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This paper presents a new approach to address the problem of allocating federal resources and identifying upgrading projects for improving the safety of at-grade crossings in Canada. The proposed approach is unique in two key aspects. First, a risk-based network screening process is adopted to identify the priority sites for providing a justifiable basis for distributing the total budget at a regional level as well as narrowing the search space in the subject optimization step. Secondly, a mathematical programming approach is applied to formalize the resource allocation process with explicit consideration of the expected benefits – risk reduction and the costs of implementing the projects. This approach is expected to improve the process of identifying the optimal set of upgrading projects within each region, thus maximizing the return on investment. A full-scale case study from the Canadian crossing network is conducted to demonstrate the application of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper describes a study focusing on performance evaluation of road weather information system (RWIS) surface temperature forecasts. To identify the factors influencing the accuracy of forecasts, five research hypotheses regarding climate patterns, geographical attributes, seasonal variations, time of day, and forecast horizon are proposed. Observation and forecast data were provided by four North American provinces, and were processed and stratified by station, hour, and month to quantify their effects. The findings based on two statistical measures; namely, mean absolute error and percent of acceptable forecasts suggested that the maritime climate group had the highest correspondence while the mixed climate group had the lowest. The geographical attributes showed that the forecasting performance of coastal maritime regions was found to have a negative correlation to the distance from nearby large bodies of water. Forecasts for the daytime were found to be less accurate than for the nighttime and deteriorate quickly as the forecasting horizon increased. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper presents the results of an extensive field study of the comparative performance of road salt on different pavement types for snow and ice control in transportation facilities. Approximately 400 tests were conducted in a real-world environment, covering three different pavement types and 27 snow events. The performance is compared on asphalt concrete (AC), portland cement concrete (PCC), and interlocked concrete (IC) pavements in terms of pavement clearing speed. The study suggests that on average, salt performs better on AC than PCC or ICC pavement, with the latter two having similar performance. The results were confirmed with a paired t-test analysis and then used to develop a performance model, the results of which were used to develop an adjustment factor for each of the different pavement types. The results from this research can be applied by pavement maintenance personnel to optimize salt usage and improve safety in transportation facilities. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning has found great successes in addressing many challenging control problems; however, real-world implementations are still scarce if not non-existent. This is primarily due to three main challenges pertaining to the implementation, the stability, the optimal settings, and a lack of knowledge on methods that can be applied to field settings. This research attempts to address these issues with an adaptive simulation-based control framework proposed specifically for the training and evaluation. The control framework has implemented simulation models to conduct an extensive sensitivity analysis on the effects of key design variables, including rewarding schemes, state spaces, and model training parameters. The feasibility of transfer learning as a training strategy is also studied on scenarios with different layouts and different driver behavior models. Complex scenarios are also evaluated and used as test cases, including multiphase ring-and-barrier control and multi-intersection control. The research has contributed a significant amount of evidence on several critical design and implementation-related questions such as input representation, data (technology) requirements, training methods, and model transferability. [ABSTRACT FROM AUTHOR]

This paper presents the results of an extensive field study on the comparative performance of alternative materials for snow and ice control of transportation facilities. Approximately 300 tests were conducted in a real-world environment, covering four alternative materials, and 21 snow events. Each of the alternatives tested were compared to regular rock salt in terms of snow melting performance - bare-pavement regain time. The study confirmed the relative advantage of these alternatives over the regular salt, but also showed that their performance varied largely depending on some external conditions. Performance models were calibrated and then used for developing application rate adjustment factors that can be applied by maintenance operations for determining the optimal application rates for specific weather events and pavement conditions. The applicability of the results is limited to parking lots and sidewalks without the traffic effects, and as such cannot be easily applied to winter roadways maintenance. [ABSTRACT FROM AUTHOR]

This paper presents a cost-benefit based approach to the problem of finding the optimal location and density of road weather information system (RWIS) stations over a regional road network. The novelty of the proposed method lies in the models that can be used to estimate the benefits of RWIS information, including reduced maintenance costs and collisions, thus determining the optimal number and location of a RWIS network. A case study based on the existing RWIS network in Northern Minnesota in the US is used to show the application of the proposed approach. Linear regression models are developed for the annual maintenance costs and the expected number of collisions that could occur on two types of highways delineated by the existence of a RWIS station nearby. The calibrated models are then applied to individual highway segments defined on the basis of a uniform grid system to determine the expected benefit of having a RWIS station installed. These benefits along with RWIS installation and maintenance costs are converted into net benefits and then the net present value, which are then used in determining the optimal number of RWIS stations and prioritizing the candidate locations. It was found from the case study that a total of 45 stations would provide the best return of investment with a 25 year net benefit of approximately $6.5 million and a life-cycle benefit-to-cost ratio of 3.5. [ABSTRACT FROM AUTHOR]

This paper describes an empirical study aimed at investigating the performance of the anti-icing strategy for snow and ice control of parking lots and sidewalks. The research is motivated by the need to address several key questions concerning various operational decisions related to the anti-icing strategy, including its relative effectiveness under different weather and site conditions, treatment options, and optimal application rates. Extensive field tests were conducted under traffic controlled environment and variety of weather events using regular solid road salt, brine, and two other liquid alternatives. Data collected from these tests was used to analyze the performance of anti-icing operations such as friction level, bare pavement regain time, and the effects of various external factors such as pavement temperature and application rate. The research has concluded with findings that are directly applicable in real world winter maintenance practices. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented about papers presented at the 4th International Conference on Transportation Information and Safety (ICTIS2017) and the 9th Annual International Conference on Urban Traffic Safety in Alberta, with themes "Transportation information and safety in intelligent and connected environments" and "Vision zero."

