this study include developing vehicular trajectory data and analyzing the lane changing and vehicle following behavior of driver on the mid block section considering the relative velocities and relative spacing between various types of vehicles under heterogeneous traffic conditions.  The videos were collected from urban roadway in the Kurnool district of Andhra Pradesh. The length of the stretch is 120m and the width is 7.0 m. The data was extracted to know the variations in terms of longitudinal and lateral speeds, velocities, vehicle following and lane changing behavior of the drivers. The data extracted was smoothened by moving average method to minimize the human errors. Lateral amplitude of the vehicles of various types was analyzed. The study revealed that vehicles in the mixed stream, in general and in particular, Bikes and Autos particularly move substantially in the lateral direction.

Modeling Speed Disturbance Absorption following Current State–Control Action–Expected State Chains

2005 ◽  
Vol 1934 (1) ◽  
pp. 83-93 ◽  
Author(s):  
Ruihua Tao ◽  
Heng Wei ◽  
Yinhai Wang ◽  
Virginia P. Sisiopiku
Keyword(s):  
Recovery Period ◽  
Control Action ◽  
State Control ◽  
Trajectory Data ◽  
Lane Changing ◽  
Car Following ◽  
Current State ◽  
Speed Advantage ◽  
Vehicle Trajectory ◽  
A Current

This paper explores driver behavior in a paired car-following mode in response to a speed disturbance from a front vehicle. A current state– control action–expected state (SAS) chain is developed to provide a framework for modeling of the hierarchy of expected actions incurred during the need for speed disturbance absorption. Three car-following scenarios and one lane-changing scenario are identified with defined perceptual informative variables to describe the process of speed disturbance absorption. Those variables include dynamic spacing versus the follower's speed, disturbance-effecting and -ending spacing, headway, acceleration– deceleration, speed recovery period, speed advantage, and lane-changing duration. A significant improvement in car-following modeling introduced in the paper is the integration of car-following and lane-changing behaviors in the SAS chain. Moreover, critical values of perceptual informative variables are statistically developed as a function of the follower's speed by using observed vehicle trajectory data. Furthermore, models that determine the probability of a lane change in response to a speed disturbance and models for acceptable lane-changing decision-making conditions at the adjacent lanes are developed on the basis of the analysis of observed vehicle trajectory data. The work presented in this paper provides an analysis of speed disturbance and speed absorption phenomena and car-following and lane-changing behaviors at the microscopic level. This work establishes the foundation for further research on multiple speed disturbance absorption and its impact on traffic stabilities at the macroscopic analysis level.

Characterizing Lane Changes via Digitized Infrastructure and Low-Cost GPS

2019 ◽  
Vol 2673 (8) ◽  
pp. 298-309 ◽  
Author(s):  
Ishtiak Ahmed ◽  
Alan Karr ◽  
Nagui M. Rouphail ◽  
Gyounghoon Chun ◽  
Shams Tanvir
Keyword(s):  
Autonomous Vehicles ◽  
Lateral Displacement ◽  
Low Cost ◽  
Detection Algorithm ◽  
Lane Change ◽  
Trajectory Data ◽  
Lane Changing ◽  
Lane Changes ◽  
Detection Systems ◽  
Level Information

With the expected increase in the availability of trajectory-level information from connected and autonomous vehicles, issues of lane changing behavior that were difficult to assess with traditional freeway detection systems can now begin to be addressed. This study presents the development and application of a lane change detection algorithm that uses trajectory data from a low-cost GPS-equipped fleet, supplemented with digitized lane markings. The proposed algorithm minimizes the effect of GPS errors by constraining the temporal duration and lateral displacement of a lane change detected using preliminary lane positioning. The algorithm was applied to 637 naturalistic trajectories traversing a long weaving segment and validated through a series of controlled lane change experiments. Analysis of naturalistic trajectory data revealed that ramp-to-freeway trips had the highest number of discretionary lane changes in excess of 1 lane change/vehicle. Overall, excessive lane change rates were highest between the two middle freeway lanes at 0.86 lane changes/vehicle. These results indicate that extreme lane changing behavior may significantly contribute to the peak-hour congestion at the site. The average lateral speed during lane change was 2.7 fps, consistent with the literature, with several freeway–freeway and ramp–ramp trajectories showing speeds up to 7.7 fps. All ramp-to-freeway vehicles executed their first mandatory lane change within 62.5% of the total weaving length, although other weaving lane changes were spread over the entire segment. These findings can be useful for implementing strategies to lessen abrupt and excessive lane changes through better lane pre-positioning.

scholarly journals A proactive lane-changing risk prediction framework considering driving intention recognition and different lane-changing patterns

2022 ◽  
Vol 164 ◽  
pp. 106500
Author(s):  
Qiangqiang Shangguan ◽  
Ting Fu ◽  
Junhua Wang ◽  
Shou'en Fang ◽  
Liping Fu
Keyword(s):  
Risk Prediction ◽  
Lane Changing ◽  
Intention Recognition ◽  
Changing Patterns

scholarly journals Evaluation of Feature Selection Techniques for Breast Cancer Risk Prediction

2021 ◽  
Vol 18 (20) ◽  
pp. 10670
Author(s):  
Nahúm Cueto López ◽  
María Teresa García-Ordás ◽  
Facundo Vitelli-Storelli ◽  
Pablo Fernández-Navarro ◽  
Camilo Palazuelos ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Random Forest ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Public Health Problem ◽  
Healthy Population ◽  
Major Public Health Problem ◽  
The Stability

This study evaluates several feature ranking techniques together with some classifiers based on machine learning to identify relevant factors regarding the probability of contracting breast cancer and improve the performance of risk prediction models for breast cancer in a healthy population. The dataset with 919 cases and 946 controls comes from the MCC-Spain study and includes only environmental and genetic features. Breast cancer is a major public health problem. Our aim is to analyze which factors in the cancer risk prediction model are the most important for breast cancer prediction. Likewise, quantifying the stability of feature selection methods becomes essential before trying to gain insight into the data. This paper assesses several feature selection algorithms in terms of performance for a set of predictive models. Furthermore, their robustness is quantified to analyze both the similarity between the feature selection rankings and their own stability. The ranking provided by the SVM-RFE approach leads to the best performance in terms of the area under the ROC curve (AUC) metric. Top-47 ranked features obtained with this approach fed to the Logistic Regression classifier achieve an AUC = 0.616. This means an improvement of 5.8% in comparison with the full feature set. Furthermore, the SVM-RFE ranking technique turned out to be highly stable (as well as Random Forest), whereas relief and the wrapper approaches are quite unstable. This study demonstrates that the stability and performance of the model should be studied together as Random Forest and SVM-RFE turned out to be the most stable algorithms, but in terms of model performance SVM-RFE outperforms Random Forest.

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