http://ieeexplore.ieee.org TOC Alert for Publication# 6979 2012 May 21 13 1 C1 C4 45 13 1 C2 C2 38 13 1 1 5 160 13 1 6 14 500 13 1 15 24 622 13 1 25 35 1501 13 1 36 48 848 13 1 49 58 1116 13 1 59 70 1026 13 1 71 80 1026 2 and 44% savings of fuel consumption.]]> 13 1 81 90 625 13 1 91 106 1926 2 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.]]> 13 1 107 114 712 13 1 115 126 1089 13 1 127 139 663 13 1 140 153 1945 13 1 154 165 1225 13 1 166 179 1146 13 1 180 192 1221 13 1 193 202 1328 13 1 203 212 275 13 1 213 220 292 13 1 221 234 575 13 1 235 248 1998 13 1 249 263 833 13 1 264 283 2678 2)emissions of Virtual Traffic Light (VTL), which is a recently proposed infrastructureless traffic control system solely based on Vehicle-to-Vehicle (V2V) communication. Our evaluation uses a real-city scenario in a complex simulation framework, involving microscopic traffic, wireless communication, and emission models. Compared with an approximation of the physical traffic light system deployed in the city, our results show a significant reduction on CO₂ emissions when using VTLs, reaching nearly 20% under high-density traffic.]]> 13 1 284 295 1179 13 1 296 306 566 13 1 307 317 426 13 1 318 328 1010 13 1 329 343 1964 13 1 344 353 286 13 1 354 364 817 13 1 365 374 1181 13 1 375 384 1388 13 1 385 394 698 13 1 395 407 515 13 1 408 413 926 13 1 413 420 607 13 1 C3 C3 34