Estimation of origin-destination matrices by fusing detector data and Floating Car Data

Vogt, Sebastian; Fourati, Walid; Schendzielorz, T.; Friedrich, Bernhard

doi:10.1016/j.trpro.2018.12.216

Cited by 9 publications

(7 citation statements)

References 4 publications

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“…The use of FCD for traffic modelling and travel demand estimation is widely discussed in literature [18][19][20][21][22][23][24][25]. For the scope of this study, FCD were used to estimate the Origin-Destination (O-D) matrices following the procedure intro-duced by Eisemann and List [26] and refined by Carrese et al [27] and Nigro et al [28].…”

Section: Traflc Simulation From Fcdmentioning

confidence: 99%

Analysing urban traffic volumes and mapping noise emissions in Rome (Italy) in the context of containment measures for the COVID-19 disease

et al. 2020

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This study presents the result of a traffic simulation analysis based on Floating Car Data and a noise emission assessment to show the impact of mobility restriction for COVID-19 containment on urban vehicular traffic and road noise pollution on the road network of Rome, Italy. The adoption of strong and severe measures to contain the spreading of Coronavirus during March-April 2020 generated a significant reduction in private vehicle trips in the city of Rome (-64.6% during the lockdown). Traffic volumes, obtained through a simulation approach, were used as input parameters for a noise emission assessment conducted using the CNOSSOS-EU method, and an overall noise emissions reduction on the entire road network was found, even if its extent varied between road types.

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Section: Traflc Simulation From Fcdmentioning

confidence: 99%

Analysing urban traffic volumes and mapping noise emissions in Rome (Italy) in the context of containment measures for the COVID-19 disease

et al. 2020

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“…FCD are also considered as the basis for estimating O-D matrices [16,39,58,[63][64][65][66][67][68]. Carrese et al [64] proposed the dynamic estimation O-D matrix approach using FCD.…”

Section: O-d Demand Flow Estimationmentioning

confidence: 99%

“…Vogt et al [67] and Dabbas et al [68] implemented the O-D matrix calculation according to a double-constrained gravity model with FCD usage for impedance function formalization. Tracking the single vehicle trajectories and defining the number of turns made according to FCD allowed Vogt et al [67] and Dabbas et al [68] to form the values of the impedance function for every O-D pair in the matrix. Thus, by tracking the route choice along with zone capacity estimation based on FCD, the authors of both studies implemented the O-D flows calculation.…”

Section: O-d Demand Flow Estimationmentioning

confidence: 99%

Private Car O-D Flow Estimation Based on Automated Vehicle Monitoring Data: Theoretical Issues and Empirical Evidence

et al. 2021

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Data on the daily activity of private cars form the basis of many studies in the field of transportation engineering. In the past, in order to obtain such data, a large number of collection techniques based on travel diaries and driver interviews were used. Telematics applied to vehicles and to a broad range of economic activities has opened up new opportunities for transportation engineers, allowing a significant increase in the volume and detail level of data collected. One of the options for obtaining information on the daily activity of private cars now consists of processing data from automated vehicle monitoring (AVM). Therefore, in this context, and in order to explore the opportunity offered by telematics, this paper presents a methodology for obtaining origin–destination flows through basic info extracted from AVM/floating car data (FCD). Then, the benefits of such a procedure are evaluated through its implementation in a real test case, i.e., the Veneto region in northern Italy where full-day AVM/FCD data were available with about 30,000 vehicles surveyed and more than 388,000 trips identified. Then, the goodness of the proposed methodology for O-D flow estimation is validated through assignment to the road network and comparison with traffic count data. Taking into account aspects of vehicle-sampling observations, this paper also points out issues related to sample representativeness, both in terms of daily activities and spatial coverage. A preliminary descriptive analysis of the O-D flows was carried out, and the analysis of the revealed trip patterns is presented.

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“…Since the entire travel paths of vehicles are captured, trajectory data have better spatial coverage than traffic volume data from loop detectors. It is thus desirable to combine these two data sources to improve link flow estimation, which has received great interest in recent years [16][17][18][23][24][25]. However, considering the limited market penetration rate of vehicle detection technologies and the data collection errors, the observed vehicle trajectory data may not reflect the true population trajectory distribution.…”

Section: Introductionmentioning

confidence: 99%

Estimating Link Flows in Road Networks With Synthetic Trajectory Data Generation: Inverse Reinforcement Learning Approach

Zhong

Kim

Zheng

2023

IEEE Open J. Intell. Transp. Syst.

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While traffic volume data from loop detectors have been the common data source for link flow estimation, the detectors only cover a subset of links. These days, other data sources such as vehicle trajectory data collected from vehicle tracking sensors are also incorporated. However, trajectory data are often sparse in that the observed trajectories only represent a small subset of the whole population, where the exact route sampling rate is unknown and may vary over space and time. In this paper, we develop a method that leverage these two limited data sources to enhance link flow estimation. This study proposes a novel generative modelling framework, where we formulate a vehicle's link-to-link movements as a sequential decision-making problem using the Markov Decision Process framework. We propose an Inverse Reinforcement Learning-based method, based on which synthetic population vehicle trajectories can be generated to estimate link flows across the whole network. The proposed method ensures the generated population vehicle trajectories are consistent with the observed traffic volume and trajectory data. The proposed generative modelling framework is compared to two existing methods in a synthetic road network and validated in a real road network.

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Estimation of origin-destination matrices by fusing detector data and Floating Car Data

Cited by 9 publications

References 4 publications

Analysing urban traffic volumes and mapping noise emissions in Rome (Italy) in the context of containment measures for the COVID-19 disease

Analysing urban traffic volumes and mapping noise emissions in Rome (Italy) in the context of containment measures for the COVID-19 disease

Private Car O-D Flow Estimation Based on Automated Vehicle Monitoring Data: Theoretical Issues and Empirical Evidence

Estimating Link Flows in Road Networks With Synthetic Trajectory Data Generation: Inverse Reinforcement Learning Approach

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