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A synchronous optimization model for rehabilitation of existing two-way two-lane (TWTL) highways is proposed in this paper considering geometric modification and existing pavement rehabilitation. The rehabilitation process of existing roadways is categorized into geometric design modifications and pavement repairs. These two types of modification apply separately in roadway life-cycle because of the difference in nature of these repairs, which impose an extra cost and time of closure to road users and owners. This paper aims to address the modification of existing highways by a new approach linking the existing pavement condition and geometric configuration changes of the developed roadway to provide the optimum vertical alignment. The proposed procedure depends on various parameters of existing pavement condition, cross-section configuration, traffic volume and composition, and current alignment composure. In the first step, all these dependencies are explained and quantified with separate cost models. For each solution, the overall cost is calculated with a life-cycle cost analysis approach. Finally, a particle swarm optimization (PSO) based model is developed to find the optimum solution. The developed model was applied in two case studies with good and adverse geometric conditions. The results show that there is a critical condition in the roadway segments which have all these conditions: (1) the grade is more than three percent, (2) the grade is following with reverse grade, and (3) decommissioning of the asphalt concrete (or extended to lower layers) exist. The optimum solution in these critical conditions suggests the modification of the geometric configuration before the pavement rehabilitation in order to reduce the overall cost. In the absence of these conditions, the optimum alignment is more analogous with the existing alignment of the highway.

Understanding and mitigating drivers' negative emotions, and stress levels, is of high importance for enhancing road safety, and human well-being. While detecting drivers' stress and negative emotions can significantly help with this goal, understanding what might be associated with increases in drivers' negative emotions and high stress level, might better help with planning interventions, which has not been explored in detail. Methods: In this study, by using a naturalistic driving study database, we analyze the changes in drivers' heart rate and facial expressions with respect to the changes in the driving scene, including road objects and the dynamical relationship between the ego vehicle and the lead vehicle. Results: Our results indicate that different road objects might be associated with varying levels of increase in drivers' HR as well as different proportions of negative facial emotions detected through computer vision. Larger vehicles on the road, such as trucks and buses, are associated with the highest amount of increase in drivers' HR as well as negative emotions. Additionally, shorter distances and higher standard deviation in the distance to the lead vehicle are associated with a higher number of abrupt increases in drivers’ HR, depicting a possible increase in stress level. Our finding indicates more positive emotions, lower facial engagement, and a lower abrupt increase in HR in highway environments. Conclusion: This research collectively shows that certain road environments such as highways with less stress-inducing objects are much more suited to promote drivers well-being and should be considered in routing use cases.

Transportation asset management helps monitor the transportation systems and optimize the construction, operation, and maintenance of assets. Many state Department of Transportations (DOTs) have already established asset management systems for high cost and low quantity assets, e.g., bridge and tunnel assets. However, due to the sheer number of traffic signs deployed by DOTs, statewide sign inventory and condition information are not well developed. Currently, using handheld devices is the most selected method by agencies to measure signs. To address safety challenge and high cost of data collection, an innovative stationary image based method has recently been proposed. This paper discusses the advantages and disadvantages of such image based method over using handheld devices in terms of the accuracy, possibility and consistency of data, speed, safety, maintenance, and cost. At its completion, this study provides suggestions to tackle the issues associated with image based method.

Maintaining an up-to-date record of the number, type, location, and condition of high-quantity low-cost roadway assets such as traffic signs is critical to transportation inventory management systems. While, databases such as Google Street View contain street-level images of all traffic signs and are updated regularly, their potential for creating an inventory databases has not been fully explored. The key benefit of such databases is that once traffic signs are detected, their geographic coordinates can also be derived and visualized within the same platform. By leveraging Google Street View images, this paper presents a new system for creating inventories of traffic signs. Using computer vision method, traffic signs are detected and classified into four categories of regulatory, warning, stop, and yield signs by processing images extracted from Google Street View API. Considering the discriminative classification scores from all images that see a sign, the most probable location of each traffic sign is derived and shown on the Google Maps using a dynamic heat map. A data card containing information about location and type of each detected traffic sign is also created. Finally, several data mining interfaces are introduced that allow for better management of the traffic sign inventories. The experiments conducted on 6.2 miles of I-57 and I-74 interstate highways in the U.S. –with an average accuracy of 94.63 % for sign classification– show the potential of the method to provide quick, inexpensive, and automatic access to asset inventory information. Given the reliability in performance shown through experiments and because collecting information from Google Street View imagery is cost-effective, the proposed method has potential to deliver inventory information on traffic signs in a timely fashion and tie into the existing DOT inventory management systems. Such spatio-temporal representations provide DOTs with information on how different types of traffic signs degrade over time and further provides useful condition information necessary for predicting sign replacement plan.

Decision makers in the transportation industry search for a systematic approach to select an appropriate structural system, construction method, and material for bridges. Simple mathematical methodologies are needed to consider different stakeholders' perspectives. With criteria that occur simultaneously in selecting appropriate material, construction technique, and structural system of bridges, invalid and unexpected results may occur from such complexity. The decision-making process is usually done subjectively by designers and requires much data and extensive experience in bridge design. To address these challenges and assume all substantial criteria within the framework, the PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) multicriteria decision-making method is used. It is not sensitive to the number and definition of the criteria. The PROMETHEE method is based on the pairwise comparison between alternatives for constructing an outranking relationship to show the degree of dominance of one alternative over another. A case study of the Kashkhan Bridge in Iran is presented to demonstrate implementation of the PROMETHEE method and show how such a decision-making methodology can assist experts in making informed decisions based on a set of comprehensive criteria in the conceptual design stage. Some novel and effective criteria in this study are combined and synthesized to select the appropriate superstructure. Criteria weights and preference and indifference thresholds are collected through questionnaires filled out by bridge experts. Results of the case study show that the most appropriate system for the Kashkhan Bridge is the box girder system with the balanced cantilever method and posttensioned concrete material.

Decision makers in the transportation industry search for a systematic approach to select an appropriate structural system, construction method, and material for bridges. Simple mathematical methodologies are needed to consider different stakeholders’ perspectives. With criteria that occur simultaneously in selecting appropriate material, construction technique, and structural system of bridges, invalid and unexpected results may occur from such complexity. The decision-making process is usually done subjectively by designers and requires much data and extensive experience in bridge design. To address these challenges and assume all substantial criteria within the framework, the PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) multi-criteria decision-making method is used. It is not sensitive to the number and definition of the criteria. The PROMETHEE method is based on the pairwise comparison between alternatives for constructing an outranking relationship to show the degree of dominance of one alternative over another. A case study of the Kashkhan Bridge in Iran is presented to demonstrate implementation of the PROMETHEE method and show how such a decision-making methodology can assist experts in making informed decisions based on a set of comprehensive criteria in the conceptual design stage. Some novel and effective criteria in this study are combined and synthesized to select the appropriate superstructure. Criteria weights and preference and indifference thresholds are collected through questionnaires filled out by bridge experts. Results of the case study show that the most appropriate system for the Kashkhan Bridge is the box girder system with the balanced cantilever method and posttensioned concrete material.