A Multi-Objective Decision-Making Framework for Transportation Investments

This paper presents a framework based on multi-objective optimization that can be used to generate and analyze the most desirable transportation investment options based on their objectives and constraints. The framework, which is based on the surrogate worth trade-off analysis, could be applied to both discrete or continuous decision-problem scenarios. In a discrete problem, a predefined set of alternatives is available, whereas continuous problems are not characterized by a predefined set of alternatives. This framework was applied with the data generated for a Capital Beltway Corridor investment study. The multi-objective decision-making framework was found to be adaptable to this typical investment case study

Current Practice of Design, Delivery and Maintenance of Online Training for Transportation Professionals at Public Agencies

Transportation officials need to address the day-to-day challenges of both traffic demands and infrastructure needs to maintain the sustainability of the existing transportation system. While participating in the decision making process, professionals from any public agency (i.e. State Department of Transportation or DOT) need diverse knowledge and dynamic skills to identify any specific points of concern. In order to make the transportation professionals efficient in their job, different public agencies offer online trainings along with the traditional instructor-led courses. These online capacity building courses augment contemporary knowledge and technical skills, which empower professionals to perform their tasks proficiently. Apart from an extensive literature review, this study compiles the outcome of the survey among 10 different state DOTs online training. In addition, this study also summarizes the results from interviewing 6 different state DOTs, of which more than 80 percent, design and develop online trainings for their employees. While outcome of the online survey has provided a general overview regarding online training practiced by state DOTs, the telephone interviews has helped to obtain detailed insight about the design considerations, delivery methods and adopted strategies for developing effective online trainings

A Multi-Objective Decision-Making Framework for Transportation Investments

This paper presents a framework based on multi-objective optimization that can be used to generate and analyze the most desirable transportation investment options based on their objectives and constraints. The framework, which is based on the surrogate worth trade-off analysis, could be applied to both discrete or continuous decision-problem scenarios. In a discrete problem, a pre-defined set of alternatives is available, whereas continuous problems are not characterized by a pre-defined set of alternatives. This framework was applied with the data generated for a Capital Beltway Corridor investment study. The multi-objective decision-making framework was found to be adaptable to this typical investment case study

Situation-Aware Left-Turning Connected and Automated Vehicle Operation at Signalized Intersections

One challenging aspect of the Connected and Automated Vehicle (CAV) operation in mixed traffic is the development of a situation-awareness module for CAVs. While operating on public roads, CAVs need to assess their surroundings, especially the intentions of non-CAVs. Generally, CAVs demonstrate a defensive driving behavior, and CAVs expect other non-autonomous entities on the road will follow the traffic rules or common driving behavior. However, the presence of aggressive human drivers in the surrounding environment, who may not follow traffic rules and behave abruptly, can lead to serious safety consequences. In this paper, we have addressed the CAV and non-CAV interaction by evaluating a situation-awareness module for left-turning CAV operations in an urban area. Existing literature does not consider the intent of the following vehicle for a CAVs left-turning movement, and existing CAV controllers do not assess the following non-CAVs intents. Based on our simulation study, the situation-aware CAV controller module reduces up to 27% of the abrupt braking of the following non-CAVs for scenarios with different opposing through movement compared to the base scenario with the autonomous vehicle, without considering the following vehicles intent. The analysis shows that the average travel time reductions for the opposite through traffic volumes of 600, 800, and 1000 vehicle/hour/lane are 58%, 52%, and 62%, respectively, for the aggressive human driver following the CAV if the following vehicles intent is considered by a CAV in making a left turn at an intersection

Assessing the Connectivity of Groundwater Wells to Surface-Water Using a Volumetric Capture Delineation Tool

Existing groundwater modeling methods for determining the degree of groundwater well connectivity to surface-water either provide weak guidance (i.e., analytical methods), only examine the hydrological impacts of pumping (i.e., mapping method developed by Leake et al. (2010)), or are too computationally expensive (i.e., solute transport modeling methods) (Ceric & Haitjema, 2005). As such, a modeling technique is required that can accurately estimate the amount of water a particular well sources from surface-water at low computational costs. Here, we present some novel applications of a software tool called FlowSource (Black and Foley, 2013) in assessing well and surface-water connectivity. FlowSource is a MODFLOW-based (Harbaugh et al., 2000) software and can completely describe the flow connectivity between specified parts of the aquifer system without the use of particle tracking or advective transport simulation. First, an algorithm was developed to reduce numerical dispersion in FlowSource calculations that result from the assumption of fully mixed groundwater model cells. For any given set of flows along the faces of a three-dimensional rectilinear finite-difference groundwater model cell, the algorithm can evaluate the volumetric flow from each inflow face to each outflow face by either applying mass balance inside the cell or analytically recreating the internal streamtube geometry using the semi-analytical particle tracking method developed by Pollock (1988). The calculations of the algorithm are exact. The algorithm may be applied on a cell-by-cell basis to establish the volumetric flow connections inside the cell before performing the flow connectivity calculations in FlowSource. Its potential to reduce numerical dispersion in FlowSource calculations is demonstrated with a hypothetical example. Second, a novel FlowSource-based modeling tool was developed for assessing well and surface-water connectivity. The tool deploys FlowSource within the framework of the mapping method developed by Leake et al. (2010) (the LRD method). The differences in the results of the novel tool and the LRD method are demonstrated. Numerous applications of the modeling tool are demonstrated using a synthetic model, which include the ability to rapidly: (1) screen for wells that are surface-water dominated and are at risk of contamination from non-point sources, (2) infer the volume of water removed by pumpage from surface-water and, (3) generate diagnostic maps that illustrate how the location and time of pumping affects: (a) volumetric connectivity with surface-water, (b) the connectivity of existing wells to surface-water and, (c) hyporheic flows. Most importantly, the use of FlowSource enables these assessments to be performed without performing any solute transport modeling. Hence, the modeling tool developed here has the potential to aid water managers to inexpensively assess the risks posed to public health and/or the local environment of existing and planned pumping operations so that they can effectively prioritize monitoring and modeling efforts

Synergizing Roadway Infrastructure Investment with Digital Infrastructure for Infrastructure-Based Connected Vehicle Applications: Review of Current Status and Future Directions

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The safety, mobility, environmental and economic benefits of Connected and Autonomous Vehicles (CAVs) are potentially dramatic. However, realization of these benefits largely hinges on the timely upgrading of the existing transportation system. CAVs must be enabled to send and receive data to and from other vehicles and drivers (V2V communication) and to and from infrastructure (V2I communication). Further, infrastructure and the transportation agencies that manage it must be able to collect, process, distribute and archive these data quickly, reliably, and securely. This paper focuses on current digital roadway infrastructure initiatives and highlights the importance of including digital infrastructure investment alongside more traditional infrastructure investment to keep up with the auto industry's push towards this real time communication and data processing capability. Agencies responsible for transportation infrastructure construction and management must collaborate, establishing national and international platforms to guide the planning, deployment and management of digital infrastructure in their jurisdictions. This will help create standardized interoperable national and international systems so that CAV technology is not deployed in a haphazard and uncoordinated manner