Bhattarai et al., 2024 - Google Patents

Proactive safety analysis using roadside LiDAR based vehicle trajectory data: a study of rear-end crashes

Bhattarai et al., 2024

Document ID
14964321415787633115
Author
Bhattarai N
Zhang Y
Liu H
Pakzad Y
Xu H
Publication year
Publication venue
Transportation research record

External Links

Snippet

This paper presents a methodology to detect rear-end conflicts at signalized intersections with the help of roadside LiDAR sensors. Raw data collected in the point cloud format from the sensors was processed using a series of data processing algorithms to obtain vehicle …
Continue reading at journals.sagepub.com (other versions)

Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0133—Traffic data processing for classifying traffic situation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063—Operations research or analysis
    • G06Q10/0639—Performance analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce, e.g. shopping or e-commerce
    • G06Q30/02—Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism

Similar Documents

Publication Publication Date Title
Zheng et al. Comparison of traffic conflict indicators for crash estimation using peak over threshold approach
Sohail et al. Data-driven approaches for road safety: A comprehensive systematic literature review
Bhattarai et al. Proactive safety analysis using roadside LiDAR based vehicle trajectory data: a study of rear-end crashes
Orsini et al. Large‐scale road safety evaluation using extreme value theory
Weng et al. Real-time bus travel speed estimation model based on bus GPS data
Berthaume et al. Variations in driver behavior: An analysis of car-following behavior heterogeneity as a function of road type and traffic condition
Zheng et al. Enabling real-time road anomaly detection via mobile edge computing
Luo et al. Arterial traffic flow estimation based on vehicle-to-cloud vehicle trajectory data considering multi-intersection interaction and coordination
Tageldin et al. Automated analysis and validation of right-turn merging behavior
Yun et al. Minimum sampling size of floating cars for urban link travel time distribution estimation
Chauhan et al. Challenges in rear-end conflict-based safety assessment of highly disordered traffic conditions
Zhang et al. Do larger sample sizes increase the reliability of traffic incident duration models? A case study of east Tennessee incidents
Wang et al. Data infrastructure for connected vehicle applications
Arafat et al. Stop sign gap assist application in a connected vehicle simulation environment
Wan et al. Identification of time-of-day breakpoints based on trajectory data of probe vehicles
Kidando et al. Use of real-time traffic and signal timing data in modeling occupant injury severity at signalized intersections
Kandiboina et al. Exploring the efficacy of large-scale connected vehicle data in real-time traffic applications
Alrassy et al. Driver behavior indices from large-scale fleet telematics data as surrogate safety measures
Anwari et al. Investigating surrogate safety measures at midblock pedestrian crossings using multivariate models with roadside camera data
Kitali et al. Impact of primary incident spatiotemporal influence thresholds on the detection of secondary crashes
Chun et al. Analytical and microsimulation model calibration and validation: application to roundabouts under sight-restricted conditions
Xin et al. Risk evaluation for conflicts between crossing pedestrians and right-turning vehicles at intersections
Iranmanesh et al. Identifying high crash risk segments in rural roads using ensemble decision tree-based models
Chen et al. Investigating the impact of influential factors on crash types for autonomous vehicles at intersections
Bhattarai et al. Crash frequency prediction based on extreme value theory using roadside lidar-based vehicle trajectory data