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See detailDynamic Origin-Destination Matrix Estimation with Interacting Demand Patterns
Cantelmo, Guido UL

Doctoral thesis (2018)

It has become very fashionable to talk about Mobility as a Service, multimodal transport networks, electrified and green vehicles, and sustainable transportation in general. Nowadays, the transportation ... [more ▼]

It has become very fashionable to talk about Mobility as a Service, multimodal transport networks, electrified and green vehicles, and sustainable transportation in general. Nowadays, the transportation field is exploring new angles to solve mobility issues, applying concepts such as using machine learning techniques to profile user behaviour. While for many years “traffic pressure” and “congestion phenomena” were the most established keywords, there is now a widespread body of research pointing out how new technologies alone will solve most of these issues. One of the main reasons for this change of direction is that earlier approaches have been proven to be more “fair” than “effective” in tackling mobility issues. The main limitation was probably to rely on simple assumptions, such as in-elastic mobility travel demand (car users will stick to their choice), when modelling travel behaviour. However, while these assumptions were questionable twenty years ago, they simply do not hold in today's society. While it is still true that high-income people usually own a car, the concept of urban mobility evolved. First, new generations are likely to buy a car ten-twenty years later than their parents. Second, in many cases, users can choose options that are more effective by combining different transport modes. Wealthy people might decide to live next to their working place or to the city centre, rather than to buy a car. Thus, it becomes clear that to understand the evolution of the mobility demand we need to question some of these assumptions. While data can help in understanding this societal transformation, we argue in this dissertation that they cannot be considered as the sole source of information for the decision maker. Although data have been there for many years, congestion levels are increasing, meaning that data alone cannot solve the problem. Although successful in many case studies, data-driven approaches have the limitation of being capable of modelling only what they observed in the past. If there is no record of a specific event, then the model will simply provide a biased information. In this manuscript we point out that both elements – data and model – are equally relevant to represent the evolution of a transport system, and specifically how important is to consider the heterogeneity of the mobility demand within the modelling framework in order to fully exploit the available data. In this manuscript, we focus on the so-called Dynamic Demand Estimation Problem (DODE), which is the problem of estimating the mobility demand patterns that are more likely to best fit all the available traffic data. While this dissertation still focuses on car-users, we stress that the activity based structure of the demand needs to be explicitly represented in order to capture the evolution of a transport system. While data show a picture of the reality, such as how many people are travelling on a certain road segment or even along a certain path, this information represents a coarse aggregation of different individuals sharing a common resource (i.e. the infrastructure). However, the traffic flow is composed of different users with different trip purposes, meaning they react differently to a certain event. If we shut down a road from one day to another, commuting and not commuting demand will react in a different way. The same concept holds when dealing with different weather conditions, which also lead to a different demand pattern with respect to the typical one. This dissertation presents different frameworks to solve the DODE, which explicitly focus on the estimation of the mobility demand when dealing with typical and atypical user behaviour. Although the approach still focuses on a single mode of transport (car-users), the proposed formulation includes the generalized travel cost within the optimization framework. This key element allows accounting for the departure time choice and, in principle, it can be extended to the mode choice in future work. The methodologies presented in this thesis have been tested with a “state of the practice” dynamic traffic assignment model. Results suggest that the models can be used for real-life networks, but also that more efficient algorithm should be considered for practical implementations in order to unleash the full potential of this new approach. [less ▲]

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See detailDemo: MAMBA: A Platform for Personalised Multimodal Trip Planning
Faye, Sébastien UL; Cantelmo, Guido UL; Tahirou, Ibrahim UL et al

Software (2017)

In recent years, multimodal transportation has become a challenging approach to route planning. Most existing planning systems usually rely on data sourced from different organisations, enabling the user ... [more ▼]

In recent years, multimodal transportation has become a challenging approach to route planning. Most existing planning systems usually rely on data sourced from different organisations, enabling the user to select a limited number of routing strategies. As part of the MAMBA project, developed in Luxembourg until 2017, we have been interested in the potential benefits of multimodal mobility systems. A key factor has been integrated into our studies: the need for a personalised experience at user level, whether when selecting the means of transport or describing user habits (e.g. route style, environment). In this context, we have developed a platform for planning personalised multimodal trips, broken down into the three main modules presented in this demonstration. More importantly, this platform has been developed to facilitate the daily mobility of people in Luxembourg, and considers datasets and characteristics that are specific to this region, which has an exceptionally high volume of daily commuting between Luxembourg and neighbouring countries. [less ▲]

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See detailEffectiveness of the Two-Step Dynamic Demand Estimation model on large networks
Cantelmo, Guido UL; Viti, Francesco UL; Derrmann, Thierry UL

in Proceedings of 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2017, June 28)

In this paper, the authors present a Two-Step approach that sequentially adjusts generation and distribution values of the (dynamic) OD matrix. While the proposed methodology already provided excellent ... [more ▼]

