A big-data model for multi-modal public transportation with application to macroscopic control and optimisation

This paper describes a Markov-chain-based approach to modelling multi-modal transportation networks. An advantage of the model is the ability to accommodate complex dynamics and handle huge amounts of data. The transition matrix of the Markov chain is built and the model is validated using the data extracted from a traffic simulator. A realistic test-case using multi-modal data from the city of London is given to further support the ability of the proposed methodology to handle big quantities of data. Then, we use the Markov chain as a control tool to improve the overall efficiency of a transportation network, and some practical examples are described to illustrate the potentials of the approach

Book of abstracts of the 24th Euro Working Group on Transportation Meeting

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Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms

Abstract The paper aims to provide an overview of the key factors to consider when performing reliable modelling of rail services. Given our underlying belief that to build a robust simulation environment a rail service cannot be considered an isolated system, also the connected systems, which influence and, in turn, are influenced by such services, must be properly modelled. For this purpose, an extensive overview of the rail simulation and optimisation models proposed in the literature is first provided. Rail simulation models are classified according to the level of detail implemented (microscopic, mesoscopic and macroscopic), the variables involved (deterministic and stochastic) and the processing techniques adopted (synchronous and asynchronous). By contrast, within rail optimisation models, both planning (timetabling) and management (rescheduling) phases are discussed. The main issues concerning the interaction of rail services with travel demand flows and the energy domain are also described. Finally, in an attempt to provide a comprehensive framework an overview of the main metaheuristic resolution techniques used in the planning and management phases is shown

2nd Symposium on Management of Future motorway and urban Traffic Systems (MFTS 2018): Booklet of abstracts: Ispra, 11-12 June 2018

The Symposium focuses on future traffic management systems, covering the subjects of traffic control, estimation, and modelling of motorway and urban networks, with particular emphasis on the presence of advanced vehicle communication and automation technologies. As connectivity and automation are being progressively introduced in our transport and mobility systems, there is indeed a growing need to understand the implications and opportunities for an enhanced traffic management as well as to identify innovative ways and tools to optimise traffic efficiency. In particular the debate on centralised versus decentralised traffic management in the presence of connected and automated vehicles has started attracting the attention of the research community. In this context, the Symposium provides a remarkable opportunity to share novel ideas and discuss future research directions.JRC.C.4-Sustainable Transpor

Resolving time conflicts in activity-based scheduling: A case study of Lausanne

In this paper, we present a novel activity-based scheduling model that combines a continuous optimisation framework for temporal scheduling decisions (i.e. activity timings and durations) with traditional discrete choice models for non-temporal choice dimensions (i.e. activity participation, number and type of tours, and considered destinations). The central concept of our approach is that individuals resolve time conflicts that arise from overlapping activities, e.g. needing to work and desiring to shop at the same time, in order to maximise their derived utility. Our proposed framework has three primary advantages over existing activity scheduling approaches: (i) the time-conflicts between different temporal scheduling decisions are considered and resolved jointly; (ii) individual behavioural preferences are incorporated in the scheduling problem using a utility-maximisation approach; and (iii) the framework is computationally scalable and can be used to estimate and simulate a city-scale case study in reasonable time. We introduce an estimation routine for the framework that allows model parameters to be calibrated using real-world historic data, as well as an efficient mixed-integer linear solver to optimally resolve temporal conflicts in simulated schedules. The estimation routine is applied and calibrated to a set of observed schedules in the Swiss mobility and transport microcensus. We then use the optimisation program with the estimated parameters to simulate activity schedules for a synthetic population for the city of Lausanne, Switzerland. We validate the model results against reported schedules in the microcensus data. The results demonstrate the capabilities of our approach to simulate realistic, flexible schedules for a real-world case-stud

Urban road network crisis response management: time-sensitive decision optimization

With the increasing global stock of vehicles, traffic congestion is becoming more severe and costly in many urban road networks. Road network modeling and optimization are essential tools in predicting traffic flow and reducing network congestion. Markov chains are remarkably capable in modeling complex, dynamic, and large-scale networks; Google’s PageRank algorithm is a living proof. In this article, we leverage Markov chains theory and its powerful statistical analysis tools to model urban road networks and infer road network performance and traffic congestion patterns, and propose an optimization approach that is based on Genetic Algorithm to model network-wide optimization decisions. Such decisions target relief from traffic congestion arising from sudden network changes (e.g. rapid increase in vehicles flow, or lanes and roads closures). The proposed network optimization approach can be used in time-sensitive decision making situations such as crisis response management, where decision time requirements for finding optimal network design to handle such abrupt changes typically don’t allow for the traditional agent-based simulation and iterative network design approaches. We detail the mathematical modeling and algorithmic optimization approach and present preliminary results from a sample application

Application of Metaheuristics in Signal Optimisation of Transportation Networks: A Comprehensive Survey

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.With rapid population growth, there is an urgent need for intelligent traffic control techniques in urban transportation networks to improve the network performance. In an urban transportation network, traffic signals have a significant effect on reducing congestion, improving safety, and improving environmental pollution. In recent years, researchers have been applied metaheuristic techniques for signal timing optimisation as one of the practical solution to enhance the performance of the transportation networks. Current study presents a comprehensive survey of such techniques and tools used in signal optimisation of transportation networks, providing a categorisation of approaches, discussion, and suggestions for future research

Transportation modelling for environmental impact assessment : Porto metropolitan area case study

Tese de mestrado. Transportes. Faculdade de Engenharia. Universidade do Porto. Departamento de Engenharia Mecânica. Universidade de Aveiro. 200

Mathematical Model and Cloud Computing of Road Network Operations under Non-Recurrent Events

Optimal traffic control under incident-driven congestion is crucial for road safety and maintaining network performance. Over the last decade, prediction and simulation of road traffic play important roles in network operation. This dissertation focuses on development of a machine learning-based prediction model, a stochastic cell transmission model (CTM), and an optimisation model. Numerical studies were performed to evaluate the proposed models. The results indicate that proposed models are helpful for road management during road incidents