Gini coefficient, dissimilarity index and Lorenz curve for the spanish port system by type of goods

This paper shows the Gini Coefficient, the dissimilarity Index and the Lorenz Curve for the Spanish Port System by type of goods from 1960 to the year 2010 for business units: Total traffic, Liquid bulk cargo, Solid bulk cargo, General Merchandise and Container (TEUs) with the aim of carcaterizar the Spanish port systems in these periods and propose future strategies

Setting the Port Planning Parameters In Container Terminals through Bayesian Networks

The correct prediction in the transport logistics has vital importance in the adequate means and resource planning and in their optimisation. Up to this date, port planning studies were based mainly on empirical, analytical or simulation models. This paper deals with the possible use of Bayesian networks in port planning. The methodology indicates the work scenario and how the network was built. The network was afterwards used in container terminals planning, with the support provided by the tools of the Elvira code. The main variables were defined and virtual scenarios inferences were realised in order to carry out the analysis of the container terminals scenarios through probabilistic graphical models. Having performed the data analysis on the different terminals and on the considered variables (berth, area, TEU, crane number), the results show the possible relationships between them. Finally, the conclusions show the obtained values on each considered scenario

Melanoma classification using deep transfer learning

Melanoma is the most lethal type of skin cancer, despite the fact that individuals who are discovered early have a decent chance of recovering. A few creators have looked at various strategies to deal with programmed location and conclusion using design recognition and AI technology. Anticipating an infection so that it does not spread It is often helpful when doctors can diagnose an illness early on and spread throughout the body. Early disease detection is quite difficult due to the small number of screening populations. Whatever the case, it will take time to determine if it is harmless or hazardous. Assume the afflicted person sees a critical specialist for analysis, unaware that the critical specialist's knowledge has resulted in a cancerous development. This is where AI and deep learning technologies become a vital component of an effective mechanised determination framework, which might help doctors forecast infections much more swiftly and even ordinary people analyse a sickness. Our study endeavour addresses the issues of increased clinical expenditures associated with discovery, lower Precision in recognition and the manual discovery framework's mobility. System for Detecting Malignant Growths in Melanoma is a deep learning-based predictive model that leverages thermoscope pictures

Digitization of the entire traffic system and mitigation of the ongoing traffic crisis across cities of developing nations

This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2015.This paper focuses on a novel approach for handling the present traffic situation in perspective of Bangladesh. We plan to moderate the ongoing traffic predicament that currently plagues Dhaka city and gradually expand it to the whole country. Road traffic congestion is apparently a borderless ordeal in Dhaka and its adjacent cities and the situation tends to worsen as new cars enter the current stream every day. The aim of the paper is to develop a threefold solution to counter the traffic clogging. The approach taken during the course of this research focused primarily on an experimental evaluation of the small-scale model of the traffic routing algorithms. Among the threefold solution, the first approach is to develop a traffic algorithm to calculate the routes with shortest possible times to destinations. We plan to implement the system‟s usability by providing feedback to our target users (car drivers) so that they can decide on which route to take. This will be done by means of an overhead display on the car dashboards backed up by an embedded OS or Android. For our input we plan to take the amount of cars that are at any specific route at a time and provide that data to the car driver by the means of modern vehicle density measurement techniques. Travelling times are calculated using Dijkstra‟s algorithm and the shortest possible time required is provided to the commuters taking into consideration the situation of the roads at any point of time. The second approach is to make use of 24-hour Dynamic Traffic Light Controllers (DTLCs) based on artificial neural networks. The DTLC will be implemented using the Intel NUC in conjunction with the Arduino Mega. The decision making algorithm is designed to replicate, in a meager form, the human brain with the system trained to learn to respond to certain traffic situations. At present the BRTA (Bangladesh Road Transport) employs Static Traffic Light Controllers (STLCs) to handle traffic flow at some intersections while other, less important, ones have manual control in the form of the traffic officer in charge

Formal development and evaluation of narrow passageway system operations

This study applies a new intelligent transportation methodology for transforming informal operations concepts for narrow passageway systems into system-level designs, which will formal enough to support automated validation of anticipated component- and system-level behaviours. Models and specifications of behaviour are formally designed as labelled transition systems. Each object is the management system is assumed to have behaviour that can be defined by a finite state machine; thus, the waterway management system architecture is modelled as a network of communicating finite state machines. Architecture-level behaviours are validated using the Labelled Transition System Analyzer (LTSA). We exercise the methodology by working step by step through the synthesis and validation of a high-level behaviour model for a vessel passing through a waterway network (i.e., canal)

