Commodity-based Freight Activity on Inland Waterways through the Fusion of Public Datasets for Multimodal Transportation Planning

Within the U.S., the 18.6 billion tons of goods currently moved along the multimodal transportation system are expected to grow 51% by 2045. Most of those goods are transported by roadways. However, several benefits can be realized by shippers and consumers by shifting freight to more efficient modes, such as inland waterways, or adopting a multimodal scheme. To support such freight growth sustainably and efficiently, federal legislation calls for the development of plans, methods, and tools to identify and prioritize future multimodal transportation infrastructure needs. However, given the historical mode-specific approach to freight data collection, analysis, and modeling, challenges remain to adopt a fully multimodal approach that integrates underrepresented modes, such as waterways, into multimodal forecasting tools to identify and prioritize transportation infrastructure needs. Examples of such challenges are data heterogeneity, confidentiality, limitations in terms of spatial and temporal coverage, high cost associated with data collection, subjectivity in surveys responses, etc. To overcome these challenges, this work fuses data across a variety of novel transportation sources to close existing gaps in freight data needed to support multimodal long-range freight planning. In particular, the objective of this work is to develop methods to allow integration of inland waterway transportation into commodity-based freight forecasting models, by leveraging Automatic Identification System (AIS) data. The following approaches are presented in this dissertation: i) Maritime Automatic Identification System (AIS) data is mapped to a detailed inland navigable waterway network, allowing for an improved representation of waterway modes into multimodal freight travel demand models which currently suffer from unbalanced representation of waterways. Validation results show the model correctly identifies 84% stops at inland waterway ports and 83.5% of trips crossing locks. ii) AIS and truck Global Positioning System (GPS) data are fused to a multimodal network to identify the area of impact of a freight investment, providing a single methodology and data source to compare and contrast diverse transportation infrastructure investments. This method identifies parallel truck and vessel flows indicating potential for modal shift. iii) Truck GPS and maritime Lock Performance Monitoring System (LPMS) data are fused via a multi-commodity assignment model to characterize and quantify annual commodity throughput at port terminals on inland waterways, generating new data from public datasets, to support estimation of commodity-based freight fluidity performance measures. Results show that 84% of ports had less than a 20% difference between estimated and observed truck volumes. iv) AIS, LPMS, and truck GPS datasets are fused to disaggregate estimated annual commodity port throughput to vessel trips on inland waterways. Vessel trips characterized by port of origin, destination, path, timestamp, and commodity carried, are mapped to a detailed inland waterway network, allowing for a detailed commodity flow analysis, previously unavailable in the public domain. The novel, repeatable, data-driven methods and models proposed in this work are applied to the 43 freight port terminals located on the Arkansas River. These models help to evaluate network performance, identify and prioritize multimodal freight transportation infrastructure needs, and introduce a unique focus on modal shift towards inland waterway transportation

