## 7 Commuters' Acceptance of the Multimodal Traffic Recommendation System
### Field Trials
When analyzing the travel times in both cities, we found that commuting peaks coincided with the morning (07:00-09:00 local time) and evening rush hours (16:00-18:00 local time). We calculated the average CO\({}_{2}\) emissions per trip from individual commuters in phase one. Overall, in 85% of the trips made in Nottingham and Sofia, users chose to commute using public transport (i.e., the eco-friendliest option), while the remaining trips were commuted via the fastest routes. A key metric for measuring the accuracy of our multimodal traffic management system is the estimated average travel time. On average, the difference between actual travel time and predicted travel time was (\(-\)9) min in Nottingham and (+0.5) min in Sofia. So, trips were faster than predicted in Nottingham and slightly later than predicted in Sofia. In addition to the quantitative analysis, we carried out qualitative analysis of the feedback and reactions of the commuters with respect to the influence of the route guidance on their commuting experience. We aimed to find evidence of the effectiveness of our approach in inflicting travel behavior change. To motivate the analysis, we posited several questions as follows:  
* How do commuters' perceptions of the usefulness and usability of a multimodal route guidance system impact its use and adoption? To answer this question, we conducted semi-structured interviews with 14 participating commuters (i.e., 12 commuters from Sofia and 2 commuters from Nottingham) about their opinions and attitudes concerning our concept and its influence on their travel  
Figure 17: Preferences of modes of transport by the trial participants. Figure 18: From-to locations of our trial commuters in (**a**) Nottingham (left) and (**b**) Sofia (right). behavior. The commuters were eight males and six females, and their age ranged between 26 and 40 years. Table 4 summarizes the general reactions of the commuters towards the concept of the multimodal traffic management system. We also asked the commuters to rate their perception toward several aspects of the multimodal system. Generally, the respondents agreed that the multimodal traffic management system is useful for promoting sustainable travel behavior. Table 5 shows that the route planning and guidance app is perceived as easy to use (5.93) and provides clear interaction (5.93). More importantly, the participating commuters perceived the app as useful for the purpose of commuting (5.57). Finally, we asked our commuters about the potential impact of multimodal travel systems on promoting sustainable travel behavior and the environment (see Table 6). Moreover, commuters indicated their intention to continue using these types of travel information systems (5.92/7). \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Theme** & **Examples of Commuter Impressions** \\ \hline Real-time traffic information provision & _“The ideas of this project are very useful providing real information for public transport traffic situation““System provides real-time information for the public transport system. More reliable because the source of information comes directly from the public transport system/SUMC”_ \\ \hline Route selection & _“Useful app for people from other cities, to make it easy when choosing routes for public transport“”Yes, it provides different routes, and I am able to choose the most suitable option for me”._ \\ \hline Multimodal transport & _“It gave me new ideas for traveling with different modes of public transport”._ \\ \hline Eco-friendliness & _“It encourages me to use other transport modes as offers me green type of transport””System shows CO\({}_{2}\) emissions for every type of transport”_ \\ \hline Traffic jam reduction & _“Avoided traffic congestions and busy junctions”._ \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Commuters’ impressions about the multimodal recommender system. SUMC, Sofia Urban Mobility Center.