Sidewalk Network Research Articles

A specialized inclusive road dataset with elevation profiles for realistic pedestrian navigation using open geospatial data and deep learning

Built environment characteristics can greatly influence pedestrians' route choices with factors beyond distance, such as accessibility, convenience, safety, and aesthetics, playing crucial roles. Although current navigation apps, such as Google Maps and Waze, have successfully provided driving directions, their navigation services are insufficient and sometimes unrealistic for addressing pedestrians' needs, largely due to the lack of dedicated pedestrian networks and the associated navigation algorithms. To address the research gaps, this paper proposes a novel approach that integrates freely available geospatial data and computer vision technology to create a specialized inclusive network dataset for outdoor pedestrian navigation. Moreover, a pedestrian navigation algorithm is developed to generate more practical “shortest” and “alternative” paths by incorporating various sidewalk attributes. We applied the method to create a pedestrian navigation network in Las Vegas. SpaceNet's open imagery dataset was used to extract Las Vegas's road networks. A virtual audit process assessed the visual and operational properties of the sidewalk networks using Google street-level images, evaluating factors including sidewalk presence, widths, surface types and conditions, missing curb ramps, greenery, protection from weather conditions, and lighting. Google Earth's open elevation data were used to analyze road elevation profiles as meaningful 3D indicators of sidewalk accessibility for wheelchair users. Further, additional geometric properties of the network, including road curviness, proximity to road intersections, and directional changes, were detected and analyzed. A navigation experiment conducted with individuals having varying mobility abilities, including regular pedestrians, older adults, and wheelchair users demonstrated the effectiveness of the newly developed network and algorithm in meeting the diverse needs of pedestrians.

Sidewalk‐based bicycle path network design incorporating equity in cycling time

AbstractMany cities find it difficult to claim enough land to build dedicated bicycle lanes. In response, this study proposes a novel framework to design a bicycle path network based on the existing sidewalks where selected sidewalk links are converted into eligible bicycle paths. The output will be a subset of the sidewalk links chosen to be converted to eligible bicycle paths with minimum cost such that all origin–destination (O‐D) pairs are connected with bicycle paths and cyclists from each O‐D pair can enjoy similar degrees of equity. The equity defined here is that cyclists from each O‐D pair will not need to travel excessively longer in time in the designed bicycle path network than in the original sidewalk network. A novel decomposition‐based dynamic dimensional search is proposed to solve the problem. The numerical experiments of a university campus and Clementi town in Singapore have shown our algorithm with varying equity parameter choices can provide tangible inclusive bicycle path network designs and improve as many as 80% equity in certain O‐D pairs with critical inequity issues.

OSM Sidewalkreator: A QGIS plugin for an automated drawing of sidewalk networks for OpenStreetMap

Sidewalks are a relevant part of the living space in urban environments but are still rarely mapped. In recent years, the mapping of sidewalks has grown in importance among the OSM and academic communities as a matter of concern for many UN SDGs. To cover this gap, we propose a GitHub-hosted, fully open-source QGIS Plugin entitled "OSM SidewalKreator" to automatically draw the geometries of sidewalks for OSM crossings and curb crossing interfaces. The plugin workflow encompasses the steps of input area selection; data fetching, data cleaning, sidewalk geometries generation; crossings and kerbs generation; optional sidewalk splitting; and data exporting. Furthermore, the tool gives the user the capacity to have control over the process. Our tests revealed that the proposed method embodied by the plugin surpasses the manual process in many contexts, highlighting completeness, topological and thematic accuracies. We conclude that deepening, improving, and increasing the amount of open sidewalk mapping, mainly in the widely available OSM, can be a valuable asset to improve the development of accessibility and mobility worldwide.