A number of traffic safety studies have investigated temporal variations in road safety indicators such as crash frequency, confirming that such variations exist. This paper examined whether speeding is more likely on weekend nights relative to all other times of the day by directly comparing speeding during weekends and weekdays. To this end, we analyzed a sample of local streets, in Montréal, for which speed data were collected automatically using traffic analyzer sensors. We found that, interestingly, weekend speeding was less likely to occur during night hours, whereas it was more likely to occur during evening and midday hours. Among other findings, the results indicated that one-way streets and those having a speed limit of 50 km/h were slightly less prevalent in speeding on weekends. Our results can be useful in designing road safety interventions, including publicity campaigns and police enforcement, which aim at reducing speeding behaviours. [ABSTRACT FROM AUTHOR]

Over five million tons of salt (NaCl) is applied in Canada every winter to improve pavement friction in the winter season. While effective for improving pavement surface condition, salts at high concentrations are detrimental to the environment and corrosive to vehicles and infrastructure. New alternative salts (bio-based products), are increasingly available in the market as an alternative to regular salt; however, limited information on the performance of these alternatives is available for transportation agencies to make informed decisions for their usage. In this study, a set of bio-based products were selected and their performances were compared using pavement friction improvement as a measure. A multivariate linear regression analysis was conducted to identify the factors influencing pavement friction by utilizing these new materials. The analysis has indicated that using bio-based materials resulted in 10%-40% improvement in the friction level. However, these materials did not significantly outperform each other. The study also concluded that an application rate as low as 3 L/1000 ft2 should be applied for parking lots or low volume roads, which is 25% less than the current application rates that are used in general for parking lot pavement maintenance. [ABSTRACT FROM AUTHOR]

Train derailments are important safety issues, and they become even more critical when dangerous goods (DG) are involved. This paper is concerned with mitigating derailment risk through improved operational strategies, with a specific focus on DG marshalling practices in the train-assembly process. A new modelling framework is proposed to investigate how the position of DG railway cars affects their chances of being involved in a derailment as the train travels over a given track segment. The underlying research problem can be formulated as a linear integer programming technique. However, since solving this formulation is computationally intractable, a heuristic method has been developed based on a genetic algorithm that gives a near-optimum solution. The proposed model is applied to a hypothetical rail corridor to demonstrate how effective marshalling of DG along a train can reduce overall derailment risks. Les déraillements de train représentent une importante question de sécurité, plus particulièrement lorsque des matières dangereuses sont impliquées. Le présent article traite de l’atténuation du risque de déraillement par l’amélioration des stratégies opérationnelles, plus spécifiquement sur le positionnement des wagons de matières dangereuses durant le classement des wagons. Un nouveau cadre de modélisation est proposé pour examiner la manière dont la position des wagons de matières dangereuses affecte leur chance d’être impliqués dans un déraillement lorsque le train roule sur un segment de voie ferrée donné. Le problème sous-jacent peut être formulé comme une technique de programmation linéaire par nombres entiers. Toutefois, étant donné que la résolution de cette formulation est difficilement calculable, une méthode heuristique a été développée en se basant sur un algorithme génétique qui offre une solution près de l’optimal. Le modèle proposé est appliqué à un corridor hypothétique de voie ferrée afin de démontrer comment un classement efficace des matières dangereuses le long d’un train peut réduire les risques lors d’un déraillement. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time winter road surface condition (RSC) monitoring solution that automatically generates descriptive RSC information in terms of snow and ice coverage by using images from fixed traffic and weather cameras. Several state-of-the-art pre-trained deep neural networks are customized and fine-tuned to address a specific domain, classifying the amount of snow coverage on a road surface. A thorough evaluation is conducted to identify and select the best model. This evaluation uses an extensive set of experiments to test the accuracy and generalization of each model and uses transfer-learning to fine-tune each of the pre-trained models on independent images from different traffic and weather cameras. The transferability of each model, relationship between model performance and data size, and the system settings of each model are then examined. Lastly, three online weight calibration methods are proposed to automatically update the model in new environments. The result shows that re-training the model using images from a mixed set of cameras has the most promising results. [ABSTRACT FROM AUTHOR]

Accurate prediction of recoverable train delay can support the train dispatchers' decision-making with timetable rescheduling and improving service reliability. In this paper, we present the results of an effort aimed to develop primary delay recovery (PDR) predictor model using train operation records from Wuhan-Guangzhou (W-G) high-speed railway. To this end, we first identified the main variables that contribute to delay, including dwell buffer time, running buffer time, magnitude of primary delay time, and individual sections' influence. Different models are applied and calibrated to predict the PDR. The validation results on test datasets indicate that the random forest regression (RFR) model outperforms the other three alternative models, namely, multiple linear regression (MLR), support vector machine (SVM), and artificial neural networks (ANN) regarding prediction accuracy measure. Specifically, the evaluation results show that when the prediction tolerance is less than 1 min, the RFR model can achieve up to 80.4% of prediction accuracy, while the accuracy level is 44.4%, 78.5%, and 78.5% for MLR, SVM, and ANN models, respectively. [ABSTRACT FROM AUTHOR]