Search

Although road surface friction is considered the most effective performance measure for maintenance operations, it is not commonly used due to the high cost of collection. As a result, most jurisdictions use subjective visual indicators that qualitatively describe the state of the road surface, even though they create measurement inconsistencies and offer less detailed maintenance tracking. For maintenance personnel to transition into using friction, the collection cost must be reduced. This paper attempts to do so by proposing a low-cost, machine-learning-based method for predicting road surface friction using dash camera imagery and demonstrates its feasibility through a case study. The dataset used for this project was collected in the City of Edmonton, Alberta, during its 2021/2022 winter season. Three models were developed using tree-based algorithms, where all three displayed high performance with an average RMSE of 0.0796 or 79.3% accuracy based on RMSPE.

Road surface condition (RSC) is an important performance indicator for winter road maintenance personnel to maintain safe driving conditions. This becomes more apparent in inclement weather events where timely clearing of snow is highly prioritized. Considering the vast road networks that need to be covered, many transportation agencies have been using camera images to view real-time RSC directly; however, monitoring conditions via these cameras is still being done manually, thereby hindering its full utilization for optimizing maintenance services. Many studies have attempted to develop a deep-learning-based approach known as convolution neural network (CNN) to automate the process of RSC image classification. When implemented, RSC can be extracted from road images without human involvement. However, efforts made thus far have been focused on rural highways, with performance in the urban context being the least explored. Furthermore, CNN models developed in previous studies have been trained either from scratch or via transfer learning, but only a few studies have investigated transfer learning using a pre-trained RSC model. To address these gaps, an urban RSC model was developed in this study via transfer learning using a pre-trained RSC CNN model. The image dataset used contains 3914 urban images collected in a residential area south of Edmonton, Alberta. With these images, the pre-trained RSC model was fine-tuned via transfer learning and underwent hyperparameter optimization to boost performance further, yielding a high classification accuracy of 98.21% and an F1-score of 98.4%, which surpassed the accuracy of the model trained from scratch.

Pavement friction has been widely recognized as an important winter road maintenance performance indicator for objectively assessing the level of service required to maintain safe driving conditions during inclement weather events. Despite significant efforts being put forth by road agencies, prevailing road surface conditions during snowstorms can yield negative consequences that compromise the safety of the traveling public. Acknowledging the vast road network that needs to be monitored and the uncertainty associated with the randomness of hazardous road weather conditions, this paper presents a novel event-based framework aimed at investigating the magnitude of the effect of varying pavement friction levels in urban environments on traffic safety during snowstorms. Negative binomial safety performance functions developed using hourly weather datasets and road surface conditions information found a strong statistically significant relationship between pavement friction and traffic safety. This meant that, with the accumulation of snow and ice during snowstorms, road surface conditions were found to deteriorate thereby increasing the likelihood of collision occurrence. The event-based models developed also suggested that the risks of driving during snowstorms varied dramatically depending on the varying surface states, represented by friction coefficient ranges. Collisions were expected to significantly decrease whenever the pavement friction was above 0.6; under conditions where pavement friction was below 0.35, collisions were predicted to significantly increase. Additionally, arterial roads were shown to experience a significantly higher number of collisions than collectors, further justifying why arterials should be prioritized in snow clearing policies that most cities adopt.

Traffic volume information has long played an important role in many transportation related works, such as traffic operations, roadway design, air quality control, and policy making. However, monitoring traffic volumes over a large spatial area is not an easy task due to the significant amount of time and manpower required to collect such large-scale datasets. In this study, a hybrid geostatistical approach, named Network Regression Kriging,has been developed to estimate urban traffic volumes by incorporating auxiliary variables such as road type, speed limit, and network accessibility.Since standard kriging is based on Euclidean distances, this study implements road network distances to improve traffic volumes estimations.A case study using 10-year of traffic volume data collected within the city of Edmonton was conducted to demonstrate the robustness of the model developed herein. Results suggest that the proposed hybrid model significantly outperforms the standard kriging method in terms of accuracy by 4.0% overall, especially for a large-scale network. It was also found that the necessary stationarity assumption for kriging did not hold true for a large network whereby separate estimations for each road type performed significantly better than a general estimation for the overall network by 4.12%.

