Effects of work zone configurations and traffic density on performance variables and subjective workload

Influence Range and Traffic Risk Analysis of Moving Work Zones on Urban Roads

There is a body of literature on the influence range and traffic risk of fixed work zones. However, relatively few studies have examined the effect of ubiquitous moving operating vehicles, such as road cleaners, on urban roads. The influence of low speed moving work zones on road traffic flow and traffic risk is still unclear. In this work, we used simulations to establish an urban expressway three lanes VISSIM model, and selected the road traffic volume and speed of the moving work zone as the independent variables. We analyzed the range of influence of the moving work zone on the rear vehicles in the left, middle and right lanes of the urban expressway and the traffic risk variation law caused by the moving work zone. The results show that the left lane was indirectly affected by the moving work zone when the traffic volume reached 2000 pcu/h. The influence of the moving work zone on the middle lane was controlled by the traffic volume and the speed of the moving work zone. Both the left and middle lanes were mainly impacted by vehicles changing lane from the right lane. Regardless of the traffic volume and the speed of the moving work zone change, the vehicles 200 m behind a moving work zone will be directly affected in the right lane. Furthermore, the average traffic risk is the highest within 50 m of the moving work zone in the right lane. When the traffic volume decreases and the speed of the moving work zone increases, the average traffic risk decreases gradually. These results provide a scientific basis for the operation and management of moving working vehicles on urban roads.

The role of pre-crash driving instability in contributing to crash intensity using naturalistic driving data

While the cost of crashes exceeds $1 Trillion a year in the U.S. alone, the availability of high-resolution naturalistic driving data provides an opportunity for researchers to conduct an in-depth analysis of crash contributing factors, and design appropriate interventions. Although police-reported crash data provides information on crashes, this study takes advantage of the SHRP2 Naturalistic Driving Study (NDS) which is a unique dataset that allows new insights due to detailed information on driver behavior in normal, pre-crash, and near-crash situations, in addition to trip and vehicle performance characteristics. This paper investigates the role of pre-crash driving instability, or driving volatility, in crash intensity (measured on a 4-point scale from a tire-strike to an injury crash) by analyzing microscopic vehicle kinematic data. NDS data are used to investigate not only the vehicle movements in space but also the instability of vehicles prior to the crash and their contribution to crash intensity using path analysis. A subset of the data containing 617 crash events with around 0.18 million temporal trajectories are analyzed. To quantify driving instability, microscopic variations or volatility in vehicular movements before a crash are analyzed. Specifically, nine measures of pre-crash driving volatility are calculated and used to explain crash intensity. While most of the measures are significantly correlated with crash intensity, substantial positive correlations are observed for two measures representing speed and deceleration volatilities. Modeling results of the fixed and random parameter probit models revealed that volatility is one of the leading factors increasing the probability of a severe crash. Additionally, the speed prior to a crash is highly correlated with intensity outcomes, as expected. Interestingly, distracted and aggressive driving are highly correlated with driving volatility and have substantial indirect effects on crash intensity. With volatile driving serving as a leading indicator of crash intensity, given the crashes analyzed in this study, early warnings and alerts for the subject vehicle driver and proximate vehicles can be helpful when volatile behavior is observed.

Optimizing Strategies for the Urban Work Zone with Time Window Constraints

Work zones that move with road maintenance tasks are enclosing and have caused severe traffic jams and the significant decline of road capacity. This paper proposes an intelligent-based multi-objects road maintenance optimization strategy based on a practical origin–destination (OD) matrix and complicated work schedules over a real urban road network. It focuses on the optimization of multi short-term maintenance tasks and the minimization of average travel delay for vehicles passing through. By taking the driving characteristic into account, static and dynamic variable speed limit strategies provide access to ensure safety on the working road network. Through this view, the problem was formulated as a mixed multi-object nonlinear program (MNLP) model with respect to the time window of the related sub-maintenance task. By using actual OD distribution matrix data, a series of microscopic simulated cases were conducted to test the model’s validity. Moreover, sensitive analyses of types of parameters (e.g., traffic safety threshold, traffic flow and working efficiency) with an optimal solution were discussed considering five different scenarios.

Drivers׳ merging behavior data in highway work zones

There have been growing research interests in finding a suitable work zone layout to improve work zone safety and traffic efficiency. This paper contains data supporting the research article entitled: Effects of work zone configurations and traffic density on performance variables and subjective workload (Shakouri et al., 2014 [1]). A full factorial experiment was conducted to compare the efficiency of two work zone configurations by using a driving simulator with two levels of work zone configuration, two levels of traffic density and three levels of sign placement as fixed factors. Seven female and 23 male participants completed the experiment. In this paper we present the data relating to demographic information of participants, driving simulator data and subjective workload evaluation of participants for each work zone.