Park et al., 2007 - Google Patents
Multivariate Poisson-lognormal models for jointly modeling crash frequency by severityPark et al., 2007
View PDF- Document ID
- 12994453423893207067
- Author
- Park E
- Lord D
- Publication year
- Publication venue
- Transportation Research Record
External Links
Snippet
A new multivariate approach is introduced for jointly modeling data on crash counts by severity on the basis of multivariate Poisson-lognormal models. Although the data on crash frequency by severity are multivariate in nature, they have often been analyzed by modeling …
- 238000004642 transportation engineering 0 description 17
Classifications
-
- 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
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- 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
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression 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
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
-
- 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
- G06F17/30861—Retrieval from the Internet, e.g. browsers
-
- 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
- G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
-
- 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
- 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
- G06Q40/06—Investment, e.g. financial instruments, portfolio management or fund management
-
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3457—Performance evaluation by simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F2217/00—Indexing scheme relating to computer aided design [CAD]
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Park et al. | Multivariate Poisson-lognormal models for jointly modeling crash frequency by severity | |
| Lord et al. | Highway safety analytics and modeling | |
| Xiong et al. | The analysis of vehicle crash injury-severity data: A Markov switching approach with road-segment heterogeneity | |
| Miaou et al. | Bayesian ranking of sites for engineering safety improvements: decision parameter, treatability concept, statistical criterion, and spatial dependence | |
| Han et al. | Investigating varying effect of road-level factors on crash frequency across regions: A Bayesian hierarchical random parameter modeling approach | |
| Alarifi et al. | Crash modeling for intersections and segments along corridors: a Bayesian multilevel joint model with random parameters | |
| El-Basyouny et al. | Collision prediction models using multivariate Poisson-lognormal regression | |
| Aguero-Valverde | Multivariate spatial models of excess crash frequency at area level: Case of Costa Rica | |
| Mohammadi et al. | Crash frequency modeling using negative binomial models: An application of generalized estimating equation to longitudinal data | |
| Zheng et al. | A full Bayes approach for traffic conflict-based before–after safety evaluation using extreme value theory | |
| Qin et al. | Hierarchical Bayesian estimation of safety performance functions for two-lane highways using Markov chain Monte Carlo modeling | |
| Park et al. | Bayesian mixture modeling approach to account for heterogeneity in speed data | |
| Zhan et al. | An efficient parallel sampling technique for Multivariate Poisson-Lognormal model: Analysis with two crash count datasets | |
| Islam et al. | Full Bayesian evaluation of the safety effects of reducing the posted speed limit in urban residential area | |
| Afghari et al. | Effects of globally obtained informative priors on Bayesian safety performance functions developed for Australian crash data | |
| Geedipally et al. | Examination of methods to estimate crash counts by collision type | |
| Karwa et al. | Causal inference in transportation safety studies: Comparison of potential outcomes and causal diagrams | |
| Haleem et al. | Using a reliability process to reduce uncertainty in predicting crashes at unsignalized intersections | |
| Chang et al. | Modelling for identifying accident-prone spots: Bayesian approach with a Poisson mixture model | |
| Ivan et al. | Estimating benefits from specific highway safety improvements | |
| Schultz et al. | Using complementary intersection and segment analyses to identify crash hot spots | |
| Jin et al. | Investigating the impacts of crash prediction models on quantifying safety effectiveness of Adaptive Signal Control Systems | |
| Navandar et al. | Headway distribution for manually operated tollbooths in India in mixed traffic conditions | |
| Park et al. | Estimation of speed differentials on rural highways using hierarchical linear regression models | |
| Ma | Bayesian multivariate Poisson-Lognormal regression for crash prediction on rural two-lane highways |