This study developed a series of strategies to reassess the service level of winter road maintenance (WRM) operations on the road network in Perth County, Ontario, Canada. These strategies include rearranging plow routes by sharing the material storage yards and optimizing their numbers and locations while considering operational and legislative constraints such as total circuit time, operating speeds, and the number of available trucks. A methodological framework was then formulated and solved by tackling four practical, yet challenging, tasks in a sequential manner. The first task was to identify existing plow routes and construct a detailed GIS database that was later used for benchmarking the current WRM operations. The second task involved the optimization of existing plow routes, while the third task focused on the optimization of plow routes and depot locations by amalgamating WRM operations of different municipalities within Perth County. The final task was to conduct a clean slate optimization to see the benefits of combining depots and route sharing. The optimization models developed were implemented to transform the existing maintenance operations networks to an optimized, integrated, and unified network of routes, with improved land use utilization for road maintenance yards. The framework then captured the impact of the service order of maintenance routes and arranged routing priority efficiently using the tabu search algorithm. The findings also revealed that, by sharing depots, the county could retire three depots and eight trucks while still meeting legislative requirements, thereby suggesting the potential for substantial monetary savings.

Optimal RWIS network can be defined as an RWIS configuration where the total number of stations (RWIS density) are determined based on a well-established guideline and the locations are allocated systematically assuming that it will provide the maximum monitoring coverage of the network. This paper examines and quantifies the benefit of an optimized RWIS network and how these benefits impact traffic safety. The methodological framework presented herein builds upon our previous efforts in RWIS location-allocation, where the kriging variance is used as a performance indicator for monitoring coverage. In this study, the network coverage index (NCI) parameter is proposed to gauge RWIS network performance and quantitatively evaluate its impact on traffic safety. The findings of this study reveal a strong dependency between the NCI and the RWIS network configuration. In terms of traffic safety, the relationship between NCI and safety effectiveness can be expressed as a polynomial function, where the two are proportional to one another. In the state of Iowa, an RWIS network with 80% monitoring coverage (NCI = 0.8) can reduce additional 40 collisions per site annually compared to a network without RWIS stations. Based on the findings obtained in this study, road agencies and RWIS planners can now be assisted with conceptualizing the capabilities of an optimized RWIS network, which will help them increase monitoring coverage, and in the process, gain a quantitative understanding on its potential impact on traffic safety.

Snowplowing is an indispensable part of winter road maintenance because it contributes to improving drivers’ mobility and safety. Most prior studies, however, limit focus to operational aspects without considering interaction with road users. This paper aims to minimize travel time in the traffic system while still maintaining the efficiency of plowing activities. The k-truck plowing with the precedence model was formulated to link road users with snowplow activities. The tabu search algorithm then followed to optimize the order of precedence of snowplow routes. Furthermore, the discrete-event simulation was used to quantify the effectiveness of the proposed method based on different scenarios and fleet sizes, and to illustrate the potential benefit of integrating both the operators’ and users’ perspectives. The methodological framework developed herein can be used to design a routing strategy that improves the performance of the snowplow trucks by reducing both plowing completion and road users’ total travel time.

Winter driving conditions pose a real hazard to road users with increased chance of collisions during inclement weather events. As such, road authorities strive to service the hazardous roads or collision hot spots by increasing road safety, mobility, and accessibility. One measure of a hot spot would be winter collision statistics. Using the ratio of winter collisions (WC) to all collisions, roads that show a high ratio of WC should be given a high priority for further diagnosis and countermeasure selection. This study presents a unique methodological framework that is built on one of the least explored yet most powerful geostatistical techniques, namely, regression kriging (RK). Unlike other variants of kriging, RK uses auxiliary variables to gain a deeper understanding of contributing factors while also utilizing the spatial autocorrelation structure for predicting WC ratios. The applicability and validity of RK for a large-scale hot spot analysis is evaluated using the northeast quarter of the State of Iowa, spanning five winter seasons from 2013/14 to 2017/18. The findings of the case study assessed via three different statistical measures (mean squared error, root mean square error, and root mean squared standardized error) suggest that RK is very effective for modeling WC ratios, thereby further supporting its robustness and feasibility for a statewide implementation.

