Nicola Sacco

One-Way Carsharing

Carsharing services allow users to benefit from the advantages of a private car without the costs of owning one. One-way systems provide users with a higher level of service than traditional carsharing systems in terms of flexibility because users do not need to return to the station of origin. Moreover, the added option to leave the vehicle at any free parking area, which is not necessarily a station, increases the flexibility offered by the one-way system. Introduction of such improvements to the carsharing system, however, leads to a vehicle relocation problem, which should be addressed carefully to avoid concentration of vehicles in certain areas. This paper reports on a study of this issue with the use of discrete event systems (DESs), which allowed an easy representation of the complex dynamics of the carsharing system. A user-based methodology was proposed on the basis of an optimal relocation policy in a rolling horizon framework. This methodology not only offers greater flexibility to users, it also maximizes operator benefits by reducing the number of required staff to relocate vehicles among the stations and determines the minimum number of vehicles needed to satisfy system demand. The DES model was applied to a case study to evaluate the proposed approach. The results showed a significant decrease in the rejection rate from the worst scenario (no relocation) to the best (relocation of all vehicles by their users). The paper concludes with suggestions for additional research and improvements to this study.

Optimization of Dynamic Ridesharing Systems

Internet-enabled technologies are becoming more widespread; users are constantly connected to the network in every place and daily activity. Access to transportation-related features—mobile payment systems, Global Positioning System connections, real-time public transit timetables or traffic congestion information, and so on—is easy. This access results in new ways to plan mobility. In the innovative mobility systems implemented and developed with these technologies, the new real-time capabilities of dynamic ridesharing, an extended version of traditional ridesharing, can play a key role if the relevant performance is improved. In other words, although ridesharing is not a new idea, recent technological advances should increase its popularity. In this paper, a proposed ridesharing system considers the interactions between drivers or riders and the system manager and the interactions between drivers and riders. The positions and speeds of the shared vehicles and the traffic flows in which such vehicles travel are omitted. To optimize the performance of the ridesharing system, a discrete event, dynamic pickup and delivery model that represents the considered dynamics and an optimal matching problem that optimally allocates an empty seat in a vehicle to a rider are proposed. The dynamic model represents the behavior of the ridesharing system and computes the relevant performance; the optimization problem finds the best match and path in the considered transportation network to minimize the difference between the desired departure and arrival times. In this paper, after the introduction of the ridesharing model, the discussion of the solution to the optimal matching problem, a simulation model is described. A real world case study is then presented and discussed.

On using petri nets for representing and controlling signalized urban areas: New model and results

A microscopic model of signalized urban areas and its representation by means of stochastic-timed Petri nets are described in this paper, where a traffic-responsive control strategy, whose objective is the optimization of green duration of each stage, is also proposed. The Petri net representation here proposed is the new version of a previously proposed one, which was based on deterministic-timed Petri nets. The motivations of the need of proposing a new version and the differences between the two Petri net representations are clearly described. In addition, some results on the application of the proposed methodology to a single signalized intersection are included in the paper.

A Deterministic and Stochastic Petri Net Model for Traffic-Responsive Signaling Control in Urban Areas

The problem of reducing congestion within urban areas by means of a traffic-responsive control strategy is addressed in this paper. The model of an urban traffic network is microscopically represented by means of deterministic and stochastic Petri nets, which allow a compact representation of the dynamic traffic network. To properly model traffic congestion, intersections are divided into crossing sections, and roads have limited capacity. Each intersection includes a multiphase traffic signal, whose sequence of phases is given and represented by a timed Petri net. The control strategy proposed in this paper aims at minimizing queue lengths by optimizing the duration of each signal phase. This is accomplished by heuristically solving a stochastic optimization problem within a receding-horizon scheme, to take into account the actual traffic flow entering the network, thus making the proposed approach traffic-responsive. In this framework, the Petri nets play a key role, as the cost function to be minimized is a function of the marking, and the constraints include the marking state evolution. The proposed strategy is applicable to both undersaturated and oversaturated traffic conditions.

