## 3 Results and Discussion
### Route Choice Toolkit  
In the route planning toolkit, the minimum travel path algorithm is complemented by the impendence of vehicular flow, specific junction details, and meteorological conditions. It was applied to the city centre of Newcastle city, depicted in Figure 4. The city centre was primarily divided into two zones. The first zone (\(Z_{1}\)) housed two major universities with more than 65,000 students, around 7000 staff, and the Newcastle civic centre. The Newcastle civic centre houses all the principal government offices and the family court. The second zone (\(Z_{2}\)) houses the major tourist destinations and nightlife venues. Newcastle is known for its nightlife across Europe, making it one of the most student-friendly cities in Europe. Two main routes connect the two zones, the first route (\(R_{1}\)) and the second route (\(R_{2}\)). Both have distinctive infrastructure features. The safety toolkit developed in the study was applied to both routes. Application of the study's findings leads to the conclusion that the route choice for a cyclist does not only follow the minimum path algorithm; it depends upon the perceived safety that a route may offer. The two equations were obtained subsequently (Equations (1) and (2)). The perceived safety depends on the traffic flow, the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Importance** & **Normalised Importance** \\ \hline Rider journey purpose & 0.058 & 100.0\% \\ \hline Hour & 0.054 & 92.8\% \\ \hline Vehicle Manoeuvre & 0.052 & 88.3\% \\ \hline Road Location of Vehicle & 0.049 & 83.7\% \\ \hline Light Conditions & 0.046 & 78.9\% \\ \hline Month & 0.045 & 76.9\% \\ \hline Weather & 0.045 & 76.7\% \\ \hline Junction Location of Vehicle & 0.045 & 76.3\% \\ \hline Junction Detail & 0.044 & 75.6\% \\ \hline Breath Test & 0.043 & 74.2\% \\ \hline Speed Limit & 0.043 & 72.9\% \\ \hline Number of Vehicles & 0.043 & 72.8\% \\ \hline Special Conditions at Site & 0.042 & 72.1\% \\ \hline Carriageway Hazards & 0.042 & 71.4\% \\ \hline Day & 0.041 & 70.6\% \\ \hline Road Type & 0.040 & 69.0\% \\ \hline
1 st Road Class & 0.040 & 68.9\% \\ \hline
2 nd Road Class & 0.039 & 67.5\% \\ \hline Road Surface Condition & 0.037 & 62.5\% \\ \hline Skidding and Overturning & 0.036 & 61.3\% \\ \hline Junction Control & 0.034 & 58.1\% \\ \hline Driver Gender & 0.027 & 46.3\% \\ \hline Weekday or Weekend & 0.027 & 45.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Normalized importance of the input variables. number of conflicts, and infrastructure route parameters. This perceived safety was varied and changes with changes in lighting and meteorological conditions. These results agree with the route choice models developed for Dublin city (see [17; 22]). This implies that the regional diversity in route intent and traffic flow conditions are significant variables that impact the safe use of infrastructure for a particular age group. This is a contribution to the present knowledge for the delivery of the last-mile mobility service. Hence, while designing a last-mile delivery smart mobility system, the route selection needs to consider such variability and choices. This is critical for promoting sustainable mobility that can help users make better-informed decisions based on their preferences rather than designing a system and expecting them to comply. Such a shift in policy and decision-making is critical for a broader stakeholder engagement for better living city spaces. The results from this study can be integrated into the new 15-min city concept that is being explored (see [23]). Presently different electric mobility hubs are being trialled across western Europe to estimate the benefits of such a scheme (see [24]). The variability of a cyclist should be modelled in such a scheme and integrated with an intelligent real-time interactive platform (with a graphical user interface) for both mode and route choices.