# Data-Driven Safe Deliveries: The Synergy of IoT and Machine Learning in Shared Mobility
## 2 Literature Review  
Ride sharing involves the practice of offering drivers the chance to add extra passengers to existing car trips. Initially, ride-sharing platforms were websites used as public noteboards where users could freely post and search for excursions, which were often grouped using keywords like cities. Through these tools, individuals might get in touch with one another and spontaneously plan collaborative trips [2]. These online platforms improved over time, gradually increasing the effectiveness of setting up carpooling arrangements by introducing a booking-based system for facilitating connections and coordinating shared journeys [3]. To facilitate the seamless connection between drivers and passengers, studies on ride-sharing algorithms focus on optimizing the count of driver-rider pairs to the maximum extent [4], minimizing the overall distance or travel time for drivers [5; 6; 7], or reducing the overall detour duration [8; 9]. A recent study [10] has proposed a real-time ride-sharing system with dynamic temporal segmentation and anticipation-based migration. The framework showed improved commuter waiting times by up to 65% and raised frequencies of successful matches by 136.11% through proper parameter tweaking using formal modeling and practical methods. Another application of shared mobility is micro-mobility services. Micro-mobility refers to the provision of mobility services via a fleet of small, low-speed vehicles (primarily bikes and e-scooters) for personal transportation in urban areas as an alternative to ride hailing, public transportation, or walking, where vehicles can be accessed by one person at a time and charged at a usage rate. Urban areas have the highest concentration of bike-sharing systems, which let people use traditional or electric bicycles whenever they need them from a network of dock-based stations or for short trips in places with good connectivity and a density of free-floating destinations based on GPS and mobile apps [1]. Studies on micro-mobility systems mainly focused on three areas: the difficulty in distributing bikes [11; 12], the planning of vehicle routes [13; 14], and prediction of bike-sharing demands [15; 16]. To address the issue of bike-sharing distribution, [12] introduced a model based on an adaptive capacity-constrained K-centers clustering algorithm and mixed-integer nonlinear programming. The study [14] introduced a comprehensive framework employing reinforcement learning to address the vehicle routing problem. To reallocate resources for bike-sharing demands, [16] suggested a hierarchical model for predicting the number of rents/returns of each bike. Due to the emergence of these shared mobility platforms among users, communities, and urban landscapes, safety issues around their use should be highly considered. The safety of passengers becomes a vital concern as people depend more and more on shared mobility services choices. Ensuring the secure operation of shared mobility systems mainly depends on monitoring the behavior of road users, especially bike and scooter riders, and minimizing the vulnerabilities they are exposed to on the road [17]. Specifically, most studies on ensuring bike/scooter rider safety focused on the use of smart helmets [18; 19; 20; 21; 22] or smart bikes [23; 24; 25] along with mobile applications [23; 24; 25; 19] or cloud-based databases [25; 22]. In the study [18], a helmet-integrated control system was presented with the goal of improving biker safety and lowering accidents, especially those with serious consequences. The proposed system enforced mandatory helmet wearing via a Radio-Frequency (RF) transmitter and receiver setup, ensuring compliance with the legal requirement of wearing a helmet. In [19], a smart helmet was implemented to prevent bike accidents caused by alcohol consumption and lack of helmet usage. Utilizing gas, infrared, vibration, and MEMS sensors, the proposed prototype detected alcohol levels, helmet usage, vehicle load, reckless driving, and accidents.