In this work, we present a system that generates customized pedestrian routes entirely based on data from OpenStreetMap (OSM). The system enables users to define to what extent they would like the route to have green areas (e.g., parks, squares, trees), social places (e.g., cafes, restaurants, shops) and quieter streets (i.e., with less road traffic). We present how the greenness, sociability, and quietness factors are defined and extracted from OSM as well as how they are integrated into a routing cost function. We intrinsically evaluate customized routes from one-thousand trips, i.e., origin–destination pairs, and observe that these are, in general, as we intended—slightly longer but significantly more social, greener, and quieter than the respective shortest routes. Based on a survey taken by 156 individuals, we also evaluate the system’s usefulness, usability, controlability, and transparency. The majority of the survey participants agree that the system is useful and easy to use and that it gives them the feeling of being in control regarding the extraction of routes in accordance with their greenness, sociability, and quietness preferences. The survey also provides valuable insights into users requirements and wishes regarding a tool for interactively generating customized pedestrian routes.

Background Walking is a good and simple way to increase people's energy expenditure, but there is limited evidence whether the neighborhood environment correlates differently with recreational and transportation walking. AimTo investigate how recreational walking and transportation walking are associated with the natural and built environmental characteristics of the living environment in the Netherlands, and examine the differences in their associations between weekdays and weekends. Method and data We extracted the total duration of daily walking (in minutes per person) for recreation and transportation of adults aged 18 years and above from the Dutch National Travel Survey 2015-2017 (N = 65,785) and analyzed it as an outcome variable. Objective measures of the natural (i.e., normalized difference vegetation index (NDVI), blue space and meteorological conditions) and built environment (i.e., crossing density, land-use mix, and residential building density) around respondents' home addresses were determined for buffers with 300, 600, and 1000 m radii using a geographic information system. To assess associations between recreational and transportation walking and the environmental exposures separately, we fitted Tobit regression models to the walking data, adjusted for multiple confounders. ResultsOn weekdays, people living in areas with less NDVI, higher land-use mix, and higher crossing density were more likely to engage in transportation walking. Recreational walking was negatively associated with NDVI, blue space, crossing density, precipitation and daily average temperature. At weekends, land-use mix supports both recreational and transportation walking. A negative association appeared for NDVI and transportation walking. Daily average rainfall and temperature were inversely correlated with recreational walking. Sensitivity tests indicated that some associations depend on the buffer size. ConclusionsOur findings suggest that the built and natural environments are differently associated with people's recreational and transportation walking. We also found differences in the walking-environment associations between weekdays and weekends. Place-based policies to design walking-friendly neighborhoods may have different implications for different types of walking.

Determining safe and fast routes for first responders is an important issue in a disaster response. Especially when different types of disasters (e.g., toxic plumes, fires, and floods) occur and affect transportation networks simultaneously, special routing strategies (e.g., detour) would be needed to ensure the safety for responders. On the other hand, after disasters happen, a quick response time is required, and the responders should move as fast as possible and even go through certain obstacles to reach the disaster sites to deliver emergency services. In this paper, we study path planning through moving obstacles, taking into account the influence of obstacles on the status of road networks and the speed of rescue vehicles. A set of algorithms is proposed to deal with not only geometries but also the properties of moving obstacles to support route generation. Based on the Dijkstra algorithm, a new routing algorithm is designed and developed, which aims at minimizing the risk while constraining the travel time of routes. We validate our approach with a set of experiments on some navigation cases. The experimental results show the promise of the algorithm in the generation of feasible and safe routes for first responders to pass through moving obstacles.

With a rapidly-growing volume of volunteered geographic information (VGI), there is an increasing trend towards using VGI to provide location-based services. In this study, we investigate using OpenStreetMap data to integrate indoor and outdoor route planning for pedestrians. To support indoor and outdoor route planning, in this paper, we focus on the connections inside buildings and propose a data model, which uses OSM primitives (nodes, ways and relations) and tags to capture horizontal and vertical indoor components, as well as the connection between indoor and outdoor environments. A set of new approaches is developed to support indoor modeling and mapping. Based on the proposed data model, we present a workflow that enables automatic generation of a routing graph and provide an algorithm to calculate integrated indoor-outdoor routes. We applied our data model to a set of test cases. The application results demonstrate the capability of our data model in modeling built environments and its feasibility for the integration of indoor and outdoor navigation.

Noise pollution is one of the main stressors in urban environments, having negative impacts on people’s quality of life and health. For some groups of citizens, such as school children, patients, and elders, there is a need to support them in finding pedestrian routes in noise polluted areas of cities. In this paper, we focus on the estimation of traffic noise, and present an approach to provide quiet routing services, taking into account the estimated noise levels of roads. By combining Volunteered Geographic Information, official socio-economic data, and open-access GPS trajectory data, we develop a set of traffic related variables, and apply machine learning methods to perform traffic volume estimations. Given the estimated traffic information, an existing traffic noise model is then employed to derive the noise polluted areas. For generation of quiet routes, a new routing algorithm is proposed based on the Dijkstra algorithm. It minimizes the exposure of pedestrians to traffic noise pollution while taking into account the route distance constraint. We apply our quiet routing approach to the city of Heidelberg (Germany). The application results demonstrate the efficacy of our algorithms in the generation of quiet routes customized to pedestrian preferences.

In this paper, we study path planning for first responders in the presence of uncertain moving obstacles. To support the path planning, in our research we use hazard simulation to provide the predicted information of moving obstacles. A major problem in using hazard simulation is that the simulation results may involve uncertainty due to model errors or noise in the real measurements. To address this problem, we provide an approach to handle the uncertainty in the information of moving obstacles, and apply it to the case of toxic plumes. Our contribution consists of two parts: 1) a spatial data model that supports the representation of uncertain obstacles from hazard simulations and their influence on the road network and 2) a modified A* algorithm that can deal with the uncertainty and generate fast and safe routes passing though the obstacles. The experimental results show the routing capability of our approach and its potential for the application to real disasters.