Cool walkability planning: Providing pedestrian thermal comfort in hot climate cities

Global warming and urbanization are increasing the number of people living in cities that experience extreme heat. This makes walking uncomfortable, unattractive, and unhealthy and causes travelers to drive for trips that could be made on foot. To address these problems hot-climate cities can create networks of shadeways (shaded sidewalks) and pedways (enclosed, climate-controlled walkways). This article introduces the Cool Walkshed Index (CWI) which rates pedestrian thermal protection from A (best) to F (worst). Currently, most urban neighborhoods have CWI E (incomplete sidewalk networks) or D (complete sidewalk networks). Moderate-heat cities should aspire to CWI C (shaded sidewalks on busy routes); high-heat cities, with temperatures that frequently exceed 38 °C (100 °F) should aspire to CWI B (most buildings located within 300 m of enclosed, climate-controlled pedways); and extreme-heat cities, with temperatures that frequently exceed 43 °C (110° F) should aspire to CWI A (most buildings are located less than 100 m of enclosed, climate-controlled pedways). Analysis in this study indicates that the additional costs of these facilities can be repaid many times over through road, parking, and vehicle savings, and increased local property values.

Automating the inventory of the navigable space for pedestrians on historical sites: Towards accurate path planning

Pedestrian mobility networks have a primary role on historical urban areas, hence knowledge of navigable space for pedestrians becomes crucial. Collection and inventory of those areas can be conducted by exploiting several techniques, including the analysis of point clouds. Existing point cloud processing techniques are typically developed for modern urban areas, which have standard layouts, and may fail when dealing with historical sites. We present a complete and automated novel method to tackle the analysis of pedestrian mobility in historic urban areas. Starting from a mobile lasers canning point cloud, the method exploits artificial intelligence to identify sidewalks and to characterize them in terms of paving material and geometric attributes. Output data are vectorized and stored in a very accurate high-definition shapefile representing the sidewalk network. It is used to automatically generate pedestrian routes. The method is tested in Sabbioneta, an Italian historic city. Paving material segmentation showed accuracy of 99.08%; urban element segmentation showed an accuracy of 88.2%; automatic data vectorization required only 1.3% of manual refinement on the generated data. Future advancements of this research will focus on testing the method on similar historical cities, using different survey techniques, and exploiting other possible uses of the generated shapefile.

Mapping the walk: A scalable computer vision approach for generating sidewalk network datasets from aerial imagery

While cities around the world are increasingly promoting streets and public spaces that prioritize pedestrians over vehicles, significant data gaps have made pedestrian mapping, analysis, and modeling challenging to carry out. Most cities, even in industrialized economies, still lack information about the location and connectivity of their sidewalks, making it difficult to implement research on pedestrian infrastructure and holding the technology industry back from developing accurate, location-based Apps for pedestrians, wheelchair users, street vendors, and other sidewalk users. To address this gap, we have designed and implemented an end-to-end open-source tool— Tile2Net —for extracting sidewalk, crosswalk, and footpath polygons from orthorectified aerial imagery using semantic segmentation. The segmentation model, trained on aerial imagery from Cambridge, MA, Washington DC, and New York City, offers the first open-source scene classification model for pedestrian infrastructure from sub-meter resolution aerial tiles, which can be used to generate planimetric sidewalk data in North American cities. Tile2Net also generates pedestrian networks from the resulting polygons, which can be used to prepare datasets for pedestrian routing applications. The work offers a low-cost and scalable data collection methodology for systematically generating sidewalk network datasets, where orthorectified aerial imagery is available, contributing to over-due efforts to equalize data opportunities for pedestrians, particularly in cities that lack the resources necessary to collect such data using more conventional methods.

The inclusive 15-minute city: Walkability analysis with sidewalk networks

In recent years, the design (and re-design) of cities to encourage walkability has taken on new urgency as part of a wider campaign for sustainable urban development. Complementary to other approaches like infrastructure improvements, increases in residential density, or traffic calming measures, here, we show how planning for walkability can be augmented by the adaptation of tools and approaches from the study of urban networks, by privileging the pedestrian perspective of short-distance access over the car (and rapid transit) perspective of flow and efficiency. Using a recently developed sidewalk network model that moves towards a more realistic representation of the pedestrian environment, we propose a framework for assessing multi-factor walkability using percolation theory and insights into pedestrian behavior. We apply our framework to the city of Barcelona, and show how it can be used to optimize service location and access for vulnerable populations (the elderly and young).

