A Multi-objective Network Design Model for Post-disaster Transportation Network Management

Despite their inherent vulnerability to structural and functional degradation, transportation networks play a vital role in the aftermath of disasters by ensuring physical access to the affected communities and providing services according to the generated needs. In this setting of operational conditions and service needs which deviate from normal, a restructuring of network functions is deemed to be beneficial for overall network serviceability. In such context, this paper explores the planning of post-disaster operations on a network following a hazardous event on one of the network’s nodes. Lane reversal, demand regulation and path activation are applied to provide an optimally reconfigured network with reallocated demand, so that the network performance is maximized. The problem is formulated as a bi-level optimization model; the upper level determines the optimal network management strategy implementation scheme while the lower level assigns traffic on the network. Three performance indices are used for that purpose: the total network travel time (TNTT), the total network flow (TNF) and the special origin-destination pair (OD pair) accessibility. A genetic algorithm coupled with a traffic assignment process is used as a solution methodology. Application of the model on a real urban network proves the computational efficiency of the algorithm; the model systematically produces robust results of enhanced network performance, indicating its value as an operation planning tool

Planning Integrated Unmanned Aerial Vehicle and Conventional Vehicle Delivery Operations under Restricted Airspace: A Mixed Nested Genetic Algorithm and Geographic Information System-Assisted Optimization Approach

Using Unmanned Aerial Vehicles (UAVs), commonly referred to as “drones”, as a supplementary mode for last-mile deliveries has been a research focus for some years now. Motivation lies in the reduced dependency on Conventional Vehicles (CVs) and fossil fuels and in serving remote areas and underprivileged populations. We are building a flexible, modular framework for integrated CV-UAV parcel delivery operations planning that is responsive to infrastructure and demand and offers an open and practical tool for future adaptations. The entire model and solution methodology are practical tools for decision making and strategic planning, with novelties such as the variable Launch Site types for Launch and Recovery Operations (LAROs), the tailored Assignment and Routing Optimization nested GA, the consideration of airspace restrictions of any shape and size, the inclusion of GIS tools in the process, the modularity of the platform, and most importantly, the inclusion of all the above in a single, comprehensive, and holistic approach. Because of the need for safe UAV deployment sites and the high presence of restricted airspace zones in urban environments, the intended field of application is assumed to be the delivery of small packages in rural and under-connected areas, the execution of inter-city deliveries, and the expansion of a city’s original service range. A single CV is equipped onboard with UAVs, while special locations, such as Remote Depots (RDs) with UAVs and Virtual Hubs (VHs) for UAV deployment facilitation, are introduced. The framework considers the presence of Restricted Zones (RZs) for UAV flights. Part of the methodology is implemented in a GIS environment, taking advantage of modern tools for spatial analysis and optimal path planning. We have designed a tailored nested GA method for solving the occurring mode assignment and vehicle routing optimization problems and have implemented our workflow on a devised case study with benchmark characteristics. Our model responds well to unfavorable network types and demand locations, while the presence of RZs notably affects the expected solution and should be considered in the decision-making process

Integrated transit route network design and infrastructure planning for on-line electric vehicles

The emergence of electromobility along with recent developments in wireless power transfer (WPT) technology offer potentials to improve the carbon footprint of bus transport, while offering quality services. Indeed, the deployment of fast charging stations and dynamic charging roadway segments (lanes) can ensure fast energy transmission to electricity-powered buses, mitigating existing energy-related concerns and limitations. Existing models for public transport network design cannot adequately capture the dependence between electric vehicle charging infrastructure requirements and route operational characteristics. In this context, this paper investigates the combined Transit Route Network Design and Charging Infrastructure Location Problem and proposes a bi-level formulation to handle both planning stages. At the upper level, candidate route sets are generated and evaluated, while at the lower-level wireless charging infrastructures are optimally deployed. A multi-objective Particle Swarm Optimization (MO-PSO) algorithm embedded with an integer programming solver is employed to handle the complexity of the problem and the conflicting design objectives related to passengers and operators. The resulting model is applied to an established benchmark network to assess the tradeoffs arising between user-oriented and operator-oriented solutions and highlight the complex decision process associated with the deployment of electric public transport networks

Ανάπτυξη μεθόδων βελτιστοποίησης των διαδικασιών λειτουργίας συστήματος σταθερής τροχιάς υπό έκτακτες συνθήκες

Metro system substitution in cases of services disruptions, is a requirement for transit operators willing to provide high quality services to the system’s passengers; a successfully organized metro substitution would considerably aid passengers and raise the credibility of the transit authority. In this context, this dissertation focuses on developing processes related to substituting metro services following a disruption. These include (i) the organization of metro substitution actions, and the development of a set of models (ii) for analyzing metro operations till their restoration and (iii) and algorithms for designing surface transit substitution networks (bus bridging). Developed models are implemented for a set of real world disruption scenarios at the Athens Metro system.Η υποκατάσταση των υπηρεσιών συστήματος Μετρό είναι αναγκαία δράση αντιμετώπισης των επιπτώσεων υποβάθμισης ή διακοπής λειτουργίας τμήματος γραμμής Μετρό και εστιάζει στην συνέχιση της παροχής μεταφορικού έργου στους επιβάτες του συστήματος μετά τη διακοπή. Η παρούσα διδακτορική διατριβή εστιάζει στο αντικείμενο της ανάλυσης των συνεπειών και του σχεδιασμού της υποκατάστασης αυτής. Στα πλαίσια της διατριβής (i) οργανώνονται οι διαδικασίες υποκατάστασης της λειτουργίας Μετρό σε περίπτωση διακοπής και αναπτύσσονται μαθηματικά μοντέλα και αλγόριθμοι (ii) για την ανάλυση των συνθηκών στο σύστημα Μετρό αμέσως μετά τη διακοπή και (iii) για το σχεδιασμό λεωφορειακού δικτύου υποκατάστασης των υπηρεσιών Μετρό που επηρεάζονται από τη διακοπή. Τα αναπτυσσόμενα πρότυπα εφαρμόζονται σε πληθώρα σεναρίων διακοπής τμήματος του συστήματος της Αθήνας

