A comparative assessment of multi-sensor data fusion techniques for freeway traffic speed estimation using microsimulation modeling

Bachmann, Chris; Abdulhai, Baher; Roorda, Matthew J.; Moshiri, Behzad

doi:10.1016/j.trc.2012.07.003

Cited by 83 publications

(52 citation statements)

References 20 publications

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“…In [6], different methods for fusing speed from loop detector data with travel times collected with Bluetooth are compared. This was done both on collected data and simulated data.…”

Section: Filtering Fusion and Assimilationmentioning

confidence: 99%

Traffic Management for Smart Cities

Allström

Barceló

Ekström

et al. 2016

Designing, Developing, and Facilitating Smart Cities

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Smart cities, participatory sensing as well as location data available in communication systems and social networks generates a vast amount of heterogeneous mobility data that can be used for traffic management. This chapter gives an overview of the different data sources and their characteristics and describes a framework for utilizing the various sources efficiently in the context of traffic management. Furthermore, different types of traffic models and algorithms are related to both the different data sources as well as some key functionalities of active traffic management, for example short-term prediction and control.

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“…In [6], different methods for fusing speed from loop detector data with travel times collected with Bluetooth are compared. This was done both on collected data and simulated data.…”

Section: Filtering Fusion and Assimilationmentioning

confidence: 99%

Traffic Management for Smart Cities

Allström

Barceló

Ekström

et al. 2016

Designing, Developing, and Facilitating Smart Cities

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“…A classification algorithm is used to identify four levels of traffic: free, dense, slow, and congested. In Bachmann et al (2013), microsimulation is used to generate synthetic loop and Bluetooth-like probe data that are then used to estimate average link speeds. Performance is evaluated for several statistical techniques that do not explicitly model the physics (Bachmann et al, 2013).…”

Section: Related Workmentioning

confidence: 99%

How much GPS data do we need?

Patire

Wright

Prodhomme

et al. 2015

Transportation Research Part C: Emerging Technologies

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“…Common in fields such as economics[6], policy analysis [7] and engineering [8]; simulation is often used where the ability to experimentally test an idea is not feasible due to cost, time constraints or ethical/legal considerations. A key distinction of microsimulation is that within the model each ‘elemental decision making unit’, as Orcutt put it [5], is indivisible; typically in health this unit is a person.…”

Section: Introductionmentioning

confidence: 99%

Developing a synthetic national population to investigate the impact of different cardiovascular disease risk management strategies: A derivation and validation study

et al. 2017

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BackgroundMany national cardiovascular disease (CVD) risk factor management guidelines now recommend that drug treatment decisions should be informed primarily by patients’ multi-variable predicted risk of CVD, rather than on the basis of single risk factor thresholds. To investigate the potential impact of treatment guidelines based on CVD risk thresholds at a national level requires individual level data representing the multi-variable CVD risk factor profiles for a country’s total adult population. As these data are seldom, if ever, available, we aimed to create a synthetic population, representing the joint CVD risk factor distributions of the adult New Zealand population.Methods and resultsA synthetic population of 2,451,278 individuals, representing the actual age, gender, ethnicity and social deprivation composition of people aged 30–84 years who completed the 2013 New Zealand census was generated using Monte Carlo sampling. Each ‘synthetic’ person was then probabilistically assigned values of the remaining cardiovascular disease (CVD) risk factors required for predicting their CVD risk, based on data from the national census national hospitalisation and drug dispensing databases and a large regional cohort study, using Monte Carlo sampling and multiple imputation. Where possible, the synthetic population CVD risk distributions for each non-demographic risk factor were validated against independent New Zealand data sources.ConclusionsWe were able to develop a synthetic national population with realistic multi-variable CVD risk characteristics. The construction of this population is the first step in the development of a micro-simulation model intended to investigate the likely impact of a range of national CVD risk management strategies that will inform CVD risk management guideline updates in New Zealand and elsewhere.

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A comparative assessment of multi-sensor data fusion techniques for freeway traffic speed estimation using microsimulation modeling

Cited by 83 publications

References 20 publications

Traffic Management for Smart Cities

Traffic Management for Smart Cities

How much GPS data do we need?

Developing a synthetic national population to investigate the impact of different cardiovascular disease risk management strategies: A derivation and validation study

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