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The purpose of this paper is to evaluate whether the additional infrastructure information in US state financial statements improves infrastructure quality.

Based on institutional theory, the authors developed six models and estimated them on a state panel data set.

The authors found that the implementation of the Government Accounting Standard Board (GASB) Statement No. 34 improved state highway infrastructure quality, and the states using the modified approach had a larger effect compared to the states using depreciation accounting. The authors further used a two-step path analysis and found that the implementation of GASB 34 indirectly improved highway quality through increasing state highway maintenance expenditures. From the empirical results, the authors conclude that the exercise of collecting and developing systems to track the additional data has provided the opportunity for officials to use the information to prioritize limited funding and improve their asset management practices.

Future research may extend this research by exploring the detailed micro-mechanisms of how decision makers use infrastructure information in their asset management practices, as well as by increasing the number of years in the panel data set to fully capture changes in behavior.

In addition, governments currently using depreciation should be encouraged to move to the modified approach.

This is the first attempt to empirically examine the effects of GASB 34 on infrastructure condition.

The purpose of this paper is to present a methodology for estimating the “price,” or the not-to-loss value, of individual highway assets, which reflects not only the assets’ capital value but also economic productivity, by adopting a productivity-based asset valuation framework. The price tags can be used in prioritizing highway assets in support of transportation asset management processes.

The methodology adopts the utility theory to consider multiple performance measures reflecting the economic productivity generated by the assets, as well as their capital value. Key performance measures are first selected, and their values are retrieved from highway asset management databases. Next, the utility functions representing decision makers’ preferences convert the performance measures into utility values, which adjust the replacement cost (RC) of each highway asset to estimate price tags. To demonstrate its applicability, case studies were conducted for the highway networks of Texas and Washington State in the USA.

The methodology yielded price tags that better reflect the importance of highways’ roles in the economy in comparison to methods where only RCs are used. Furthermore, it was proven to be flexible enough to accommodate local conditions such as varying data availability.

The research provides a practical and reasonable way to prioritize critical highway assets in purport of maintenance and rehabilitation resource allocations, based on their economic productivity as well as physical condition and historical cost information, enhancing the overall efficiency and effectiveness of highway asset management.

This paper examines the relationship between transport connectivity and regional economic development in China. It develops measurements appropriate for transport connectivity based on a set of evaluation models. This model is used to analyze the logistic connectivity of China’s 31 provinces by focusing on 11 variables, including some new factors (Density of road network, Density of railway network, Number of Internet Users) not used in previous studies, over the 13-year period from 2002 to 2014. Using panel data regression analysis, the empirical results show a statistically significant and positive impact of transport connectivity (factors like Density of road network, Density of railway network and Number of Internet Users) on economic development in China. In particular, the Number of internet users is a key factor reflecting information connectivity in all the variables. Comparative analysis regarding economic development is conducted to benchmark between coastal provinces and interior provinces. Like most previous research, this study yields the same finding of higher impact of transport connectivity on economic development in eastern provinces than in western provinces. This study suggests that decentralized decision-making will be significantly more efficient for analyzing regional infrastructure development. It also shows that the influence of transport connectivity on economic development is dependent on a certain developmental stage. This suggests that an economic region should adopt different development strategies for transport connectivity during different stages of development.

The anticipated benefits of connected and autonomous vehicles (CAVs) include safety and mobility enhancement. Small headways between successive vehicles, on one hand, can cause increased capacity and throughput and thereby improve overall mobility. On the other hand, small headways can cause vehicle occupant discomfort and unsafety. Therefore, in a CAV environment, it is important to determine appropriate headways that offer a good balance between mobility and user safety/comfort.

In addressing this research question, this study carried out a pilot experiment using a driving simulator equipped with a Level-3 automated driving system, to measure the threshold headways. The Method of Constant Stimuli (MCS) procedure was modified to enable the estimation of two comfort thresholds. The participants (drivers) were placed in three categories (“Cautious,” “Neutral” and “Confident”) and 250 driving tests were carried out for each category. Probit analysis was then used to estimate the threshold headways that differentiate drivers' discomfort and their intention to re-engage the driving tasks.

The results indicate that “Cautious” drivers tend to be more sensitive to the decrease in headways, and therefore exhibit greater propensity to deactivate the automated driving mode under a longer headway relative to other driver groups. Also, there seems to exist no driver discomfort when the CAV maintains headway up to 5%–9% shorter than the headways they typically adopt. Further reduction in headways tends to cause discomfort to drivers and trigger take over control maneuver.

In future studies, the number of observations could be increased further.

The study findings can help guide specification of user-friendly headways specified in the algorithms used for CAV control, by vehicle manufacturers and technology companies. By measuring and learning from a human driver's perception, AV manufacturers can produce personalized AVs to suit the user's preferences regarding headway. Also, the identified headway thresholds could be applied by practitioners and researchers to update highway lane capacities and passenger-car-equivalents in the autonomous mobility era.

The study represents a pioneering effort and preliminary pilot driving simulator experiment to assess the tradeoffs between comfortable headways versus mobility-enhancing headways in an automated driving environment.

Hiring, selecting or assessing plant operatives' proficiency in the UK construction industry is an increasingly difficult task. A number of plant operator certification schemes are available to practitioners and each scheme trains to a myriad of bespoke standards. Consequently, the decision to employ a candidate often rests upon the employer's intuition and judgement and creates an unnecessary dilemma. To address this aforementioned problem, findings of research work that modelled plant operators' maintenance proficiency is presented. A UK nationwide survey was conducted to elicit plant professional opinion on what ‘training and educational’ (T&E) attributes constitute ‘good’ operator proficiency. The data was then arranged into three categories of operator maintenance proficiency: good, average and poor Multivariate Discriminant Analysis (MDA) was used on 75 percent of a simulated data set. The model utilised five T&E attributes, namely: duration of training provided, operator holder of alternative training card (not Certificate of Training Achievement (CTA) or Scottish/National Vocational Qualifications (S/NVQ)), operator's oral communication skills, operator's planning skills and operator's mechanical knowledge. Performance analysis revealed that model classification accuracy was 89.10 percent. The remaining 25 percent hold out sample was then modelled for validation purposes using the derived MDA model. Accuracy of the sub‐sample model was high at 77.60 percent whilst a paired sample T‐tests for the 75 percent and 25 percent sample data established that there was no significant statistical difference between actual and predicted classifications. Future work is proposed that aims to model other factors that influence operator maintenance proficiency; namely, work situational, motivational management and personal factors.