# Data-Driven Safe Deliveries: The Synergy of IoT and Machine Learning in Shared Mobility
## 5 Results and Discussion
### Overall System Performance  
The overall system performance was assessed through a series of tests on specific maneuver types that can be safely tested without putting human subjects in danger using an electric bike. The system's performance was evaluated on maneuver types that could be safely replicated during short rides on the motorbike. The tested maneuver types included fall-like, harsh braking, degraded track, acceleration on a curve, and acceleration on a straight line. Each of these maneuvers was attempted three times, and during the rides, the system recorded risky instances along with their classifications. The percentage of positive classifications for the tested maneuver type in the different trials was computed and used to evaluate the system's performance.  
It is evident that the performance of the system varied for different maneuvers. The average positive classification for the fall-like maneuver was 74%. The harsh braking maneuver had an average positive classification of 80%. In the case of the degraded track maneuver, the system showed an average positive classification of 85%. For acceleration on a curve, the average positive classification was 77%. Finally, acceleration on a straight line had an average positive classification of 77%.  
Overall, the system's performance is acceptable for the tested maneuver types. It is worth mentioning that several factors might have affected the system's performance and contributed to the observed variations. Firstly, the system's performance heavily relied on the expertise of the rider, which could be a limitation, as none of the participants were licensed motorbike drivers, thus lacking the finesse needed to perform maneuvers accurately. Furthermore, the dataset used for evaluation was conducted on a single track by one driver, which might limit the generalizability of the results. Additionally, the testing environment was confined within the university campus and thus may not fully reflect real-world scenarios, which might have impacted the system's performance under different  
Figure 5: Confusion matrix of the decision tree model.  
conditions. Due to these limitations, the system's performance evaluation results may not be as accurate as desired. However, the preliminary tests suggest that the system has the potential to be a valuable tool for motorcycle safety.  
Table 2 compares the proposed system with other machine learning-based systems documented in the literature.  
Notably, [27; 28] primarily focus on classifying normal driving behaviors: left turn, right turn, U-turn, and going straight in [27] and left turn, right turn, stopping, and going straight in [28]. These works only address routine driving activities. Moreover, only five of the identified maneuver types in [26] are risky driving behaviors, while the other four types are normal driving behaviors. Our model, in contrast, concentrates on detecting a wider range of nine risky maneuver types, extending the classification beyond normal maneuver types. Our system's approach is intentionally designed to be more general, unlike the solution proposed in [26], which demonstrates optimal performance at a specific speed of 30 km/h and was trained on a relatively small dataset comprising 718 samples. Instead, our system utilizes over 90,000 data samples and does not impose speed limitations. The preliminary tests of our proposed system were intentionally conducted across various speed scenarios to assess its adaptability to real-world motorcycle scenarios.