On the public citation graph
Evaluating Surrogate Safety Indicators Across Different Manner of Collisions Using Roadside Lidar Trajectory Data
0 signed · 56 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-1-4471-3675-0openalex-extracted
An Introduction to Statistical Modeling of Extreme Values
inferredcites →https://doi.org/10.1007/s11036-012-0417-8openalex-extracted
Real Time Anomalous Trajectory Detection and Analysis
inferredcites →https://doi.org/10.1016/0001-4575(79)90066-6openalex-extracted
Proceedings of the first workshop on traffic conflicts
inferredcites →https://doi.org/10.1016/j.aap.2006.02.003openalex-extracted
The extreme value theory approach to safety estimation
inferredcites →https://doi.org/10.1016/j.aap.2011.09.002openalex-extracted
Crashes and crash-surrogate events: Exploratory modeling with naturalistic driving data
inferredcites →https://doi.org/10.1016/j.aap.2013.04.005openalex-extracted
Traffic conflicts on bicycle paths: A systematic observation of behaviour from video
inferredcites →https://doi.org/10.1016/j.aap.2013.09.006openalex-extracted
Freeway safety estimation using extreme value theory approaches: A comparative study
inferredcites →https://doi.org/10.1016/j.aap.2015.04.024openalex-extracted
Safety surrogate histograms (SSH): A novel real-time safety assessment of dilemma zone related conflicts at signalized intersections
inferredcites →https://doi.org/10.1016/j.aap.2017.02.022openalex-extracted
Crash data quality for road safety research: Current state and future directions
inferredcites →https://doi.org/10.1016/j.aap.2018.02.024openalex-extracted
Assessing rear-end crash potential in urban locations based on vehicle-by-vehicle interactions, geometric characteristics and operational conditions
inferredcites →https://doi.org/10.1016/j.aap.2018.03.005openalex-extracted
Vehicle manoeuvers as surrogate safety measures: Extracting data from the gps-enabled smartphones of regular drivers
inferredcites →https://doi.org/10.1016/j.aap.2018.09.023openalex-extracted
Before-after safety analysis using extreme value theory: A case of left-turn bay extension
inferredcites →https://doi.org/10.1016/j.aap.2018.12.013openalex-extracted
A crash prediction method based on bivariate extreme value theory and video-based vehicle trajectory data
inferredcites →https://doi.org/10.1016/j.aap.2018.12.019openalex-extracted
Evaluating the safety impact of connected and autonomous vehicles on motorways
inferredcites →https://doi.org/10.1016/j.aap.2019.105315openalex-extracted
Road safety of passing maneuvers: A bivariate extreme value theory approach under non-stationary conditions
inferredcites →https://doi.org/10.1016/j.aap.2021.105971openalex-extracted
Fusing crash data and surrogate safety measures for safety assessment: Development of a structural equation model with conditional autoregressive spatial effect and random parameters
inferredcites →https://doi.org/10.1016/j.aap.2021.106016openalex-extracted
A systematic mapping review of surrogate safety assessment using traffic conflict techniques
inferredcites →https://doi.org/10.1016/j.aap.2021.106157openalex-extracted
A review of surrogate safety measures and their applications in connected and automated vehicles safety modeling
inferredcites →https://doi.org/10.1016/j.aap.2021.106350openalex-extracted
A relative approach to the validation of surrogate measures of safety
inferredcites →https://doi.org/10.1016/j.aap.2021.106503openalex-extracted
A high-resolution trajectory data driven method for real-time evaluation of traffic safety
inferredcites →https://doi.org/10.1016/j.aap.2022.106879openalex-extracted
Driver behavior indices from large-scale fleet telematics data as surrogate safety measures
inferredcites →https://doi.org/10.1016/j.aap.2023.107306openalex-extracted
Crash frequency prediction based on extreme value theory using roadside lidar-based vehicle trajectory data
inferredcites →https://doi.org/10.1016/j.amar.2020.100136openalex-extracted
The relationship between driving volatility in time to collision and crash-injury severity in a naturalistic driving environment
inferredcites →https://doi.org/10.1016/j.amar.2020.100142openalex-extracted
Modeling traffic conflicts for use in road safety analysis: A review of analytic methods and future directions
inferredcites →https://doi.org/10.1016/j.iatssr.2016.07.001openalex-extracted
Safety analysis of passing maneuvers using extreme value theory
inferredcites →https://doi.org/10.1016/j.iatssr.2017.02.001openalex-extracted
Application of proximal surrogate indicators for safety evaluation: A review of recent developments and research needs
inferredcites →https://doi.org/10.1016/j.iatssr.2022.03.001openalex-extracted
Review of traffic safety evaluation at T-intersections using surrogate safety measures in developing countries context
inferredcites →https://doi.org/10.1016/j.jsr.2020.03.006openalex-extracted
