Autonomous vehicle (AV) stakeholders continue to seek assurance of the safety performance of this new technology through AV testing on in‐service roads, AV‐dedicated road networks, and AV test tracks. However, recent AV‐related fatalities on in‐service roads have exacerbated public skepticism and eroded some public trust in the safety of AV operations. Further, test tracks are unable to characterize adequately the real‐world driving environment. For this reason, driving simulators continue to serve as an attractive means of AV testing. However, in most AV driving simulators, the AV operation is based on commands external to the vehicle and embedded in the code for the driving environment. To address the simulation shortfalls associated with this approach, this paper develops a deep convolutional neural network–long short‐term memory (CNN–LSTM) algorithm for self‐driving simulation. This algorithm observes and characterizes the AV's driving environment, and controls the AV movement in the driving simulation. The CNN part extracts features that use transfer learning to introduce human prior knowledge, and the LSTM part uses temporal information to process the extracted features, and incorporates temporal dynamics to predict driving decisions. The AV may also use an external server with a database containing road environment data as an additional source of information. It is acknowledged that different driving simulators differ in their functions and their capabilities to access driving‐environment data. Therefore, to make it sufficiently flexible to facilitate replication by other researchers that use driving simulators, the algorithm has been designed and demonstrated using only image data of the driving environment as input. This is because roadway image data are easily and readily accessible from the screen of any driving simulator. The proposed algorithm was tested using the open racing car simulator test track platform and was found to be able to mimic human driving decisions with a high degree of accuracy.

Construction cost overrun and time overrun (delay) are a significant problem in highway-construction project delivery. Previous research studies have provided insight into the factors that affect overruns; however the findings may have been limited because they do not explicitly consider the simultaneous relationship between cost and time overruns. In this paper, we use data from Indiana highway projects to provide empirical evidence that a simultaneous relationship exists between cost and time overruns and that analysis of these two contractual outputs need to take due cognizance of such simultaneity. Using the three-stage least-squares technique, we identify a number of factors that significantly affect cost overrun and time overrun and we show how the effect of these variables vary by attributes such as project type and results of the bidding process. The models developed in this paper can help agencies enhance the estimation of the expected overruns of final cost and the delay in completion time for t...

This study investigates the cost effectiveness of various levels of life-cycle preventive maintenance (PM) for three asphaltic concrete pavement functional class families. For each family, the effectiveness and cost associated with each of several alternative life-cycle PM strategies were estimated. For each strategy, effectiveness was estimated as the increase in service life relative to a base-case strategy, and the cost was estimated in terms of agency and user costs associated with the treatments comprising that strategy. Using the estimated costs and effectiveness, statistical models were developed to describe the relationship between life-cycle PM effort and its efficacy in extending the pavement life, per unit cost. It is shown that increasing PM is generally associated with increasing cost effectiveness (but only up to a certain turning point beyond which cost effectiveness decreases). It was determined that the maximum cost effectiveness and the corresponding level of annualized PM are influenced...

Delays in the completion of highway construction and maintenance projects are important concerns to state highway agencies and contractors alike because such time delays can have a number of adverse consequences, such as extending the duration of active work zones, contributing to road-user dissatisfaction and increasing the risk of litigation regarding delay responsibility. In this paper, using data from 1,722 highway projects in Indiana, random-parameter statistical models are estimated to study the factors that contribute to the likelihood of encountering a project time delay and its duration. The model estimation results show that the likelihood and duration of project time delays are significantly influenced by factors such as project cost (contract bid amount), project type, planned project duration, and the likelihood of adverse weather.