To facilitate more efficient winter maintenance decision support, road weather information systems (RWIS) have been widely used by highway agencies. However, the cost of RWIS stations is high, and they have limited monitoring coverage. To address this challenge, this paper presents an innovative framework that applies regression kriging to integrate stationary and mobile RWIS data to improve the accuracy of road surface temperature (RST) estimation. Furthermore, an optimal RWIS network expansion strategy is introduced by incorporating a modified particle swarm optimization method with the objective of minimizing spatially averaged kriging estimation errors. A sensitivity analysis is also conducted to investigate the influence of station densities on model performance. The case study from Alberta, Canada, demonstrates the feasibility and applicability of the proposed method. The findings provide insights for continuous monitoring and visualization of both road weather and surface conditions and for optimizing RWIS network planning.

Speeding is a leading factor that contributes to approximately one-third of all fatal collisions. Over the past decades, various passive/active countermeasures have been adopted to improve drivers’ compliance to posted speed limits to improve traffic safety. The driver feedback sign (DFS) is considered a low-cost innovative intervention that is being widely used, in growing numbers, in urban cities to provide positive guidance for motorists. Despite their documented effectiveness in reducing speeds, limited literature exists on their impact on reducing collisions. This study addresses this gap by designing a before-and-after study using the empirical Bayes method for a large sample of urban road segments. Safety performance functions and yearly calibration factors are developed to quantify the sole effectiveness of DFS using large-scale spatial data and a set of reference road segments within the city of Edmonton, Alberta, Canada. Likewise, the study followed a detailed economic analysis based on three collision-costing criteria to investigate if DFS was indeed a cost-effective intervention. The results showed significant collision reductions that ranged from 32.5% to 44.9%, with the highest reductions observed for severe speed-related collisions. The results further attested that the benefit–cost ratios, combining severe and property-damage-only collisions, ranged from 8.2 to 20.2 indicating that DFS can be an extremely economical countermeasure. The findings from this study can provide transportation agencies in need of implementing cost-efficient countermeasures with a tool they need to design a long-term strategic deployment plan to ensure the safety of traveling public.

The City of Edmonton has invested in the installation of Driver Feedback Signs (DFSs) throughout the city starting from 2011. DFSs are dynamic speed display signs aimed at providing positive guidan...

During the winter season, snowplowing has a significant effect on road users as it is critical to winter road maintenance and operations. The main goal of this study is to generate optimal routes for snowplowing trucks for efficient road maintenance. In addition to the conventional problem of reducing travel time and distance, this study also incorporates actual operational constraints, such as minimum maintenance standards and driver safety, to improve the overall efficiency of operations. To achieve the objectives, we first implemented the Chinese Postman Problem (CPP) to create Euler circuits from the initial routes and then identified the shortest paths by applying Dijkstra’s algorithm. Then, the Tabu search algorithm was chosen as a metaheuristic algorithm for the optimization process that finds near-optimal solutions by considering operational constraints for snowplow routes. Unsafe turning conditions and minimum maintenance standards were taken into account in the objective function defined for the optimization process. In simulations, the route obtained by our approach was compared to one with the application of CPP only in terms of travel distance, time, turning conditions, and road maintenance priority.

