Instant delivery has become an essential service in daily life, requiring strict delivery timelines. However, traditional delivery methods that employ human couriers struggle to meet the soaring delivery demands due to labor shortages. While researchers have explored alternative solutions using ground vehicles (e.g., crowdsourced taxis) and Unmanned Aerial Vehicles (UAVs), their inherent limitations, such as constrained delivery detour for crowdsourced taxis and limited battery capacity of UAVs, greatly constrain their effectiveness. To address these challenges, this paper proposes a novel air-ground delivery paradigm that cooperatively integrates UAVs and crowdsourced taxis. First, UAV stations are strategically deployed based on delivery gaps between the delivery demands and taxis' delivery capacity, instead of delivery demands only; Then, a predictive UAV repositioning strategy is designed to bridge instantaneously dynamic delivery gaps. Thereafter, a transfer learning-based (TL-based) algorithm that mines the delivery knowledge of human couriers is designed to optimize the cooperative performance. This algorithm extracts behavioral insights from human couriers and transfers them to enhance the delivery capabilities of UAVs and taxis. Finally, parcel assignment is formulated as optimization problems aimed at maximizing total preferences of UAVs and taxis, and maximizing delivery number while minimizing cost, respectively. Evaluations on real-world datasets demonstrate that the proposed method delivers 27.4% more parcels, saves 19.2% delivery cost, and preserves 36.3% more of the travel experience of taxi passengers than the state-of-the-art (SOTA) air-ground cooperative approach for instant delivery.

Instant delivery has become a fundamental service in people's daily lives. Different from the traditional express service, the instant delivery has a strict shipping time constraint after being ordered. However, the labor shortage makes it challenging to realize efficient instant delivery. To tackle the problem, researchers have studied to introduce vehicles (i.e., taxis) or Unmanned Aerial Vehicles (UAVs or drones) into instant delivery tasks. Unfortunately, the delivery detour of taxis and the limited battery of UAVs make it hard to meet the rapidly increasing instant delivery demands. Under this circumstance, this paper proposes an air-ground cooperative instant delivery paradigm to maximize the delivery performance and meanwhile minimize the negative effects on the taxi passengers. Specifically, a data-driven delivery potential-demands-aware cooperative strategy is designed to improve the overall delivery performance of both UAVs and taxis as well as the taxi passengers' experience. The experimental results show that the proposed method improves the delivery number by 30.1% and 114.5% compared to the taxi-based and UAV-based instant delivery respectively, and shortens the delivery time by 35.7% compared to the taxi-based instant delivery.

Unmanned Aerial Vehicles (UAVs) are widely applied in smart city applications such as urban sensing and delivery, due to the UAVs’ agility, low cost and not being restricted by ground road conditions. However, the limited battery capacity becomes one of the biggest obstacles to the application of UAVs. To address this issue, this paper investigates an emergency response application, in which UAVs generally ride crowdsourced buses to save energy and respond to a stochastic emergency event (such as a traffic accident) when the event occurs. For the bus-based UAV response paradigm, a single UAV response process with the constraint of the bus mobility is first modeled. Subsequently, a data-driven UAV path planning algorithm is designed. Then two emergency response cases by multi-UAV are investigated. One case is irregular emergency response, whose objective is to maximize the temporal-spatial coverage of the urban area. The other case is predictable emergency response, which optimizes the response performance to these emergencies. Thereafter, the bus-stimulating problems for the two cases are formulated and solved. Finally, utilizing a real-world bus trajectory dataset generated by a large-scale bus fleet and a traffic event dataset, the emergency response performance of the bus-based UAV response paradigm is comprehensively evaluated. The results show that (1) with only 30 UAVs, 90% of Shenzhen city can be covered in the irregular emergency response case; (2) with only 50 UAVs, the average response delay to the emergencies is shorter than 1.5 minutes, which is 56% shorter than baselines, in the predictable emergencies response case.

