## 8 Implications and Limitations
\begin{table}
\begin{tabular}{c c} \hline \hline
**Aspect** & **Exemplary Commuting Feedback** \\ \hline Reduction of traffic jams & “People will find easier routes to unknown places and will rarely be late. \\  & - & To reduce emissions \\  & - & Less traffic jams” \\ \hline Reduction in traffic carbon emissions & “Reduction in polluting emissions and environmental benefits. Less usage of personal vehicles”. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Impact of the multimodal travel guidance system on commuters’ travel behavior. be used (e.g., inductive loop inductors, radar, cameras, acoustic sensors, infrared sensors, and so on [71]); and (3) the purpose of using the sensors [72]. The costs of road sensing and monitories technologies ranges from 385 to 26,000 USD per unit [73]. In fact, there are advantages and disadvantages to using different road sensing technologies and, thereby, making an accurate estimation of the cost of our system is not a straightforward activity. However, we recommend installing road sensors for the main road segments and frequent bottlenecks, where high traffic volumes are anticipated. The data collected from these transport segments should supply sufficient lead information for the system to predict and organize the traffic to an acceptable degree. There are further limitations that we would like to acknowledge here. The duration of field experiments could have been prolonged to observe more realistic and stable route calculations and predictions by the proposed system in different scenarios. Moreover, the number of commuters who took part in the experiments could be increased to solidify the findings of the study. The trial studies were conducted in two European cities with a specific set of conditions and infrastructure. The expansion of the trial studies to other metropolitan cities with huge and varying infrastructure, like London or Paris, could have properly tested the system reliability. Likewise, modelling a bigger city requires stronger computing power and infrastructure to simulate its transport segments. Furthermore, the focus of this study was not on route optimization with respect to execution time; therefore, only the k-shortest path Dijkstra algorithm was deployed. Some other algorithms could have been tested to observe the change in the route predictions and optimization of travel behavior. The main purpose of the system is to convince commuters to take eco-friendly means of transport; however, we also provide the fastest route for those commuters who do not wish to take public transport. The recommender system uses estimations of future journey times (refer to Figure 1) instead of actual journey times to produce the fastest route. Travel time predictions are received every 30 min. This way, traffic is actually dispersed from bottleneck areas before traffic jams occur. However, we did not calculate the time reduction in the overall travel time. We believe this necessitates a separate experiment. Our architecture conducted macroscopic modelling where the whole road segments were modeled; however, the microscopic modelling, at the level of individual vehicles, could have been included to inspect its impact on the overall behavior of the system and the route recommendations. Lastly, a follow-up study with the users should have been conducted to accurately determine whether our system truly has a long-lasting impact on the choice of transportation modes of the commuters.