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The Nomenclature
Published in Madapuri Rudra Kumar, Kalli Srinivasa Nageswara Prasad, Annaluri Sreenivasa Rao, Vinit Kumar Gunjan, Change Request Impacts in Software Maintenance, 2020
Madapuri Rudra Kumar, Kalli Srinivasa Nageswara Prasad, Annaluri Sreenivasa Rao, Vinit Kumar Gunjan
Unlike other proposed techniques, MSR approaches do not involve retrieving dependencies from artefacts. Instead, these methods derive artefacts from repositories of software. While maintaining track of the version history of artefacts, this approach examines efficient associations between various software artefacts which are only accessible from repositories. Few of the broadly used version control systems include CVS, SVN or Git.
Real-time traffic flow topology sensing in partial vehicular ad hoc network: a deep learning solution
Published in Transportmetrica A: Transport Science, 2023
Data from more CVs would lead to better inference results. However, when these CVs are near to each other, their inference results may be in conflict. For instance, two neighboring CVs infer one HDVs location, and the two inference results are extremely near. It is possible that only one HDV exists, which influences both CVs. This scenario is shown in Figure 12. A coordination mechanism is necessary to merge the inferences from multiple CVs when they are near to each other. Under such circumstances, the procedure is shown in Figure 11(b). Compared with Figure 11(a), two extra components are introduced: confliction criterion, and inference results fusion. The former one determines which inference result (i.e. which one of the five surrounding vehicles) of a CV need to be fused, and the latter one integrates the results of the conflicted CVs’ inference.
Leveraging existing high-occupancy vehicle lanes for mixed-autonomy traffic management with emerging connected automated vehicle applications
Published in Transportmetrica A: Transport Science, 2020
Comparing cases (0.33, 0.25), (0.67, 0.25) and (1, 0.25), an interesting phenomenon can be found that at the same low CAV market penetration rate of 0.25, the traffic performance is getting worse with the increase of the market penetration. This is because in this study CVs also perform cooperative merge. When the CV market penetration increases, more vehicles are eligible for cooperative merge, and they create gaps for on-ramp vehicles. As CV’s driving behavior (i.e. manually driving behavior) is quite stochastic and incurs errors, the lane change, acceleration, and deceleration process can have an impact on the mainline traffic performance. However, this phenomenon is gone when the CAV market penetration becomes high because of the stabilizing effect of AV traffic flow originated from deterministic machine driving behavior coded in the ACC/CACC vehicle algorithms.