Interpolating Sparse GPS Measurements Via Relaxation Labeling and Belief Propagation for the Redeployment of Ambulances

Phan, Andrew; Ferrie, Frank P.

doi:10.1109/tits.2011.2165281

Cited by 6 publications

(3 citation statements)

References 25 publications

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“…After a thorough literature review, we discovered only a few papers that worked with GPS-based data that came from ambulances. Motivated by the issue of the redeployment of ambulances, the authors of [38] used a GPS-based vehicle locator data to predict the most likely ambulance speeds. The speed profile was estimated for each road segment while separately considering weekdays and weekends, with the mean average error reaching 15 km/h.…”

Section: Literature Reviewmentioning

confidence: 99%

On the Modelling of Emergency Ambulance Trips: The Case of the Žilina Region in Slovakia

Buzna

Czimmermann

2021

Mathematics

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The efficient operation of emergency medical services is critical for any society. Typically, optimisation and simulation models support decisions on emergency ambulance stations’ locations and ambulance management strategies. Essential inputs for such models are the spatiotemporal characteristics of ambulance trips. Access to data on the movements of ambulances is limited, and therefore modelling efforts often rely on assumptions (e.g., the Euclidean distance is used as a surrogate of the ambulance travel time; the closest available ambulance is dispatched to a call; or the travel time estimates, offered by application programming interfaces for ordinary vehicles, are applied to ambulances). These simplifying assumptions are often based on incomplete data or common sense without being fully supported by the evidence. Thus, data-driven research to model ambulance trips is required. We investigated a unique dataset of global positioning system-based measurements collected from seventeen emergency ambulances over three years. We enriched the data by exploring external sources and designed a rule-based procedure to extract ambulance trips for emergency cases. Trips were split into training and test sets. The training set was used to develop a series of statistical models that capture the spatiotemporal characteristics of emergency ambulance trips. The models were used to generate synthetic ambulance trips, and those were compared with the test set to decide which models are the most suitable and to evaluate degrees to which they fit the statistical properties of real-world trips. As confirmed by the low values of the Kullback–Leibler divergence (0.004–0.229) and by the Kolmogorov–Smirnov test at the significance level of 0.05, we found a very good fit between the probability distributions of spatiotemporal properties of synthetic and real trips. A reasonable modelling choice is a model where the exponential dependency on the population density is used to locate emergency cases, emergency cases are allocated to hospitals following empirical probabilities, and ambulances are routed using the fastest paths. The models we developed can be used in optimisations and simulations to improve their validity.

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Section: Literature Reviewmentioning

confidence: 99%

On the Modelling of Emergency Ambulance Trips: The Case of the Žilina Region in Slovakia

Buzna

Czimmermann

2021

Mathematics

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“…The increasing availability of GPS data provides new means for conducting large-scale estimation of traffic conditions. However, as GPS data are inherently noisy, the estimated traffic conditions usually do not satisfy the flow conservation requirement assumed by many simulation models (Phan and Ferrie, 2011;Kong et al, 2013;Zhang et al, 2013). As a result, new studies that consist of several steps for the estimation task, are emerging.…”

Section: Related Workmentioning

confidence: 99%

Simulation and Learning for Urban Mobility: City-scale Traffic Reconstruction and Autonomous Driving

Li¹

2019

Preprint

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Weizi Li: Simulation and Learning for Urban Mobility: City-scale Traffic Reconstruction and Autonomous Driving (Under the direction of Ming C. Lin) Traffic congestion has become one of the most critical issues worldwide. The costs due to traffic gridlock and jams are approximately $160 billion in the United States, more than £13 billion in the United Kingdom, and over one trillion dollars across the globe annually. As more metropolitan areas will experience increasingly severe traffic conditions, the ability to analyze, understand, and improve traffic dynamics becomes critical. This dissertation is an effort towards achieving such an ability. I propose various techniques combining simulation and machine learning to tackle the problem of traffic from two perspectives: city-scale traffic reconstruction and autonomous driving.Traffic, by its definition, appears in an aggregate form. In order to study it, we have to take a holistic approach. I address the problem of efficient and accurate estimation and reconstruction of city-scale traffic.The reconstructed traffic can be used to analyze congestion causes, identify network bottlenecks, and experiment with novel transport policies. City-scale traffic estimation and reconstruction have proven to be challenging for two particular reasons: first, traffic conditions that depend on individual drivers are intrinsically stochastic; second, the availability and quality of traffic data are limited. Traditional traffic monitoring systems that exist on highways and major roads can not produce sufficient data to recover traffic at scale. GPS data, in contrast, provide much broader coverage of a city thus are more promising sources for traffic estimation and reconstruction. However, GPS data are limited by their spatial-temporal sparsity in practice. I develop a framework to statically estimate and dynamically reconstruct traffic over a city-scale road network by addressing the limitations of GPS data.Traffic is also formed of individual vehicles propagating through space and time. If we can improve the efficiency of them, collectively, we can improve traffic dynamics as a whole. Recent advancements in automation and its implication for improving the safety and efficiency of the traffic system have prompted widespread research of autonomous driving. While exciting, autonomous driving is a complex task, consider iii the dynamics of an environment and the lack of accurate descriptions of a desired driving behavior. Learning a robust control policy for driving remains challenging as it requires an effective policy architecture, an efficient learning mechanism, and substantial training data covering a variety of scenarios, including rare cases such as accidents. I develop a framework, named ADAPS (Autonomous Driving via Principled Simulations), for producing robust control policies for autonomous driving. ADAPS consists of two simulation platforms which are used to generate and analyze simulated accidents while automatically generating labeled training data, and a hierarchical control...

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“…the estimated traffic conditions usually do not satisfy the flow conservation requirement assumed by many simulation models [26]- [28]. Consequently, new studies are emerging and these studies commonly take several steps to perform the estimation.…”

Section: Related Workmentioning

confidence: 99%

Citywide Estimation of Traffic Dynamics via Sparse GPS Traces

Li¹,

Nie²,

Wilkie³

2017

IEEE Intell. Transport. Syst. Mag.

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Interpolating Sparse GPS Measurements Via Relaxation Labeling and Belief Propagation for the Redeployment of Ambulances

Cited by 6 publications

References 25 publications

On the Modelling of Emergency Ambulance Trips: The Case of the Žilina Region in Slovakia

On the Modelling of Emergency Ambulance Trips: The Case of the Žilina Region in Slovakia

Simulation and Learning for Urban Mobility: City-scale Traffic Reconstruction and Autonomous Driving

Citywide Estimation of Traffic Dynamics via Sparse GPS Traces

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