Measuring Nonmotorized Accessibility and Connectivity in a Robust Pedestrian Network

With the advanced capabilities of mobile devices and the success of car navigation systems, interest in pedestrian navigation systems is on the rise. A critical component of any navigation system is a map database, which represents a network (e.g., road networks for car navigation) and supports key functionality such as map display, geocoding, and routing. Road networks, mainly due to the popularity of car navigation systems, are well defined and publicly available. However, in pedestrian navigation systems, as well as other applications including urban planning and physical activity studies, road networks do not adequately represent the paths that pedestrians usually travel. Currently, there is a void in literatures discussing the challenges, methods, and best practices for pedestrian network map generation. This coupled with the increased demand for pedestrian networks is the prime motivation for development of new approaches and algorithms to automatically generating pedestrian networks. Three approaches, network buffering, using existing road networks, collaborative mapping, using Global Positioning System (GPS) traces collected by volunteers, and image processing, using high-resolution satellite and laser imageries, were implemented and evaluated with a pedestrian network baseline as a ground truth. The results of the experiments indicate that these three approaches, while differing in complexity and outcome, are viable for automatic pedestrian network map generation. The recommendation of a suitable approach for generating pedestrian networks for a given set of sources and requirements is provided.

Central to every pedestrian network analysis is the mapping of the network. Many studies have used street networks (SNs) to analyze connectivity. Recent studies suggest that analyses based on informal pedestrian networks (e.g., park trails, greenways, or shortcuts) may provide a more robust network representation, however, no studies have examined whether switching to a network consisting of formal pedestrian facilities (FPFs) (e.g., sidewalks and crosswalks) would provide more effective representation of real pedestrian movement and perceptions. Does it yield different outcomes compared to an SN? When does an SN still remain a good proxy, and when should a FPF network (FPFN) be adopted? How worthwhile is it to switch to a FPFN? To address these questions, this paper examines the difference between SNs and FPFNs and how the differences affect the outcomes of network measures. Four U.S. neighborhoods were selected to represent different types of networks. Four classic connectivity measures and four new measures related to crossings were evaluated using both SNs and FPFNs. Comparisons were conducted and the following conclusions drawn: (i) using FPFNs yields greatly different outcomes from using SNs, (ii) an SN can broadly describe the morphological features of a network, especially for a gridiron pattern with narrow streets and small blocks full of sidewalks and crosswalks; however, an FPFN performs much better in describing features which are invisible when using SNs; and (iii) using FPFNs can provide opportunities to develop new measures of performance, such as safety or convenience.

Do footbridge and underpass improve pedestrian safety? A Hong Kong case study using three-dimensional digital map of pedestrian network

This chapter gives our measures of unconditional (or traditional) and conditional connectivity and fault-tolerance of two-dimensional k-covered wireless sensor networks. The latter measures are more realistic than the former as they impose a restriction on the subsets of sensors that can fail at the same time. Precisely, conditional measures take into consideration the inherent properties of k-covered wireless sensor networks, such as high sensor density and sensor heterogeneity. In particular, the neighbour set of a sensor is defined as a forbidden faulty set, and hence cannot fail at the same time. This concept defines the new measure of connectivity, called conditional connectivity, which seems to be more realistic.

<p>My dissertation focused on understanding how different areas of the brain coordinate in networks to drive higher cognitive functions, and how damage, changes the brain’s synchronized activity (or functional connectivity) in the short and long term. In this dissertation, I studied the functional connectivity of brain networks that are thought to underlie emotion and decision-making, and how these networks change in the face of neurological injury.</p><p>In my first set of experiments, I studied participants with chronic focal brain damage to determine how damage to brain areas which have been identified as important in emotion and decision-making behaviors (amygdala, ventromedial prefrontal cortex, & insula), affected connectivity of brain networks, and how changes in connectivity following damage to these areas related to emotion and decision-making behavior. Supporting my predictions, I found evidence that damage to the amygdala, ventromedial prefrontal cortex, and insula all result in significantly weaker connections between a network of areas important for assigning value to stimuli. Additionally, I found that stronger connectivity in this valuation network was significantly positively associated with performance on ratings of disgusted faces, while stronger connectivity in a network important for processing emotional salience was significantly positively correlated with decision-making performance.</p><p>In the second set of studies in this dissertation, I utilized a population of epilepsy patients who were undergoing brain surgery to treat their seizures to investigate how a brain network related to emotional salience changed from before to after surgery. This approach allowed me to study how the connectivity and associated behavior of this network changed from preoperative baseline, to the weeks and months after part of this network was removed. While I expected a decline in this network in the weeks following surgery, instead I found a significant positive correlation between preoperative and acute postoperative connectivity in a subset of this network. However, my hypothesis that there would be a significant increase in the connectivity of this network between acute and chronic postoperative epochs was supported. I only have partial evidence for a significant correlation between the change in salience network connectivity between preoperative and acute postoperative assessments and the associated change in decision-making behavior. This correlation was in the opposite direction of my hypothesis, with increased change in connectivity being positively associated with change in risk-taking behavior. I did not observe a significant correlation between the change in network connectivity and change in behavior across acute and chronic measurements.</p><p>These findings provide important insight on how measures of network connectivity can inform theories of neuroplasticity and reorganization following brain damage. Understanding how these networks change over time, and how changes in these networks relate to behavioral outcomes, are critical for the development and effective deployment of therapeutic interventions. Together, these studies provide a foundation for further study, demonstrating that these networks change over time with damage, and the residual network strength is associated with performance on measures of emotion and decision-making.</p>

