Development of crash prediction models with individual vehicular data

Chen

Journal of Advanced Transportation

et al. 2018

The objective of this study is to investigate lane-changing characteristics in freeway off-ramp areas using Shanghai Naturalistic Driving Study (SH-NDS) data, considering a four-lane freeway stretch in various traffic conditions. In SH-NDS, the behavior of drivers is observed unobtrusively in a natural setting for a long period of time. We identified 433 lane-changing events with valid time series data from the whole dataset. Based on the logit model developed to analyze the choice of target lanes, a likelihood analysis of lane-changing behavior was graphed with respect to three traffic conditions: free flow, medium flow, and heavy flow. The results suggested that lane-changing behavior of exiting vehicles is the consequence of the balance between route plan (mandatory incentive) and expectation to improve driving condition (discretionary incentive). In higher traffic density, the latter seems to play a significant role. Furthermore, we found that lane-change from the slow lane to the fast lane would lead to higher speed variance value, which indicates a higher crash risk. The findings contribute to a better understanding on drivers' natural driving behavior in freeway off-ramp areas and can provide important insight into road network design and safety management strategies.

Section: Introductionmentioning

confidence: 99%

Modeling Lane-Changing Behavior in Freeway Off-Ramp Areas from the Shanghai Naturalistic Driving Study

Chen

Journal of Advanced Transportation

et al. 2018

“…However, they have rarely been adopted in road safety literature (Boucher, Santolino 2010;Hosseinpour et al 2013;Son et al 2011). …”

Section: Hurdle Modelsmentioning

confidence: 99%

“…The comparison and selection among the candidate models is based on the presence and the source of overdispersion in the crash data (Son, 2011). To check if over-dispersion exists in the rollover crashes, a Wald t-statistical test on the dispersion parameter and a likelihood ratio test (LRT) were performed where the Poisson, NB, HP, and ZIP models were nested within the NB, HTNB, HNB, and ZINB models, respectively (Isgin et al 2008).…”

Section: Model Selection Criteriamentioning

confidence: 99%

Evaluating the Effects of Road Geometry, Environment, and Traffic Volume on Rollover Crashes

et al. 2016

There are a number of factors that cause motor vehicles to rollover. However, the impacts of roadway characteristics on rollover crashes have rarely been addressed in the literature. This study aims to apply a set of crash prediction models in order to estimate the number of rollovers as a function of road geometry, the environment, and traffic conditions. To this end, seven count-data models, including Poisson (PM), negative binomial (NB), heterogeneous negative binomial (HTNB), zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), hurdle Poisson (HP), and hurdle negative binomial (HNB) models, were developed and compared using crash data collected on 448 segments of Malaysian federal roads. The results showed that the HTNB was the best-fit model among the others to model the frequency of rollovers. The variables Light-Vehicle Traffic (LVT), horizontal curvature, access points, speed limit, and centreline median were positively associated with the crash frequency, while UnPaved Shoulder Width (UPSW) and Heavy-Vehicle Traffic (HVT) were found to have the opposite effect. The findings of this study suggest that rollovers could potentially be reduced by developing road safety countermeasures, such as access management of driveways, straightening sharp horizontal curves, widening shoulder width, better design of centreline medians, and posting lower speed limits and warning signs in areas with higher rollover tendency.

Transportation Research Part C: Emerging Technologies

“…Other research studies also correlated the actual crash data with microsimulation-based surrogate safety measures to develop credible crash prediction models, as well as to examine the relationships between real world conflicts and surrogate measures (Son et al, 2011;Park et al, 2011). Tan et al (2012) developed microscopic simulation model which reasonably represented safety related parameters of signalized intersections which could be used for signal optimization and evaluation of various countermeasures.…”

Section: Safety Metrics As Part Of Traffic Signal Optimizationsmentioning

confidence: 99%

Multi-criteria optimization of traffic signals: Mobility, safety, and environment

Stevanović

Stevanovic

et al. 2015

a b s t r a c tTwo-dimensional multi-objective optimizations have been used for decades for the problems in traffic engineering although only few times so far in the optimization of signal timings. While the other engineering and science disciplines have utilized visualization of 3-dimensional Pareto fronts in the optimization studies, we have not seen many of those concepts applied to traffic signal optimization problems. To bridge the gap in the existing knowledge this study presents a methodology where 3-dimensional Pareto Fronts of signal timings, which are expressed through mobility, (surrogate) safety, and environmental factors, are optimized by use of an evolutionary algorithm. The study uses a segment of 5 signalized intersections in West Valley City, Utah, to test signal timings which provide a balance between mobility, safety and environment. In addition, a set of previous developed signal timing scenarios, including some of the Connected Vehicle technologies such as GLOSA, were conducted to evaluate the quality of the 3-dimensional Pareto front solutions. The results show success of 3-dimensinal Pareto fronts moving towards optimality. The resulting signal timing plans do not show large differences between themselves but all improve on the signal timings from the field, significantly. The commonly used optimization of standard single-objective functions shows robust solutions. The new set of Connected Vehicle technologies also shows promising benefits, especially in the area of reducing inter-vehicular friction. The resulting timing plans from two optimization sets (constrained and unconstrained) show that environmental and safe signal timings coincide but somewhat contradict mobility. Further research is needed to apply similar concepts on a variety of networks and traffic conditions before generalizing findings.