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Comparison of the number of PAVs by state (parked, rebalancing, picking up, and carrying passengers) for each policy on a single testing instance.

Comparison of the number of PAVs by state (parked, rebalancing, picking up, and carrying passengers) for each policy on a single testing instance.

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Article
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Current mobility services cannot compete on equal terms with self-owned mobility products concerning service quality. Due to supply and demand imbalances, ridesharing users invariably experience delays, price surges, and rejections. Traditional approaches often fail to respond to demand fluctuations adequately since service levels are, to some exte...

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Context 1
... the baseline parameters described in Table 2, we consider the best tuning settings achieved in Section 6.2, namely, the three hierarchical aggregation levels presented in Table 3, the five-vehicle limit per location, and the rebalancing strategy 8 × RC1. Figure 9 and Figure 10 compare the performance of the proposed VFA policy against the myopic policy on a single testing instance. Since the myopic policy reacts to request rejection, from Figure 9, we can see that the fleet can fulfill the demand entirely until about 6:45 AM, when the first rebalancing movement appears. ...
Context 2
... the baseline parameters described in Table 2, we consider the best tuning settings achieved in Section 6.2, namely, the three hierarchical aggregation levels presented in Table 3, the five-vehicle limit per location, and the rebalancing strategy 8 × RC1. Figure 9 and Figure 10 compare the performance of the proposed VFA policy against the myopic policy on a single testing instance. Since the myopic policy reacts to request rejection, from Figure 9, we can see that the fleet can fulfill the demand entirely until about 6:45 AM, when the first rebalancing movement appears. In contrast, under our VFA policy, most vehicles are rebalancing before 6:30 AM. ...
Context 3
... results indicate that this inability to cover the demand entirely is due to insufficient vehicle supply. As can be seen in Figure 9, under our VFA policy, most vehicles are busy (i.e., rebalancing, picking up, or carrying users) during the demand peak. When rejections start to accumulate from about 6:30 and on (see Figure 10), we can see that the number of parked vehicles drop dramatically, especially in the myopic policy. ...
Context 4
... a vehicle is better off parking in high-demand areas than traveling to pick up users in low-demand areas, associated with unpromising future returns. This fleet management strategy can also be seen during the busiest period in Figure 9, which features two "idleness peaks" (at around 7:15 and 8:30) where about fifty AVs are parked, waiting for future requests. ...

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... Sharing rides with other passengers is an alternative way of sharing transportation resources. Beirigo, Schulte, and Negenborn (2021) address the problem of traditional ridesharing platforms, namely, that they often do not adequately cope with demand fluctuation because they are bounded by fleet size. The authors propose to overcome this by including autonomous vehicles, and thereby they introduce the autonomous ridesharing problem, where idle vehicles can be hired on demand. ...
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... These idle hireable vehicles, which we refer to as FAVs, may be readily available during predefined time windows to join larger, centrally controlled fleets in exchange for compensation. As suggested by Hyland and Mahmassani (2017) and shown by Beirigo et al. (2021), AV fleet managers can significantly benefit from such short-term fleet size elasticity, increasing or decreasing the fleet to adequately meet the demand, by either hiring privately-owned AVs or drivers with non-AVs. ...
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