MAPPING SIDEWALK FALL RISKS USING BIG DATA AND MACHINE LEARNING

Abstract Outdoor falls are a leading cause of fatal and non-fatal injuries in the US. These falls are more likely to occur due to environmental hazards, inclement weather, unsafe construction zones, or poor sidewalk condition. Fall prevention programs targeted at older adults must therefore be informed by the interplay between weather condition, urban development, and built environment. Current practices, however, are limited by the lack of comprehensive data describing the condition of pedestrian facilities at fine, human scale, limiting the effectiveness of these programs. To address these shortcomings, we propose a multi-pronged approach leveraging urban data and machine learning techniques to create city-scale inventories describing sidewalk features that can inform safe strategies and programs for community mobility. We will cover the creation of multiple data sets, including computing shadow / shade from building geometries, detection of sidewalk surface material from street-level images, and creation of sidewalk networks from satellite images.

Dynamic pedestrian traffic assignment with link transmission model for bidirectional sidewalk networks

Planning assessment of urban walking infrastructure requires appropriate modeling methodologies that capture the time-dependent and unique microscopic characteristics of bidirectional pedestrian streams. In this paper, we develop a simulation-based dynamic pedestrian traffic assignment (DPTA) model specifically formulated for walking networks (e.g. sidewalks) with bidirectional links. The model consists of a dynamic user equilibrium (DUE) based walking route choice and a link transmission model (LTM) for network loading. The formulated DUE adopts a pedestrian volume delay function (pVDF) taking into account the properties of bidirectional pedestrian streams such as self-organization. The adopted LTM uses a three-dimensional triangular bidirectional fundamental diagram as well as a generalized first-order node model. The applicability and validity of the model is demonstrated in hypothetical small networks as well as a real-world large-scale network of sidewalks in Sydney. The model successfully replicates formation and propagation of shockwaves in walking corridors and networks due to bidirectional effects.

Disparate Approaches to Maintaining Roads and Sidewalks: An Interview Study of 16 U.S. Cities

America’s “crumbling roads” currently headline the infrastructure zeitgeist, but deteriorating sidewalks are also in desperate need. Sidewalks can be vibrant, social conduits in cities that, when safe and well-maintained, enhance public health and enable accessible passage for all residents, particularly the most vulnerable. The scant but burgeoning research on pedestrian infrastructure has largely focused on data collection technologies and spatial analysis. This research instead seeks to uncover the similarities and differences in how American cities treat their road versus sidewalk networks by asking: are these two indispensable assets of daily urban public life repaired, maintained, and funded disparately? The research was structured around interviews with employees of 16 U.S. cities about their road and sidewalk policies and procedures. We found multiple disparities between roads and sidewalks in and among American cities, including: municipality responsibility, repair timeliness, data availability, and condition assessment. We also discuss cities’ pressing concerns for these assets and directions for future research. The implications of this study could affect several themes across transportation infrastructure, including project prioritization, equity, and efficient government spending.

Converting street view images to land cover maps for metric mapping: A case study on sidewalk network extraction for the wheelchair users

Street view images are now widely used in web map services, providing on-site photos of street scenes for users to explore without physically being in the field. These photos record detailed visual information of the street environment with geospatial controls; therefore, they can be used for metric mapping purposes. In this study, we present a method to convert street view images to measurable land cover maps using their associated depthmap data. The proposed method can autonomously extract and measure land cover objects over large areas covered by a mosaic of street view images. In the case study, we demonstrated the use of land cover maps derived from Google Street View images to extract sidewalk features and to measure sidewalk clear widths for wheelchair users. Sidewalk feature slopes were also extracted from the metadata of street view images. Using the Washington D.C., U.S. as the study area, our method extracted a sidewalk network of 2561 km in length with the precision of 0.8662 and recall of 0.8525. The width mean error of extracted sidewalks wide between 1 and 2 m is 0.24 m, and the slope mean error is 0.638°. In Washington D.C., most sidewalks meet the minimum width requirement (0.9 m), but 20% of them have slopes that exceed the maximum allowance (1:20 or about 2.9°). These results demonstrate the converted land cover maps from street view images can be used for metric mapping purposes. The extracted sidewalk network can serve as a valuable inventory for urban planners to promote equitable walkability for mobility disabled users. And if widely available, mobility-impaired users could consult them prior to planning a route.

ACCESSIBLE PATH FINDING FOR HISTORIC URBAN ENVIRONMENTS: FEATURE EXTRACTION AND VECTORIZATION FROM POINT CLOUDS

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.

