Better Routing in Developing Regions:Weather and Satellite-Informed Road Speed Prediction

Inaccurate digital road networks significantly complicate the use of analytics in developing, data scarce, environments. For routing purposes, the most important characteristic of a digital road network is the information about travel times/speeds of roads. In developing regions, these are often unknown, and heavily dependent on the weather (e.g., rainfall). This may, for instance, cause vehicles to experience longer travel times than expected. Current methods to predict the travel speeds are designed for the short upcoming period (minutes or hours). They make use of data about the position of the vehicle, the average speed on a given road (section), or patterns of trafic flow in certain periods, which are typically not available in more developing regions. This paper presents a novel deep learning method that predicts the travel speeds for all roads in a data scarce environment using GPS trajectory data and open-source satellite imagery. The method is capable of predicting speeds for previously unobserved roads and incorporates specific circumstances, which are characterized by the time of the day and the rainfall during the last hour. In collaboration with the organization PemPem, we perform a case study in which we show that our proposed procedure predicts the average travel speed of roads in the area (that may not exist in the GPS trajectory data) with an average RMSE of 8.5 km/h

Enhancing Digital Road Networks for Better Operations in Developing Countries

Data scarcity in developing countries often significantly complicates the use of analytics to address development challenges. One of the most fundamental data structures needed in operations management is digitized road data; e.g., a poorly digitized road network significantly reduces our ability to optimize trade of micro-enterprises (SDG 8) and placement of hospitals (SDG 3). Unfortunately, current methods to extend or create digital road networks are not well-adapted to regions with sparse geospatial data and, as a result, road networks are often poorly represented digitally in less-developed regions such as rural areas of developing countries. To address this, we propose a novel method to create digital road networks in regions with sparse geospatial data, by adapting existing methods to ensure they extract as much information as possible from the limited available data. Our proposed method combines projection-based incremental insertion methods that incrementally add new information to existing road networks when it becomes available, with a simple edge adjustment procedure that allows edge geometries to be improved when more information becomes available. This method is well-suited to either incrementally adjust a large existing road network (e.g., OSM) or combine multiple sources of road networks in regions with sparse data (e.g., OSM and eStrada, a dataset provided by the World Bank). Our method significantly improves the digital road network for smallholder farmers in Indonesia, where only 40% of the origin-destination pairs in our dataset were previously digitized. In a case study of optimizing geospatial accessibility to healthcare in Timor-Leste, we find that the improved road network detects an additional 5% of people to be in the vicinity of a hospital

Blood pressure in the first 6 hours following endovascular treatment for ischemic stroke is associated with outcome

Background and Purpose: Optimal blood pressure (BP) management in the acute phase of ischemic stroke remains an unresolved issue. It is uncertain whether guidelines for BP management during and after intravenous alteplase can be extrapolated to endovascular treatment (EVT) for stroke due to large artery occlusion in the anterior circulation. We evaluated the associations between systolic BP (SBP) in the first 6 hours following EVT and functional outcome as well as symptomatic intracranial hemorrhage. Methods: Patients of 8 MR CLEAN (Multicenter Randomized Controlled Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands) Registry centers, with available data on SBP in the 6 hours following EVT, were analyzed. We evaluated maximum, minimum, and mean SBP. Study outcomes were functional outcome (modified Rankin Scale) at 90 days and symptomatic intracranial hemorrhage. We used multivariable ordinal and binary regression analysis to adjust for important prognostic factors and studied possible effect modification by successful reperfusion. Results: Post-EVT SBP data were available for 1161/1796 patients. Higher maximum SBP (per 10 mm Hg increments) was associated with worse functional outcome (adjusted common odds ratio, 0.93 [95% CI, 0.88-0.98]) and a higher rate of symptomatic intracranial hemorrhage (adjusted odds ratio, 1.17 [95% CI, 1.02-1.36]). The association between minimum SBP and functional outcome was nonlinear with an inflection point at 124 mm Hg. Minimum SBP lower and higher than the inflection point were associated with worse functional outcomes (adjusted common odds ratio, 0.85 per 10 mm Hg decrements [95% CI, 0.76-0.95] and adjusted common odds ratio, 0.81 per 10 mm Hg increments [95% CI, 0.71-0.92]). No association between mean SBP and functional outcome was observed. Successful reperfusion did not modify the relation of SBP with any of the outcomes. Conclusions: Maximum SBP in the first 6 hours following EVT is positively associated with worse functional outcome and an increased risk of symptomatic intracranial hemorrhage. Both lower and higher minimum SBP are associated with worse outcomes. A randomized trial to evaluate whether modifying post-intervention SBP results in better outcomes after EVT for ischemic stroke seems justified.Neuro Imaging Researc