An improved vehicle-pedestrian near-crash identification method with a roadside LiDAR sensor
inferredcites →https://doi.org/10.1016/j.trc.2013.08.015openalex-extracted
An automated surrogate safety analysis at protected highway ramps using cross-sectional and before–after video data
inferredcites →https://doi.org/10.1016/j.trc.2018.03.011openalex-extracted
A combined use of microscopic traffic simulation and extreme value methods for traffic safety evaluation
inferredcites →https://doi.org/10.1016/j.trc.2019.01.007openalex-extracted
Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors
inferredcites →https://doi.org/10.1016/j.trc.2019.01.016openalex-extracted
A hybrid approach for identifying factors affecting driver reaction time using naturalistic driving data
inferredcites →https://doi.org/10.1016/j.trc.2022.103655openalex-extracted
Anticipated Collision Time (ACT): A two-dimensional surrogate safety indicator for trajectory-based proactive safety assessment
inferredcites →https://doi.org/10.1016/j.trf.2013.12.022openalex-extracted
Crash risk evaluation of aggressive driving on motorways: Microscopic traffic simulation approach
inferredcites →https://doi.org/10.1016/j.trf.2018.03.012openalex-extracted
Yielding behavior and traffic conflicts at cyclist crossing facilities on channelized right-turn lanes
inferredcites →https://doi.org/10.1016/j.trf.2023.10.006openalex-extracted
A comparison of pedestrian behavior in interactions with autonomous and human-driven vehicles: An extreme value theory approach
inferredcites →https://doi.org/10.1049/iet-its.2018.5258openalex-extracted
Trajectory tracking and prediction of pedestrian's crossing intention using roadside LiDAR
inferredcites →https://doi.org/10.1080/01441647.2018.1442888openalex-extracted
In search of surrogate safety indicators for vulnerable road users: a review of surrogate safety indicators
inferredcites →https://doi.org/10.1080/17457300.2022.2147194openalex-extracted
Investigating and modeling the influence of PET-types on crossing conflicts at urban unsignalized intersections in India
inferredcites →https://doi.org/10.1080/19427867.2019.1650430openalex-extracted
An evaluation of time-to-collision as a surrogate safety measure and a proposal of a new method for its application in safety analysis
inferredcites →https://doi.org/10.1080/23249935.2018.1515271openalex-extracted
Collision prediction in roundabouts: a comparative study of extreme value theory approaches
inferredcites →https://doi.org/10.1109/access.2019.2951763openalex-extracted
Automatic Vehicle-Pedestrian Conflict Identification With Trajectories of Road Users Extracted From Roadside LiDAR Sensors Using a Rule-Based Method
inferredcites →https://doi.org/10.1109/icara.2011.6144918openalex-extracted
Radar/Lidar sensor fusion for car-following on highways
inferredcites →https://doi.org/10.1109/itsc.2018.8569552openalex-extracted
The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems
inferredcites →https://doi.org/10.1109/jsen.2022.3166957openalex-extracted
An Unsupervised Clustering Method for Processing Roadside LiDAR Data With Improved Computational Efficiency
inferredcites →https://doi.org/10.1109/mits.2019.2926362openalex-extracted
Automatic Vehicle Detection With Roadside LiDAR Data Under Rainy and Snowy Conditions
inferredcites →https://doi.org/10.1177/0361198118775839openalex-extracted
3-D Data Processing to Extract Vehicle Trajectories from Roadside LiDAR Data
inferredcites →https://doi.org/10.1177/0361198119843857openalex-extracted
Automatic Vehicle Classification using Roadside LiDAR Data
inferredcites →https://doi.org/10.1177/03611981211069347openalex-extracted
Automatic Identification of Vehicle Partial Occlusion in Data Collected by Roadside LiDAR Sensors
inferredcites →https://doi.org/10.1177/03611981231182704openalex-extracted
Proactive Safety Analysis Using Roadside LiDAR Based Vehicle Trajectory Data: A Study of Rear-End Crashes
inferredcites →https://doi.org/10.3141/2083-12openalex-extracted
Derivation and Validation of New Simulation-Based Surrogate Safety Measure
inferredcites →https://doi.org/10.3141/2102-10openalex-extracted
Probabilistic Determination of Crash Locations in a Road Network with Imperfect Data
inferredcites →https://doi.org/10.3141/2237-02openalex-extracted
Methodologies for Aggregating Indicators of Traffic Conflict
inferredcites →https://doi.org/10.3141/2365-03openalex-extracted
Longitudinal Driving Behavior with Integrated Crash-Warning System
inferredcites →https://doi.org/10.3141/2386-06openalex-extracted
Evaluation of Postencroachment Time as Surrogate for Opposing Left-Turn Crashes
inferredcites →https://doi.org/10.3141/2661-03openalex-extracted
Comparison of Time-Proximity and Evasive Action Conflict Measures: Case Studies from Five Cities
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.