Mohammadali Shirazi
Mohammadali Shirazi
Assistant Professor, University of Maine
Verified email at maine.edu
Title
Cited by
Cited by
Year
A semiparametric negative binomial generalized linear model for modeling over-dispersed count data with a heavy tail: Characteristics and applications to crash data
M Shirazi, D Lord, SS Dhavala, SR Geedipally
Accident Analysis & Prevention 91, 10-18, 2016
422016
Sample-size guidelines for recalibrating crash prediction models: Recommendations for the highway safety manual
M Shirazi, D Lord, SR Geedipally
Accident Analysis & Prevention 93, 160-168, 2016
412016
Developing a Random Parameters Negative Binomial-Lindley Model to analyze highly over-dispersed crash count data
MRR Shaon, X Qin, M Shirazi, D Lord, SR Geedipally
Analytic methods in accident research 18, 33-44, 2018
312018
A Methodology to Design Heuristics for Model Selection Based on Characteristics of Data: Application to Investigate When the Negative Binomial Lindley (NB-L) is Preferred Over …
M Shirazi, SS Dhavala, D Lord, SR Geedipally
Accident Analysis and Prevention, 2017
152017
A Procedure to Determine When Safety Performance Functions Should Be Recalibrated
M Shirazi, S Geedipally, D Lord
Journal of Transportation Safety & Security, 2016
122016
Improved Guidelines for Estimating the Highway Safety Manual Calibration Factors
D Lord, S Geedipally, M Shirazi
122016
Characteristics Based Heuristics to Select a Logical Distribution between the Poisson-Gamma and the Poisson-Lognormal for Crash Data Modelling
M Shirazi, D Lord
Transportmetrica A: Transport Science, 1-22, 2019
102019
Exploring the Need for Region-Specific Calibration Factors
SR Geedipally, M Shirazi, D Lord
Transportation Research Record: Journal of the Transportation Research Board …, 2017
102017
Estimating the minimal revenue tolls in large-scale roadway networks using the dynamic penalty function method
M Shirazi, HZ Aashtiani, L Quadrifoglio
Computers & Industrial Engineering 107, 120-127, 2017
92017
A Monte-Carlo simulation analysis for evaluating the severity distribution functions (SDFs) calibration methodology and determining the minimum sample-size requirements
M Shirazi, SR Geedipally, D Lord
Accident Analysis & Prevention 98, 303-311, 2017
92017
Solving the minimum toll revenue problem in real transportation networks
M Shirazi, HZ Aashtiani
Optimization Letters 9 (6), 1187-1197, 2015
62015
Familiar versus unfamiliar drivers on curves: naturalistic data study
MP Pratt, SR Geedipally, B Dadashova, L Wu, M Shirazi
Transportation research record 2673 (6), 225-235, 2019
42019
Analyzing Highway Safety Datasets: Simplifying Statistical Analyses from Sparse to Big Data
D Lord, SR Geedipally, F Guo, A Jahangiri, M Shirazi, H Mao, X Deng
Safe-D: Safety Through Disruption, 2019
22019
Assessing Curve Severity and Crash Rates at Horizontal Curves on Rural Two-Lane Highways Using SHRP 2 Safety Data
L Wu, B Dadashova, S Geedipally, MP Pratt, M Shirazi
Transportation Research Board 97th Annual MeetingTransportation Research Board, 2018
22018
Using naturalistic driving study data to explore the association between horizontal curve safety and operation on rural two-lane highways
L Wu, B Dadashova, S Geedipally, MP Pratt, M Shirazi
Journal of Transportation Safety & Security, 1-18, 2019
12019
Evaluating Curve Speed Behavior Using Shrp 2 Data
SR Geedipally, MP Pratt, B Dadashova, L Wu, M Shirazi
ATLAS Center (Mich.), 2017
12017
Using a Prospect Theory Approach to Studying the Car-Following Model
M Khodakarami, Y Zhang, BX Wang, M Shirazi, MS Dastgiri
Advances in Human Aspects of Transportation, 287-300, 2017
12017
A simulation analysis to study the temporal and spatial aggregations of safety datasets with excess zero observations
M Shirazi, SR Geedipally, D Lord
Transportmetrica A: Transport Science 17 (4), 1305-1317, 2021
2021
Examining the feasibility of using naturalistic driving study data for validating speed prediction models
SR Geedipally, MP Pratt, B Dadashova, L Wu, M Shirazi
Transportation research procedia 48, 1084-1094, 2020
2020
Advanced Statistical Methods for Analyzing Crash Datasets with Many Zero Observations and a Long Tail: Semiparametric Negative Binomial Dirichlet Process Mixture and Model …
M Shirazi
2018
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Articles 1–20