A collision between a truck and an overpass bridge on an interstate highway is rare but can be catastrophic, especially if the bridge involved was designed and built in the early interstate highway era. Such collisions highlight the importance of developing a systematic and scientific method for evaluating at-risk bridges. The findings of this research offer a method for screening the safety risk of highway bridges and identifying bridges that require further review. A risk-based approach has been developed for this study from statistical models, probabilistic theories, and a comprehensive data set. Data include a five-year history of run-off-road (ROR) truck crashes, highway geometric characteristics, and traffic and weather information. The random coefficient Poisson model was used to model truck crashes so that data heterogeneity among highway segments could be captured. Monte Carlo simulation was employed to estimate the collision hazard envelope, given the uncertainties of truck size, encroachment, and vehicle orientation angle. Finally, collision risk was calculated for each bridge bent, and the maximum value was considered as the bridge collision risk. A risk analysis can effectively model rare events when there are uncertainties. Moreover, the bent-specific predictive method improves collision estimate accuracy because the impact is usually between the truck and the bridge bent.

Pavement maintenance solutions have been traditionally developed based mainly on two quantities: pavement condition index (PCI) and structural condition index (SCI). The PCI represents the functional condition, whereas the SCI reflects the structural condition of a pavement. Using a system dynamic concept, this study has developed two approaches for the first time: (1) the PCI approach, where a specific maintenance treatment is applied when the PCI value of a pavement section reaches a minimum defined value; and (2) the combined PCI-SCI approach, where a specific maintenance is applied when either the PCI or the SCI reaches a minimum value. In particular, two different system dynamic modules have been developed to calculate the relative benefit and cost associated with different maintenance scenarios. It is shown that for low initial SCI values, the PCI-SCI approach shows higher relative benefits and lower life-cycle treatment cost than the PCI approach. However, if the difference between the PCI and SCI is more than 10, the PCI-SCI approach does not give a higher benefit and is thus not a recommended approach for airport pavement. Airports with higher initial PCI have lower functional benefits and lower life cycle cost (LCC) for different maintenance solutions.

Precise milepoint measurement data are essential for better subway track management and maintenance practices within railroads and subways. For milepoint estimation, dead reckoning systems of some subway track geometry cars use as pesudolite markers having no unique identification information. In such cases, milepoint measurement data have to be postprocessed. However, the postprocessing is conducted in a manual fashion and is time consuming and labor intensive. This paper presents an optimal match method to automatically postprocess milepoint measurement data. The presented method consists of three submodels: (1) dynamic-programming-based distribution-pattern match model for differentiating actual markers from false-positive ones, (2) correlation-analysis-based algorithm determining milepoints for recognized markers, and (3) a linear interpolation equation for milepoint revision. The method was applied to 124 inspection runs for 15 tracks of the Beijing subway system whose track geometry car is such a case. It is shown that the developed method outperforms the manual method in milepoint accuracy. It takes the developed method less than 3 min to complete milepoint revision for an inspection run of the longest track in Beijing subways.

Real survey data are of considerable significance for transit planners and operators to assess the performance of a bus route and then improve its level of service. Automatic vehicle location (AVL) system is a convenient tool to collect a large amount of real data from buses. This paper aims to propose a methodology to evaluate the operational performance of a bus route based on AVL data. First, several statistical indexes are selected for the evaluation, including percentile travel times, coefficient of variation (COV) of travel times, and average commercial speed and travel time distribution. Moreover, spatial and temporal features of travel time variation and transit regulation indexes are analyzed. Then the bus route with transit signal priority and dedicated bus lane in Suzhou, China, is taken as a case study to validate the proposed methodology. Numerical tests indicate that the most influential feature of travel time is its spatial and temporal patterns, which vary across segments and time-of-day intervals. Bus lane violation and route repetition may undermine the efficiency of priority measures. In addition, the schedule design has important impacts on the adherence and headway regularity.

This paper investigates the effects of segment length, line types, and imputed retroreflectivity values on pavement-marking retroreflectivity and crash frequency analyses. Road data, crash data, pavement condition data, and pavement-marking retroreflectivity data from Iowa Department of Transportation (DOT) databases were acquired and spatially integrated. Data sets for 1-, 3-, and 5-mi segments were prepared to investigate the effect of segmentation. Additional data sets with imputed and measured retroreflectivity data were prepared for comparison. A series of negative binomial regression analyses were run to estimate the expected number of crashes on varying segment lengths and data subsets based on the two retroreflectivity collection methods. The findings show that using smaller segments and data sets with measured retroreflectivity rather than imputed retroreflectivity leads to a more significant relationship between the retroreflectivity of longitudinal pavement markings and crash frequency. The findings also suggest that keeping longitudinal pavement markings in good condition has significant positive effects on safety. The results further suggested that the expected number of annual crashes significantly decreased with the increasing retroreflectivity of white-edge lines (WELs) and yellow-edge lines (YELs) for four-lane road segments. In addition, a significant relationship between pavement condition, measured with the International Roughness Index (IRI) and the expected number of crashes was found for all data sets.

The precast ballastless track slab has been widely applied in the Chinese high-speed railway system. However, the current slabs, made of normal concrete (NC) materials and designed in a “plane” structural configuration, were found vulnerable to the train loads and temperature effects. This paper proposed an innovative slab type, which was made of an alternative reactive powder concrete (RPC) material and designed in a “frame” structural type. Four frame slabs with different cross section profiles were parametrically investigated, and the best configuration was proposed. The results demonstrated the proposed RPC slabs can well meet the requirements of the stiffness and strength. Compared with the NC plane slabs, the warping deformation of the RPC frame slabs were decreased as much as by 41.7% and 37.0%, respectively, under the $-10°\mathrm{C}$ temperature gradients and the combination of train and temperature effects. The simulated tensile stresses were within the corresponding RPC strength capacity. The crack and arch failures at the slab, cement and asphalt (CA) mortar, and concrete roadbed were significantly minimized.

The approach to spot speed prediction in two-lane highways followed in this study aims to evaluate the effects of a comprehensive set of speed factors, with a special focus on the geometric characteristics of the road segment to which the element belongs. Two flexible models were developed for different types of roads based on a stochastic frontier formulation in which the maximum operating speed is estimated as a function of road geometrics, and the one-sided disturbance accounts for diversity in driving behavior and vehicle characteristics, allowing the estimation of any percentile speed. The models are applicable to horizontal curves and tangents and consider both on-site characteristics and aggregated variables characterizing the road segment. The results show a clear influence of segment features on different percentile spot speeds, revealing that recent driving experience and expectations about the quality of the geometric design influence the way a driver approaches a specific road element. Additionally, this study contributes to addressing some of the limitations of existing speed models identified in the literature.