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En las áreas metropolitanas la movilidad cotidiana por motivos laborales, de estudio, trámites cotidianos, ocio y tiempolibre adquiere una importancia crucial. En esta investigación se pretende construir una metodología de análisis demovilidad cotidiana basada en la utilización de sistemas inteligentes que permitan investigar la forma de encontrar...
Currently, there is a notable prevalence of substantial traffic congestion and frequent vehicular accidents on roadways in contemporary times. Amalgamation of latest front-line technologies involving Internet of Things (IoT) and image classification has immense potential to advance the progress of a proficient traffic regulation system. To mitigate...
In recent years, many cities have formed a complete modern transportation system, but the phenomenon of “phantom traffic jam” is also frequent, the reason of which has been concerned by people. At present, much research has analyzed this phenomenon, but there are still many questions about its occurrence. Therefore, this paper makes an in-depth stu...
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... Costs for equipment (i.e., crane, conveyor, hooper, forklift) and storage facilities (i.e., warehouse, storage tank, paved and unpaved storage) are obtained from (Braham et al., 2017). These costs include labor and materials, as well as general overhead. ...
... These costs include labor and materials, as well as general overhead. (Braham et al., 2017) selected these costs from a material, construction, and equipment cost database from RS Means (2014, 2017) and validated through interviews with industry representatives. All costs for our study are calculated based on the 2020 dollar value. ...
This paper investigates inland port infrastructure investment planning under uncertain commodity (such as coal, petroleum, manufactured products, nonmetallic minerals) demand conditions. A two-stage stochastic optimization is developed to model the impact of demand uncertainty on infrastructure planning and transportation decisions. The model minimizes expected total costs, including capacity expansion costs, associated with handling equipment and storage infrastructure, and the expected transportation costs. To solve the problem, an accelerated Benders decomposition algorithm is implemented. The use of a stochastic approach is justified by comparing the value of stochastic solution with its corresponding deterministic solution. For demonstration, the model is applied to the Arkansas section of the McClellan-Kerr Arkansas River Navigation System (MKARNS). Given data availability, the model is generalizable to other regions. Results show that as investment in port capacities (handling equipment and storage infrastructure) increases by 21 M per year. The model serves as a decision-making tool for optimal, distributed allocation of monetary investments, that encourages mode shift to inland waterways.
... Costs for equipment (i.e., crane, conveyor, hooper, forklift) and storage facilities (i.e., warehouse, storage tank, paved and unpaved storage) are obtained from (Braham et al., 2017). These costs include labor and materials, as well as general overhead. ...
... These costs include labor and materials, as well as general overhead. (Braham et al., 2017) selected these costs from a material, construction, and equipment cost database from RS Means (2014, 2017) and validated through interviews with industry representatives. All costs for our study are calculated based on the 2020 dollar value. ...
This paper investigates inland port infrastructure investment planning under uncertain commodity demand conditions. A two-stage stochastic optimization is developed to model the impact of demand uncertainty on infrastructure planning and transportation decisions. The two-stage stochastic model minimizes the total expected costs, including the capacity expansion investment costs associated with handling equipment and storage, and the expected transportation costs. To solve the problem, an accelerated Benders decomposition algorithm is implemented. The Arkansas section of the McCllean-Kerr Arkansas River Navigation System (MKARNS) is used as a testing ground for the model. Results show that commodity volume and, as expected, the percent of that volume that moves via waterways (in ton-miles) increases with increasing investment in port infrastructure. The model is able to identify a cluster of ports that should receive investment in port capacity under any investment scenario. The use of a stochastic approach is justified by calculating the value of the stochastic solution (VSS).
... In doing this, estimates of system usage by mode may provide a means to more accurately reflect costs and benefits of a project. Benefits, for example, may be associated with reduce roadway maintenance costs and/or emissions by shifting cargo from truck to barge (Braham et al. 2017). ...
To estimate impacts, support cost–benefit analyses, and enable project prioritization, it is necessary to identify the area of influence of a transportation infrastructure project. For freight related projects, like ports, state-of-the-practice methods to estimate such areas ignore complex interactions among multimodal supply chains and can be improved by examining the multimodal trips made to and from the facility. While travel demand models estimate multimodal trips, they may not contain robust depictions of water and rail, and do not provide direct observation. Project-specific data including local traffic counts and surveys can be expensive and subjective. This work develops a systematic, objective methodology to identify multimodal “freight-shed” (or “catchment” areas) for a facility from vehicle tracking data and demonstrates application with a case study involving diverse freight port terminals. Observed truck Global Positioning System and maritime Automatic Identification System data are subjected to robust pre-processing algorithms to handle noise, cluster stops, assign data points to the network (map-matching), and address spatial and temporal conflation. The method is applied to 43 port terminals on the Arkansas River to estimate vehicle miles and hours travelled, origin, destination, and pass-through zones, and areas of modal overlap within the catchment areas. Case studies show that the state-of-the-practice 100-mile diameter influence areas include between 15 and 34% of the multimodal freight-shed areas mined from vehicle tracking data, demonstrating that adoption of an arbitrary radial area for different ports would lead to inaccurate estimates of project benefits.
Inland waterways play a key role within the freight transportation system by connecting productive heartland areas to international gateways, while keeping costs competitive. Quantifying commodity flow is important because it affects cost-based supply chain decision-making. However, data on commodity movements to inform investment and planning decisions is elusive. Publicly available commodity data on U.S. inland waterways is limited in its spatial aggregation to the location of locks, which is insufficient to identify inter-port commodity flows. Automatic Identification System (AIS) data has the potential to disaggregate freight-flows to the port and river segment levels but it does not identify the commodity carried. This paper characterizes and quantifies vessel trips by port of origin-destination, timestamp, commodity carried, and path (mapped to an inland waterway network), allowing for disaggregated commodity flow analysis, previously unavailable in the public domain in the U.S.
This is accomplished through a multi-commodity assignment model which conflates AIS vessel movement data with commodity-specific port throughput. A stochastic approach is introduced to handle uncertainty in cargo-to-vessel ratios. Validation using data from the Arkansas River show agreement between model predictions and aggregated commodity volumes with differences lower than 1.82% by commodity and lock. Ubiquitous AIS data permit the transferability of the proposed work.
