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Transportation is a fundamental tool to develop communities, cities, and countries on a larger scale, and more extensive transportation networks have developed ubiquitously. However, it is needed to consider the fact that animals also live in the same environment without using the same means, and there is always a chance of colliding with them while driving vehicles. Animal-Vehicle Collision (AVC) is a principal concern for transportation agencies and roadway hazards that influences human safety, property, and wildlife. State of Tennessee animal crash data has been collected for 23 years containing different types of information for each collision. This paper presents and evaluates the performance of five machine learning-based prediction models for animal collisions in the presence of both categorical and non-categorical features. These five models are developed using Logistic Regression, Random Forest, CatBoost, eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LGBM). The CatBoost model has the highest accuracy level at 78.52%. Therefore, it seems to be the most suitable model to predict animal collisions based on 23-year data from Tennessee. The experimental results demonstrate the potential of leveraging categorical data with CatBoost as a viable solution for creating up-to-date and complete analysis for animal-vehicle collision data. [ABSTRACT FROM AUTHOR]

Continuous condition monitoring and inspection of traffic signs are essential to ensure that safety and performance criteria are met. The use of 3D point cloud modeling by the construction industry has been significantly increased in recent years especially for recording the as-is conditions of facilities. The high-precision and dense 3D point clouds generated by photogrammetry can facilitate the process of asset condition assessment. This paper presents an automated computer-vision based method that detects, classifies, and localizes traffic signs via street-level image-based 3D point cloud models. The proposed pipeline integrates 3D object detection algorithm. An improved Structure-from-Motion (SfM) procedure is developed to create a 3D point cloud of roadway assets from the street level imagery. In order to assist with accurate 3D recognition and localization by color and texture features extraction, an automated process of point cloud cleaning and noise removal is proposed. Using camera pose information from SfM, the points within the bounding box of detected traffic signs are then projected into the cleaned point cloud by using the triangulation method (linear and non-linear) and the 3D points corresponding to the traffic sign in question are labeled and visualized in 3D. The proposed framework is validated using real-life data, which represent the most common types of traffic signs. The robustness of the proposed pipeline is evaluated by analyzing the accuracy in detection of traffic signs as well as the accuracy in localization in 3D point cloud model. The results promise to better and more accurate visualize the location of the traffic signs with respect to other roadway assets in 3D environment. [ABSTRACT FROM AUTHOR]

Frequent analysis and updating the condition of traffic signs and mile markers are among the most important aspects of a highway asset management system. Today's practices mainly involve manual data collection and analysis, which have to be done for millions of miles of roads and the practice needs to be repeated regularly. While significant progress has been made on improving the data collection practice by leveraging video streams collected from car-mounted cameras, the analysis has primarily remained a manual and labor-intensive process. Automating the analysis from the collected videos is also challenging due to the interclass variability of traffic signs, expected changes in illumination, occlusion, sign position, and orientation. To address these challenges, this paper presents and evaluates the performance of three computer vision algorithms for detection and classification of traffic signs in presence of cluttered backgrounds and static and dynamic occlusions. The task particularly focuses on (1) extracting two-dimensional (2D) candidate windows from already collected video streams that potentially contain traffic signs-without making any prior assumption about their locations; (2) detecting the presence of signs in these 2D candidate windows; and (3) classifying them into warning, regulatory, stop, and yield sign categories based on their shape and color. For validation, a new comprehensive benchmark data set of over 11,000 annotated U.S. traffic sign images with a large range of pose, scale, background, illumination, and occlusion variation is introduced. Experimental results show an average accuracy of 76.20%, 89.31%, and 94.83% for the methods of (1) Haar-like features with Cascade classifiers, (2) histograms of oriented gradients (HOG) with multiple one-versus-all support vector machine (SVM) classifiers, and (3) HOG+C with the SVM classifiers, a variant of the second method with histograms of colors concatenated to HOG. The experimental results demonstrate the potential of leveraging joint representation of texture and color in HOG+C together with SVM discriminative classifiers as a viable solution for creating up-to-date and complete inventories of traffic signs for U.S. roadways. [ABSTRACT FROM AUTHOR]

