# Data-Driven Safe Deliveries: The Synergy of IoT and Machine Learning in Shared Mobility
## 2 Literature Review
The prototype included a PIC microcontroller, LCD display, and Android application, sending accident information via GPS to hospitals and providing alerts to riders in case of non-compliance. Similarly, [20] used gas sensors and infrared sensors along with a GSM/GPRS module to warm medical staff in emergency situations. The study [21] proposed a helmet-based system using a PIC microcontroller. The system used a force-sensitive resistor to detect helmet wearing, an activated buzzer for helmet reminders, and an LED that flashes when the speed sensor detects exceeding speed limits. The study in [23] proposed a smart bike system that incorporated an Android application on the smartphone for data transmission through the 4G network between the app and a cloud-based real-time database and a microcontroller for communication via Bluetooth 4.0 Low Energy (BLE) between sensors and the phone. The study used ultrasonic sensors to detect nearby vehicles and an inertial measurement unit to measure acceleration and angular velocities. A smart bike architecture, proposed by [24], integrated a microcontroller, an accelerometer/gyroscope module to monitor bike movements, and a GSM/GPRS module to collect the bike's location and send it over the cellular network to emergency contacts. The study in [25] used a similar system along with the use of MQTT protocol to transmit the collected data to a cloud-based database. Several machine-learning approaches were used in the literature [26; 27; 28] to analyze sensor-collected data patterns in driving behavior that affect safety. An Artificial Neural Network (ANN) algorithm is proposed in [26] for detecting abnormal movements among motorcyclists. The study utilized smartphone accelerometer and gyroscopes sensors data. The ANN algorithm processed these collected data to make decisions. The system is trained to identify nine distinct types of movements, and it achieved detection with varying accuracies. On average, the ANN demonstrated an accuracy rate of 96.2%. The embedded sensors of smartphones are also utilized in [27] to identify four distinct driving activities among motorcyclists. The study evaluated various classifiers, with the random forest (RF) classifier achieving the highest accuracy of 86.51%. Another system that deploys the random forest classifier based on mobile phone sensors data is introduced in [28]. This system, named the Vehicle mode-driving Activity Detection System, comprises two primary modules. The Vehicle mode Detection Mod ule (VDM) is designed to determine the user's current mode of transportation (such as walking, biking, motorcycling, driving a car, or riding a bus) based on the smartphone's accelerometer input. The second module, referred to as the Activity Detection Module (ADM), is dedicated to recognizing four core driving activities by analyzing data from the smartphone's accelerometer, gyroscope, and magnetometer sensors. The system managed to achieve an average accuracy of 98.33% in identifying vehicle modes and an average accuracy of 98.95% in identifying the motorist movements. This literature review provides a broad overview of the transformative effects of shared mobility on transportation systems, focusing on the shift away from traditional ownership paradigms and toward cooperative resource utilization [1]. The growth of ride-sharing services and other shared mobility platforms has sparked the creation of sophisticated algorithms that optimize interactions between drivers and passengers and boost the effectiveness of shared travel [2; 3; 4; 5; 6; 7; 8; 9; 10]. Research has not only focused on mobility sharing effects on urban transportation but also on distribution techniques, route optimization, and precise demand forecasting [11; 12; 13; 14; 15; 16]. Safety concerns sparked creative solutions, such as smart helmets, smart bikes, and mobile applications intended to protect road users, as these platforms have grown in popularity [17; 18; 19; 20; 21; 22; 23; 24].