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Figure 4 Trajectories of equipped, unequipped, and estimated unequipped vehicles over a portion of I-80 at a 25% equipped vehicle penetration rate.

Figure 4 Trajectories of equipped, unequipped, and estimated unequipped vehicles over a portion of I-80 at a 25% equipped vehicle penetration rate.

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‘(Smith et al., 2010b), (He et al., 2012), *(Priemer & Friedrich, 2009) “(Barria & Thajchayapong, 2011), °(Rim, Oh, Kang, & Kim, 2011), (Li, Zou, Bu, & Zhang, 2008), ‘Ban, Herring, Hao, & Bayen, 2009), (Ban et al., 2011), °(Hao, Ban, Bennett, Ji, & Sun, 2012).
‘(Smith et al., 2010b), (He et al., 2012), *(Priemer & Friedrich, 2009) “(Barria & Thajchayapong, 2011), °(Rim, Oh, Kang, & Kim, 2011), (Li, Zou, Bu, & Zhang, 2008), ‘Ban, Herring, Hao, & Bayen, 2009), (Ban et al., 2011), °(Hao, Ban, Bennett, Ji, & Sun, 2012).
The speed of the estimated vehicle can be predicted from an empirical relationship between lea vehicle speed, following vehicle speed, and following vehicle acceleration. The relationship is defined in Equation 1.
The speed of the estimated vehicle can be predicted from an empirical relationship between lea vehicle speed, following vehicle speed, and following vehicle acceleration. The relationship is defined in Equation 1.
The proposed algorithm was tested using vehicle trajectories from the Next-Generation Simulation (NGSIM) project, a field-collected data set of vehicle movements along several corridors in the United States (Federal Highway Administration, 2010). Vehicle movements were collected from video recordings, and then extracted via specialized software. This study used the data set collected from a 500 meter section of Interstate 80 in Emeryville, California between 5:00 PM and 5:30 PM on April 13, 2005. The roadway has five lanes in the northbound direction, along with a one on-ramp and a weave area. The activity on the ramps and merge lanes are not analyzed, only the behavior of vehicles traveling in the through lanes. Figure 2 shows an overview of the network. Because the NGSIM data list the position of every vehicle ten times per second, they are considered ground truth data. Because the location estimation algorithm requires vehicle interactions to produce any estimates, it requires not only warm up time, but also warm up space. Figure 3 shows estimated vehicle densities for the final 220 meters of the freeway segment after a 60 second initialization at various penetration rates. The left side shows only equipped vehicles, while the right shows the densities of both equipped vehicles, and estimated vehicle locations. Densities are much more accurate downstream of congestion (vehicles travel bottom to top in the figures) as seen at 25% penetration rates. The algorithm occasionally misses traffic phenomenon at low penetration rates, such as the wide moving jam at between 5:18 and 5:20 which only becomes visible at 50% penetration rate using the location estimation algorithm. At higher penetration rates, because there are so few unequipped vehicles in the network, the algorithm often overestimates densities as seen in at 100% penetration rate, and to a lesser extent at 70% penetration. In spite of its shortcomings, the algorithm provides a remarkable improvement over equipped vehicles alone at low and mid penetration rates near congestion.
The proposed algorithm was tested using vehicle trajectories from the Next-Generation Simulation (NGSIM) project, a field-collected data set of vehicle movements along several corridors in the United States (Federal Highway Administration, 2010). Vehicle movements were collected from video recordings, and then extracted via specialized software. This study used the data set collected from a 500 meter section of Interstate 80 in Emeryville, California between 5:00 PM and 5:30 PM on April 13, 2005. The roadway has five lanes in the northbound direction, along with a one on-ramp and a weave area. The activity on the ramps and merge lanes are not analyzed, only the behavior of vehicles traveling in the through lanes. Figure 2 shows an overview of the network. Because the NGSIM data list the position of every vehicle ten times per second, they are considered ground truth data. Because the location estimation algorithm requires vehicle interactions to produce any estimates, it requires not only warm up time, but also warm up space. Figure 3 shows estimated vehicle densities for the final 220 meters of the freeway segment after a 60 second initialization at various penetration rates. The left side shows only equipped vehicles, while the right shows the densities of both equipped vehicles, and estimated vehicle locations. Densities are much more accurate downstream of congestion (vehicles travel bottom to top in the figures) as seen at 25% penetration rates. The algorithm occasionally misses traffic phenomenon at low penetration rates, such as the wide moving jam at between 5:18 and 5:20 which only becomes visible at 50% penetration rate using the location estimation algorithm. At higher penetration rates, because there are so few unequipped vehicles in the network, the algorithm often overestimates densities as seen in at 100% penetration rate, and to a lesser extent at 70% penetration. In spite of its shortcomings, the algorithm provides a remarkable improvement over equipped vehicles alone at low and mid penetration rates near congestion.
Figure 3 Heat map of densities each second for 30-meter segments of I-80 at various penetration rates, both with and without the location estimation algorithm. Accuracy is improved at low-penetration rates, but densities are often overestimated at high penetration rates.
Figure 3 Heat map of densities each second for 30-meter segments of I-80 at various penetration rates, both with and without the location estimation algorithm. Accuracy is improved at low-penetration rates, but densities are often overestimated at high penetration rates.
Table 2 Effective penetration rates of I-80 at various accuracies and actual penetration rates. Highlighted values indicate improvements over the actual penetration rates. Ramp Metering Application Used alone, ‘the location estimation algorithm is useful for detecting highway conditions and providing an estimate of densities in low- or no-detection segments. However, by providing estimates of individual vehicle locations, the algorithm should also be able to improve the performance of some connected vehicle applications at low penetration rates. To test this theory, the location estimation algorithm was applied to a connected vehicle ramp metering algorithm called the GAP algorithm (Park, 2008).
Table 2 Effective penetration rates of I-80 at various accuracies and actual penetration rates. Highlighted values indicate improvements over the actual penetration rates. Ramp Metering Application Used alone, ‘the location estimation algorithm is useful for detecting highway conditions and providing an estimate of densities in low- or no-detection segments. However, by providing estimates of individual vehicle locations, the algorithm should also be able to improve the performance of some connected vehicle applications at low penetration rates. To test this theory, the location estimation algorithm was applied to a connected vehicle ramp metering algorithm called the GAP algorithm (Park, 2008).
Related topics:
Intelligent Transportation SystemsConnected Vehicles
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