A pedestrian road network is a complex non-linear dynamic system, and it is prone to severe accidents due to the congestion of spatiotemporal mass gathering. This paper proposes a framework of hybrid crowd dynamics modeling and model predictive control to ease the pressure of pedestrian road networks. The framework is integrated on the basis of a mature paradigm in the field of traffic signal control; the theory is yet new to pedestrian road networks except general traffic signals on the streets. The novelty also lies in its ability to simultaneously monitor both the discrete and continuous dynamics changes in the overall network. We verify the framework through comparison with simulation of manual control commands reproduced from real cases of a large-scale outdoor event. The results indicate that the proposed framework has a superior performance in keeping the balance in the overall distribution of large-scale crowd and reducing the risk of saturation in a pedestrian road network.

최근 들어 모바일 단말기를 휴대하고 이동하는 사용자에게 경로안내 및 주변 위치정보 안내와 같은 개인화된 서비스가 가능해졌다는 점에서 보행자용 도로망의 중요성이 커지고 있다. 한편, 전국단위 도로망 신규 구축과 갱신에 많은 비용이 소요된다는 점은 활성화의 제약조건으로 작용하고 있어 래스터 데이터를 기반으로 한 보행자용 도로망 추출 알고리즘을 적용한 자동 생성 방안이 필요한 상황이다. 그러나 생성된 도로망은 불필요한 결절점이 다수 포함되어 경로 안내 시 과도한 방향전환을 야기하고 데이터 용량 증가를 초래하는 등 유지관리 차원에서의 비효율이 발생한다. 본 연구에서는 이를 제거하기 위해 Douglas-Peucker 알고리듬 적용 과정에서 수치지도 도로의 속성정보를 이용하여 각 선형 객체별로 적합한 임계값을 부여함으로써 선형단순화의 효과는 극대화하고 실제 도로의 형태를 왜곡하지 않도록 최적의 임계값을 자동 선정하였다. 실험 대상 지역의 보행자용 도로망에 적용한 결과 결절점 감소율과 위치정확도 측면에서 제안된 방법이 자동 선형단순화에 적합하다는 결과를 얻을 수 있었다. Recently, importance of pedestrian road network is getting emphasized as it is possible to provide mobile device users with both route guidance services and surrounding spatial information. However, it costs a tremendous amount of budget for generating and renovating pedestrian road network nationally, which hinder further advances of these services. Hence, algorithms extracting pedestrian road network automatically based on raster data are needed. On the other hand, road dataset generated from raster data usually has unnecessary vertices which lead to maintenance disutility such as excessive turns and increase in data memory. Therefore, this study proposed a method of selecting a proper threshold automatically for separate road entity using not only Douglas-Peucker algorithm but also road attribute data of digital map in order to remove redundant vertices, which maximizes line simplification efficiency and minimizes distortion of shape of roads simultaneously. As a result of the test, proposed method was suitable for automatic line simplification in terms of reduction ratio of vertices and accuracy of position.