Mapping the Walk: A Scalable Computer Vision Approach for Generating Sidewalk Network Datasets from Aerial Imagery

Mapping the Walk: A Scalable Computer Vision Approach for Generating Sidewalk Network Datasets from Aerial Imagery

A sustainable strategy for Open Streets in (post)pandemic cities

Cities world-wide have taken the opportunity presented by the COVID-19 pandemic to improve and expand pedestrian infrastructure, providing residents with a sense of relief and pursuing long-standing goals to decrease automobile dependence and increase walkability. So far, due to a scarcity of data and methodological shortcomings, these efforts have lacked the system-level view of treating sidewalks as a network. Here, we leverage sidewalk data from ten cities in three continents, to first analyse the distribution of sidewalk and roadbed geometries, and find that cities present an unbalanced distribution of public space, favouring automobiles at the expense of pedestrians. Next, we connect these geometries to build a sidewalk network –adjacent, but irreducible to the road network. Finally, we compare a no-intervention scenario with a shared-effort heuristic, in relation to the performance of sidewalk infrastructures to guarantee physical distancing. The heuristic prevents the sidewalk connectivity breakdown, while preserving the road network’s functionality.

Optimizing underground shelter location and mass pedestrian evacuation in urban community areas: A case study of Shanghai

In recent years, natural disasters happening over the world pose severe challenges to emergency evacuation systems, especially for highly-populated urban community areas. Underground spaces not systematically utilized yet provide a new development direction for the construction of emergency shelters. Emergency response for mitigating negative impacts of disasters calls for well-designed shelter system and effective management of pedestrian evacuation. In this paper, we study the location selection of underground emergency shelters in urban community areas, along with pedestrian evacuation planning once disaster happens. The aim is to effectively use the existing underground space and sidewalk network to satisfy pedestrian evacuation demand as much as possible. We formulate the problem as a network flow model, and develop a minimum-cost-maximum-flow solution approach. A real-world case study based on the East Nanjing Road community area in central Shanghai is conducted. The results show that sidewalks are the bottleneck of the pedestrian evacuation network due to their limited capacity. Reserving vehicle lanes, implementing one-way evacuation, and opening local small-scale underground shelters, are shown to be effective measures for improving the pedestrian evacuation performance. Overall, the proposed methodology and managerial insights based on the case study in Shanghai show a great potential of employing underground space as shelters for pedestrian evacuation system design in urban community areas.

Independent mobility for persons with VIB using GIS

Purpose The study aims to promote independent mobility for persons with visual impairment or legal blindness (VIB) by developing a Web-based wayfinding application using geographic information systems (GIS). While the literature mainly focuses on technical devices presenting results for wayfinding, a lack of integration of user needs is identified. The inclusive, participative developed application offers step-by-step directions for pre-trip planning through an accessible user interface. Design/methodology/approach The paper presents a semi-automated approach to extract a pedestrian routing network data set based on open government data and field survey. User profiles calculate different routes using a weighting scheme for landmarks, orientation hints, infrastructure of crossings and sidewalks. The adoption of ArcGIS Web AppBuilder widgets allows access of the user interface additionally through keyboard-only navigation and therefore screen-reader capability. Findings GIS offers a powerful tool to design network analysis for persons with VIB. The routing algorithm accesses different user profiles, returning individualized turn-by-turn directions. The complex set of attributes, including shorelines, landmarks and barriers, can be integrated by semi-automated processes. Practical implications The paper illustrates the benefit of GIS applications for wayfinding of persons with VIB to raise self-determination and independence. Originality/value A ubiquitous pedestrian sidewalk network for a medium-size city comprises a novelty, as research has mainly focused on small areas. The integration of shorelines next to a various number of hints, landmarks and potential barriers through semi-automated processes allows reproducibility and transferability of the model to other cities.

Towards routine, city-scale accessibility metrics: Graph theoretic interpretations of pedestrian access using personalized pedestrian network analysis.

