A map matching algorithm based on a particle filter

Mokhtari, Karim El; Reboul, Serge; Azmani, Monir; Choquel, Jean-Bernard; Hamdoune, Salaheddine; Amami, Benaissa; Benjelloun, M.

doi:10.1109/icmcs.2014.6911291

Cited by 6 publications

(3 citation statements)

References 8 publications

(11 reference statements)

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“…Two popular map-matching methods were considered in detail, one employing particle filters [32]- [34] and the second the HMM/V variant proposed by Newson and Krumm [35]. Our experiments suggest that AVLS data are best suited to the HMM/V approach which we found to produce consistently higher fidelity routes (the details of these experiments are not included in this paper due to lack of space).…”

Section: A Map-matching Avls Tracksmentioning

confidence: 88%

Modeling Metropolitan-Area Ambulance Mobility Under Blue Light Conditions

et al. 2019

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Actions taken immediately following a life-threatening personal health incident are critical for the survival of the sufferer. The timely arrival of specialist ambulance crew in particular often makes the difference between life and death. As a consequence, it is critical that emergency ambulance services achieve short response times. This objective sets a considerable challenge to ambulance services worldwide, especially in metropolitan areas, where the density of incident occurrence and traffic congestion are high. Using London as a case study, in this paper, we consider the advantages and limitations of data-driven methods for ambulance routing and navigation. Our long-term aim is to enable considerable improvements to their operational efficiency through the automated generation of more effective response strategies and tactics. A key ingredient of our approach is to use a large historical dataset of incidents and ambulance location traces to model route selection and arrival times. Working on the London road network graph modified to reflect the differences between emergency and civilian vehicle traffic, we develop a methodology for the precise estimation of expected ambulance speed at the individual road segment level. We demonstrate how a model that exploits this information achieves best predictive performance by implicitly capturing route-specific persistent patterns in changing traffic conditions. We then present a predictive method that achieves a high route similarity score while minimising journey duration error. This is achieved through the combination of a technique that correctly predicts routes selected by the current LAS navigation system and our best performing speed estimation model. This hybrid approach outperforms alternative mobility models. To the best of our knowledge, this paper represents the first attempt to apply a data-driven methodology for route selection and the estimation of arrival times of ambulances travelling with blue lights and sirens on. INDEX TERMS Ambulance mobility, emergency services, routing and navigation, smart cities. MARCUS POULTON received the master's degree in research in computer science from the Birkbeck, University of London, in 2013, where he is currently pursuing the Ph.D. degree with the Department of Computer Science and Information Systems. He is a specialist in emergency services computer systems, and has worked with many U.K. emergency services, including the Metropolitan Police and the London Ambulance Service. His work currently focuses on ambulance mobility and simulation. His research interests also include intelligent systems for the emergency services and smart city technology.

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Section: A Map-matching Avls Tracksmentioning

confidence: 88%

Modeling Metropolitan-Area Ambulance Mobility Under Blue Light Conditions

et al. 2019

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“…Trajectory estimation of moving targets can achieve better performance by using map constraints as additional information [6,7]. In summary, the state estimation accuracy of the filter with state constraints is higher than that of the unconstrained filter [8]. The adaptive Kalman filter with an observer of vehicle velocity and heading angle can provide robust and highly accurate estimates of vehicle position [9].…”

Section: Introductionmentioning

confidence: 99%

Adaptive H<sub>∞</sub> Filtering Algorithm for Train Positioning Based on Prior Combination Constraints

Diao,

Wang,

et al. 2024

CMES

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To solve the problem of data fusion for prior information such as track information and train status in train positioning, an adaptive H ∞ filtering algorithm with combination constraint is proposed, which fuses prior information with other sensor information in the form of constraints. Firstly, the train precise track constraint method of the train is proposed, and the plane position constraint and train motion state constraints are analysed. A model for combining prior information with constraints is established. Then an adaptive H ∞ filter with combination constraints is derived based on the adaptive adjustment method of the robustness factor. Finally, the positioning effect of the proposed algorithm is simulated and analysed under the conditions of a straight track and a curved track. The results show that the positioning accuracy of the algorithm with constrained filtering is significantly better than that of the algorithm without constrained filtering and that the algorithm with constrained filtering can achieve better performance when combined with track and condition information, which can significantly reduce the train positioning error. The effectiveness of the proposed algorithm is verified.

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“…Blazquez and Vonderohe (2009) consider the difference of different positioning data, propose a parameter adjustment idea for the MM algorithm of rule decision and find that different parameters have an influence on the matching result accuracy of the positioning data of different sampling frequencies. Mokhtari et al (2014) proposed an integrated-weighted MMA for particle filtering. The algorithm considers two factors: heading angle and speed.…”

Section: Introductionmentioning

confidence: 99%

Research on map matching algorithm based on priority rule for low sampling frequency vehicle navigation data

Liu

Yao

Zhao

et al. 2019

IJCS

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Purpose There is a certain error in the satellite positioning of the vehicle. The error will cause the drift point of the positioning point, which makes the vehicle trajectory shift to the real road. This paper aims to solve this problem. Design/methodology/approach The key technology to solve the problem is map matching (MM). The low sampling frequency of the vehicle is far from the distance between adjacent points, which weakens the correlation between the points, making MM more difficult. In this paper, an MM algorithm based on priority rules is designed for vehicle trajectory characteristics at low sampling frequencies. Findings The experimental results show that the MM based on priority rule algorithm can effectively match the trajectory data of low sampling frequency with the actual road, and the matching accuracy is better than other similar algorithms, the processing speed reaches 73 per second. Research limitations/implications In the algorithm verification of this paper, although the algorithm design and experimental verification are considered considering the diversity of GPS data sampling frequency, the experimental data used are still a single source. Originality/value Based on the GPS trajectory data of the Ministry of Transport, the experimental results show that the accuracy of the priority-based weight-based algorithm is higher. The accuracy of this algorithm is over 98.1 per cent, which is better than other similar algorithms.

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A map matching algorithm based on a particle filter

Cited by 6 publications

References 8 publications

Modeling Metropolitan-Area Ambulance Mobility Under Blue Light Conditions

Modeling Metropolitan-Area Ambulance Mobility Under Blue Light Conditions

Adaptive H<sub>∞</sub> Filtering Algorithm for Train Positioning Based on Prior Combination Constraints

Research on map matching algorithm based on priority rule for low sampling frequency vehicle navigation data

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