An application of the Sequential Monte Carlo to increase the accuracy of travel time estimation in urban areas

Hadachi, Amnir; Lecomte, Christèle; Mousset, Stéphane; Bensrhair, Abdelaziz

doi:10.1109/itsc.2011.6082969

Cited by 6 publications

(11 citation statements)

References 13 publications

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“…Where tp i is the estimated time of the vehicle when he passed by the PUMAS points and for more details refer to our previous article [4] .…”

Section: A Monte Carlo Methodsmentioning

confidence: 99%

“…The project PUMAS [17] (Plateforme Urbaine de Mobilite Avancee et Soutenable / Sustainable and Advanced Mobility and Urban Platform) is a preindustrial project that has the objective to inform about the traffic situation, to evaluate the gas emission, and also to develop and implement a platform for sustainable mobility in order to evaluate it in the region, specifically Rouen, France [4]. By taking into consideration the article [8] it is important to start by preprocessing the data before computing the travel estimation by the two methods in order to reduce data errors such as the GPS errors [9] or the Map-matching errors [4]. Then we will give a clear explanation on how we adapt our two methods in order to use them in our case; therefore, we can conduct the two methods and analyze the results obtained.…”

Section: A Overviewmentioning

confidence: 99%

“…Map Matching then allows us to fix the GPS errors and match the position on the appropriate road section [4]. To filter the data, we checked coherence by veryfiying the distance and the frequency between two successive GPS data inputs.…”

Section: System Architecturementioning

confidence: 99%

“…To achieve this comparison, we collected the raw data, simulated our historical database and processed it. We used the map-matching [4] to filter the GPS errors and another filter concerning the coherence of the data. The two methods results were compared and analyzed.…”

Section: A Conclusionmentioning

confidence: 99%

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Practical testing application of travel time estimation using applied Monte Carlo Method and adaptive estimation from probes

Hadachi

Mousset

Bensrhair

2012

2012 IEEE Intelligent Vehicles Symposium

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This paper presents a practical testing of two different methods to estimate the travel time in urban areas. The purpose behind this testing is to validate the behavior of each method regarding the road aspect in urban areas. The first method is based on Monte Carlo Method and the second one is based on adaptive estimation from probes. Both methods were modified to be adapted to our case and also to the nature of our data. The paper also describes an experiment with realworld data that was used in the testing of the two methods. Moreover it contains the architecture of the system used in order to make the tests.This work yeilded interesting results based on real-world experiments which give clear feedback about the application of the two methods to compute the travel time estimation per road section that can be used for processing the historical database as well as real time data. In general this work is a suitable validation of the two methods and encouraging for our future perspectives.

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“…Where tp i is the estimated time of the vehicle when he passed by the PUMAS points and for more details refer to our previous article [4] .…”

Section: A Monte Carlo Methodsmentioning

confidence: 99%

Section: A Overviewmentioning

confidence: 99%

Section: System Architecturementioning

confidence: 99%

Section: A Conclusionmentioning

confidence: 99%

See 2 more Smart Citations

Practical testing application of travel time estimation using applied Monte Carlo Method and adaptive estimation from probes

Hadachi

Mousset

Bensrhair

2012

2012 IEEE Intelligent Vehicles Symposium

Self Cite

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“…Hadachi et al [22] used the Sequential Monte Carlo to increase the accuracy of travel time estimations. Tabibiazar et al [23] utilized a kernelbased density estimation method and developed a probabilistic framework to model the traffic data with generalized Gaussian density.…”

Section: Related Workmentioning

confidence: 99%

Efficient traffic congestion estimation using multiple spatio-temporal properties

Yang

Han

et al. 2017

Neurocomputing

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Abstract-Traffic estimation is an important issue to analyze the traffic congestion in large-scale urban traffic situations.Recently, many researchers have used GPS data to estimate traffic congestion. However, how to fuse the multiple data reasonably and guarantee the accuracy and efficiency of these methods are still challenging problems. In this paper, we propose a novel method Multiple Data Estimation (MDE) to estimate the congestion status in urban environment with GPS trajectory data efficiently, where we estimate the congestion status of the area through utilizing multiple properties, including density, velocity, inflow and previous status. Among them, traffic inflow and previous status (combination of time and space factors) are not both used in other existing methods. In order to ensure the accuracy and efficiency, we apply dynamic weights of data and parameters in MDE method. To evaluate our methods, we apply it on large-scale taxi GPS data of Beijing and Shanghai. Extensive experiments on these two real-world datasets demonstrate the significant improvements of our method over several state-ofthe-art methods.

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Persistent Schedule Evaluation and Adaptive Re-planning for Maritime Search Tasks

Bays,

Wettergren,

Shin

et al. 2024

J Intell Robot Syst

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Search operations performed by adaptive autonomous maritime vehicles have been a topic of considerable interest for many years. Such operations require carefully scheduled coordination of multiple vehicles performing search tasks across the region of interest. Due to the inherent uncertainty of the maritime environment, however, an initially planned search schedule may not be maintained if the vehicles have significant capability to adapt their tasks to match the environment they detect in real time. We propose a multi-vehicle adaptive algorithm for dynamic evaluation and elastic re-planning of variable-length tasks commonly found in the maritime environments. In adaptive evaluation and re-planning problems, a set of tasks are initially planned for execution by adaptive, autonomous search vehicles. Tasks are allocated to search vehicles under a pre-defined schedule based on prior knowledge and desired outcome. Because of the vehicles’ autonomy and reactivity to in situ conditions such as environment or target pose, the precise duration and actions required by each task are unknown a priori. We develop a hidden Markov model (HMM) for propagating task estimates, coupled with a quadratic-programming-based elastic re-scheduler. The result is an integrated estimate-and-schedule adaptation scheme that quickly and efficiently re-plans the vehicles’ schedules based on in situ observations. The numerical simulation results show that this novel HMM approach decreases avoidable schedule variation by over a factor of two compared to existing methods.

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An application of the Sequential Monte Carlo to increase the accuracy of travel time estimation in urban areas

Cited by 6 publications

References 13 publications

Practical testing application of travel time estimation using applied Monte Carlo Method and adaptive estimation from probes

Practical testing application of travel time estimation using applied Monte Carlo Method and adaptive estimation from probes

Efficient traffic congestion estimation using multiple spatio-temporal properties

Persistent Schedule Evaluation and Adaptive Re-planning for Maritime Search Tasks

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