An application of Bayesian multilevel model to evaluate variations in stochastic and dynamic transition of traffic conditions

This study seeks to investigate the variations associated with lane lateral locations and days of the week in the stochastic and dynamic transition of traffic regimes(DTTR).In the proposed analysis,hierarchical regression models fitted using Bayesian frameworks were used to calibrate the transition probabilities that describe the DTTR.Datasets of two sites on a freeway facility located in Jacksonville,Florida,were selected for the analysis.The traffic speed thresholds to define traffic regimes were estimated using the Gaussian mixture model(GMM).The GMM revealed that two and three regimes were adequate mixture components for estimating the traffic speed distributions for Site 1 and 2 datasets,respectively.The results of hierarchical regression models show that there is considerable evidence that there are heterogeneity characteristics in the DTTR associated with lateral lane locations.In particular,the hierarchical regressions reveal that the breakdown process is more affected by the variations compared to other evaluated transition processes with the estimated intra-class correlation(ICC)of about 73%.The transition from congestion on-set/dissolution(COD)to the congested regime is estimated with the highest ICC of 49.4%in the three-regime model,and the lowest ICC of 1%was observed on the transition from the congested to COD regime.On the other hand,different days of the week are not found to contribute to the variations(the highest ICC was 1.44%)on the DTTR.These findings can be used in developing effective congestion countermeasures,particularly in the application of intelligent transportation systems,such as dynamic lane-management strategies.

Incorporating travel time reliability in predicting the likelihood of severe crashes on arterial highways using non-parametric random-effect regression

Travel time reliability(TTR) modeling has gain attention among researchers’ due to its ability to represent road user satisfaction as well as providing a predictability of a trip travel time.Despite this significant effort,its impact on the severity of a crash is not well explored.This study analyzes the effect of TTR and other variables on the probability of the crash severity occurring on arterial roads.To address the unobserved heterogeneity problem,two random-effect regressions were applied;the Dirichlet random-effect(DRE)and the traditional random-effect(TRE) logistic regression.The difference between the two models is that the random-effect in the DRE is non-parametrically specified while in the TRE model is parametrically specified.The Markov Chain Monte Carlo simulations were adopted to infer the parameters’ posterior distributions of the two developed models.Using four-year police-reported crash data and travel speeds from Northeast Florida,the analysis of goodness-of-fit found the DRE model to best fit the data.Hence,it was used in studying the influence of TTR and other variables on crash severity.The DRE model findings suggest that TTR is statistically significant,at 95 percent credible intervals,influencing the severity level of a crash.A unit increases in TTR reduces the likelihood of a severe crash occurrence by 25 percent.Moreover,among the significant variables,alcohol/drug impairment was found to have the highest impact in influencing the occurrence of severe crashes.Other significant factors included traffic volume,weekends,speed,work-zone,land use,visibility,seatbelt usage,segment length,undivided/divided highway,and age.

Assessment of traffic performance measures and safety based on driver age and experience:A microsimulation based analysis for an unsignalized T-intersection

Traffic safety and performance measures such as crash risk and queue lengths or travel times are influenced by several important factors including those related to environment,human,and roadway design,especially at intersections.Previous research has studied different aspects related to these factors,yet these characteristics are not fully investigated with a focus on age and experience of drivers.In this paper,we investigate this issue by using a two-phase approach via a case study application on a critical T-intersection in the City of Tallahassee,Florida.The first phase includes a scenario-based microsimulation analysis through the use of a microscopic simulation software,namely VISSIM,to illustrate the variations in traffic performance measures with respect to driver compositions of different age groups in the traffic stream.A variety of scenarios is created where the driving characteristics are provided as inputs to these scenarios in terms of decision making and risk taking.This is also supported by a sensitivity analysis conducted based on the driver composition in the traffic.The second phase includes the analysis of microsimulation outputs via a tool developed by Federal Highway Administration tool,namely the Surrogate Safety Assessment Model(SSAM),in order to determine the traffic conflicts that occur in each scenario.These conflicts are also compared with real-life crash data for validation purposes.Results show that(a) the differences in risk perception that affect driving behavior might be significant in influencing traffic safety and performance measures,and(b) the proposed approach is considerably successful in simulating the actual crash conflict points.