# Assessment of Affordable Real-Time PPP Solutions for Transportation Applications
## 1 Introduction  
The accurate positioning, velocity, and time (PVT) estimation of a vehicle is essential for intelligent transportation system (ITS) applications. PVT can be effectively determined using global navigation satellite system (GNSS) techniques, which include differential, real-time kinematic (RTK), precise point positioning (PPP), and PPP-RTK solutions. GNSS has some key applications in the vehicle transportation domain, including navigation,vehicle tracking, optimal route determination, and traffic monitoring, and can be used to track road networks and enhance their safety and efficiency. GNSS is commonly used in self-driving cars, parking automation, and advanced driver assistance systems (ADAS), such as pedestrian detection and lane departure warning applications; moreover, GNSS can be integrated with internet of things (IoT) and 5G technologies to foster connectivity, which significantly helps in real-time transportation and monitoring applications. On the other hand, GNSS has some limitations in urban areas, such as signal blocks and signal losses in tunnels or under bridges; as a result, GNSS accuracy may be degraded. To address this problem, the inertial navigation system (INS) can be combined with GNSS to bridge the gaps during signal outages by tracking a vehicle's motion and orientation; furthermore, the sensor fusion technique can be utilized by integrating GNSS with an additional sensor, such as an odometer or light detection and ranging (LiDAR). Another possible solution is to apply the visual-inertial odometry (VIO) technique, which combines a high-end camera with the INS to determine vehicle positions and directions when GNSS signals are lost. This technology is widely used in autonomous vehicle navigation domains. In recent years, numerous studies have investigated the performance of GNSS and other integrating system strategies in both open and urban areas; see, e.g., [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. [PERSON] et al. [1] developed a PPP-RTK model using real-time state space representation (SSR) corrections, which exhibited instantaneous ambiguity resolution and rapid convergence time; their model has been examined for vehicular transportation applications. Their PPP-RTK solution had a precision of 0.1 m, except in the case of signal outages due to bridge passages; additionally, about 92% and 94% of the PPP-RTK estimates converged within 5 s in the east and north components, respectively. Furthermore, [PERSON] et al. [10] proposed a multi-sensor fusion system for vehicle navigation based on PPP-RTK, INS, and an odometer along with vehicle motion constraints (VMCs); hence, the collected datasets were tightly coupled into a Kalman filter. Their developed system has been explored in complex urban scenarios, and the positioning errors were at the centimeter level. In addition, [PERSON] et al. [11] created an RTK/LiDAR/INS integrated system that focused on enhancing the RTK in GNSS-denied environments; thereafter, their developed system was examined in a road test. The proposed system enhanced RTK performance in GNSS-denied areas; however, LiDAR data processing and the use of a particle filter increased the system's computation time, resulting in RTK output latencies. Notably, these techniques are costly because they depend on geodetic-grade receivers and high-end sensors; hence, several researchers have used low-cost GNSS modules and sensors in GNSS transportation applications [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37]. For example, [PERSON] and [PERSON] [20] developed a navigation system that consisted of a triple-frequency low-cost GNSS receiver, a micro-electromechanical (MEMS)-based inertial measurement unit (IMU), and a patch antenna; their system was assessed in urban and suburban areas. Their developed system demonstrated a positioning accuracy at the decimeter level, as well as an overall root mean square error (RMSE) that was 10-40% superior to that obtained with the dual-frequency PPP/IMU solution. [PERSON] et al.