The visibility of a traffic sign at night depends on its retro-reflectivity, a property that needs to be frequently monitored to ensure transportation safety. In the U.S., Federal Highway Administration (FHWA) maintains regulations to ensure minimum retro-reflectivity levels. Current measurement techniques either (a) use vehicle mounted device during the night, or (b) use manual handheld devices during the day. The former is expensive due to nighttime labor cost. The latter is time-consuming and unsafe. To address these limitations, this paper presents a computer vision-based technique to measure retro-reflectivity during daytime using a vehicle mounted device. The presented algorithms simulate nighttime visibility of traffic signs from images taken during daytime and measure their retro-reflectivity. The technique is faster, cheaper, and safer as it neither requires nighttime operation nor requires manual sign inspection. It also satisfies FHWA measurement guidelines both in terms of granularity and accuracy. The performance of the presented technique is evaluated under various testing conditions. The results are promising and demonstrate a strong potential in lowering inspection cost and improving safety in practical applications on retro-reflectivity measurement. [ABSTRACT FROM AUTHOR]

This paper presents a non-parametric image parsing method for segmentation and recognition of roadway assets such as traffic signs, traffic lights, pavement markings, and guardrails from 2D car-mounted video streams. The method can be easily scaled to thousands of video frames captured during data collection and does not need training. Instead, it retrieves a set of most relevant video frames (e.g. highway vs. secondary road) which serve as candidates for superpixel-level annotation. It then obtains superpixels from the video frames and using the retrieval set encodes their visual characteristics using a histogram of different shape, appearance, and color descriptors. Neighborhood contexts are incorporated by using Markov Random Field (MRF) optimization and two types of semantic (e.g. guardrail) and geometric (e.g. horizontal) labels are simultaneously assigned to the superpixels. We introduce a new dataset from I-57 together with its ground truth and present experimental results on both I-57 and SmartRoad datasets. Experimental results with an average accuracy of 88.24% for recognition and 82.02% for segmentation show that our local visual features provide acceptable performance, while the method overall does not require any significant supervised training. This scalable method has potential to reduce the time and effort required for developing road inventories, especially for those such as guardrails and traffic lights that are not typically considered in 2D asset recognition methods. [ABSTRACT FROM AUTHOR]

The problem of selection or ranking of alternatives based on multiple criteria is not an easy problem, neither economically nor mathematically. The recent advancements in various structural systems have made the selection of the appropriate structural system for a specific project a challenging decision-making process. This process involves different economic and technical criteria representing the availability of experienced technicians and engineers and necessary machinery and construction materials. Economic life cycle, environmental protection related issues, safety of the project site, and vulnerability to natural disasters such as earthquakes are also issues related to the country and location in which the construction project takes place, and they should also be addressed in the decision-making process. In this paper, a new algorithm combining two well-known multicriteria decision-making (MCDM) techniques, namely, elimination and choice expressing the reality III (ELECTRE III) and preference ranking organization method for enrich evaluation II (PROMETHEE II), have been specially developed for ranking of structural systems. The proposed algorithm is also applicable to group decision-making cases. In addition, the decision matrix data, criteria weights, preference, indifference, and veto thresholds are set as interval values to consider uncertainties in the decision-making procedure. The proposed methodology considers veto threshold, provide complete ranking, exempts the preference functions and includes conceptions such as risk, uncertainty, and reliability with interval approach. The proposed algorithm has been utilized in selecting the appropriate structural system in a low-rise multihousing project in Iran. In this case study, sixteen criteria have been used to select the best structural system between eight alternatives considering relevant uncertainties in the judgments of three groups of stakeholders (owner, consultants, and contractors). The results of the case study showed that the proposed methodology can be used as a proper tool for decision making in the construction management processes. [ABSTRACT FROM AUTHOR]