A road weather information system (RWIS) is a combination of advanced technologies which collect, process, and disseminate road weather and condition information. This information is used by road maintenance authorities to make operative decisions that improve safety and mobility during inclement weather events. Many North American transportation agencies have invested millions of dollars to deploy RWIS stations to improve the monitoring coverage of winter road surface conditions. The design of these networks often varies by region, however, and it is not entirely clear how many stations are necessary to provide adequate monitoring coverage under different conditions; substantial gaps remain in knowledge about optimal design. To fill these gaps, an investigation was conducted to determine how optimized RWIS station densities relate to topographic and weather characteristics. A series of geostatistical semivariogram models were constructed and compared using topographic position index (TPI) and weather severity index (WSI) to measure relative topographic variation and weather severity, respectively. The geostatistical approach was then applied to map the optimum number of RWIS stations across several topographic and weather zones. The study area captured varying environmental characteristics, including regions with flat or varied terrain and warm or cold regions. This study suggests that RWIS data collected from a specific region can be used to estimate the number of stations required for regions with similar zonal characteristics. The outcome of this study can be used as a decision-making tool for RWIS network expansion, thus maximizing monitoring capability of RWIS networks using topographic and weather-related zonal classifications.

In winter, it is critical for cold regions to have a full understanding of the spatial variation of road surface conditions such that hot spots (e.g., black ice) can be identified for an effective mobilization of winter road maintenance operations. Acknowledging the limitations in present study, this paper proposes a systematic framework to estimate road surface temperature (RST) via the geographic information system (GIS). The proposed method uses a robust regression kriging method to take account for various geographical factors that may affect the variation of RST. A case study of highway segments in Alberta, Canada is used to demonstrate the feasibility and applicability of the method proposed herein. The findings of this study suggest that the geostatistical modelling framework proposed in this paper can accurately estimate RST with help of various covariates included in the model and further promote the possibility of continuous monitoring and visualization of road surface conditions.

This paper aims to investigate the effect of network signal timing strategy with dynamic variable guidance lanes based on a two-step approach, where the first step is an interactive traffic signal optimization model for each single interaction (e.g., lane allocation plans, cycle length) in the network, and the second refers to network signal control (e.g., split, off-sets). The optimization problem in the first step is solved using the Non-dominated Sorting Genetic Algorithm (NSGA-ΙΙ), and the network signal control problem in the second step is solved through SYNCHRO. To verify the effect of dynamic variable guidance lanes and also the reliability and validity of the proposed approach, a numerical case study is carried out. The results show that the average vehicle delay in the entire road network was reduced by 25.06% after optimization using the proposed model. Moreover, the sensitivity of influencing factors of the proposed model is also analyzed. The results show that when the traffic flow is increased by 60% of the original traffic flow, the optimization effect of the model is more significant. However, when the lane capacity is more than 1300 pcu/h, the vehicle delay will increase slowly. To sum up, this method can improve the regional traffic efficiency of the traffic-stressed lanes and further promote the full utilization of space-time resources of the road network.

The study deals with the design of control algorithms to enhance the steering performance of all-terrain cranes. For this, its hydraulic steering system was modeled using a multiphysics simulation tool and the self-tuning fuzzy PID controller was developed to control hydraulic steering actuators with nonlinearities. The designed control algorithm was validated through co-simulation using a real-time simulator and showed satisfactory performance.

Inclement weather acutely affects road surface and driving conditions and can negatively impact traffic mobility and safety. Highway authorities have long been using road weather information systems (RWISs) to mitigate the risk of adverse weather on traffic. The data gathered, processed, and disseminated by such systems can improve both the safety of the traveling public as well as the effectiveness of winter road maintenance operations. As the road authorities continue to invest in expanding their existing RWIS networks, there is a growing need to determine the optimal deployment strategies for RWISs. To meet such demand, this study presents an innovative geostatistical approach to quantitatively analyze the spatiotemporal variations of the road weather and surface conditions. With help of constructed semivariograms, this study quantifies and examines both the spatial and temporal coverage of RWIS data. A case study of Alberta, which is one of the leaders in Canada in the use of RWISs, was conducted to indicate the reliability and applicability of the method proposed herein. The findings of this research offer insight for constructing a detailed spatiotemporal RWIS database to manage and deploy different types of RWISs, optimize winter road maintenance resources, and provide timely information on inclement road weather conditions for the traveling public.

