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

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

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%

On Transport Monitoring and Forecasting during COVID-19 Pandemic in Rome

This paper presents the results of a study on the Rome mobility system aiming at estimating the impacts of the progressive lockdown, imposed by the government, due to the Covid-19 pandemic as well as to support decision makers in planning the transport system for the restart towards a post-Covid "new normal". The analysis of data obtained by the transport monitoring system has been fundamental for both investigating effects of the lockdown and feeding transport models to predict the impacts on future actions. At first, the paper focuses on the so-called transport analytics, by describing mobility trends for the multimodal transportation system of Rome. Then, the results of the simulated scenarios to design public transport services, able to ensure passengers social distancing required in the first post-Covid months, are presented and discussed

Short-term speed predictions exploiting big data on large urban road networks

Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road network and provide great opportunities for enhanced short-term traffic predictions based on real-time information on the whole network. Two network-based machine learning models, a Bayesian network and a neural network, are formulated with a double star framework that reflects time and space correlation among traffic variables and because of its modular structure is suitable for an automatic implementation on large road networks. Among different mono-dimensional time-series models, a seasonal autoregressive moving average model (SARMA) is selected for comparison. The time-series model is also used in a hybrid modeling framework to provide the Bayesian network with an a priori estimation of the predicted speed, which is then corrected exploiting the information collected on other links. A large floating car data set on a sub-area of the road network of Rome is used for validation. To account for the variable accuracy of the speed estimated from floating car data, a new error indicator is introduced that relates accuracy of prediction to accuracy of measure. Validation results highlighted that the spatial architecture of the Bayesian network is advantageous in standard conditions, where a priori knowledge is more significant, while mono-dimensional time series revealed to be more valuable in the few cases of non-recurrent congestion conditions observed in the data set. The results obtained suggested introducing a supervisor framework that selects the most suitable prediction depending on the detected traffic regimes

Traffic dynamics estimation by using raw floating car data

Massive datasets of Floating Car Data (FCD) are collected and thereafter processed to estimate and predict traffic conditions. In the framework of short-term traffic forecasting, machine learning techniques have become very popular. However, the big datasets available today contain for the most part easily predictable data, that are data observed during recurrent conditions. Integration of different machine learning techniques with traffic engineering notions must contribute to obtain new transportation-oriented data-driven methods. In this paper we address traffic dynamics estimation by using individual FCD in order to develop an integrative framework able to recognize and select the suitable method for traffic forecasting. Taking into account the spatial distributions of individual FCD positions we retrieve a new spatial-based criterion for the integration of models

'Big Data' opportunities for new transport models and services

La diffusione di dispositivi mobili di localizzazione e comunicazione produce con continuità un'immensa quantità di dati – i cosiddetti Big Data– che possono essere sfruttati per migliorare la conoscenza dello stato del sistema di trasporto e per mettere in atto opportune politiche di mobilità e appropriate azioni di regolazione. Peraltro, gli attuali sistemi di informazione presentano tuttora notevoli lacune e sono quindi suscettibili di significativi miglioramenti. La presente memoria esamina i principali problemi connessi all’uso dei Big Data e tratta dell’opportunità offerta da questi di realizzare modelli di mobilità individuale basati su un aggiornamento continuo dell’informazione. La prima parte, di carattere discorsivo, è completata da una sintetica rassegna delle ricerche eseguite dagli autori e finalizzate ad analizzare i Big Data sotto diversi aspetti: per ottenere una caratterizzazione quantitativa dei fenomeni di mobilità, per comprendere i comportamenti e le preferenze degli utenti, per migliorare i modelli di previsione e studiare nuovi servizi di mobilità.The vast diffusion of mobile location and communication devices continuously produce a huge amount of data ('Big Data') that can be exploited to improve the knowledge about the state of the transport system and perform appropriate regulatory actions and execute suitable policy actions. However, current information systems still have noteworthy drawbacks and are in the meantime open to significant improvements. The paper discusses the main problems related to the use of the Big Data and examines the opportunities offered by Big Data to introduce new transport models based on continuous updating of microscopic models. The first part of the paper, of conversational nature, is complemented by a concise overview of the research conducted by the authors with multiple objectives: derive a quantitative characterization of the mobility phenomena, understand users' behaviour and preferences, develop enhanced prediction models, and study new mobility services

Optimization and Simulation Approach of Containers handling operations at Intermodal Terminals

The paper deals with the problem of minimizing reshuffling of containers in an inland intermodal terminal. The problem is tackled according to a simulation-optimization approach. A simulation model computes the operational costs of containers, related to storage and pick-up operations in an inland yard. The optimization is carried out by a double genetic algorithm that applies two genetic algorithms in series. The first optimizes the locations of the containers to store in the yard and identifies the blocking containers that have to be reshuffled. The second genetic algorithm takes the solution of the first and optimizes the reshuffling of blocking containers together with the unloaded ones. The proposed optimization method has been tested on a theoretical example of a realistic size. Results highlighted that the double genetic algorithm reduces the total operational costs by 5% with respect to the single genetic algorithm