Intelligent extended floating car data collection

Messelodi, Stefano; Modena, Carla Maria; Zanin, Michele; Natale, Francesco G. B. De; Granelli, Fabrizio; Betterle, Enrico; Guarise, Andrea

doi:10.1016/j.eswa.2008.04.008

Cited by 71 publications

(39 citation statements)

References 4 publications

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“…Even though GPS is becoming more and more used and affordable, so far only a limited number of cars are equipped with this system, typically fleet management services. Traffic data obtained from private vehicles or trucks is more suitable for estimating traffic under motorways and rural areas [11]. In case of urban traffic, taxi fleets are particularly useful due to their high number and their on-board communication systems already in place.…”

Section: Introductionmentioning

confidence: 99%

Analysis of GPS Based Vehicle Trajectory Data for Road Traffic Congestion Learning

Lanka

Jena

2014

Advanced Computing, Networking and Informatics- Volume 2

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Abstract. Successful developments of effective real-time traffic management and information systems demand high quality real time traffic information. In the era of intelligent transportation convergence, traffic monitoring requires traffic sensory technologies. We tabulate various realistic traffic sensors which aim to address the technicalities of both point and mobile sensors and also increase the scope to prefer an optimal sensor for real time traffic data collection. The present analysis extracted data from Mobile Century experiment. The data obtained in the experiment was pre-processed successfully by applying data mining pre-processing techniques such as data transformation, normalization and integration. Finally as a result of the availability of pre-processed Global Position System (GPS) sensors trace data a road map has been generated.

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Section: Introductionmentioning

confidence: 99%

Analysis of GPS Based Vehicle Trajectory Data for Road Traffic Congestion Learning

Lanka

Jena

2014

Advanced Computing, Networking and Informatics- Volume 2

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“…On the one hand, the minimum number of probe cars should be larger that 2% of the traffic flow [19]- [21], and on the other hand, the minimum information rate should be between 10 min [18] and 3 min [17]. With these features, it is possible to detect abnormal events in traffic as well as calculate the travel time [22].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, a different solution for obtaining traffic information is to use "floating vehicles" [11]- [17]. Huber et al [11] raised the possibility of having "sensorized floating vehicles" circulating that could gather information on the condition of the roadway, meteorological conditions (rain, fog, temperature, etc.…”

Section: Introductionmentioning

confidence: 99%

Floating Car Data Augmentation Based on Infrastructure Sensors and Neural Networks

Naranjo

Jiménez²,

Serradilla

et al. 2012

IEEE Trans. Intell. Transport. Syst.

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Abstract-The development of new-generation intelligent vehicle technologies will lead to a better level of road safety and CO2 emission reductions. However, the weak point of all these systems is their need for comprehensive and reliable data. For traffic data acquisition, two sources are currently available: 1) infrastructure sensors and 2) floating vehicles. The former consists of a set of fixed point detectors installed in the roads, and the latter consists of the use of mobile probe vehicles as mobile sensors. However, both systems still have some deficiencies. The infrastructure sensors retrieve information from static points of the road, which are spaced, in some cases, kilometers apart. This means that the picture of the actual traffic situation is not a real one. This deficiency is corrected by floating cars, which retrieve dynamic information on the traffic situation. Unfortunately, the number of floating data vehicles currently available is too small and insufficient to give a complete picture of the road traffic. In this paper, we present a floating car data (FCD) augmentation system that combines information from floating data vehicles and infrastructure sensors, and that, by using neural networks, is capable of incrementing the amount of FCD with virtual information. This system has been implemented and tested on actual roads, and the results show little difference between the data supplied by the floating vehicles and the virtual vehicles.

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“…The car spatial and driving information is called Floating Car Data or FCD for short. Floating car like mobile sensors distributed in road network [1] to capture the traffic information (such as congestion, transport road speed) in current time. FCD has become one of the key technologies in the field of intelligent transportation, which is widely used in the current-time traffic surveillance and traffic management.…”

Section: Introductionmentioning

confidence: 99%