Dada la relevancia de la accesibilidad a bienes y servicios urbanos en la calidad de vida de los habitantes, y considerando que la caminata representa el principal modo de transporte en el Gran Santiago —34,6% de los viajes, según la Encuesta de Origen y Destino de 2012 (Universidad Alberto Hurtado, 2014)—, la presente investigación tiene como objetivo analizar y comparar las barreras estructurales que inciden en el área caminable a escala de barrio y estimar sus posibles efectos en los patrones de movilidad y acceso a bienes y servicios, en tres conjuntos habitacionales del Gran Santiago: población Juan Antonio Ríos sector 3B (Independencia), villa San Cristóbal (Recoleta) y villa Los Sauces (La Florida). Para cada caso, se identificaron las principales barreras materiales, ya sean naturales (ríos, cerros) o artificiales (carreteras, pasos bajo/sobre nivel) y algunas barreras intangibles (asociadas a la seguridad vial o delictual). Se comparó la superficie teórica máxima del barrio caminable (euclidiana) con la “real” calculada mediante análisis de red (sobre la base de recorridos de 20 min), considerando las condiciones de cruce de las principales vías, cuantificando sus efectos en tiempo y distancia y la consiguiente accesibilidad a equipamiento y servicios. Además, se analizó la configuración de la trama vial y peatonal, contrastando su integración a escala local y global basada en mapas axiales. Finalmente, se analizaron los patrones de movilidad de la población según datos de zona EOD 2012.Los resultados muestran una clara disminución del área real del barrio caminable (a un tercio del área euclidiana), producto de la trama y de las diversas barreras existentes, lo que se refleja en la accesibilidad a bienes y servicios vía caminata. Estos resultados permiten proponer además que, si bien una mayor distancia promedio de caminata no se relaciona con una mayor área caminable, la cercanía a centralidades sí se asocia con la prevalencia de este modo de transporte.

The lack of associating pedestrian networks, i.e. the paths and roads used for non-vehicular travel, with information about semantic attribution is a major weakness for many applications, especially those supporting accurate pedestrian routing. Researchers have developed various algorithms to generate pedestrian walkways based on datasets, including high-resolution images, existing map databases, and GPS data; however, the semantic attribution of pedestrian walkways is often ignored. The objective of our study is to automatically extract semantic information including incline values and the different categories of pedestrian paths from multi-source spatial data, such as crowdsourced GPS tracking data, land use data, and motor vehicle road (MVR) networks. Incline values for each pedestrian path were derived from tracking data through elevation filtering using wavelet theory and a similarity-based map-matching method. To automatically categorize pedestrian paths into five classes including sidewalk, crosswalk, entrance walkway, indoor path, and greenway, we developed a hierarchical strategy of spatial analysis using land use data and MVR networks. The effectiveness of our proposed method is demonstrated using real datasets including GPS tracking data collected by volunteers, land use data acquired from OpenStreetMap, and MVR network data downloaded from Gaode Map.

BackgroundAccessibility to community pharmacies is crucial for ensuring timely access to medications and essential health services. While accessibility to community pharmacies is critical, disparities driven by temporal and spatial factors persist, resulting in inequities in healthcare access. This study aims to comprehensively assess spatiotemporal and multimodal accessibility to community pharmacies in Lisbon, highlighting the influence of transport modes and time of day on accessibility disparities.Data and methodsThe study employed a methodology that considered five daily time slots and two modes of transport—walking and public transport—to evaluate accessibility to community pharmacies. Data was sourced from road and pedestrian networks, Google API, and GTFS data. Descriptive statistics and spatial analysis were utilized to assess travel time and accessibility disparities across different regions of Lisbon. The analysis focused on both the percentage of residents able to access pharmacies within 10 min and the total number of pharmacies accessible.Resultsndings reveal significant temporal variations in accessibility, with public transport consistently improving access compared to walking. Accessibility peaks in the evening (6–7 PM), when 83.3% of residential buildings are within a 10-min walking distance of a pharmacy, and 92.7% are reachable by public transport. In contrast, early morning hours (4–5 AM) show the lowest accessibility, with only 8.9% of buildings accessible by walking and 16.1% by public transport. During the daytime (8–9 AM), notable disparities emerge across the city: public transport enhances access in the southwest, northwest, and central areas, while limited pharmacy opening hours constrain accessibility in the north and southeast, where only 108 of 258 pharmacies are operational. Finally, travel time to pharmacy services for city residents highlight significant spatial and temporal disparities in pharmacy accessibility, emphasizing the role of transport modes and service hours in shaping urban healthcare access.ConclusionsThis study underscores the importance of addressing both temporal and spatial factors to ensure equitable accessibility to community pharmacies. The findings suggest the need for targeted policies to improve public transport services during off-peak hours and to extend pharmacy operating hours. Future research should focus on comparative studies across different urban contexts and incorporate more granular data to better understand accessibility to urban services.

