Taxi services play an important role in the public transportation system of large cities. Improving taxi business efficiency is an important societal problem. Most of the recent analytical approaches on this topic only considered how to maximize the pickup chance, energy efficiency, or profit for the immediate next trip when recommending seeking routes, therefore may not be optimal for the overall profit over an extended period of time due to ignoring the destination choice of potential passengers. To tackle this issue, we propose a novel Spatial Network-based Markov Decision Process (SN-MDP) with a rolling horizon configuration to recommend better driving directions. Given a set of historical taxi records and the current status (e.g., road segment and time) of a vacant taxi, we find the best move for this taxi to maximize the profit in the near future. We propose statistical models to estimate the necessary time-variant parameters of SN-MDP from data to avoid competition between drivers. In addition, we take into account fuel cost to assess profit, rather than only income. A case study and several experimental evaluations on a real taxi dataset from a major city in China show that our proposed approach improves the profit efficiency by up to 13.7 percent and outperforms baseline methods in all the time slots.

With the development of Unmanned Aerial Vehicles (UAVs) in recent years, UAV-mounted Mobile Edge Computing (MEC) systems are widely used for broadband connectivity and emergency communications in areas without internet. However, the onboard energy of UAVs is limited, and how to provide long-term computing services for ground User Equipments (UEs) still faces significant challenges. In this paper, UAVs are used as mobile base stations to offer MEC services for UEs. Unlike existing solutions that mainly solve UAV path planning from convex optimization methods, we propose a Multi-Agent Path planning (MAP) scheme based on deep reinforcement learning. We aim to minimize the total energy consumption of UAVs while completing the offloading tasks of UEs. At the same time, fairness is taken into account to ensure UE offloading balance and UAV load balance. To this end, we establish the optimization problem and design the state space, action space, and reward function for modeling each UAV through Deep Neural Networks (DNNs). Extensive simulation experiments show that the proposed scheme outperforms other benchmark schemes.

Gathering extensive public data from the IoT devices scattered throughout a city is a significant challenge in Smart City. In this paper, a novel urban vehicle network we call the location-based Urban Vehicle Network (LUV), is proposed to perform the non-real time data gathering task in smart cities. Different from the most works in Vehicle Networks, the empirical research on the real-life 8,900 private cars trace data in Changsha, China compels us to focus on the parked vehicles and the parking places, rather than on the moving cars and the urban roads. The Location-based mechanism not only provides more reliable and predictable wireless connections but also dramatically simplifies the system topology. It ensures that the vehicle network deployed on an intricate metropolitan area reaches the desired scale for gathering data.

The communication infrastructures are likely to be a failure, in case of undesirable disasters like earthquake and debris flow, resulting in blind areas and the inconvenience of residents’ communication. In this paper, we propose a novel scheme connecting these infrastructure failure areas, namely NEED (short for aN Energy-awarE D2D communication scheme). Our proposed scheme, taking advantage of clustering technology, connects users within the infrastructure failure areas that often have no direct access to the cellular network. Compared with the clustering used in traditional cases, we add the process of determining candidate cluster heads before determining final cluster heads. Based on location and residual energy, the final cluster heads are selected in the candidate cluster heads, and dual cluster heads in the cluster run alternately to share the communication cost. Besides, a modified ant colony algorithm (MACA) is developed to increase routing efficiency. The simulation results show its effectiveness of our proposed NEED scheme in terms of energy consumption and energy balance, and demonstrate that the scheme significantly extends the lifetime of the whole network.

Taxi drivers always look for strategies to locate passengers quickly and therefore increase their profit margin. In reality, the passenger seeking strategies are mostly empirical and substantially vary among taxi drivers. From the history taxi data, the top performing taxi drivers can earn 25% more than the ones with mediocre seeking strategy in the same period of time. A better strategy not only helps taxi drivers earn more with less effort, but also reduce fuel consumption and carbon emissions. It is interesting to examine the influential factors in passenger seeking strategies and find algorithms to guide taxi drivers to passenger hotspots with the right timing. With the abundant availability of history taxicab traces, the existing methods of doing taxi business have been radically changed. This paper focuses on the problem of mining efficient operation strategies from a large-scale history taxi traces collected over one year. Our approach presents generic insights into the dynamics of taxicab services with the objective of maximizing the profit margins for the concerned parties. We propose important metrics, such as trip frequency, hot spots, and taxi mileage, and provide valuable insights toward more efficient operation strategies. We analyze these metrics using techniques, such as Newton's polynomial interpolation and Gamma distribution, to understand their dynamics. Our strategies use the real taxicab traces from the city of Changsha (P.R.China), may predict the taxi rides at different times by 90.68% per day, and increase the taxi drivers income levels up to 19.38% by controlling appropriate mileage per trip and following the route across more urban hot spots.