1,720,979 research outputs found
A Dynamic Simulation based Model for Optimal Fleet Repositioning in Bike-sharing Systems
Full text linkAbstractIn this paper a simulation model for dynamic bikes redistribution process is presented. The objective of the model is to minimize the vehicles repositioning costs for bike-sharing operators, aiming at a high level users satisfaction, and assuming that it increases with the probability to find an available bike or a free docking point in any station at any time. The proposed model considers the dynamic variation of the demand (for both bikes and free docking slot) and micro-simulate the BSS in space and time determining the optimal repositioning flows, distribution patterns and time intervals between relocation operations by explicitly considering the route choice for trucks among the stations
A Modular Soft Computing based Method for Vehicles Repositioning in Bike-sharing Systems
Full text linkAbstractA crucial issue in bike-sharing systems (BSS) is the unbalanced distribution in space and time of the bikes among the stations. Literature shows several methods, to solve the vehicle reallocation problem and most of them are based on rigid control thresholds and refer to car-sharing systems. In this paper a more flexible fuzzy decision support system for redistribution process in BSS is presented. The aim of the proposed method is to minimize the redistribution costs for bike-sharing companies, determining the optimal bikes repositioning flows, distribution patterns and time intervals between relocation operations, with the objective of a high level for users satisfaction. The proposed method allows to define the best bikes repositioning jointly to the best route for the carrier vehicles. The optimization method has been applied to a simulated BSS that can be considered as a module of a wider real BSS thanks to the scalable architecture of the decision support system. The results of this first tests are interesting even if further investigation are in progress
Fuzzy linear programming for O-D matrix estimation using traffic counts and uncertain data
No full textSIMULTANEOUS PATH CHOICE MODEL CALIBRATION AND O-D MATRIX ESTIMATION USING TRAFFIC COUNTS: A GLS ESTIMATOR FOR CONGESTED NETWORK
No full textIn this paper a Generalized Least Square estimator for the simultaneous path choice model parameters calibration and Origin-Destination (O-D) matrix estimation is presented. The paper will assume as available information a set of link traffic counts, a starting estimate of the unknown parameters and of the O-D travel demand vector. The problem is formulated as fixed-point model (equilibrium programming) assuming the congested network case, the variability of both O-D demand vector and the matrix of link choice probabilities. Along the paper, the theoretical aspects of the proposed estimator as well as numerical results of numerical applications are described
A GLS estimator for combined path choice model and O-D Matrix aggregate calibration on congested network
No full text
GIFT Transport Network Census - Corridor V Selected Stretches
No full textThe report therefore raises the objective of providing an overview of the currently available transport infrastructure, going to define the size, problems and potential. For this purpose we have chosen to outline all the data collected with synthetic indicators, better known as Key Performance Indicators (KPIs).The report therefore raises the objective of providing an overview of the currently available transport infrastructure, going to define the size, problems and potential. For this purpose we have chosen to outline all the data collected with synthetic indicators, better known as Key Performance Indicators (KPIs)
A fuzzy data meta training system for ranking hub container terminals
No full textThe potential and critical aspects of any transport service can be highlighted through the estimation of appropriate performance indicators of the examined system. Commonly, container terminal analysis is based first on the evaluation and comparison of quantitative parameters that describe the level of service of the terminal and, on the other side by means of performance indicators related to terminal productivity. In this paper a Fuzzy Inference System for evaluation of a synthetic performance indicator is proposed. This tool could help planners and managers in terminals performances analysis and ranking as well as in assessing the effects of possible intervention on the systems. The proposed approach is suitable in the case of hub container ports. In fact this system is characterised by significant uncertainties and it is not always governed by certain rules, rational behaviour, so that it cannot be easily represented by traditional mathematical techniques and models. In our opinion, could be convenient to define the values of the considered parameters by explicitly define them in an approximate way, that is to say by fuzzy set
Traffic equilibrium network design problem under uncertain constraints
Full text linkAbstractNetwork design models allow to define an optimal network configuration by means of objective functions subject to a series of constraints. The starting data and/or the constraints of the problem can be affected by uncertainty. These uncertain values are better managed through the use of fuzzy values/constraints. In this paper we present a fuzzy non-linear programming to solve the equilibrium Network Design problem for urban areas. This problem is formulated as a fixed point optimization subject to fuzzy constraints. The proposed method has been applied to a test network. The obtained results show that the proposed approach is very interesting
Measuring Transport Systems Efficiency Under Uncertainty by Fuzzy Sets Theory Based Data Envelopment Analysis: Theoretical and Practical Comparison with Traditional DEA Model
Full text linkAbstractIn transportation management the measure of systems efficiency is a key issue in order to verify the performances and propose the best countermeasure to achieve the prefixed goals. Many efforts have been made in this field to provide satisfactory answer to this problem. One of the most used methodologies is the Data Envelopment Analysis (DEA) that has been in many fields. The DEA technique is a useful is non-parametric method that allow to handle many output and input at the same time. In many real world applications, input and output data cannot be precisely measured. Imprecision (or approximation) and vagueness may be originated from indirect measurements, model estimation, subjective interpretation, and expert judgment or available information from different sources. Therefore, methodologies that allow the analyst to explicitly deal with imprecise or approximate data are of great interest, especially in freight transport where available data as well as stakeholders’ behavior often suffer from vagueness or ambiguity. This is particularly worrying when assessing efficiency with frontier-type models, such as Data Envelopment Analysis (DEA) models, since they are very sensitive to possible imprecision in the data set. In this paper, we have specified a Fuzzy Theory-based DEA model to assess efficiency of transportation systems and services considering uncertainty in data, as well as in the evaluation result. In particular, we have applied the proposed fuzzy DEA model to evaluate the efficiency of a selected set of international container ports. In particular, we focus on the “delay time” that is an important input data that is usually non easy to measure and then is considered as uncertain. Finally, a comparison of ports efficiency obtained by the proposed fuzzy DEA model and traditional DEA has been carried out in order to evaluate the differences between the two methods
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