Christina Iliopoulou

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Iliopoulou

First Name

Christina

ORCID

0000-0002-9888-1947

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Publications1 - 10 of 31

Electric bus charging station location optimization considering queues
Tzamakos, Dionysios; Iliopoulou, Christina; Kepaptsoglou, Konstantinos (2023)
International Journal of Transportation Science and Technology
The transition to new forms of urban transport, which are environment friendly, efficient, and cost-effective has become a pressing issue of modern times. Battery electric buses are being deployed worldwide, marking a change in public transport planning and operations. The emergence of fast and ultra-fast wireless charging for electric buses has brought about the need for new planning processes and associated decision-support models for their deployment. The objective of this paper is the development of a model for optimally locating fast wireless chargers in an electric bus network, which considers delays of buses queuing for opportunity charging at charger locations. An integer linear programming model is formulated for that purpose: the model minimizes the investment cost for deploying opportunity charging facilities. An M/M/1 queuing model is employed to incorporate bus queuing considerations at terminal charging locations. The proposed model is demonstrated to a benchmark instance and results are discussed.
Modeling traveler recovery time following man-made incidents: the case of the Athens metro
Milioti, Christina; Kepaptsoglou, Konstantinos; Deloukas, Alexandros; et al. (2019)
Journal of Transportation Security
Man-man, life-threating incidents such as terrorist attacks, can have a significant impact in travel behavior and public transport ridership. Based on data collected from an extensive personal interview survey undertaken in Athens (Greece), factors affecting post-incident recovery time of metro users (i.e. the time till travelers will start re-using the metro system) are investigated and modeled. A preliminary statistical analysis reveals that most survey participants would return in the metro system within a week, while almost 16% of them exhibits a persistent change in traveler behavior as they would avoid using the metro system for more than 6 months. A clustering methodology and a discrete duration model are applied to further analyze and model metro user recovery time. Results show that women, less educated travelers, non-frequent users and travelers with higher risk perception, are less likely to use the metro system after a man-made incident.
Solving the Greek school timetabling problem by a mixed integer programming model
Tassopoulos, Ioannis X.; Iliopoulou, Christina; Beligiannis, Grigorios N. (2020)
Journal of the Operational Research Society
This study deals with the school timetabling problem for the case of Greek high schools. At first, the problem is modelled as a Mixed Integer Programming problem for ten instances referring to Greek high schools. Then, the problem is coded using the MathProg programming language. Two different linear programming solvers are employed, Gurobi and CPLEX, to solve the problem for the instances at hand. Two methodologies are proposed. The first one deals with the problem utilising a model that includes all hard and soft constraints, called “monolithic” model, while the second one is based on a decomposition of the problem to six sub-problems. It should be stated that Gurobi and CPLEX did not produced satisfactory results when the monolithic model was the case. Computational results demonstrate the effectiveness of the second proposed methodology, as optimal solutions or new lower bounds were found. In addition, the results produced by Mixed Integer Programming are compared with the best so far published results, obtained by two Nature Inspired algorithms namely Particle Swarm Optimization and Cat Swarm Optimization.
Factors affecting bus bunching at the stop level: A geographically weighted regression approach
Chioni, Evangelia; Iliopoulou, Christina; Milioti, Christina; et al. (2020)
International Journal of Transportation Science and Technology
Efficient operation of bus networks is vital for urban centers. Unfortunately, factors such as uneven passenger loads and congestion hinder the adherence to posted schedules, leading to reliability issues. Most notably, bus bunching has been identified as a significant reliability problem, impacting both users and operators. Bus bunching is treated as a route-level problem in the relevant literature, while spatial patterns in explanatory factors are overlooked. Diverging from the typically performed route-level analysis, this study exploits Automatic Vehicle Location data to investigate factors affecting bus bunching at the network level, while taking into account their spatial variability. For this purpose, a Geographically Weighted Regression Model is applied to model bus bunching, using bus stop and network attributes as explanatory variables. Results for approximately 360 bus stops in Athens, Greece underline the superiority of the proposed model to Ordinary Least Squares Regression and corroborate the presence of spatial variability in the factors affecting bus bunching. Indeed, the number of traffic lanes at the stop level is positively associated with bunching in heavy traffic segments, whereas a higher number of lanes is negatively linked to bunching in less congested regions. Further, the number of bunching occurrences generally increases with the number of routes serving each stop, as well as with the distance from subway stops in the outer parts of the city. Such findings highlight the need to consider spatial structures in relevant models and can help improve their reliability and accuracy.
Decision-Making Framework to Allocate Real-Time Passenger Information Signs at Bus Stops: Model Application in Athens, Greece
Papagianni, Styliani; Iliopoulou, Christina; Kepaptsoglou, Konstantinos; et al. (2017)
Transportation Research Record