An optimisation-oriented model of distributed supply-chain

In this paper, a suitable model of distributed supply-chains (DSCs) is presented with the aim of providing a tool for DSC decentralised optimisation. To cope with this challenge, in the first part of the paper, a general model for distributed supply-chain including suppliers, processing units, assemblers, and transportation systems is presented with the aim of keeping the framework as general as possible. In the second part of the paper, an optimisation algorithm is also discussed.

A model for performance evaluation and sensitivity analysis of seaport container terminals

Abstract In the last decades, the growth of the containerised freight demand has led to a fast development of seaport container terminals. As an evidence of the relevance and of the interest that such intermodal terminals have reached, consider the significant amount of existing scientific works facing the problem of optimising their performances at operative, tactic, and strategic levels. In this framework, the present paper provides a model to face the problem of finding the best typology, and number, of resources for a given seaport container terminal. Evidently, on one hand, such problem is subject to “static” constraints due, for instance, to budget, manpower and space limitations, and on the other hand, to “dynamic” constraints that rise when the sequence of operations in the terminal, and the relevant timing, are taken into account. The paper is organised as follows: after a brief bibliography review, the proposed PN and (max,+)-algebra models are introduced. Then, some simulation results and the relevant sensitivity analysis are discussed.

Experimental Validation of a Hybrid Petri-Net Based Model of Urban Transportation Networks

Abstract In this paper, a class of Hybrid Petri Nets is proved to provide a valuable model of urban networks of signalized intersections. Such networks can be fruitfully considered to be hybrid systems, where the vehicle flow behaviour is represented by means of a time-driven model, and the traffic light dynamics is represented by an event-driven model. The proposed model has been validated using real traffic data about the Italian city of Turin. Some relevant experimental results are reported and discussed

Stochastic scheduling approach for predictive risk-based railway maintenance

This paper presents a stochastic model for scheduling predictive and risk-based maintenance activities in rail sector. The novelty of the approach consists of the risk concept introduction in railway maintenance scheduling, thus implying that the maintenance priorities are based on criticality of assets, determined by the relevant failure probability, related to asset degradation conditions, and by the consequent direct and indirect damages. This approach belongs to the framework of “predictive maintenance” which aims at intervening when an asset has reached a certain degradation state, being the future track conditions forecasted by appropriate models. In particular, this work explicitly considers the stochastic nature of risk and of the real-world maintenance operations, introducing stochastic deadlines. In doing so, it is worth noting that, the adaptive rescheduling models only partially solve this issue, since they consider deterministic sub-problems of the overall problem and they cannot vary continuously the stochastic input variables. Therefore, to cope with this problem, in this paper, the risk-based maintenance planning problem is formulated in term of stochastic programming. After providing a formal methodology description, some experimental results are reported and some indications about its future developments are given.

A control system for the individual route guidance in traffic flow networks

Abstract The problem of optimally routing a class of guided vehicles on an urban traffic network is considered in this paper. The guided vehicles give their requests in terms of origin, destination, and time window, and the control system returns to each of them the start time and the path to be followed, as provided by the solution of an original individual route guidance (IRG) problem. The control system integrates the IRG problem with a macroscopic traffic model in order to take into account the evolution of the traffic (in particular, of the congestions) on the network links. The traffic model and the IRG problem are mutually dependent one from the other, and for this reason the ultimate solution of the route guidance problem is iteratively sought. The control system has the objective of minimizing both the impact of the whole traffic and the individual costs of the guided vehicles.

A cooperative framework for freight distribution in multimodal corridors

Nowadays, there is a growing agreement that sustainable mobility is about effectively interconnecting transport systems which have to provide a door-to-door service. Intermodal transportation of freights is an important issue in this context, due to the increasing amount of freight movements all over the world. The current study proposes and examines an approach for optimizing such transport chains. In this approach, the freight distribution corridor is decomposed into the different elements with individual functions that interact two-by-two based on a cooperative scheme relying on information flow in the corridor. The methodology is evaluated by means of an example corridor and the results are presented.