The current and expected future proliferation of mobile and embedded technology provides unique opportunities for crowdsourcing platforms to gather more user data for making data-driven decisions at the system level. Intelligent Transportation Systems (ITS) and Vehicular Social Networks (VSN) can be leveraged by mobile, spatial, and passive sensing crowdsourcing techniques due to improved connectivity, higher throughput, smart vehicles containing many embedded systems and sensors, and novel distributed processing techniques. These crowdsourcing systems have the capability of profoundly transforming transportation systems for the better by providing more data regarding (but not limited to) infrastructure health, navigation pathways, and congestion management. In this paper, we review and discuss the architecture and types of ITS crowdsourcing. Then, we delve into the techniques and technologies that serve as the foundation for these systems to function while providing some simulation results to show benefits from the implementation of these techniques and technologies on specific crowdsourcing-based ITS systems. Afterward, we provide an overview of cutting edge work associated with ITS crowdsourcing challenges. Finally, we propose various use-cases and applications for ITS crowdsourcing, and suggest some open research directions.
Modern urbanization is demanding smarter technologies to improve a variety of applications in intelligent transportation systems. Mobile crowdsourcing enabling automatic sensing tasks constitutes an excellent mean to complement existing technologies. In this paper, we exploit the high amount of data that can be collected by on-board and infrastructure-based sensors to evaluate traffic network statuses and improve the navigation of vehicles in urban areas. The objective is to design real-time route planning algorithms that determine fastest trajectories for both single and multiple destinations, in a real-time manner based on the frequent data inputs. We first formulate the routing problems as integer linear programs (ILPs) and then, design iterative approaches levels to iteratively solve the ILPs while considering updated traffic data. Afterwards, lower complexity sub-optimal graph-based algorithms are designed to solve the real-time routing problems. Unlike traditional navigation solutions, the proposed approaches update the vehicle trajectory after a certain period characterized by timely correlated data. Uncertainty and erroneous data inputs are also considered to determine fastest and least risky trajectory. Our results show that crowdsourcing-based realtime navigation outperforms outperform traditional navigation solutions by selecting less congested roads and avoiding blocked streets. INDEX TERMS Intelligent transportation systems, mobile crowdsourcing, uncertainty, real-time navigation, delivery vehicle problem.
Multi-rotor drones have witnessed a drastic usage increase in several smart city applications due to their 3D mobility, flexibility, and low cost. Collectively, they can be used to accomplish different short-and long-term missions that require low-altitude motion in urban areas. Therefore, it is important to efficiently manage the operation of the fleet to leverage its use and maximize its application performances. In this paper, we propose to investigate the path routing problem for the multiple drones in urban areas, where obstacles with different heights exist. The objective is to find the best trajectories in this 3D environment while ensuring collision-free navigation. The collision is prevented by three possible alternatives: forcing the drone to statically hover, so its peer can pass first, making it fly at a different altitude, or completely changing its path. Multiple charging stations are made available to allow the drones to recharge their batteries when needed. A mixed integer linear program is first developed to model the problem and achieve optimal navigation of the fleet. Afterward, two heuristic algorithms with different conceptual constructions are designed to solve the trajectory planning problem with faster convergence speed. The selected simulation results illustrate the performance of our framework in realistic 3D maps and show that the designed heuristic approaches provide close performances to the optimal ones. INDEX TERMS Unmanned aerial vehicles (UAVs), fleet path planning, energy management, collision avoidance, smart city.
Modern taxi services are usually classified into two major categories: traditional taxicabs and ride-hailing services. For both services, it is required to design highly efficient recommendation systems to satisfy passengers' quality of experience and drivers' benefits. Customers desire to minimize their waiting time before rides, while drivers aim to speed up their customer hunting. In this paper, we propose to leverage taxi service efficiency by designing a generic and smart recommendation system that exploits the benefits of Vehicular Social Networks (VSNs). Aiming at optimizing three key performance metrics, number of pick-ups, customer waiting time, and vacant traveled distance for both taxi services, the proposed recommendation system starts by efficiently estimating the future customer demands in different clusters of the area of interest. Then, it proposes an optimal taxi-to-region matching according to the location of each taxi and the future requested demand of each region. Finally, an optimized geo-routing algorithm is developed to minimize the navigation time spent by drivers. Our simulation model is applied to the borough of Manhattan and is validated with realistic data. Selected results show that significant performance gains are achieved thanks to the additional cooperation among taxi drivers enabled by VSN, as compared to traditional cases.Electronics 2020, 9, 648 2 of 24 a means of transportation and enhance the efficiency of both services for the benefits of both customers and drivers.In regular taxi services, traditional ways for taxi drivers to find potential customers include driving around the city and waiting at some 'hot spots', e.g., taxicab stands. For the first option, taxi drivers usually follow an intuition-based trajectory hoping to find customers as soon as possible, while for the second option, most of the drivers will target the same hot spots since based on their personnel experience, they know when and where customers will be gathered. In the latter case, regular taxi drivers may be subject to an unfair competition since the number of taxis is higher than the demand or vice versa. Hence, traditional solutions for customer hunting are usually exhaustive and inaccurate. On the other hand, for the ride-hailing taxi services, although a central server is dedicated to manage the requests of customers and allocate them to drivers, similar problems that face regular taxi services still exist. Customers' requests might still be raised far away from drivers' locations and high vacant distances are accumulated, resulting in huge and redundant fuel consumption. In Portland, the average waiting times are estimated to be around six and ten minutes for regular and ride-hailing taxi services, respectively, according KGW News [2]. Therefore, it is recommended to enhance the efficiency of such transportation services by tackling the offer/demand problem in both taxi categories.Thanks to the spread of on-board and infrastructure-based sensors [3], collecting and sharing data have become very com...
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