Network Analysis and Routing with QGIS

Pella, Hervé; Osé, Kenji

doi:10.1002/9781119476726.ch4

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…Then, from each sidewalk segment, some geometric attributes were computed, that have to be considered for an accessibility map. Each sidewalk segment was then stored into a vector layer and used as network for routes computation implementing processes already existing in the software QGIS (Pella and Ose, 2018). A summary of the workflow is presented in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

Accessible Path Finding for Historic Urban Environments: Feature Extraction and Vectorization From Point Clouds

Treccani

Díaz-Vilariño

Adami

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Sidewalk inventory is a topic whose importance is increasing together with the widespread use of smart city management. In order to manage the city properly and to make informed decisions, it is necessary to know the real conditions of the city. Furthermore, when planning and calculating cultural routes within the city, these routes must take into account the specific needs of all users. Therefore, it is important to know the conditions of the city’s sidewalk network and also their physical and geometrical characteristics. Typically, sidewalk network are generated basing on existing cartographic data, and sidewalk attributes are gathered through crowdsourcing. In this paper, the sidewalk network of an historic city was produced starting from point cloud data. The point cloud was semantically segmented in ”roads” and ”sidewalks”, and then the cluster of points of sidewalks surfaces were used to compute sidewalk attributes and to generate a vector layer composed of nodes and edges. The vector layer was then used to compute accessible paths between Points of Interest, using QGIS. The tests made on a real case study, the historic city and UNESCO site of Sabbioneta (Italy), shows a vectorization accuracy of 98.7%. In future, the vector layers and the computed paths could be used to generate maps for city planners, and to develop web or mobile phones routing apps.

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Section: Methodsmentioning

confidence: 99%

Accessible Path Finding for Historic Urban Environments: Feature Extraction and Vectorization From Point Clouds

Treccani

Díaz-Vilariño

Adami

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…In optimizing fire vehicle routes, this study uses GIS network analysis over existing methods (Chen et al 2012, Qin et al 2017, Pella and Ose 2018, Anuar et al 2021. The GIS based network analysis tool is a versatile approach for resolving any cost-distance queries.…”

Section: Optimized Fire Vehicle Routesmentioning

confidence: 99%

Fire Vehicle Route, Response Time, and Service Coverage Optimizations in Pekojan Urban Village,tambora Subdistrict Fire Hotspot of Jakarta City Indonesia

Zainal

Lestari

Gunawan³

et al. 2022

PREPOTIF

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One challenge in managing fire hazards in an urban setting is how to optimize the fire service route, increase the response time, and increase service coverage. Recently, this challenge is becoming imminent due to road traffic congestion and insufficient road widths that are common in populated cities in the Southeast Asia regions. One of the urban fire hotspots in populated Jakarta City is Pekojan Urban Village, Tambora Subdistrict. This subdistrict is served by Angke Fire Station located in Pekojan’s southwestern parts. Then this research aims to evaluate and compare optimized routes for fire vehicle dispatched from Angke Fire Station to serve 12 neighborhood units (in Bahasa is RW) in Pekojan. The method used the route optimization and network analysis tools in Geographic Information System (GIS) and its related geospatial data including neighborhood units, road networks, traffic congestion, and fire station locations. Geospatial network analysis of data by GIS has an advantage as a method to design and analyze the routing strategy and determine the most optimized route for fire vehicles. Based on the results and with the fire vehicle speed of 40 km/h, the average optimized route distances to travel from the fire station to RWs were 1.092 km (95%CI: 0.888-1.3 km) with an average response time of 1.638 minutes (95%CI: 0.869-2.41 min.). According to the GIS, model, response time of 1 minute only covers 22.77% of Pekojan areas. By increasing response time to 2 minutes, then fire vehicle can cover 98.9% of Pekojan area (AIC= 0.06). Despite the fact that the fire vehicle routes and response times can be optimized, those routes are challenged by the road traffic congestion. This congestion limits the speeds of fire vehicles to less than 20 km/h, as observed in 11.59% of the optimized routes. The service coverages of fire vehicles was also limited due to the narrow street.

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“…Each small town is linked to the Civil Protection headquarters, connecting one origin to several destinations. Each O-D pair P includes n P paths; each p 2 R n P can be created using existing routing algorithms (31). Each path is made up by a set of links k = 1.m (Figure 2), each characterized by a traffic volume v k .…”

Section: Setting Up Of the Gis Environment And Preliminary Analysis Of The Area Under Studymentioning

confidence: 99%

Detour-Impact Index Method and Traffic Gathering Algorithm for Assessing Alternative Paths of Disrupted Roads

Pucci

Puppio

Sousa

et al. 2021

Transportation Research Record

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Infrastructure plays a key role in society. Recent collapses of bridges have underlined their importance for road functionality, causing disruptions to commuters and emergency vehicles. Major issues arise on rural roads, where the lack of redundancy leads to the isolation of entire communities. Actual approaches to assess the resilience of countryside roads rely on the availability of specific datasets, limiting their practical application; this issue is typically related to traffic data. This research aims to propose innovative algorithms to assess the road network’s vulnerability in rural areas, including a novel traffic data collection process and its calibration. The aggregate metric is called Detour-Impact Index (DII) and compares user costs before and after a disruptive event. The method uses traditional network-impact metrics in combination with a new algorithm that allows us to gather quantitative traffic data starting from qualitative information. User travel time showed good agreement between the proposed procedure and traditional web-based methods. Furthermore, the paper provides user delay costs functions accounting for traffic composition, trip purposes, vehicle operative costs, nonlinear volume–capacity relation, and average daily traffic. A significant aspect is the adaptability of this framework, as it is designed to be coupled with existing approaches. The method is demonstrated on a case study in Tuscany (Italy).

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Network Analysis and Routing with QGIS

Cited by 4 publications

References 7 publications

Accessible Path Finding for Historic Urban Environments: Feature Extraction and Vectorization From Point Clouds

Accessible Path Finding for Historic Urban Environments: Feature Extraction and Vectorization From Point Clouds

Fire Vehicle Route, Response Time, and Service Coverage Optimizations in Pekojan Urban Village,tambora Subdistrict Fire Hotspot of Jakarta City Indonesia

Detour-Impact Index Method and Traffic Gathering Algorithm for Assessing Alternative Paths of Disrupted Roads

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