How is Intraday Metro Ridership related to Station Centrality in Athens, Greece?

In this study, intraday correlations between station centralities and ridership at stations of the Athens metro system in Greece are explored. An unweighted L-space representation of the physical metro network is developed, and degree, closeness and betweenness are selected as station centrality measures. Hourly smart-card data are used for representing passenger flows. For station classification, principal component analysis and k-means clustering are utilized. The findings suggest that centrality and ridership usually move in opposite directions, morning peak-hour boardings are completely uncorrelated with station centrality, and metro stations can be classified as ‘central destinations’, ‘averagely central origins’, and ‘underutilized peripheral stations’

Combining ITS and optimization in public transportation planning: state of the art and future research paths

Intelligent Transportation Systems (ITS) applications in public transportation have allowed for automated data collection, which is particularly useful for planning and operations. While technological advancement of ITS has so far been extensive, their usage for developing relevant planning and operational tools is rather limited. Research on planning and operations of public transportation systems has not widely investigated the potential of combining optimization models with data originating from ITS. Such applications, which could benefit from such an approach include route planning, scheduling and resource allocation in real time. In this context, this paper investigates and critically discusses potential models and methodologies in public transport planning and operations, which can benefit from ITS data, highlights their potential and identifies possible research paths on that area. The overview of literature collectively points to a series of common challenges faced by transportation professionals and underlines the need for better decision support tools for ITS data.ISSN:1867-0717ISSN:1866-888

Robust electric transit route network design problem (RE-TRNDP) with delay considerations: Model and application

Electric bus networks are steadily gaining ground as the prominent option for urban public transport. However, in contrast to conventional transit systems operated by diesel buses, electric bus networks are particularly vulnerable with respect to energy supply, both in terms of power level availability and the unobstructed access to charging points. Indeed, power fluctuations can prevent buses from adequately recharging at designated points, affecting extended areas of operation. Similarly, queue formation at terminal stops can lead to poor schedule adherence and excessive delays. In this context, this study addresses research gaps by presenting a realistic and flexible design framework for fully electric public transport networks, tackling both the route network and the charging infrastructure design. To handle the uncertainty associated with power supply, Robust Optimization (RO) is employed for solving the charging infrastructure location problem while maintaining computational tractability. Queuing delays due to charging are also modeled and minimized. To address the integrated design of route networks and wireless chargers, RO is coupled with Multi-Objective Particle Swarm Optimization within a bi-level methodological framework. Different scenarios for power supply variability are considered. Results show that depending on policy priority, the cost of robustness significantly changes

An exact approach for the multi-depot electric bus scheduling problem with time windows

This study extends the multi-depot vehicle scheduling problem with time windows (MDVSPTW) to the case of electric vehicles which can recharge at charging stations located at any point of the service operation area. We propose a mixed-integer nonlinear model for the electric bus multi-depot vehicle scheduling problem with time windows (EB-MDVSPTW). Our formulation considers not only the operational cost of vehicles, but also the waiting times. In addition, it explicitly considers the capacity of charging stations and prohibits the simultaneous charging of different vehicles at the same charger. Chargers are modeled as task nodes of an extended network and can be placed at any location utilizing the charging infrastructure of a city instead of using only bus-dedicated chargers. Further, we linearize the MINLP formulation of the EB-MDVSPTW by reformulating it to a mixed-integer linear program (MILP) that can be solved to global optimality. Because EB-MDVSPTW is NP-Hard, we also introduce valid inequalities to tighten the search space of the MILP and we investigate the trade-off between the compactness and the tightness of the problem in benchmark instances with up to 30 trips. In the numerical experiments, we show that the valid inequalities reduce the problem’s compactness by increasing up to three times the number of constraints, but, at the same time, improve tightness resulting in computational time improvements of up to 73% in 20-trip instances. The implementation of our exact approach is demonstrated in a toy network and in the benchmark instances of Carpaneto et al. (1989).ISSN:0377-2217ISSN:1872-686

The bus bridging problem in metro operations: conceptual framework, models and algorithms

Metro networks provide efficient transportation services to large numbers of travelers in urban areas around the World; any unexpected operational disruption can lead to rapid degradation of the provided level of service by a city’s public transportation system. In such instances, quick and efficient substitution of services is necessary for accommodating metro passengers including the widely used practice of “bridging” metro stations using bus services. Despite its widespread application, bus bridging is largely done ad-hoc and not as part of an integrated optimization procedure. In this paper we propose a methodological framework for planning and designing an efficient bus bridging network. Furthermore, we offer a set of structured steps and optimization models and algorithms for handling bus bridging problems