An integrated method for short-term prediction of road traffic conditions for intelligent transportation systems applications

The paper deals with the short-term prediction of road traffic conditions within Intelligent Transportation Systems applications. First, the problem of traffic modeling and the potential of different traffic monitoring technologies are discussed. Then, an integrated method for short-term traffic prediction is presented, which integrates an Artificial Neural Network predictor that forecasts future states in standard conditions, an anomaly detection module that exploits floating car data to individuate possible occurrences of anomalous traffic conditions, and a macroscopic traffic model that predicts speeds and queue progressions in case of anomalies. Results of offline applications on a primary Italian motorway are presented

Modeling Car following with Feed-Forward and Long-Short Term Memory Neural Networks

The paper investigates the capability of modeling the car following behavior by training shallow and deep recurrent neural networks to reproduce observed driving profiles, collected in several experiments with pairs of GPS-equipped vehicles running in typical urban traffic conditions. The input variables are relative speed, spacing, and vehicle speed. In the model, we assume that the reaction is not instantaneous. However, it may occur during a time interval of the order of a few tenth of seconds because of both the psychophysical driver’s reaction process and the mechanical activation of braking or dispensing the traction power to the wheels. Experimental results confirm the reliability of this assumption and highlight that the deep recurrent neural network outperforms the simpler feed-forward neural network

Accessibility analysis for Urban Freight Transport with Electric Vehicles

Urban Freight Transport is a continuously growing market mainly based on the use of vehicles with combustion engines, whose environmental impact has become unsustainable. Because of the technological improvement of electric vehicles and their growing economic feasibility, the introduction of electric fleets for urban freight distribution is now a considerable opportunity. Cities are rapidly adapting, in need of tools to properly guide and manage these changes, as the rise of electric vehicles must be encouraged by an appropriate infrastructural system, from charging stations to dedicated areas. What is proposed in this work is an aggregate approach to the freight system, transport demand and supply, to support the design of a distribution system based on electric vehicles by means of an accessibility indicator that takes into account the supply of facilities, vehicle performances, and freight demand patterns. A study case regarding the Metropolitan City of Rome is also presented to interpret and understand the potentialities of this approach

Application of a Mamdani-Based Fuzzy Traffic State Identifier to a Real Case Study

This paper presents a fuzzy-logic application based on the Mamdani inference method to get the range of road traffic conditions. It was tested with real data extracted from the Padua-Venice motorway in Italy, which contains a dense network of monitoring that provide continuous measurements of flow, occupancy, and speed. The empirical results show that the proposed study functions well in qualitative classification. The experiment can provide another perspective on motorway traffic control

Comparative analysis of implicit models for real-time short-term traffic predictions

Predicting future traffic conditions in real-time is a crucial issue for applications of intelligent transportation systems devoted to traffic management and traveller information. The increasing number of connected vehicles equipped with locating technologies provides a ubiquitous updated source of information on the whole network. This offers great opportunities for developing data-driven models that extrapolate short-term future trend directly from data without modelling traffic phenomenon explicitly. Among several different approaches to implicit modelling, machinelearning models based on a network structure are expected to be more suitable to catch traffic phenomenon because of their capability to account for spatial correlations existing between traffic measures taken on different elements of the road network. The study analyses and applies different implicit models for short-term prediction on a large road network: namely, time-dependent artificial neural networks and Bayesian networks. These models are validated and compared by exploiting a large database of link speeds recorded on the metropolitan area of Rome during seven months. © The Institution of Engineering and Technology 2016

Work Extraction and Energy Storage in the Dicke Model

We study work extraction from the Dicke model achieved using simple unitary cyclic transformations keeping into account both a non optimal unitary protocol, and the energetic cost of creating the initial state. By analyzing the role of entanglement, we find that highly entangled states can be inefficient for energy storage when considering the energetic cost of creating the state. Such surprising result holds notwithstanding the fact that the criticality of the model at hand can sensibly improve the extraction of work. While showing the advantages of using a many-body system for work extraction, our results demonstrate that entanglement is not necessarily advantageous for energy storage purposes, when non optimal processes are considered. Our work shows the importance of better understanding the complex interconnections between non-equilibrium thermodynamics of quantum systems and correlations among their subparts.Comment: 5 pages + supplementary informatio

Short-term traffic predictions on large urban traffic networks: applications of network-based machine learning models and dynamic traffic assignment models

The paper discusses the issues to face in applications of short-term traffic predictions on urban road networks and the opportunities provided by explicit and implicit models. Different specifications of Bayesian Networks and Artificial Neural Networks are applied for prediction of road link speed and are tested on a large floating car data set. Moreover, two traffic assignment models of different complexity are applied on a sub-area of the road network of Rome and validated on the same floating car data set

Hybrid Metaheuristic Approach to Solve the Problem of Containers Reshuffling in an Inland Terminal

The paper deals with the problem of minimizing the reshuffling of containers in an inland intermodal terminal. The problem is tackled according to a hybrid approach that combines a preliminary selection of heuristics and a genetic algorithm. The heuristics are used to determine the initial population for the genetic algorithm, which aims to optimize the locations of the containers to store in the yard in order to minimize the operational costs. A simulation model computes the costs related to storage and pick-up operations in the yard bay. The proposed optimization method has been calibrated by selecting the optimal parameters of the genetic algorithm in a toy case and has been tested on a theoretical example of realistic size. Results highlighted that the use of a suitable heuristic to generate the initial population outperforms the genetic algorithm, initialized with a random solution, by 20%

Effectiveness of link and path information on simultaneous adjustment of dynamic O-D demand matrix

Introduction The paper deals with the adjustment of time-dependent Origin–destination (O-D) demand matrix, which is the fundamental input of ITS application for traffic predictions. The usual problem is to search for temporal O-D matrices that are "near" an a priori estimate (seed matrices) and that best fit traffic counts. However information on link flows is not fully effective in describing the state of the network; recent technologies for tracking vehicles provide a new kind of information on route travel times that can integrate usual information on traffic flows at count sections