This research conducted a two-stage assessment to investigate sun glare effects on road safety in Edmonton. The first stage developed a methodology to model sun glare occurrence, aiming to identify when and where drivers are most likely to be exposed to sun glare. Safety risks at those identified locations during sun glare periods were assessed in the second stage. By contrasting the collisions during glare and non-glare conditions, this second stage aimed to quantify the effects of sun glare on road collisions. Consequently, three major findings were concluded. First, sun glare was found to significantly contribute to collision occurrence, especially at road intersections. Second, the effects of sun glare on collision occurrence during mornings on the eastbound and evenings on the westbound were observed to be particularly worse in the spring and fall months. In fact, safety in the southbound direction was significantly affected by sun glare during most daytime hours in the main winter months (November, December, and January). Lastly, the analysis revealed that certain collision types were more likely to occur during periods of sun glare. For example, collisions due to signal violations and failing to yield to pedestrians/cyclist were more likely to occur at intersections. At mid-block locations, the proportion of collisions occurring due to improper turning and lane changes were observed to be significantly higher. Overall, the approach proposed in this research provides a holistic method to quantify the effects of sun glare on road collisions and offers additional insights into the extent by which sun glare affects road safety.

This paper aims to assess the effects of snow and ice control operations by investigating the interdependency between weather variables, maintenance operations, pavement friction, and collisions. Using a disaggregated event-based and location-specific framework, and employing the statistical techniques of Structural Equation Modeling and Path Analysis, all the significant direct and indirect effects of weather variables and maintenance operations on pavement friction and collision occurrence during snowstorms have been identified. It was revealed that precipitation, extremely low temperatures, and the potential of black ice formation all had significant negative direct effects on pavement friction and significant indirect negative effects on traffic safety. Moreover, the application of anti-icing agents and plowing operations have been shown to significantly improve pavement friction and in return improve traffic safety indirectly. To illustrate how the maintenance operations improve traffic safety, a hypothetical snowstorm example was considered. According to the model, anti-icing application was associated with a 14% reduction in collisions, plowing operations resulted in a 33% reduction in collisions, and combining the two tools reduced collisions per snowstorm by 42%. The findings of this paper can help transportation agencies make more informed decisions to promote an efficient mobilization of the existing winter road maintenance services and resources while improving the safety of the traveling public during the winter months.

This paper presents a study aiming at understanding the relationship between the spatiotemporal characteristics of road weather conditions and a number of road weather information systems (RWIS) stations using real-world case studies. Semivariogram models are constructed to determine the spatial variability of road weather conditions, especially, autocorrelation range which describes a separation distance at which the measurements are no longer correlated to each other. An optimal RWIS density is then determined through an optimization process that minimizes the total inference errors across the underlying road network. The findings suggest that the regions with less varied topography tend to have a longer spatial correlation range than the regions with more varied topography. The study further reveals that the range of spatial autocorrelation is related to the optimal density of RWIS network — the region with a longer range requires fewer RWIS stations, than the region having a shorter range.

This paper presents a risk-based approach for classifying the road surface conditions of a highway network under winter weather events. A relative risk index (RRI) is developed to capture the effect of adverse weather conditions on the collision risk of a highway in reference to the normal driving conditions. Based on this index, multiple risk factors related to adverse winter weather conditions can be considered either jointly or separately. The index can also be used to aggregate different types of road conditions observed on any given route into a single class for risk-consistent condition classification and reporting. Two example applications are shown to illustrate the advantages of the proposed approach.

Accumulated snow on roads poses a threat to traffic systems and rouses significant safety concerns. Snow plowing is often used to recover roads in the event of heavy snow. Due to the unpredictability of weather conditions, it is difficult to determine the overall performance of a certain truck fleet size, thus make it challenging to estimate the number of snow plow trucks needed for a given highway area. The objective of this research is to estimate the truck fleet performance under uncertain weather conditions, and to provide decision support for selecting a reasonable fleet size. A generic simulation model is developed in the Simphony.NET environment. Weather, road network, and truck speed data are entered as inputs, and Monte Carlo simulation is used to generate random snow events to quantify the performance. A case study is developed and presented to demonstrate the practicality and feasibility of the proposed model.

