Intelligent Real-Time Modelling of Rider Personal Attributes for Safe Last-Mile Delivery to Provide Mobility as a Service

Malik, Faheem Ahmed; Dala, Laurent; Khalid, Muhammad; Busawon, Krishna

Abstract

This paper develops an intelligent real-time learning framework for the last-mile delivery of mobility as a service in city planning, based upon safe infrastructure use. Through a hybrid approach integrating statistics and supervised machine learning techniques, knowledge-driven solutions based on the specific user rather than generalized safe mobility practices are suggested. One of the most important aspects influencing transport mode and route selection, and safe infrastructure usage, i.e., the age of the user, is simulated. This is because this variable has been described in the literature as a significant variable. Nonetheless, few works deal with such modelling or the learning system. The learning system was applied in the Northumbria region of England’s northeast as a case study. It comprised four building toolkits: (a) Input toolkit, (b) Safety Predictive toolkit, (c) Variable causation toolkit, and (d) Route choice toolkit. An accurate dynamic road safety model and understanding of the critical parameters influencing bicycle rider safety is created. The developed deep learning model’s average distinguishing power to reliably predict the riskiest age group was 95%, with a standard deviation of 0.02, suggesting a good prediction accuracy across all age groups. According to the study’s findings, different infrastructural networks represent varying risks to bicycle riders of different ages. The rider’s age impacts how other road users engage with them. The regional diversity in trip intent and traffic flow conditions were significant elements influencing the safe use of infrastructure for a specific age group. The study’s findings have the potential to considerably influence infrastructure route selection, modelling, and planning. The constructed model, which integrates the rider’s fragility, sensitivity to externalities, and the varied safety impact dependent on its features, may even be used for the infrastructure still in the planning/design phase. It is envisaged that this research would aid in adopting sustainable (green) transportation options and the last-mile delivery of mobility as a service. Future work should aim to uncover the sensitivities of a rider from different countries and make a baseline comparison scenario.

Citation

Malik, F. A., Dala, L., Khalid, M., & Busawon, K. (2022). Intelligent Real-Time Modelling of Rider Personal Attributes for Safe Last-Mile Delivery to Provide Mobility as a Service. Applied Sciences, 12(20), Article 10643. https://doi.org/10.3390/app122010643

• Journal Article Type: Article
• Acceptance Date: Oct 18, 2022
• Online Publication Date: Oct 21, 2022
• Publication Date: 2022
• Deposit Date: Apr 29, 2025
• Publicly Available Date: Apr 29, 2025
• Journal: Applied Sciences
• Electronic ISSN: 2076-3417
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Issue: 20
• Article Number: 10643
• DOI: https://doi.org/10.3390/app122010643
• Keywords: mobility as a service; safe mobility; sustainable mobility; green and intelligent mobility; machine learning; rider safety
• Public URL: http://researchrepository.napier.ac.uk/Output/4248182