Quasi-Dynamic Estimation of OD Flows From Traffic Counts Without Prior OD Matrix

Bauer, Dietmar; Richter, Gerald; Asamer, Johannes; Heilmann, Bernhard; Lenz, Gernot; Kölbl, Robert

doi:10.1109/tits.2017.2741528

Cited by 24 publications

(30 citation statements)

References 15 publications

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“…Real-time estimations and predictions of time-dependent OD flows have been developed and evaluated with real traffic data using state space models, but the estimation is dependent on the assignment matrix [ 32 ]. OD flows can also be estimated without a prior OD matrix under the assumption of a quasi-dynamic framework [ 1 , 7 , 9 ]. However, quasi-dynamic estimation techniques also require a specific setting of the transportation network where path choice is not an issue (e.g., highways or urban areas) [ 1 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Origin–destination (OD) flow models estimate the number of vehicles in a given transportation network that are travelling between origins and destinations within a specific time interval [ 1 ]. Such estimations answer questions related to traffic congestion, and evaluate performances of theoretical models [ 2 ] and accessibility measures of public transport in urban areas [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, the quality of OD flow estimation remains uncertain for such OD flow estimations due to the lack of complete validation datasets. Existing literature validates an OD flow model compared either with a sample of known OD flow obtained from surveys or other sources of historical trip data [ 1 , 19 , 24 ], or with simulating synthetic data [ 10 , 12 , 23 ]. Surveying a complete OD flow is expensive, and is not feasible for the real world on a city scale.…”

Section: Introductionmentioning

confidence: 99%

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Origin–Destination Flow Estimation from Link Count Data Only

Dey

Winter

Tomko

2020

Sensors

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All established models in transportation engineering that estimate the numbers of trips between origins and destinations from vehicle counts use some form of a priori knowledge of the traffic. This paper, in contrast, presents a new origin–destination flow estimation model that uses only vehicle counts observed by traffic count sensors; it requires neither historical origin–destination trip data for the estimation nor any assumed distribution of flow. This approach utilises a method of statistical origin–destination flow estimation in computer networks, and transfers the principles to the domain of road traffic by applying transport-geographic constraints in order to keep traffic embedded in physical space. Being purely stochastic, our model overcomes the conceptual weaknesses of the existing models, and additionally estimates travel times of individual vehicles. The model has been implemented in a real-world road network in the city of Melbourne, Australia. The model was validated with simulated data and real-world observations from two different data sources. The validation results show that all the origin–destination flows were estimated with a good accuracy score using link count data only. Additionally, the estimated travel times by the model were close approximations to the observed travel times in the real world.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Origin–Destination Flow Estimation from Link Count Data Only

Dey

Winter

Tomko

2020

Sensors

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“…Among these components, GPS data are applied more extensively due to their finer spatial and temporal resolution, which can be exploited to yield high quality route and travel time data by transportation researchers, planners, and modellers. In practice, due to the low penetration rates of these devices among transportation networks' users, excessive costs and the probability of statistical bias for specific social groups such as young people or business people (Bauer et al 2018), most applications of GPS data are going through developing process as to modelling components, calibration and validation (Lee et al 2016). The CDRs are the recent attempts made in improving travel surveys.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Sensor Location and Origin–destination Matrix Observation With and Without Sensors on Uncongested Networks

2019

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The Origin–Destination (O–D) matrix, is an important information in transportation planning and traffic control. Rapid changes in land use, particularly in developing countries, have been and are on an increase, which makes the estimation and observation of this matrix more significant. The objective of this paper is to observe O–D matrix under two scenarios. In the first scenario, it is assumed that the traffic network is equipped with path-ID sensors. In this situation, the goal is to determine the optimal number and location of these sensors in the network, where by applying collected information through these sensors, the O–D matrix is observed. Because path-ID sensors are not available in many cities, in the second scenario the interview alternative is proposed in order to observe O–D matrix. The interview method has encountered some restrictions. Several mathematical programming models have been developed to overcome these restrictions. To illustrate these proposed methodologies, they are applied in the Nguyen–Dupuis transportation network and the results are analysed. By applying the model on the intercity road network in the Province of Isfahan (Iran), a large network, the efficiency of these proposed models is demonstrated. Finally, some conclusions and final recommendations are included.

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“…The above-described OD matrices estimation methods make it possible to identify their general characteristic: the availability of presented in various forms information on the existing distribution of passenger and transport flows or the presence of an "old" OD matrix. Any preliminary information on the flows distribution improves significantly the accuracy of the OD matrix estimation [1][2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Improving Methods of Estimation of Transport Demand for Urban Passenger Transportation

Sharov¹

2019

Proceedings of the International Conference on Aviamechanical Engineering and Transport (AviaENT 2019)

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The article is devoted to the issues of the transportation demand estimation. Existing methods to bring up to date the Origin-Destination matrix are reviewed. A regression method to update the passenger Origin-Destination matrix is proposed. The proposed method is compared with the method of calculating the Origin-Destination matrix using the population mobility data. The comparability of the proposed method with the standard method of calculating the Origin-Destination matrix is established. The characteristics of the transport mobility of Irkutsk city population are also established. The relevance of the research topic is determined by the growth of the level of vehicleto-population ratio and transport mobility of our country population that complicates the process of designing and managing urban passenger transport systems (UPT). One of the urgent objectives is the development of efficient methods to estimate the demand for transportation services, including ones to determine the distribution of this demand between UPT and individual road transport. Constantly updated information on transportation demand improves the efficiency of the management of the UPT system and improves the quality of transportation services provided to the population. Keywordstransport and urban planning, transportation demand, origin-destination matrix, travel time distribution, urban passenger transport. I.

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Quasi-Dynamic Estimation of OD Flows From Traffic Counts Without Prior OD Matrix

Cited by 24 publications

References 15 publications

Origin–Destination Flow Estimation from Link Count Data Only

Origin–Destination Flow Estimation from Link Count Data Only

Optimal Sensor Location and Origin–destination Matrix Observation With and Without Sensors on Uncongested Networks

Improving Methods of Estimation of Transport Demand for Urban Passenger Transportation

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