Translating Roadway Alerts into Pedestrian Impedance Factors in Real-Time
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Bhosale, Kalyani Sanjay
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Abstract
The ITS4US Georgia project has established a navigation system for ADA mobility mode users that employs a complete paths network in OpenStreetMap (OSM) for use with OpenTripPlanner's (OTP’s) shortest path navigation system. Impedance factors that affect each ADA mode are assigned to each link in the complete paths network so that preferred paths can be found for each mode. For example, if the sidewalk is closed on one side due to maintenance, user may choose the other operational sidewalk which will be considered the preferable path for travel. Such alerts are typically not gathered or made available to improve accessible travel within a pedestrian network. Pedestrians usually encounter these closure events only when they are already navigating the affected path. This study uses crowd-sourced roadway alerts to associate the impact of roadway related events over pedestrian networks. For this research these roadway alerts are integrated with in the OSM network. The OSM-based ADA network used for this study was developed by the Georgia Tech ITS4US Spatial Analytics Team. The rectified OSM pedestrian network is represented in the ITS4US space-time-memory (STM) system in a graph database architecture (Amazon Neptune). This network assigns link-by-link impedance based on physical attributes (e.g. sidewalk width, surface quality, cross-slope, obstruction presence, etc.), asset condition (e.g. surface roughness, cracking) and traffic/roadway related events and alerts.
As mentioned, the overall route accessibility is also dependent on how the link performs during the roadway related events and alerts nearby, which is also considered the fundamental element of shortest-path routing, by adding impedance to such affected network links. For the ITS4US project, one source of roadway alerts information is Waze data, which can be spatially tied to the OSM network. Waze data is chosen based on its data coverage and attribute information for all road types. By analyzing dynamic data from Waze alerts, this research will identify and assesses sidewalk segments that appear to be affected by incidents, influencing routing decisions through impedance calculations based on alert severity (for example, the presence of an ambulance or fire engine that may impede the walking path). The research also explored the potential of Waze traffic data for more accessible travel, extending the analysis to adjacent roadways to optimize routing in real-time and mitigate hazards. The objective is to enhance the overall accessibility of the routes for vulnerable road users. Accessibility is determined by whether the alert affects the link and how it is affected (if the link is fully or partially obstructed by the alert). This research proposed a framework to associate the impact using spatiotemporal buffers conducted to identify the optimal spatial coverage of the Waze alert on the sidewalks. This algorithm models the nature of the Waze subtypes and their integration across space and time to detect link affected by alerts over time.
Challenges such as report reliability, incomplete descriptions, and disparities between actual incident locations and user-reported ones, underscore the need for cautious interpretation. Based on the results from two case studies, the scenario-based study concludes that for most cases the alert impedances are mostly effective to analyze new routes which are more accessible. While the case study considering the overall impact of Waze alerts in the study area concludes that the alternative routes are longer but offer greater unobstructed accessibility compared to the original route. These outcomes were attributed to the complete reliance on alert-based impedance factors. Since these factors are derived from roadway alerts, the impedance values were set higher to prioritize accessibility, accounting for sudden obstructions caused by the magnitude and nature of the roadway alerts. These final results are also subject to certain limitations such as links assigned to current attribute-based algorithm, impedance derivation, alerts impact coverage and alert accuracy determined for analysis. The limitations of the shortest path analysis included the use of ArcGIS Pro and its network, which typically identifies more desirable routes, but there are potential drawbacks to this approach. Despite employing impedance values for the analysis, if the network has not been adequately segmented into links for routing assignments, ArcGIS Pro may overlook such configurations and route based on the existing network structure. Nevertheless, by integrating real-time traffic alert information with spatial analysis, this research aims to associate the impact over the pedestrian network and improve the overall accessibility for vulnerable road users.
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Date
2024-12-08
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Thesis (Masters Degree)