The battery-powered Unmanned Aerial Vehicle (UAV) is a promising alternative to traditional logistics trucks. Using UAVs can achieve much more speedy, cost-effective, and environment-friendly delivery on an urban scale. However, UAVs suffer from insufficient delivery range and battery aging. This paper presents an innovative logistics UAV scheduling framework using public buses, in which logistics UAVs Land and Recharge its battery on Buses (ULRB) to extend its delivery range and decelerate its fading battery capacity. This work correlates physical layer parameters such as the energy consumption rate, the parcels weight, UAV velocity, the battery temperature to the UAVs path planning, the battery discharging, and the capacity fading models. Specifically, the ULRB framework consists of a single-UAV scheduling module and a multi-UAV dispatching module. In the single-UAV module, a Markov-based algorithm is utilized to plan the UAVs flying path to land and dynamically get recharged on the bus. The latter module optimized the delivery progress in a multi-UAV, multi-parcel, and multi-bus scenario. Finally, using a large-scale real-world bus trajectory dataset, extensive evaluations are conducted to verify ULRB. The results show that ULRB can extend the UAVs delivery range by 5.54 and decelerate the battery aging by 3.26 on average.

Parcel delivery and Point of Interest (PoI) surveillance are two fundaches were conducted for an isolated application, separately. UAV-sharing in the two heterogeneous applications bears significant but unexplored benefit potentials, similar to current sharingmental applications of Unmanned Aerial Vehicles (UAVs) in city. Traditional resear economy such as taxi sharing. However, the inconsistency of the two heterogeneous applications in both temporal and spatial domains would impact the sharing performance. This work illustrates the first quantified studies of the UAV-sharing performance in the two heterogeneous applications. Specifically, some critical constraints of the UAV-sharing process are first discussed. Thereafter, a data-driven evaluation is conducted to understand the sharing process of the UAVs with a delivery dataset and a traffic accident dataset obtained from Shanghai city. Some inspiring results verify the particularly excellent UAV-sharing performance in the two heterogeneous applications.

Unmanned Aerial Vehicles (UAVs) are widely employed in smart city. However, the limited battery capacity is one of the UAV's most critical obstacles to monitoring mission. Recently, leveraging public buses to relay UAVs has been shown to be a promising solution to this critical issue. Existing works on this solution focused on pre-determined scenarios, namely the mission time and location of the UAVs are determined in advance. While in on-demand mission such as emergency response, mission time and location of the UAV is on-demand and stochastic. How the UAV riding on a bus perform in such stochastic missions remains open. In this paper, driven by the bus mobility data, a sampling-based algorithm is designed to navigate UAVs to land on buses in on-demand applications. A simple greedy algorithm is proposed to determine the appropriate buses to relay UAVs, so that the scheduling performance of the UAVs is optimized. Comprehensive evaluation using a large-scale bus trajectory data is conducted.

This paper constructs a GMCR (Graph Model for Conflict Resolution) model to analyze the financing dilemma existing in the brownfield remediation projects in China. In addition to the stability analysis, status quo and inverse analyses are conducted to obtain further managerial insights. The findings of this research are threefold. First, the local government should not redevelop the brownfield without remediation; otherwise, the situation will raise great public concerns, which may lead to severe conflicts among various stakeholders. Second, the cooperation between the local government and enterprises (developers and/or investors) is the most favorable state to successfully conduct the remediation. Third, in order to achieve the desired outcomes, an intervention party who can influence the decision makers' preference has to be intervened in the unsolved situations.

Based on the main mode of development and research status of aviation industry cluster, and the combined method of literature analysis and expert investigation, the grey-fuzzy comprehensive evaluation model for evaluation of maturity of aviation industry cluster is proposed. The evaluation index system and factors set of the proposed model are established and determined sequentially. After that, the index weight is determined by using Likert Scale questionnaire analysis. The established index system combined qualitative and quantitative indicators together, which is within the employment of Grey-fuzzy comprehensive evaluation method. By taking Xi'an Yanliang aviation industry park as an example, the feasibility and practicability of the evaluation model was verified.