This paper presents a study aimed at developing zonal-specific semivariograms for zones with different climates using regionalized random variables for a strategic road weather information ...

This study presents an innovative approach to the planning of a critical highway sensor infrastructure - road weather information system RWIS. The problem is formulated to minimize the spatially averaged kriging variance of hazardous road surface conditions while maximizing the coverage of accident-prone areas. This optimization framework takes explicit account of the value of information from an RWIS network, providing the potential to enhance the overall efficacy of winter maintenance operations and the safety of the travelers. Spatial simulated annealing is used to solve the resulting optimization problem and its performance is demonstrated using a real-world case study from Minnesota, United States. The case study illustrates the distinct features of the proposed model, assesses the effectiveness of the current location setting, and recommends additional stations locations. The findings of our study suggest that the proposed model could become a valuable decision-support tool for planning a new RWIS network and evaluating the performance of alternative RWIS expansion plans.

Driver feedback signs (DFSs) are being adopted increasingly by municipalities around the world, as they have been proven to be a cost-effective countermeasure that improves road safety. However, research is still needed on developing a location-allocation framework to determine the optimal implementation strategies for DFS placement. Hence, the main aim of this paper is to formulate a location-allocation optimization problem with the objective of reducing vehicular collisions (&Delta, C) while enhancing spatial coverage for vulnerable road users and facilities (Cov). Two distinct planning scenarios, namely, an all-new and expansion scenario, were proposed in the framework. It was found that &Delta, C and Cov can be improved by up to 149.44% and 69.27%, respectively, in the all-new scenario. Two expansion scenarios were done with 10 and 20 additional units into the system. It was found that &Delta, C can be improved by up to 30.22% and 51.61% for the additional 10 and 20 DFSs, respectively. Likewise, the Cov can be improved by up to 14.64% and 29.27%, respectively. This framework provides decision makers with the freedom to simulate and optimize their DFS network by balancing the needs of the road users, vulnerable facilities, and traffic safety in locating DFSs over an urban road network.

In wintery regions, it is critical to have a full understanding of how road surface conditions would change over space to make more informed decision on where (i.e., hot-spots such as historically cold spots that are likely to create slippery conditions) and how much of winter maintenance operations (i.e., salting and plowing) are of necessity. Thermal mapping has been a primary tool used to determine the hot-spots by identifying the variation pattern of measured pavement surface temperature along roadways. It, however, requires substantial amount of time and effort, particularly for cities that are in need of a large-scaled implementation, posing a significant limitation of its applicability at the regional level. Hence, this paper presents a standard framework to estimate road surface temperature (RST) variable using GIS and advanced geostatistical technique. A case study based on highway segments in Alberta is used to illustrate the distinct features of the proposed method and model estimation performance. The findings of the paper suggest that the proposed GIS-based approach provides a reliable solution posing a great possibility to replace the existing technique and further applied for determining the potential location of an RWIS station to promote more efficient and cost-effective winter maintenance operations.

This paper presents an empirical study focusing on identifying the main factors that affect the capacity and free-flow speed (FFS) of urban freeways under inclement winter weather conditions. The weather and road surface condition factors examined include air temperature, wind speed, hourly snow intensity, visibility, snow on ground, and road surface condition describing the road slipperiness caused mainly by snow events. Data on traffic operations and the associated weather and road conditions observed at two freeway locations over the 2010–2012 winter seasons were used in an extensive statistical analysis. Linear regression models were calibrated for both capacity and FFS reductions as related to various weather and road condition variables. It was found that visibility and road surface conditions had a statistically significant effect on both capacity and FFS. Snow intensity was found to be significant only when the visibility factor was excluded; this finding suggests a refutation of these two factors on capacity and FFS. The modeling results were compared with those recommended by the Highway Capacity Manual 2010, showing that, in many cases, the manual could underestimate or overestimate the effects of winter weather conditions and that the proposed models provided a more reasonable estimate at a higher level of granularity.