Leveraging Resources on Anonymous Mobile Edge Nodes

Smart devices have become an essential component in the life of mankind. The quick rise of smartphones, IoTs, and wearable devices enabled applications that were not possible few years ago, e.g., health monitoring and online banking. Meanwhile, smart sensing laid the infrastructure for smart homes and smart cities. The intrusive nature of smart devices granted access to huge amounts of raw data. Researchers seized the moment with complex algorithms and data models to process the data over the cloud and extract as much information as possible. However, the pace and amount of data generation, in addition to, networking protocols transmitting data to cloud servers failed short in touching more than 20% of what was generated on the edge of the network. On the other hand, smart devices carry a large set of resources, e.g., CPU, memory, and camera, that sit idle most of the time. Studies showed that for plenty of the time resources are either idle, e.g., sleeping and eating, or underutilized, e.g. inertial sensors during phone calls. These findings articulate a problem in processing large data sets, while having idle resources in the close proximity. In this dissertation, we propose harvesting underutilized edge resources then use them in processing the huge data generated, and currently wasted, through applications running at the edge of the network. We propose flipping the concept of cloud computing, instead of sending massive amounts of data for processing over the cloud, we distribute lightweight applications to process data on users\u27 smart devices. We envision this approach to enhance the network\u27s bandwidth, grant access to larger datasets, provide low latency responses, and more importantly involve up-to-date user\u27s contextual information in processing. However, such benefits come with a set of challenges: How to locate suitable resources? How to match resources with data providers? How to inform resources what to do? and When? How to orchestrate applications\u27 execution on multiple devices? and How to communicate between devices on the edge? Communication between devices at the edge has different parameters in terms of device mobility, topology, and data rate. Standard protocols, e.g., Wi-Fi or Bluetooth, were not designed for edge computing, hence, does not offer a perfect match. Edge computing requires a lightweight protocol that provides quick device discovery, decent data rate, and multicasting to devices in the proximity. Bluetooth features wide acceptance within the IoT community, however, the low data rate and unicast communication limits its use on the edge. Despite being the most suitable communication protocol for edge computing and unlike other protocols, Bluetooth has a closed source code that blocks lower layer in front of all forms of research study, enhancement, and customization. Hence, we offer an open source version of Bluetooth and then customize it for edge computing applications. In this dissertation, we propose Leveraging Resources on Anonymous Mobile Edge Nodes (LAMEN), a three-tier framework where edge devices are clustered by proximities. On having an application to execute, LAMEN clusters discover and allocate resources, share application\u27s executable with resources, and estimate incentives for each participating resource. In a cluster, a single head node, i.e., mediator, is responsible for resource discovery and allocation. Mediators orchestrate cluster resources and present them as a virtually large homogeneous resource. For example, two devices each offering either a camera or a speaker are presented outside the cluster as a single device with both camera and speaker, this can be extended to any combination of resources. Then, mediator handles applications\u27 distribution within a cluster as needed. Also, we provide a communication protocol that is customizable to the edge environment and application\u27s need. Pushing lightweight applications that end devices can execute over their locally generated data have the following benefits: First, avoid sharing user data with cloud server, which is a privacy concern for many of them; Second, introduce mediators as a local cloud controller closer to the edge; Third, hide the user\u27s identity behind mediators; and Finally, enhance bandwidth utilization by keeping raw data at the edge and transmitting processed information. Our evaluation shows an optimized resource lookup and application assignment schemes. In addition to, scalability in handling networks with large number of devices. In order to overcome the communication challenges, we provide an open source communication protocol that we customize for edge computing applications, however, it can be used beyond the scope of LAMEN. Finally, we present three applications to show how LAMEN enables various application domains on the edge of the network. In summary, we propose a framework to orchestrate underutilized resources at the edge of the network towards processing data that are generated in their proximity. Using the approaches explained later in the dissertation, we show how LAMEN enhances the performance of applications and enables a new set of applications that were not feasible

Technical, economic and energetic competitiveness of rail-road combined transport: evaluation of the effects of possible ITS applications on intermodal terminal.

L'abstract è presente nell'allegato / the abstract is in the attachmen

Methodology for Quantifying Resiliency of Transportation Systems

The National Science Foundation’s definition of resiliency is “the ability to prepare and plan for, absorb, recover from, or more successfully adapt to actual or potential adverse events” (National Science Foundation, 2016). While this definition is informative and useful, it lacks a quantitative reference. There is a need for a method of quantifying resilience to better plan and prepare for system wide disruptions. The research effort described herein provides a quantifiable measures of system resiliency, consistent with NSF’s definition. Fundamentally, a system disruption can be partitioned into five distinctive states: the stable pre-event state, the absorption state, the disrupted state, the recovered state, and stable recovered state. The proposed method identifies these states by measuring system output and quantifies each component on a value scale between zero and one. The resiliency measure then unifies these metrics to provide an overall assessment of resiliency, which accounts for the system’s ability to absorb, recover, and adapt. This approach to quantifying resiliency is applicable to any real-world or simulated system with measureable outputs. This paper first documents the development of the resiliency quantification method and then applies the method toward four complex, real world, transportation systems undergoing disruptions. These case studies consisted of six maritime port, three airports, two localized refueling systems, and the Colorado Department of Transportation’s cyber network. Each system had a measurable drop in functionality due to a disruption. In general the results of this research showed that the proposed method of quantifying resiliency can be utilized for any transportation system