A wide range of analytical methods applied to urban systems address the modeling of pedestrian behavior. These include methods for multimodal trip service areas, access to businesses and public services, diverse metrics of “walkability”, and the interpretation of location data. Infrastructure performance metrics in particular are an increasingly important means by which to understand and provide services to an urbanizing population. In contrast to traditional one-size-fits all analyses of street networks, as more detailed pedestrian-specific transportation network data becomes available, the opportunity arises to model the pedestrian network in terms of individual experiences. Here, we present a formalized and city-scale framework, personalized pedestrian network analysis (PPNA), for embedding and retrieving pedestrian experiences. PPNA enables evaluation of new, detailed, and open pedestrian transportation network data using a quantitative parameterization of a pedestrian’s preferences and requirements, producing one or more weighted network(s) that provide a basis for posing varied urban pedestrian experience research questions, with four approaches provided as examples. We introduce normalized sidewalk reach (NSR), a walkshed-based metric of individual pedestrian access to the sidewalk network, and sidewalk reach quotient (SRQ), an estimate of inequity based on comparing the normalized sidewalk reach values for different pedestrian profiles at the same location. Next, we investigate a higher-order and combinatorial research question that enumerates pedestrian network-based amenity access between pedestrians. Finally, we present city-scale betweenness centrality calculations between unique pedestrian experiences, highlighting disagreement between pedestrians on the “importance” of various pedestrian network corridors. Taken together, this framework and examples represent a significant emerging opportunity to promote the embedding of more explicit and inclusive hypotheses of pedestrian experience into research on urban pedestrian accessibility, multimodal transportation modeling, urban network analysis, and a broader range of research questions.

Sidewalk extraction using aerial and street view images

A reliable, punctual, and spatially accurate dataset of sidewalks is vital for identifying where improvements can be made upon urban environment to enhance multi-modal accessibility, social cohesion, and residents' physical activity. This paper develops a synthetically new spatial procedure to extract the sidewalk by integrating the detected results from aerial and street view imagery. We first train neural networks to extract sidewalks from aerial images, and then use pre-trained models to restore occluded and missing sidewalks from street view images. By combining the results from both data sources, a complete network of sidewalks can be produced. Our case study includes four counties in the U.S., and both precision and recall reach about 0.9. The street view imagery helps restore the occluded sidewalks and largely enhances the sidewalk network's connectivity by linking 20% of dangles.

Assessing physical accessibility conditions to tourist attractions. The case of Maspalomas Costa Canaria urban area (Gran Canaria, Spain)

In this research, a methodology for the analysis of accessibility conditions for people with physical disabilities was developed. The analysis is based on an assessment of the sidewalk network in two tourist areas located in Maspalomas Costa Canaria (Gran Canaria, Spain). ArcMap's Network Analyst was used, and Spanish regulations on accessibility in urbanized public spaces, as well as the points of origin (tourist accommodation, bus stops and reserved parking lots for people with reduced mobility) and destination (access points to the beach and other tourist elements, some of them with heritage value) for typical users were considered. After application of the methodology, a set of potential routes were obtained. The results reveal deficient walkability, with breaks in the accessibility chain in both study areas. It is concluded that new measures to improve accessibility are required which can be used as a basis for the development of new tourist products to reinforce the competitiveness of the tourist destination.

Methodology and evidence from a case study in Rome to increase pedestrian safety along home-to-school routes

Abstract Home-to-school routes are very sensitive areas: they represent, for children, a learning tool for their everyday activities, but if poorly designed, maintained and equipped they can expose them to traffic risks. Sidewalks' inappropriate level of service and poor maintenance, especially, are main factors contributing to walking unsuitability, thus to poor comfort and safety levels for young pedestrians, and more in general for all the vulnerable non-motorized road users. This paper deals with a methodology specifically developed to highlight the quality of the urban environment where the home-to-school routes are located, according to four main criteria: wellbeing, usage, appearance, and safety and security. Each criterion is associated with a checklist including the most relevant features to assess, with a focus on maintenance as a key parameter to create safe and comfortable routes to school. An application, a case study in Rome, where the lack of regular maintenance results into a network of unsafe sidewalks, is also presented, analyzing three different areas where a number of schools are located. Detours when approaching school premises were surveyed, due to the levels of distresses and linked to the poor comfort and safety levels. As the mutual influence of built environment over road safety and maintenance requirements for home-to-school paths is not largely investigated thus far, the paper's goal is to provide advanced knowledge for studies and applications further afield.