Changwei Yuan

A risk-based optimisation for pavement preventative maintenance with probabilistic LCCA: a Chinese case

Compared to major structural repair or even replacement, preventative preservation of in-service pavements has been more popular in engineering practices, but recently, pavement preventative maintenance (PPM) has become more complex in China as the competition for pavement preservation funds has grown and the need to justify decisions has increased. Therefore, the life cycle cost analysis (LCCA) has increasingly attracted attention from transportation agencies. However, most of previous studies were conducted deterministically or only focused on a single factor, while PPM is apparently affected by many potential sources of uncertainty. The risk-based analysis to investigate potential risks and combined effects of multiple factors is a necessary component of PPM. This paper aims at presenting a risk-based PPM with the probabilistic LCCA for a Chinese highway case. Major analysis variables of different range are examined to probe risks of different scenarios, investigate combined effects of multiple variables and identify an optimal preservation strategy.

The assessment of damage to Texas highways due to oversize and overweight loads considering climatic factors

ABSTRACT Transportation agencies should have the capacity to evaluate the damage caused by oversize and overweight (OS/OW) vehicles in order to develop effective infrastructure management and rehabilitation strategies. In this paper, we discussed how to combine different paradigms that influence pavement performance into a single evaluation methodology by integrating the most available historical data with respect to the characteristics of OS/OW vehicles (i.e. dimension and weight), their origin and destination, permitted routes, frequency of the routes, pavement condition data, and climatic effects. The proposed methodology is then implemented into three case studies to indicate its applicability and practicality. In the case studies, we evaluate the impacts of OS/OW loads on pavements under different scenarios and combinations of the related factors. The corresponding relationships between pavement conditions and passing OS/OW loads are quantified with a well-accepted sigmoidal function. The results also indicate that, at the early age of the road, higher OS/OW loading would bring a faster deterioration rate (e.g. about 6% of the reduction in service life for extreme high OS/OW loading, while only 2.35% for low OS/OW loading). At the end of road life, the reduction trend slows down, nearly above 2% for all OS/OW loading levels.

Evaluation, Classification, and Influential Factors Analysis of Traffic Congestion in Chinese Cities Using the Online Map Data

This study proposes a new method to describe, compare, and classify the traffic congestion states in 23 Chinese cities using the online map data and further reveals the influential factors that may affect them. First, the real-time traffic congestion information is obtained from the online map of AutoNavi in a 15-minute interval. Next, a new measuring index is introduced to describe the overall characterization of congestion patterns in each city based on online map data, which is named as the congestion ratio. The next analysis is the cluster analysis based on the temporal distribution of the congestion ratio, which helps to identify groups of the selected cities with similar traffic congestion states. These cities are categorized as four groups according to the severity of traffic congestion: severely congested, less severely congested, amble, and smooth cities. Lastly, multiple linear regression models are developed to identify the primary factors that affect the congestion ratio. The result shows that the influences of per capita road area, car ownership, and vehicle miles traveled (VMT) on the congestion ratio are significant. Sensitivity analyses are also implemented in order to reveal more effective policy measures in mitigating traffic congestion in urban areas.

Modeling and Analyzing Taxi Congestion Premium in Congested Cities

Traffic congestion is a significant problem in many major cities. Getting stuck in traffic, the mileage per unit time that a taxicab travels will decline significantly. Congestion premium (or so-called low-speed fare) has become an increasingly important income source for taxi drivers. However, the impact of congestion premium on the taxicab market is not widely understood yet. In particular, modeling and analyzing of the taxi fare structure with congestion premium are extremely limited. In this paper, we developed a taxi price equilibrium model, in which the adjustment mechanism of congestion premium on optimizing the taxi driver’s income, balancing the supply and demand, and eventually improving the level of service in the whole taxicab market was investigated. In the final part, we provided a case study to demonstrate the feasibility of the proposed model. The results indicated that the current taxi fare scheme in Beijing is suboptimal, since the gain from the raise of congestion premium cannot compensate for the loss from the demand reduction. Conversely, the optimal fare scheme suggested by our model can effectively reduce the excessive demand and reach the supply-demand equilibrium, while keeping the stability of the driver’s income to the maximum extent.