A gradient approximation approach for adjusting temporal origin–destination matrices

Cipriani, Ernesto; Florian, Michaël; Mahut, Michael; Nigro, Marialisa

doi:10.1016/j.trc.2010.05.013

Cited by 104 publications

(71 citation statements)

References 7 publications

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“…In order to overcome solution under-determinedness, Balakrishna et al [6] suggested to use a simulation-based DTA model to generate traffic measures and to include additional information, such as link speed, within the objective function, in order to represent the congested/uncongested network conditions. Following this seminal work, many researchers developed new and more robust algorithms able to properly capture the non-linearity between link-flow propagation and time-varying OD demand [7][8][9][10]. Despite this intense effort, the resulting optimization problem remains highly non-linear and non-convex.…”

Section: The Demand Estimation Problemmentioning

confidence: 99%

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A Big Data Demand Estimation Framework for Modelling Urban Congested Networks

Viti¹,

Cantelmo²

2018

EasyChair Preprints

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This paper deals with the problem of estimating daily mobility flows using different sources of data, and in particular from mobile devices such as mobile phones and floating car data. First, the dynamic origin-destination (OD) estimation problem is presented in its classical bi-level formulation, and opportunities and challenges of introducing new data types with respect to the classical fixed sensors are discussed. We show how mobile phone data can be used to better estimate the structure of the demand matrix, both temporally (i.e. the daily generated flows from each zone) and spatially (i.e. distributing the flows on the different OD pairs). Then, floating car data and traffic counts can be used to further distribute the demand on the available modes and routes, according to the classical 4-step procedure. During this phase, a behavioural modelling approach is used, according to traditional dynamic user equilibrium using a joint route and departure time choice model. Floating car data information is used to estimate speed profiles at all links where information is available, and for route travel times, which feed the utility-based models. A two-step approach is then proposed to solve the problem for large scale networks, in which the total demand is first generated, and then equilibrium is calculated through a dynamic traffic assignment model. The effectiveness and reliability of the proposed modelling framework is then shown on a realistic case study involving the multimodal network of Luxembourg City and its surrounding, and is compared to the traditional bi-level formulation solved using the Generalised Least Square Estimation. The comparison shows how the two-step approach is more robust in generating realistic daily OD flows, and in exploiting the information collected from mobile sensors.

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Section: The Demand Estimation Problemmentioning

confidence: 99%

“…Clearly, the availability of more detailed probe vehicles data such as GPS position would strongly be an asset, as shown e.g. in [22].…”

Section: 3mentioning

confidence: 99%

A Big Data Demand Estimation Framework for Modelling Urban Congested Networks

Viti¹,

Cantelmo²

2018

EasyChair Preprints

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“…Details on phases 1 and 2 can be found in [34]. Likewise details on phase 3 are provided by [36][37][38] and [39].…”

Section: Simulating Interaction Among Rail System Componentsmentioning

confidence: 99%

“…These sets of data were adopted to implement the procedures described in detail by [36][37][38] and [39]. Figure 1 provides the Gamma distribution function by highlighting the three considered levels of travel demand.…”

Section: Travel Demand Definitionmentioning

confidence: 99%

The use of microsimulation models for the planning and management of metro systems

Placido¹,

D’Acierno²,

Montella³

2014

WIT Transactions on the Built Environment

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The management of public transport for rebalancing the use of transportation systems is a useful tool for reducing negative externalities without excessively affecting zone accessibility. In this context, a rail or metro system can be a key element for producing a high-quality supply of public transport. Obviously, due to the great vulnerability of rail technology to system failures, it is necessary to develop suitable tools to identify rapidly, even with off-line procedures, the best operational strategies which minimise user discomfort produced by such failures. Hence, our proposal is to extend previous models proposed in the literature by considering travel demand as an outcome of a random variable and not only in terms of average values. The proposed approach is applied in the case of a real dimension metro network, considering a wider class of failure contexts.

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“…Many authors dealt with the problem of increasing the amount of information required by dynamic O-D estimation problem and included, for example, speed and link occupancy [7][8][9], probe data from vehicle equipped by AVI tags [10-14, 15,16], aggregate demand data such as traffic emissions and attractions by zones [8,9,17], total demand for sub-networks, or the temporal distribution of trips in some areas on the network.…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of link and path information on simultaneous adjustment of dynamic O-D demand matrix

Cipriani

Nigro

Fusco

et al. 2013

Eur. Transp. Res. Rev.

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Introduction The paper deals with the adjustment of timedependent Origin-destination (O-D) demand matrix, which is the fundamental input of ITS application for traffic predictions. The usual problem is to search for temporal O-D matrices that are "near" an a priori estimate (seed matrices) and that best fit traffic counts. However information on link flows is not fully effective in describing the state of the network; recent technologies for tracking vehicles provide a new kind of information on route travel times that can integrate usual information on traffic flows at count sections. Objective The object of the paper is to analyse the effectiveness of different types of information in the off-line simultaneous adjustment of dynamic O-D demand, starting from seed matrices with different degrees of reliability.

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A gradient approximation approach for adjusting temporal origin–destination matrices

Cited by 104 publications

References 7 publications

A Big Data Demand Estimation Framework for Modelling Urban Congested Networks

A Big Data Demand Estimation Framework for Modelling Urban Congested Networks

The use of microsimulation models for the planning and management of metro systems

Effectiveness of link and path information on simultaneous adjustment of dynamic O-D demand matrix

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