Crowdsourcing traffic data for travel time estimation

Travel time estimation is a fundamental measure used in routing and navigation applications, in particular in emerging intelligent transportation systems (ITS). For example, many users may prefer the fastest route to their destination and would rely on real-time predicted travel times. It also helps real-time traffic management and traffic light control. Accurate estimation of travel time requires collecting a lot of real-time data from road networks. This data can be collected using a wide variety of sources like inductive loop detectors, video cameras, radio frequency identification (RFID) transponders etc. But these systems include deployment of infrastructure which has some limitations and drawbacks. The main drawbacks in these modes are the high cost and the high probability of error caused by prevalence of equipment malfunctions and in the case of sensor based methods, the problem of spatial coverage.;As an alternative to traditional way of collecting data using expensive equipment, development of cellular & mobile technology allows for leveraging embedded GPS sensors in smartphones carried by millions of road users. Crowd-sourcing GPS data will allow building traffic monitoring systems that utilize this opportunity for the purpose of accurate and real-time prediction of traffic measures. However, the effectiveness of these systems have not yet been proven or shown in real applications. In this thesis, we study some of the current available data sets and identify the requirements for accurate prediction. In our work, we propose the design for a crowd-sourcing traffic application, including an android-based mobile client and a server architecture. We also develop map-matching method. More importantly, we present prediction methods using machine learning techniques such as support vector regression.;Machine learning provides an alternative to traditional statistical method such as using averaged historic data for estimation of travel time. Machine Learning techniques played a key role in estimation in the last two decades. They are proved by providing better accuracy in estimation and in classification. However, employing a machine learning technique in any application requires creative modeling of the system and its sensory data. In this thesis, we model the road network as a graph and train different models for different links on the road. Modeling a road network as graph with nodes and links enables the learner to capture patterns occurring on each segment of road, thereby providing better accuracy. To evaluate the prediction models, we use three sets of data out of which two sets are collected using mobile probing and one set is generated using VISSIM traffic simulator. The results show that crowdsourcing is only more accurate than traditional statistical methods if the input values for input data are very close to the actual values. In particular, when speed of vehicles on a link are concerned, we need to provide the machine learning model with data that is only few minutes old; using average speed of vehicles, for example from the past half hour, as is usually seen in many web based traffic information sources may not allow for better performance

Planning and Scheduling Interrelated Road Network Projects by Integrating Cell Transmission Model and Genetic Algorithm

In systems with interrelated alternatives, the benefits or costs of each alternative depend on which other alternatives are selected and when they are implemented. System interrelations and uncertainties in various elements of transportation systems such as future demand, make it difficult to evaluate project impacts with analytical methods. This study proposes a general and modular framework for planning and scheduling interrelated infrastructure projects under uncertainties. The method should be general enough to address the planning problem for any interrelated system in a wide range of applications. The goal is to determine which projects should be selected and when they should be implemented to minimize the present value of total system cost, subject to a cumulative budget flow constraint. For this purpose, the scheduling problem is formulated as a non-linear integer optimization problem that minimizes the present value of system cost over a planning horizon. The first part of this dissertation employs a simple traffic assignment model to evaluate improvement alternatives. The algorithm identifies potential locations within a network that needs improvements and considers multiple improvement alternatives at each location. Accordingly, a probabilistic procedure is introduced to select the optimal improvement type for the candidate locations. The traffic assignment model is used to evaluate the objective function and implicitly compute project interrelations, with a Genetic Algorithm (GA) developed to solve the optimization problem. In the second part of the dissertation, the traffic assignment model is replaced with a more detailed evaluation model, namely a Cell Transmission Model (CTM). The use of CTM significantly improves the model by tracking queues and predicating queue build-up and dissipation, as well as backward propagation of congestion waves. Finally, since GA does not guarantee global optimum, a statistical test is employed to test the optimality of the GA solution by estimating the probability of arriving at a better solution. In effect, it is shown that the probability of finding a better solution is negligible, thus demonstrating the soundness of the GA solution

Analysed potential of big data and supervised machine learning techniques in effectively forecasting travel times from fused data

Travel time forecasting is an interesting topic for many ITS services. Increased availability of data collection sensors increases the availability of the predictor variables but also highlights the high processing issues related to this big data availability. In this paper we aimed to analyse the potential of big data and supervised machine learning techniques in effectively forecasting travel times. For this purpose we used fused data from three data sources (Global Positioning System vehicles tracks, road network infrastructure data and meteorological data) and four machine learning techniques (k-nearest neighbours, support vector machines, boosting trees and random forest). To evaluate the forecasting results we compared them in-between different road classes in the context of absolute values, measured in minutes, and the mean squared percentage error. For the road classes with the high average speed and long road segments, machine learning techniques forecasted travel times with small relative error, while for the road classes with the small average speeds and segment lengths this was a more demanding task. All three data sources were proven itself to have a high impact on the travel time forecast accuracy and the best results (taking into account all road classes) were achieved for the k-nearest neighbours and random forest techniques.</p