A testbed for evaluating network construction algorithms from GPS traces

Over the last two decades, cycling has seen a rise in popularity in North American cities, which are continuously expanding their bicycle networks. While studies highlight that a good utilitarian bicycle network should provide direct routes for cyclists to reach their desired destinations, most network assessments simply measure the length and growth of bicycle facilities. This study evaluates the performance of the bicycle network in Montreal, Canada using a set of complementary indicators accounting for the directness of bicycle facilities between observed origins and destinations of cyclists. The study first uses data on routes taken by cyclists from an online survey conducted in Montreal in 2009 to derive a perceived reduced cost of traveling on bicycle facilities. By applying the derived cost reduction coefficient to bicycle facilities (0.77), bicycle routes are then predicted for 1482 cycling trips from the 2013 Montreal Origin-Destination survey. For each predicted route, the proportion of route on bicycle facilities and the diversion from the shortest path are calculated to assess the directness allowed by bicycle facilities. These two indicators are then used to measure network connectivity. The findings reveal that only 33% of the routes are connected with a diversion lower than 12% and a route proportion on facilities higher than 50%. The results also highlight underperforming neighborhoods and demonstrate the importance of designing well-connected facilities between the boroughs. This study offers a proof of concept towards the development of a new methodology to measure bicycle network connectivity and support bicycle infrastructure planning.

Space, flow rate, speed, and the volume-to-capacity ratio have been taken into account in the existing evaluations of pedestrian environments, such as those focusing on traffic facilities. However, these measures are insufficient when assessing pedestrian environments in favored places where many pedestrians congregate. Therefore, this study attempts to develop a “walk score” to evaluate the spatial characteristics of pedestrian environments and the facility characteristics of pedestrian networks in relation to major subway stations in Seoul, Korea. In particular, the walk score that we have developed focuses on qualitative convenience factors using GIS (Geographic Information System) and quantitative network connectivity indices of the Space Syntax technique. The scores of the amenities have been calculated as avenue-oriented type, destination-oriented type, or general amenities, considering the individual distances and the number of facilities available. The convenience and connectivity of the pedestrian networks have been measured quantitatively using the Space Syntax model (Hillier 1996; Penn et al. 1998). Furthermore, our walk score has been calculated by integrating amenity and pedestrian network scores using the Analytical Hierarchy Process (AHP). With the walk score proposed in this study, it is easily possible to evaluate pedestrian environments around subway-adjacent areas, and to compare and analyze the types and number of facilities and the connectivity of the pedestrian networks. This will be helpful in suggesting future improvements, as well as evaluating the level of quality of current pedestrian environments.

The assessment of OpenStreetMap (OSM) data quality has become an interdisciplinary research area over the recent years. The question of whether the OSM road network should be updated through periodic data imports from public domain data, or whether the currency of OSM data should rather rely on more traditional data collection efforts by active contributors, has led to perpetual debates within the OSM community. A US Census TIGER/Line 2005 import into OSM was accomplished in early 2008, which generated a road network foundation for the active community members in the US. In this study we perform a longitudinal analysis of road data for the US by comparing the development of OSM and TIGER/Line data since the initial TIGER/Line import. The analysis is performed for the 50 US states and the District of Columbia, and 70 Urbanized Areas. In almost all tested states and Urbanized Areas, OSM misses roads for motorized traffic when compared with TIGER/Line street data, while significant contributions could be observed in pedestrian related network data in OSM compared with corresponding TIGER/Line data. We conclude that the quality of OSM road data could be improved through new OSM editor tools allowing contributors to trace current TIGER/Line data.

With industrial transformation and the rise in the 15 min community life circle, optimizing walkability and preserving industrial heritage are key to revitalizing former industrial areas. This study, focusing on Shijingshan District in Beijing, proposes a walkability evaluation framework integrating multi-source big data and street-level perception. Using Points of Interest (POI) classification, which refers to the categorization of key urban amenities, pedestrian network modeling, and street view image data, a Walkability Friendliness Index is developed across four dimensions: accessibility, convenience, diversity, and safety. POI data provide insights into the spatial distribution of essential services, while pedestrian network data, derived from OpenStreetMap, model the walkable road network. Street view image data, processed through semantic segmentation, are used to assess the quality and safety of pedestrian pathways. Results indicate that core communities exhibit higher Walkability Friendliness Index scores due to better connectivity and land use diversity, while older and newly developed areas face challenges such as street discontinuity and service gaps. Accordingly, targeted optimization strategies are proposed: enhancing accessibility by repairing fragmented alleys and improving network connectivity; promoting functional diversity through infill commercial and service facilities; upgrading lighting, greenery, and barrier-free infrastructure to ensure safety; and delineating priority zones and balanced enhancement zones for differentiated improvement. This study presents a replicable technical framework encompassing data acquisition, model evaluation, and strategy development for enhancing walkability, providing valuable insights for the revitalization of industrial districts worldwide. Future research will incorporate virtual reality and subjective user feedback to further enhance the adaptability of the model to dynamic spatiotemporal changes.