Dynamic Origin-Destination Matrix Estimation in Large-Scale Congested Networks (Schatting van dynamische herkomst-bestemmingsmatrices in grootschalige, congestiegevoelige netwerken)

Dynamic Network Loading (DNL) models and Dynamic Traffic Assignment (DTA)models are an important tool in the context of traffic planning and trafficmanagement. An essential input of these models is a dynamic origin-destinationmatrix. The dynamic origin-destination (OD) matrix specifies the number of travelersfrom each origin to each destination, and this for each time period. In general, theOD matrix is not directly observable. Therefore, estimation methods are used toderive this OD matrix. They make use of available information such as measuredflows on a road, measured travel times, etc. When reviewing the available literaturethat discusses these methods, we can identify an important gap in current state-ofthe-art methods, namely the impact of congestion on dynamic OD estimation. Thepresence of congestion has a non-linear effect on the relationship between link flowsand OD flows. This relationship is crucial in the dynamic OD estimation problem. Inthis thesis, this relationship is studies in two phases.In a first phase of this research, the effect of congestion spillback is analyzed by theuse of synthetic experiments. It is found that, when congestion spillback is present,the accuracy of the estimated OD matrix depends on the spillback mechanism that isused in the OD estimation. More importantly, the presence of congestion spillbackitself is identified as a source of inaccuracy. The ambiguity of traffic flowmeasurements is conceived as the source of this inaccuracy: a certain flowmeasurement can indicate both slow moving vehicles with a small headway (incongested conditions), and fast moving vehicles with a large headway (in free flowconditions). To overcome these problems, the use of additional information sourcesis suggested, combined with the use of specialized estimation methods. A novel ODestimation procedure based on density measurements is presented and tested in casestudy on a real-world network. It is shown that the manner in which this informationis used, has an impact on the performance of the estimation: when the speeds werecombined with the flow measurements into densities, better results were obtained.However, the performance of the developed methods on a real-world case study isnot yet satisfactory in terms of the match between estimated and measured link flowsand speeds.The second phase of the research focuses on the approximation of the relationshipbetween link flows and OD flows, and its impact on the OD estimation problem.When solving the OD estimation problem, this relationship is approximated. In stateof-the-art methods, this is usually done by assuming a constant mapping, whichimplicitly assumes separability of the link flows to the OD flows, which can lead toinaccurate results when dealing with congested networks. Another important sourceof error attributable to congestion dynamics is the presence of multiple local minimain the objective function. It is illustrated that these local minima are the result of anincorrect interpretation of the information from the detectors. This work proposessensitivity-based OD estimation (SBODE), which uses a linear instead of constantapproximation. It is shown to lead to superior OD estimated.Although the proposed SBODE method yields superior OD-estimates with respect toexisting methods, its computational complexity is also much larger due to the need tonumerically approximate the sensitivity of link flows to OD flows. Hence, in thefinal phase of this research, we focus on efficient algorithms allowing the applicationof the developed methodologies to real-world networks. A first technique is MarginalComputation (MaC), which is a computationally efficient method that performs aperturbation analysis (using kinematic wave theory principles) for deriving thesensitivity of the link flows to the OD flows. The calculation of this sensitivity is arequirement of the SBODE methodology. Next, a hierarchical approach fordecomposing the dynamic OD estimation problem in a number of smaller problemson subareas is presented. The main idea is to perform a more accurate dynamic ODestimation only on subareas with substantial congestion. Both approaches aresuccessfully tested, both on a study network and on a real network.status: publishe

A hierarchical approach for dynamic origin-destination matrix estimation on large-scale congested networks

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A density-based OD estimation method that reproduces within-day congestion dynamics

The OD estimation procedure is of paramount importance in traffic engineering problems. However, existing procedures are built upon the well-known static OD estimation methods, with the result that in practice the estimated OD flows sometimes hardly reproduce current traffic conditions. In this paper we identify the reasons that can cause this problem. Further, we propose a novel density-based OD estimation procedure that outperforms traditional dynamic estimation approaches, which tend to misinterpret the data, confusing free-flow and congestion regimes. The advantage of this approach is shown by performing demand estimation on the highway network around the city of Antwerp, Belgium.no ISBNstatus: publishe

A hierarchical approach for dynamic origin-destination matrix estimation on large-scale congested networks

In this paper we propose a hierarchical approach for decomposing and simplifying the dynamic OD estimation procedure for large-scale congested networks. The main idea is to subdivide the network into multiple hierarchical levels. Next an OD estimation is performed on each level separately, starting with the highest level, and the output of this estimation is used as input for the OD estimation on a lower level. The main advantage of this approach is that different levels of complexity (of the DNL/DTA model and of the estimation method) can be used for different parts of the network as required. In addition, this hierarchical approach solves many practical and theoretical limitations of traditional OD estimation methods, which have been identified in previous research.status: publishe