Performance Evaluation And Anomaly detection in Mobile BroadBand Across Europe

With the rapidly growing market for smartphones and user’s confidence for immediate access to high-quality multimedia content, the delivery of video over wireless networks has become a big challenge. It makes it challenging to accommodate end-users with flawless quality of service. The growth of the smartphone market goes hand in hand with the development of the Internet, in which current transport protocols are being re-evaluated to deal with traffic growth. QUIC and WebRTC are new and evolving standards. The latter is a unique and evolving standard explicitly developed to meet this demand and enable a high-quality experience for mobile users of real-time communication services. QUIC has been designed to reduce Web latency, integrate security features, and allow a highquality experience for mobile users. Thus, the need to evaluate the performance of these rising protocols in a non-systematic environment is essential to understand the behavior of the network and provide the end user with a better multimedia delivery service. Since most of the work in the research community is conducted in a controlled environment, we leverage the MONROE platform to investigate the performance of QUIC and WebRTC in real cellular networks using static and mobile nodes. During this Thesis, we conduct measurements ofWebRTC and QUIC while making their data-sets public to the interested experimenter. Building such data-sets is very welcomed with the research community, opening doors to applying data science to network data-sets. The development part of the experiments involves building Docker containers that act as QUIC and WebRTC clients. These containers are publicly available to be used candidly or within the MONROE platform. These key contributions span from Chapter 4 to Chapter 5 presented in Part II of the Thesis. We exploit data collection from MONROE to apply data science over network data-sets, which will help identify networking problems shifting the Thesis focus from performance evaluation to a data science problem. Indeed, the second part of the Thesis focuses on interpretable data science. Identifying network problems leveraging Machine Learning (ML) has gained much visibility in the past few years, resulting in dramatically improved cellular network services. However, critical tasks like troubleshooting cellular networks are still performed manually by experts who monitor the network around the clock. In this context, this Thesis contributes by proposing the use of simple interpretable ML algorithms, moving away from the current trend of high-accuracy ML algorithms (e.g., deep learning) that do not allow interpretation (and hence understanding) of their outcome. We prefer having lower accuracy since we consider it interesting (anomalous) the scenarios misclassified by the ML algorithms, and we do not want to miss them by overfitting. To this aim, we present CIAN (from Causality Inference of Anomalies in Networks), a practical and interpretable ML methodology, which we implement in the form of a software tool named TTrees (from Troubleshooting Trees) and compare it to a supervised counterpart, named STress (from Supervised Trees). Both methodologies require small volumes of data and are quick at training. Our experiments using real data from operational commercial mobile networks e.g., sampled with MONROE probes, show that STrees and CIAN can automatically identify and accurately classify network anomalies—e.g., cases for which a low network performance is not justified by operational conditions—training with just a few hundreds of data samples, hence enabling precise troubleshooting actions. Most importantly, our experiments show that a fully automated unsupervised approach is viable and efficient. In Part III of the Thesis which includes Chapter 6 and 7. In conclusion, in this Thesis, we go through a data-driven networking roller coaster, from performance evaluating upcoming network protocols in real mobile networks to building methodologies that help identify and classify the root cause of networking problems, emphasizing the fact that these methodologies are easy to implement and can be deployed in production environments.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Matteo Sereno.- Secretario: Antonio de la Oliva Delgado.- Vocal: Raquel Barco Moren

Deep mobile traffic forecast and complementary base station clustering for C-RAN optimization

The increasingly growing data traffic has posed great challenges for mobile operators to increase their data processing capacity, which incurs a significant energy consumption and deployment cost. With the emergence of the Cloud Radio Access Network (C-RAN) architecture, the data processing units can now be centralized in data centers and shared among base stations. By mapping a cluster of base stations with complementary traffic patterns to a data processing unit, the processing unit can be fully utilized in different periods of time, and the required capacity to be deployed is expected to be smaller than the sum of capacities of single base stations. However, since the traffic patterns of base stations are highly dynamic in different time and locations, it is challenging to foresee and characterize the traffic patterns in advance to make optimal clustering schemes. In this paper, we address these issues by proposing a deep-learning-based C-RAN optimization framework. First, we exploit a Multivariate Long Short-Term Memory (MuLSTM) model to learn the temporal dependency and spatial correlation among base station traffic patterns, and make accurate traffic forecast for a future period of time. Afterwards, we build a weighted graph to model the complementarity of base stations according to their traffic patterns, and propose a Distance-Constrained Complementarity-Aware (DCCA) algorithm to find optimal base station clustering schemes with the objectives of optimizing capacity utility and deployment cost. We evaluate the performance of our framework using data in two months from real-world mobile networks in Milan and Trentino, Italy. Results show that our method effectively increases the average capacity utility to 83.4% and 76.7%, and reduces the overall deployment cost to 48.4% and 51.7% of the traditional RAN architecture in the two datasets, respectively, which consistently outperforms the state-of-the-art baseline methods

Book of abstracts of the 24th Euro Working Group on Transportation Meeting

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Safety evaluation of the ports along the maritime silk road

21st Century Maritime Silk Road (MSR) is of significant importance for world freight transport. The ports along the MSR present a key element of the involved shipping networks to support the connectivity of the MSR. Therefore, it is crucial to carry an effective safety assessment of the ports to ensure the robustness and sustainability of the growing MSR. However, traditional quantitative risk analysis approaches (QRA) used in ports face many challenges when being applied within the context of the MSR, such as risk data incompleteness and ambiguity, and operational and environmental uncertainties. This paper proposes a novel safety evaluation approach to address these issues encountered during the risk analysis process in the MSR ports. The fuzzy set theory (FST), an evidential reasoning (ER) approach, and the expected utility theory are integrated in a holistic way in the proposed methodology. The proposed methodology is used to analyse five key ports along the MSR. The results provide decision-makers with useful insights on enhancing port safety, effective route planning as well as improving operational efficiency