Existing navigation applications such as Google Maps, Apple maps provide a core service to users to find the shortest path or the path that takes the least amount of time towards a user's destination. The applications and other research efforts have been sought to include other features such as 3D maps, neighboring facilities, traffic information, and multi-modal alternate route suggestion based on user constraints. None of these, however, take into account an important factor: "user safety while commuting". Although the common perception of street crime is that it is primarily a problem in the third-world countries, current popular hashtags or topics (e.g. "blacklifematters") indicate that it is now prevalent in other parts of the world as well. Even existing multi-modal alternative route recommender systems are incapable of adapting to a dynamic set of safety features, are unable to provide new safe path updates in response to real-time commuter responses, and take little or no account of historical on-road events when designing the safe algorithm. In light of the above background, we begin by developing a generic framework, namely On-road Risk Minimization Problem (ORMP). We then introduce a dynamic population-based algorithm, that we call Safe Path for Everyone (SPaFE), that solves ORMP using multi-modal historical and crowdsourced data. Finally, our extensive empirical results demonstrate that SPaFE markedly outperforms the state-of-the-art.

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In the age of information technology, location-based services such as Google Maps, Bing Maps, and Apple Maps have become popular for navigation and traffic information. These services usually consider the shortest path, traffic information, nearby places, and multi-modal alternative route suggestions based on a user's constraints. Nevertheless, these services do not always provide the best choice in terms of "user safety". Recently, some research and mobile applications have considered safety issues. Notably, none of these are capable of adapting to dynamic, conflicting safety features and real-time user feedback. Recently, a population-based algorithm called "SPaFE" has been introduced, which deals with crowdsourced data along with historic data. This population-based approach, however, does not give more weight to recent feedback than to earlier feedback and does not consider updated crime reports. Furthermore, this approach does not consider distance and performs poorly in the area with insignificant or zero feedback. Considering the above background, we have introduced the population based algorithm "CrowdSPaFE" to adapt with dynamic crime reports, latest feedback, navigation in areas with insignificant feedback, and a trade-off between distance and safety factors. Finally, our empirical results of the "CrowdSPaFE" algorithm depict that it significantly outperforms the state of the art.