Smart City Taxi Trajectory Coverage and Capacity Evaluation Model for Vehicular Sensor Networks

Abstract

In a smart city, a large number of smart sensors are operating and creating a large amount of data for a large number of applications. Collecting data from these sensors poses some challenges, such as the connectivity of the sensors to the data center through the communication network, which in turn requires expensive infrastructure. The delay-tolerant networks are of interest to connect smart sensors at a large scale with their data centers through the smart vehicles (e.g., transport fleets or taxi cabs) due to a number of virtues such as data offloading, operations, and communication on asymmetric links. In this article, we analyze the coverage and capacity of vehicular sensor networks for data dissemination between smart sensors and their data centers using delay-tolerant networks. Therein, we observed the temporal and spatial movement of vehicles in a very large coverage area (25 × 25 km2) in Beijing. Our algorithm sorts the entire city into different rectangular grids of various sizes and calculates the possible chances of contact between smart sensors and taxis. We further calculate the vehicle density, coverage, and capacity of each grid through a real-time taxi trajectory. In our proposed study, numerical and spatial mining show that even with a relatively small subset of vehicles (100 to 400) in a smart city, the potential for data dissemination is as high as several petabytes. Our proposed network can use different cell sizes and various wireless technologies to achieve significant network area coverage. When the cell size is greater than 500 m2, we observe a coverage rate of 90% every day. Our findings prove that the proposed network model is suitable for those systems that can tolerate delays and have large data dissemination networks since the performance is insensitive to the delay with high data offloading capacity.

Analysis of the implementation of urban computing in smart cities: A framework for the transformation of Saudi cities

“…However, collecting data from these sensors presents several obstacles, including connecting the sensors to the data center via a communication network, which requires expensive infrastructure. Nevertheless, due to its advantages, such as data offloading, operations, and communication on asymmetric links, simple strategies such as connecting smart sensors with data centers on a broad scale via smart vehicles (transport fleets or taxi cabs) is a good place to start for an efficient and economical collection of everyday data ( Naseer et al., 2021 ).…”

Matrix Factorization-Based Trajectory Completion for Addressing Missing Trajectories in Mobile Crowd Sensing

“…This dataset is derived from Microsoft's T-Drive project and records the trajectories of over ten thousand taxis in Beijing during a week from August 2, 2008, to August 8, 2008. [28] It includes 15 million coordinate points, with a total trajectory distance of over 9 million kilometers, as shown in The dataset only provides latitude and longitude data. Latitude and longitude matrices, labeled as LatM atrix and LonM atrix, were created, each having dimensions of 815x120("815" corresponds to the number of trajectories.…”

Dynamic Graph Convolutional Network-Based Prediction of the Urban Grid-Level Taxi Demand–Supply Imbalance Using GPS Trajectories

“…Nonetheless, these methods present issues with reliability and timeliness. With the widespread use of GPS devices, a significant number of taxis outfitted with GPS systems traverse the city's road 2 of 21 networks, gathering trajectory data that depict aspects of urban traffic operations, such as the traffic flow, speed, and density [14]. The GPS systems of most city taxis can trace information such as the vehicle ID, location coordinates, and time, as well as whether the vehicle is occupied [15][16][17].…”

“…such as the traffic flow, speed, and density [14]. The GPS systems of most city taxis can trace information such as the vehicle ID, location coordinates, and time, as well as whether the vehicle is occupied [15][16][17].…”