The use of intelligent transport systems for the provision of real-time passenger information is an important incentive in efforts to strengthen the role of public transport and improve livability in large cities. Electronic signs installed at bus stops to disseminate information on bus arrivals are an important component of these systems with a significant capital cost. Nonetheless, to the best of the authors’ knowledge, there is no systematic approach for the selection of location sites for deployment of dynamic message signs (DMS). Public transport authorities often follow ad hoc procedures that are based on various location criteria—namely, passenger boardings, availability of power, and number of routes served at bus stops—to derive a set of candidate location sites. This was the case with the methodology implemented by the Athens Urban Transport Organization in Athens, Greece. With data from Athens, this paper proposes a modeling framework for the decision-making process regarding DMS locations in bus networks. The framework is formulated as a linear programming model, and the results show that the proposed model constitutes a systematic and transferable approach to tackle the problem at hand.
An exact approach for the multi-depot electric bus scheduling problem with time windows
Gkiotsalitis, Konstantinos; Iliopoulou, Christina; Kepaptsoglou, Konstantinos L. (2023)
European Journal of Operational Research
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).
Critical multi-link disruption identification for public transport networks: A multi-objective optimization framework
Iliopoulou, Christina; Makridis, Michail (2023)
Physica A: Statistical Mechanics and its Applications
Public transportation networks are vulnerable to uncertainty, which manifests in various forms, disrupting their operations and leading to delays and passenger dissatisfaction. Strategic-level planning of public transport networks should include the identification of critical disruption scenarios that may result in the loss of network functionality and increased travel times for users. Existing studies on transit network vulnerability have focused on identifying isolated critical links, overlooking simultaneous failures and their impacts. In this context, this study presents a multi-objective algorithm based on Adaptive Variable Neighborhood Search (MO-AVNS) to identify critical disruption scenarios affecting transit network serviceability, using a transit assignment model to capture passenger reactions to these. A set of critical combinations is generated, reflecting transit network link failures that maximize unsatisfied demand and additional travel time, capturing both users without viable travel options and passengers whose shortest-paths are disrupted. Results on a test network are presented for scenarios featuring up to five simultaneous link failures and compared to those based on centrality-based attacks. Empirical findings demonstrate that optimization-based attacks can identify scenarios that result in significant shares of disconnected passengers and high detour costs for the remaining passengers, that would be missed under single-objective approaches or centrality metrics.
DRT route design for the first/last mile problem: model and application to Athens, Greece
Charisis, Anastasios; Iliopoulou, Christina; Kepaptsoglou, Konstantinos (2018)
Public Transport
The first/last mile problem in urban transportation services refers to limited connectivity and accessibility to high capacity commuter lines. This is often encountered in low-density residential areas, where low flexibility and resources of traditional public transportation systems lead to reduced service coverage. Demand-responsive transit (DRT) offers an alternative for providing first/last mile feeder services to low density areas, because of its flexibility in adjusting to different demand patterns. This paper presents a mathematical model and a genetic algorithm for efficiently designing DRT type first/last mile routes. The model is applied for the case of a residential area in Athens, Greece and results are discussed.
Metaheuristics for the transit route network design problem: a review and comparative analysis
Iliopoulou, Christina; Kepaptsoglou, Konstantinos; Vlahogianni, Eleni (2019)
Public Transport
This paper critically reviews applications of metaheuristics for solving the Transit Route Network Design Problem (TRNDP). A structured review is offered and prominent metaheuristics for tackling the TRNDP are evaluated, according to a benchmark network. The review findings yield a unified implementation framework, which contains common algorithmic components and different solution representations and methods, which are considered important for obtaining solutions of good quality. The paper concludes with identified gaps in research and opportunities for future research on the application of metaheuristic algorithms for solving the TRNDP.
Transit route network redesign under Electrification: Model and application
Pylarinou, Constantina; Iliopoulou, Christina; Kepaptsoglou, Konstantinos (2021)
International Journal of Transportation Science and Technology
Electrification of surface transportation networks has the potential to reduce oil consumption and transport-related emissions, with many relevant projects currently underway worldwide. Typically, transition to electric public transport services is gradual, as operators select specific lines to electrify during initial stages of electric vehicle deployment. In this context, this study proposes a model for the optimal redesign of an existing transit route network, so that electric buses may be deployed where possible. The proposed model seeks to minimize the implementation cost for electrification while improving the level of service provided to transit passengers. A hybridized Genetic Algorithm is employed to solve the problem at hand while the model is validated using benchmark networks. Scenario analysis is carried out to investigate the effect of important parameters such as battery capacity and charger costs. Results show that the transition to electrification may be achieved with positive impacts on the service quality of the current public transportation network.

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Christina Iliopoulou

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