Clear-cell carcinoma is a convenient and frequently used descriptive term for a malignant epithelial neoplasm that is entirely or largely composed of cells with optically clear cytoplasm in hematoxylin-eosin-stained sections. Transmission electron microscopy was performed on 57 tumors from various sites to investigate the fine structural basis for the clarity of the cytoplasm. The clear appearance resulted from the presence of one or several of the following features, as the sole or predominant cause or in combination: glycogen, lipid droplets, mucin vacuoles or diffuse mucosubstances, dilated cisternae, swollen or unusually large mitochondria, large solitary membrane-limited vacuoles or numerous smaller vacuoles, intracytoplasmic lumens, expanded intercellular spaces, cytoplasmic pseudoinclusions, and a paucity of organelles. Degenerative changes contributed to the clear appearance by inducing swelling of mitochondria and creating lucent cytosol. The factors responsible for the clear cytoplasm were not always consistent with regard to tumor type or site of origin, but glycogen was the commonest reason among the 57 tumors studied and the principal cause in tumors of the female genital tract, skin, and salivary glands, while renal cell carcinomas tended to possess an admixture of glycogen and lipid droplets. Because of the heterogeneity of the subcellular changes that can produce optically clear cytoplasm, electron microscopy has a limited role in determining the primary site of a metastatic clear-cell carcinoma.

Clear-cell carcinomas of the female genital tract are uncommon tumors that usually arise in the ovary, cervix, or vagina. The objectives of the present study were to examine the immunohistochemical and ultrastructural characteristics of 18 of these tumors and to determine whether any distinctive features were present at the different locations. The fine structure of the neoplastic cells was similar at the several sites but there was a striking difference in the appearance of the basal lamina. Prominent reduplication of the basal lamina was seen in all 7 ovarian carcinomas, whereas it was mild in 3 and absent in 7 of the cervico-vaginal tumors. The distribution of eosinophilic hyaline material in hematoxylin/eosin sections and of immunohistochemical staining for type IV collagen and larminin correlated with the ultrastructural observations. Reduplication of the basal lamina has been observed in a number of epithelial neoplasms and viewed as a criterion of differentiation, but there is currently no evidence that its presence influences the prognosis of clear-cell carcinomas of the ovary.

This article presents a comprehensive framework for determining the location of road weather information system (RWIS) stations over a regional road network. In the proposed methodology, the region is divided into a grid of equal-sized zones which are considered as the minimum spatial unit for allocating a candidate set of RWIS stations. These zones are ranked according to a set of pre-specified criteria that reflect the needs for, and potential benefits from, real-time road weather information, including road surface temperature variability, precipitation, network traffic, and collision patterns. A case study based on the existing RWIS network in the province of Ontario was conducted to illustrate the major features of the proposed method and evaluate the implications of alternative location selection criteria. The findings of the study suggest that it is feasible to develop a systematic process for locating RWIS stations using an integrated location criterion to capture multiple factors being considered in practice. The study has also revealed the need to establish quantitative models for estimating the benefit of real-time information from RWIS stations, which is the foundation of a cost–benefit-based RWIS location optimization model.

This paper presents a study aimed at comparing the outcome of two geostatistical-based approaches, namely kernel density estimation (KDE) and kriging, for identifying crash hotspots in a road network. Aiming at locating high-risk locations for potential intervention, hotspot identification is an integral component of any comprehensive road safety management programs. A case study was conducted with historical crash data collected between 2003 and 2007 in the Hennepin County of Minnesota, U.S. The two methods were evaluated on the basis of a prediction accuracy index (PAI) and a comparison in hotspot ranking. It was found that, based on the PAI measure, the kriging method outperformed the KDE method in its ability to detect hotspots, for all four tested groups of crash data with different times of day. Furthermore, the lists of hotspots identified by the two methods were found to be moderately different, indicating the importance of selecting the right geostatistical method for hotspot identification. Notwithstanding the fact that the comparison study presented herein is limited to one case study, the findings have shown the promising perspective of the kriging technique for road safety analysis.