Dynamic Origin-Destination Matrix Estimation on Large-Scale Congested Networks Using A Hierarchical Decomposition Scheme

Despite the ever increasing computing power, dynamic Origin-Destination (OD) estimation in congested networks remains troublesome. In previous research, we have shown that an unbiased estimation requires the calculation of the sensitivity of the link flows to all Origin Destination flows, in order to incorporate the effects of congestion spillback. This is however computationally infeasible for large-scale networks. To overcome this issue, we propose a hierarchical approach for off-line application that decomposes the dynamic OD estimation procedure in space. The main idea is to perform a more accurate dynamic OD estimation only on subareas where there is congestion spillback. The output of this estimation is then used as input for the OD estimation on the whole network. This hierarchical approach solves many practical and theoretical limitations of traditional OD estimation methods. The main advantage is that different OD estimation method can be used for different parts of the network as necessary. This allows applying more advanced and accurate, but more time consuming methods only where necessary. The hierarchical approach is tested on a study network and on a real network. In both cases the proposed methodology performs better than traditional OD estimation approaches, indicating its merit

Dynamic origin-destination estimation in congested networks

In this paper three conditions for unbiased origin-destination (OD) estimation in congested networks are presented. A first condition deals with the choice of the network loading model. It is shown that an incorrect representation of queuing leads to an incorrect interpretation of information from the detectors, and thus leads to biased results. A second aspect is the relationship that is used in the minimization problem. In many OD estimation methods a linear relationship expressed by the assignment matrix is chosen. However, this relationship assumes separability of the link flows to the OD flows, which can lead to biased results when dealing with congested networks. A final source of error is the presence of multiple local minima in the objective function. An important cause of local minima is an incorrect interpretation of the information from the detectors. Since flow measurements can correspond to two distinguished traffic regimes, free flow and congestion, when the assigned initial OD matrix reproduces a different traffic regime at the detectors compared to reality, the measurements can be interpreted incorrectly. Finally a case study is presented that illustrates the bias that may be introduced if not all three conditions are met.no ISBNstatus: publishe

The performance of short distanced traffic lights with probabilistic spillback

Queues at controlled intersections play an important role in the performance of urban networks, as they are a main determinant of the delay experienced by the drivers. These queues have upper bounds, which, once exceeded, cause extra delays also to other links and other traffic streams. Often this effect is neglected in practice, or it is calculated as a deterministic phenomenon. In this paper we describe queue length probabilities at each moment in time for closely-spaced pre-timed traffic lights, using renewal theory. Special attention is given to the influence of the upstream signal settings on the downstream arrival pattern, and to the effects of downstream queues to the upstream signal, which can be blocked temporarily due to an excess of the available buffer space. Based on this model, a case study is used to illustrate the performance of uncoordinated signals. We show that if two signals are not well coordinated and if insufficient green is assigned to the downstream direction spillback will be likely to occur, causing extra delay in the system even when the signal is under saturated.no ISBNstatus: publishe

Dynamic Origin-Destination estimation in congested networks: theoretical findings and implications in practice

In this study we analyse the impact of congestion in dynamic origin–destination (OD) estimation. This problem is typically expressed using a bi-level formulation. When solving this problem the relationship between OD flows and link flows is linearised. In this article the effect of using two types of linear relationship on the estimation process is analysed. It is shown that one type of linearisation implicitly assumes separability of the link flows, which can lead to biased results when dealing with congested networks. Advantages and disadvantages of adopting non-separable relationships are discussed. Another important source of error attributable to congestion dynamics is the presence of local minima in the objective function. It is illustrated that these local minima are the result of an incorrect interpretation of the information from the detectors. The theoretical findings are cast into a new methodology, which is successfully tested in a proof of concept.status: publishe

A hierarchical approach for dynamic origin-destination matrix estimation on large-scale congested networks

In this paper we propose a hierarchical approach for decomposing and simplifying the dynamic OD estimation procedure for large-scale congested networks. The main idea is to subdivide the network into multiple hierarchical levels. Next an OD estimation is performed on each level separately, starting with the highest level, and the output of this estimation is used as input for the OD estimation on a lower level. The main advantage of this approach is that different levels of complexity (of the DNL/DTA model and of the estimation method) can be used for different parts of the network as required. In addition, this hierarchical approach solves many practical and theoretical limitations of traditional OD estimation methods, which have been identified in previous research. ispartof: pages:1543-1548 ispartof: 2011 14TH INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) pages:1543-1548 ispartof: IEEE ITSC 2011 location:Washington DC date:5 Oct - 7 Oct 2011 status: publishe

Marginal Dynamic Network Loading for Large-Scale Simulation-Based Applications

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