Chiara Colombaroni

Artificial Neural Network Models for Car Following: Experimental Analysis and Calibration Issues

This article deals with the application of artificial neural networks to model car following drivers’ behavior. The study is based on experimental data collected by several global positioning system-equipped vehicles that follow each other on urban roads. A “swarm” stochastic evolutionary algorithm has been applied in the training phase to improve convergence of the usual error-back propagation algorithm. Validation tests show that artificial neural networks (ANNs) provide a good approximation of driving patterns. Therefore, ANN can be suitably implemented in microsimulation models. In this regard, a new experimental calibration method for microsimulation software might consist of training one specific ANN for each representative individual of the driver population through systematic observations in the field or in virtual environment trials.

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.

Traffic monitoring and incident detection through VANETs

Road traffic monitoring is one of the key applications in the Intelligent Transport System field. New technologies are now provided in this field and among the most relevant ones there is the DSRC (Dedicated Short Range Communication) set of protocols and standards where vehicles wirelessly communicate. In this paper, we deal with the application of Vehicular Ad-Hoc Networks to road traffic monitoring and we present the design of two distributed protocols based on the DSRC. A realistic simulation of a main expressway in Rome, Italy, is implemented and the performances of the two proposed monitoring methodologies are evaluated in case of regular traffic conditions and in case of a car accident. In both cases the protocols are able to capture in a very quick time (few seconds) the current traffic conditions even on a quite long road of about 70 km. A discussion about the impact of the market penetration rate of the on-board DSRC devices on the protocols performance is also provided.

Optimization of container operations at inland 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 two genetic algorithms that work in series. The introduction of the second genetic algorithm and the concept of trust region are the original contributions of the paper to the literature. The proposed optimization method has been tested on a theoretical example of realistic size. Results highlighted that the double genetic algorithm reduces the total operational costs by 7% with respect to the single genetic algorithm.

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.

Dynamic O-D Demand Estimation: Application of SPSA AD-PI Method in Conjunction with Different Assignment Strategies

This paper examines the impact of applying dynamic traffic assignment (DTA) and quasi-dynamic traffic assignment (QDTA) models, which apply different route choice approaches (shortest paths based on current travel times, User Equilibrium: UE, and system optimum: SO), on the accuracy of the solution of the offline dynamic demand estimation problem. The evaluation scheme is based on the adoption of a bilevel approach, where the upper level consists of the adjustment of a starting demand using traffic measures and the lower level of the solution of the traffic network assignment problem. The SPSA AD-PI (Simultaneous Perturbation Stochastic Approximation Asymmetric Design Polynomial Interpolation) is adopted as a solution algorithm. A comparative analysis is conducted on a test network and the results highlight the importance of route choice model and information for the stability and the quality of the offline dynamic demand estimations.