Within a plausible scenario for the sensorization related to Smart Mobility in a Smart City, coexist different types of technologies for traffic data gathering and data sources. The processing of such data will provide more or less Smart information as a function of the sophistication and computational effort put into play. Roughly the technologies that may coexist in this scenario could be:
The situation de facto that all these types of scenarios present may be described in the following way:
Therefore, sets of heterogeneous measures to which may be added complementary data such as meteorological data, events or seasonal variations.
The immediate questions are: What to do with those sets of heterogeneous data? How to extract the most, best and more efficient traffic information from that data?
Betterways proposal relies on two components:
The first steps of the process correspond to the generation of the integrated database, which combines data coming from different technologies with that of the different types of events for the generation of profiles.
There is a global consensus among experts with respect to the fundamental outcome of the Data Fusion techniques, which Dailey (1996) and Ou (2011) summarize in the following way:
There is also global consensus in classifying the Data Fusion techniques in three different levels. Their generic characteristics are described in Dailey (1996) as:
|Fusion Level||General Method||Specific Technology|
|Level 1||Data association
|Level 2||Fusion through ID
|Bayesian theory of Decision, Evidential Reasoning of Dempster-Schafer, Adaptive Neuronal Networks
|Level 3||Artificial Intelligence||Systems based on knowledge, Blackboard Architecture, Fuzzy Logic|
Varshney (1997) proposes a more elaborated version which suits better the Data Fusion applications for the case of traffic data.
|Level 1||Raw data processing||Methods for the estimation of the state, Digital filters, Kalman filter, Particles filters, etc|
|Level 2||Derivation of the distinctive characteristics and behavioural patterns||Classification/inference methods (Statistical recognition of patterns, Evidential Reasoning of Demster-Chaffer, Bayesian methods, Neuronal networks, correlation measurements, Fuzzy Sets theory, ...)|
|Level 3||Decision making and event/incident detection||Decision Support systems, knowledge based systems|
Level 1 fusion is oriented towards processing the raw data coming from sensors and the estimation of the basic states of the system under analysis. The objective of data fusion at this level is to translate these data into basic information for determining the system’s traffic state: intensities, occupancies, speeds. The objective of Level 2 fusion is to derivate distinctive characteristics and behavioral patterns from the estimation of the previous level. In case of vehicular traffic systems this means the sort-term prediction of the evolution of the traffic state, the detection of incidents, etc. Level 3 fusion may be considered like a level for decision making about the system based on the information provided by Level 1 and Level 2 fusion.
Ou (2011) proposes an approach that allows characterizing the methods for traffic data fusion as if they were composed by two main components: a core and a capsule.
The CORE represents the physical laws and the hypothesis that support the traffic flow theory like, for instance:
The SHELL represents the assimilation techniques like, for instance, the statistical and Kalman Filter techniques able to combine models and data in an optimum way.
Betterways has followed the approaches from Varshney (1997) and Ou (2011) in order to implement a data processing method with three fusion levels.
Level 1 implies the previous execution of two processes:
A complementary application of the methods implemented by Betterways is the space-time traffic state reconstruction through interpolation between data points and the removal of high-frequency noise, preserving most of the relevant dynamic information. The application of this method for the fusion of heterogeneous data sources for space-time reconstruction makes it more robust and allows obtaining estimations at spots for which a single data source would not be enough.
Figure: (a) Spots with data from loops within space-time (b) Average speeds from the trips between Bluetooth sensors (c) Space-time traffic state reconstruction from loop data (d) Space-time traffic state reconstruction from Bluetooth data (e) Space-time traffic state reconstruction for a section of the network coming from all data sources (Data from loops and trips between three Bluetooth sensors at Gran Vía avenue on the 03/02/2012)
Last, the Macro Fundamental Diagram (MFD) estimation from Geroliminis and Daganzo (2008) is also included: a new global information that can be read as a measure of the global capacity of the network and its state in a given moment can be generated from local data provided by RSU deployed all over the transport network plus GPS data coming from vehicles.