In this paper, the authors present a Two-Step approach that sequentially adjusts generation and distribution values of the (dynamic) OD matrix. While the proposed methodology already provided excellent results for updating demand flows on a motorway, the aim of this paper is to validate this conclusion on a real network: Luxembourg City. This network represents the typical middle-sized European city in terms of network dimension. Moreover, Luxembourg City has the typical structure of a metropolitan area, composed of a city centre, ring, and suburb areas. An innovative element of this paper is to use mobile network data to create a time-dependent profile of the generated demand inside and outside the ring. To support the claim that the model is ready for practical implementation, it is interfaced with PTV Visum, one of the most widely adopted software tools for traffic analysis. Results of these experiments provide a solid empirical ground in order to further develop this model and to understand if its assumptions hold for urban scenarios. [less ▲]

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See detailGenerating purpose-dependent production factors through Monte Carlo sampling techniques.
Scheffer, Ariane; Cantelmo, Guido UL; Viti, Francesco UL

Scientific Conference (2017, May)

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See detailA network-wide assessment of local signal control policies’ performance in practical implementations
Cantelmo, Guido UL; Viti, Francesco UL; Rinaldi, Marco UL

in Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on (2016, November)

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See detailSystematic analysis of global and local control policies
Cantelmo, Guido UL; Viti, Francesco UL; Rinaldi, Marco et al

in Periodica Polytechnica (2015)

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See detailA Markov chain dynamic model for trip generation and distribution based on CDR
Viti, Francesco UL; Cantelmo, Guido UL

in Periodica Polytechnica (2015)

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See detailA two-steps dynamic demand estimation approach sequentially adjusting generations and distributions
Cantelmo, Guido UL; Viti, Francesco UL; Cipriani, Ernesto et al

in Proceedings of IEEE-ITS Conference (2015, September)

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See detailThe Impact of Route Choice Modeling on Dynamic OD Estimation
Cipriani, Ernesto; Del Giudice, Andrea; Nigro, Marialisa et al

in Proceedings of IEEE-ITS Conference (2015, September)

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See detailSystematic assessment of local & global control policies: A methodological perspective
Cantelmo, Guido UL; Viti, Francesco UL; Rinaldi, Marco et al

in Proceedings of the MT-ITS Conference (2015, June)

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See detailA Markov Chain dynamic model for trip generation and distribution based on CDR
Di Donna, Simone Aniello UL; Cantelmo, Guido UL; Viti, Francesco UL

in Proceedings of the MT-ITS Conference (2015, June)

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See detailAssessing the consistency between observed and modelled route choices through GPS data
Hadjidimitriou, Selini N.; Dell'Amico, Mauro; Cantelmo, Guido UL et al

in Proceedings of the MT-ITS Conference (2015, June)

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See detailSystematic assessment of local & global signal control policies: A methodological perspective
Cantelmo, Guido UL; Viti, Francesco UL; Rinaldi, Marco UL et al

in Proceedings of 2015 International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2015 (2015)

Traffic control performance on networks depends on the flow response to the policy adopted, which in turn contributes to determine the optimal signal settings. This paper focuses on the relationship ... [more ▼]

Traffic control performance on networks depends on the flow response to the policy adopted, which in turn contributes to determine the optimal signal settings. This paper focuses on the relationship between local and network wide traffic control policies within the combined traffic control and assignment problem. Through a full exploration of the solution space, an in depth cross comparison is performed between the well-known local policies P0 and Equisaturation, versus the global policies Maximum Throughput and Minimum Delay, to verify how the two local policies approximate the optimal settings for signalized intersections. Realistic traffic dynamics, such as congestion, multiple controllers and spillback are considered, to empirically determine the conditions under which the local policies are able to approximate global performances. After presenting the different local and global control policies, experiments are performed on simple toy networks. The complexity of the underlying network and, therefore, of the problems' boundary conditions is then increased, allowing us to showcase how the different metrics perform in different situations. Finally, conclusions on the results are drawn. © 2015 BME. [less ▲]

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See detailA TWO-STEP APPROACH FOR THE CORRECTION OF THE SEED MATRIX IN THE DYNAMIC DEMAND ESTIMATION
Cantelmo, Guido UL; Viti, Francesco UL; Tampère, Chris M.J. et al

in Transportation Research Record: Journal of the Transportation Research Board (2014), 2466

In this work deterministic and stochastic optimization methods are tested for solving the Dynamic Demand Estimation problem. All the adopted methods demonstrate the difficulty in reproducing the correct ... [more ▼]

In this work deterministic and stochastic optimization methods are tested for solving the Dynamic Demand Estimation problem. All the adopted methods demonstrate the difficulty in reproducing the correct traffic regime, especially if the seed matrix is not sufficiently close to the real one. Therefore, in this paper a new and intuitive procedure to specify an opportune starting seed matrix is proposed: it is a two-step procedure based on the concept of dividing the problem into small-size problems, focusing on specific OD pairs in different steps. Specifically, the first step focuses on the optimization of a subset of OD variables (the ones who generate the higher flows or the ones who generate the bottlenecks on the network). In the second step the optimization works on all the OD pairs, using as starting matrix the matrix derived from the first step. In this way is possible to use a more performance optimization method for every step, improving the performance of the method and the quality of the result with respect to the classical “one-step” approach. The procedure has been tested on the real network of Antwerp, Belgium, demonstrating its efficacy in combination with different optimization methods. [less ▲]

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