Urban navigation beyond shortest route: The case of safe paths

Abstract

Advancements in mobile technology and computing have fostered the collection of a large number of civic datasets that capture the pulse of urban life. Furthermore, the open government and data initiative has led many local authorities to make these datasets publicly available, hoping to drive innovation that will further improve the quality of life for the city-dwellers. In this paper, we develop a novel application that utilizes crime data to provide safe urban navigation. Specifically, using crime data from Chicago and Philadelphia we develop a risk model for their street urban network, which allows us to estimate the relative probability of a crime on any road segment. Given such model we define two variants of the SAFEPATHS problem where the goal is to find a short and low-risk path between a source and a destination location. Since both the length and the risk of the path are equally important but cannot be combined into a single objective, we approach the urban-navigation problem as a biobjective shortest path problem. Our algorithms aim to output a small set of paths that provide tradeoffs between distance and safety. Our experiments demonstrate the efficacy of our algorithms and their practical applicability.

Towards a Persuasive Recommender for Bike Sharing Systems: A Defeasible Argumentation Approach

“…Rather than focusing on monetary incentives, we attempt to appeal to users by considering their characteristics and travel preferences. Recently, scholars have discovered that distance is not the only factor that humans take into consideration when preferring a route over another [17][18][19]. In fact, some studies have suggested that some individuals may even deviate up to approximately a 30% from the shortest path in exchange for a pleasant journey [20].…”

“…Users suggested that proposed routes were more apt for cheering up (i.e., happy route), a stressful day (i.e., quiet route), and for sightseeing (i.e., beautiful route). Later on, Galbrun et al [18] proposed an algorithm that calculates safer paths as an alternative to the shortest path. For that, the authors employ open crime data from the city of Chicago to map those city segments with the highest crime rates.…”

“…The rationale behind the recommendations provided by the persuasion system may reside in the balance of the bike sharing system, or the preferences of the user. Differently to [17][18][19], instead of employing a single criteria to recommend routes, the proposed system can incorporate a variety of criteria such as the ones mentioned above, the balancing of the system, and other criteria. This result is achieved by providing a defeasible logic-based persuasion engine that is capable of aggregating the results from multiple recommendation rules.…”

Where Google Maps Doesn’t Go: Mobility Decisions of People in Underserved Neighborhoods †

“…We consider the factors that neighbors deemed necessary in the interview, and we incorporate them as criteria for generating routes suggestions. For example, a user might want to avoid certain parts of a route based on the (in)security of the area [14], the walkability (which we define further on), the closeness to bus routes and many other factors that are not usually considered when calculating routes for users.…”

Methodology for the navigation optimization of a terrain-adaptive unmanned ground vehicle

“…The criterion for optimization can be chosen depending on the desired goal (minimum energy expended, minimum time, minimum torque required, or one combining these). [26][27][28] The algorithm also works for non-smooth, nondifferentiable profiles. In fact, it performs a discretization of the track, calculates the slope of the ground at the ground-wheel contacts.…”