Chapter 9
Conclusion and Future Work
The study aimed to determine how incorporating driving suitability in route rec- ommendations affected route choice behavior. We provided a simple framework for accomplishing this which can be adapted to other kinds of user preferences:
first, we determined which road features contribute to road comfort and safety.
Then, we built a dataset for us to be able to perform remote sensing on our tar- get road network. We adapted the U-Net architecture - originally designed for biomedical segmentation - to segment road images and determine the presence of road surface irregularities which contribute to the road comfort score. We then calculated the perceived brightness of various regions of a nighttime road image to determine road safety. Third, we developed a routing system that incorporated these factors in the route generation process and created a navigation app that used the system for route recommendations. Lastly, we ran a field study using this app to determine how drivers behave with these route recommendations presented to them.
We were able to show that using image segmentation to detect road surface irregularities is a viable method of determining road comfort score, yielding an accuracy between 48% and 95% depending on the class label. On the other hand, we simply used the perceived road brightness as a proxy for safety. Feeding these features through various regression techniques yielded a best RMSE of 1.16 for comfort and 1.22 for safety. When we we generated driving suitability scores for our road network and generated route recommendations for typical inter-city and intra-city points, we were able to find the good balance of road comfort and safety.
Comparing these generated routes to the top recommendations from Google, our routes provided a better average comfort, safety, and distance score, at the expense of having more turns. When we ran a field study, we found that in practice, road surface irregularities and road lighting are not enough to completely represent
99
what drivers consider comfortable and safe. Participants reported other static features such as road width and turn angles, as well as temporal features such as the presence of pedestrians and specific types of vehicles, as factors affecting their perceptions of comfort and safety. Additionally, properties of the area where routes pass through, such as whether or not it is a slum area or an area known to be shady to the driver also contribute to the driving suitability perception. None of the most popular driving navigation apps provide options or optimize for these driver-specific preferences. Whether the route is driving suitability optimized or Google-recommended, drivers still deviate from the recommended routes between 40% and 58% of the time.
To improve upon the image segmentation technique, we can identify more specific classes, such as differentiating between types of paved roads and vehicles, which drivers have different preferences for. Using a larger ground truth will also help with the class label prediction accuracy. Additionally, the Cityscapes study frequently accepts benchmarks for image segmentation techniques against their dataset, and it might be possible to explore these state-of-the-art techniques de- spite the difference in use cases. When modeling both comfort and safety scores, more features can be taken into account when modeling. For example, as proxy for shady or slum areas, we can use real estate pricing, the crime index, or popula- tion density for each area. Additionally, we recommend to explore the possibility of a multi-modal approach, such as combining road images with inertial sensor data for measuring road vibrations. As far as the routing systems go, we also recommend to use a different approach to pathfinding such as Contraction Hier- archies (Geisberger et al., 2008) or Hub Labeling (Abraham et al., 2011), which requires more pre-processing time and computing resources, but offer a much faster query time. Additionally, these algorithms exploit the hierarchical nature of roads which seems to coincide with some feedback from field study volunteers, as well as performing similarly to how the leading apps recommend routes.
Based on feedback from our field study, there are also several ways to improve upon studies using navigation apps. One important recommendation is that al- ternative navigation apps will have to support real-time information about roads, including traffic and temporal features. These features are now a staple in modern navigation apps and in some cases, drivers look to the most popular apps only for this data, and not for the directions themselves. It only seems fitting that real- time information be included in future route choice analysis studies, at least when comparing against other applications. Explicit options for routing preferences can also be added. For instance, when routing based on safety and comfort, the type or size of vehicle can affect the safety or comfort of the driver and can be taken into account. Another possibility is adding an option for fragile payloads, which might route drivers away from severely uncomfortable roads.
100