Efficient data collection of high-quantity and low-cost highway assets such as road signs, traffic signals, light poles, and guardrails is a critical element to the operation, maintenance, and preservation of transportation infrastructure systems. Despite its importance, current practice of highway asset data collection is time-consuming, subjective, and potentially unsafe. The high volume of the data that needs to be collected can also negatively impact the quality of the analysis. To address these limitations, this paper proposes a new algorithm for semantic segmentation and recognition of highway assets using video frames collected from a car-mounted camera. The proposed set of algorithms (1) takes the captured frames and using a pipeline of structure from motion and multiview stereo reconstructs a three-dimensional (3D) point cloud model of the highway and surrounding assets; (2) using a Semantic Texton Forest classifier, each geo-registered two-dimensional (2D) video frame at the pixel-level is segmented based on shape, texture, and color of the highway assets; and finally, (3) based on the results of the 2D segmentation and a new voting scheme, each reconstructed 3D point in the cloud is also categorized for one type of asset and is color coded accordingly. The resulting augmented reality environment that integrates the color-coded point clouds with the geo-registered video frames enables a user to conduct visual walk through and query different categories of assets. Experiments were performed on a challenging video data set containing sequences filmed from a moving car on a 2.2-mi-long, two-lane highway research facility. Experimental results with an average accuracy of 76.50 and 86.75% in segmentation and pixel-level recognition of 12 types of asset categories reflect the promise of the applicability of this approach for segmentation and recognition of highway assets from image-based 3D point clouds. It also enables future algorithmic developments for 3D localization of traffic signs and other assets that are detected using the state-of-the-art vision-based methods. [ABSTRACT FROM AUTHOR]

It is challenging to devise a group decision-making process that supports compromise solutions reflecting diverse stakeholders' preferences about multiple criteria. This study illustrates how the combination of multicriteria decision analysis and a group decision-making technique can be used to arrive at solutions that are mutually acceptable to multiple stakeholders when selecting sustainable transport projects. The proposed methodology, in which the collective preference is determined based on the aggregation of individual preferences from all stakeholders, is illustrated through a case study of the prioritization of nine prospective transport projects affecting three stakeholder groups (decision-makers, project designers, and system users) in Tianjin Binhai New Area, China. Specifically, the various projects were evaluated for their probable sustainability performance according to 14 criteria. The results show that the proposed methodology can enable transportation managers to achieve an acceptable solution to a complex project-prioritization problem by understanding the different needs and concerns of diverse stakeholders. It is hoped that the present research will serve as a practical tool for infrastructure managers seeking to build consensus, and helping them to assess the degree of consensus among stakeholders at various points in the process, thereby facilitating effective communication and the coordination of decisions. [ABSTRACT FROM AUTHOR]

Conflicts inevitably occur in major transport projects as various stakeholders express diverse, often conflicting needs and concerns, and failing to address and manage these conflicts often leads to project failures. Understanding stakeholders' perceptions and the discrepancies among them is crucial to an effective dialogue among the parties seeking to build consensus. This study investigates the potential influences of stakeholder characteristics as well as project environments on conflicts and consensuses in stakeholders' overall preferences about sustainable transport projects. Data on the perspectives of three stakeholder-group types (system providers, project designers, and system users) with regard to 14 sustainability criteria are drawn from a survey of eight major transport projects across China. Two-way analysis of variance is then carried out to investigate whether or not the differences in the mean scores among stakeholder-group types and cities were statistically significant. The results show that discrepancies of opinion about transport-project sustainability criteria prevail among stakeholder-group types in every region we studied, due to these types' different needs and concerns. Our findings also suggest that special attention should be given to cases in which multiple stakeholder groups assign the same priorities to certain criteria, as those criteria are likely to represent the most serious issues affecting all stakeholders in a given project environment. With regard to the potential influences of project environments, we found that, in general, the discrepancies in stakeholders' prior concerns about sustainability criteria exhibited no obvious geographic pattern. However, exceptions did occur when a specific criterion was of major concern in a given project environment. Taken together, our findings emphasize the necessity of addressing and managing specific local issues, and the divergent concerns of stakeholders, when planning sustainable-transport projects. [ABSTRACT FROM AUTHOR]