Computers Environment and Urban Systems最新文献

Considering that the number of wheelchair users is on the rise at the global level due to population aging, it is crucial to secure their rights to have adequate access to healthcare services. Spatial accessibility to healthcare services has been well recognized to influence people's health. However, research on healthcare accessibility of wheelchair users is scarce. This study proposes a barrier-free path planning method to estimate wheelchair users' travel time as the measurement of their accessibility. A study on Wuhan, China, is conducted to evaluate the spatial accessibility to healthcare services for wheelchair users and compare it with the general population. The results show that: (1) the levels of healthcare accessibility are unevenly distributed across the city center and the periphery of the study area for both wheelchair users and the general population, while wheelchair users have lower accessibility overall; (2) both similarities and differences in hospital and travel mode selection to access healthcare services co-exist in the study area between the two groups; (3) significant inequality in healthcare accessibility is observed in Hongshan and Qingshan districts. The research findings are beneficial for policymakers to further improve healthcare accessibility and its equality by optimizing the allocation of hospital resources and barrier-free public transport.

Bicycling can improve the sustainability and liveability of cities, many of which desperately require better active transport infrastructure. Urban and transport planners need to examine how improvements in infrastructure change bicyclists' behaviour. With this knowledge, investment in bicycling networks can be more efficient and encourage the use of bicycling for transportation. This study developed a simple Agent-Based Model (ABM) to simulate bicyclists' movements in response to the built environment and road network characteristics in the City of Penrith, in the Greater Sydney Area, Australia. In this case study, the GAMA platform was used to build the ABM and Strava and Riderlog data were used to calibrate and validate the model. The model outputs give insights into bicyclist movements through the road network. The incorporated built environment characteristics include the type of bicycling infrastructure, tree canopy, slope, land use mix, and vehicle traffic. These choice factors also allowed the computation of rider levels of comfort and safety on each trip. Potential refinements of the model include additional bicycling behaviour factors (such as aesthetic preferences), and bicyclists' interactions with each other and other modes of transport.

Road networks in the past are imperative for understanding evolution of transportation infrastructure, urban sprawl, and route planning, etc. Various approaches have been developed for road extraction from historical maps, among which deep learning techniques stand out as the most effective ones. However, little attention has been paid to investigating road vectorization and classification from historical maps. Moreover, road classification via machine learning methods usually requires large amounts of dedicated training data. To address these issues, this paper proposes a novel and comprehensive framework for road vectorization and classification on the basis of road segmentation from historical maps. First, deep learning is used to get pixel-wise raster road segmentation results, which are further skeletonized using morphological operations. Then, considering that each road class is represented with a certain symbol, a painting function is defined for each class able to paint the corresponding symbol. These painting functions are then used to draw road segments along the skeletons. Since the start and end points in each painting function are used to vectorise the segment, this method achieves vectorization and classification at the same time. Our method is validated on four Siegfried map sheets in Switzerland, and evaluated via both visual and quantitative assessments. The results indicate that the method is capable of classifying roads accurately. In particular, two evaluation metrics completeness and correctness achieve 90.69% and 72.71% respectively for road class 2 which accounts for the highest portion in the map. Moreover, the results of this method avoid the saw-toothed issue of vectorised road lines. This research is beneficial for creating complete vector road network datasets with class information to support decision-making in urban planning and transportation.

The accuracy of retail location models depends on their precise calibration, but the data necessary for such a key task is seldom available. In this research, we use synthetic human mobility data, which introduces commuting dynamics, to improve the reliability of such models. We use the origin-destination flows to distribute households' potential expenditures in their home and commuting locations with the aim of modeling non-residential-driven demand in the commercial streets of Tokyo. We estimate potential revenues of commercial streets using the Huff model with its conventional specification as well as a variation of it that adopts pedestrian trajectory counts as the deterrence variable. We found that redistributing the potential expenditures toward the households' daytime locations significantly increased the model's performance. Additionally, we found that our use of pedestrian trajectory counts is comparable to using distance within the Huff model framework, but our proposed model was still outperformed by the conventional Huff model specification. We conclude that combining synthetic human mobility simulations and retail location models significantly increases the reliability of analysis in data-constrained situations.

Data are essential for planning walkable cities that are comfortable, convenient and safe to pedestrians. Yet, in contrast to massive vehicular traffic data, data on pedestrian traffic have not been systematically collected by municipal governments. Nowadays, geospatial big data provide rich information related to human activities and, hence, can capture street scenes in an innovative way. Using bus dashcam videos (on 244.36 km of roads covered by 33 bus routes in Hong Kong) and deep learning methods (Fast R-CNN and Deepsort), this study proposes a new method for estimating pedestrian volume from this data source. In comparison, we generate two alternative measures from household travel surveys and Google Street View images. The estimates are validated by manual counts at selected locations on a main road. Using five different modelling approaches (including three variants of negative binomial and two variants of random forest models), the pedestrian volume estimates are used for predicting pedestrian-vehicle crashes. The results show that pedestrian volumes calculated from bus dashcam videos consistently show comparable, if not better, performance in explaining crash frequency. In the future, different data sources should be used to supplement each other so that a more complete picture of pedestrian flows at the city level can be obtained.

The detection of critical situations through the adoption of multi-sensor Emergency Detection Units (EDUs) can significantly reduce the time between the initial stages of urban emergencies and the actual responses to relieve its negative effects, usually through the rescuing of endangered people, the attending to eventual victims, and the mitigating of its causes. However, although the benefits of such units are well known, their proper positioning in a city is challenging when considering a limited set of available units. In this sense, data-driven approaches can be leveraged to provide a better perception of the urban environments under consideration, allowing emergency management systems to be tailored to the specificities of a target city, thus improving the positioning of EDUs. This article proposes the processing of geospatial data of emergency-related urban infrastructure to support the computing of risk zones in a city, which is retrieved from the OpenStreetMap database together with the map of streets within a defined area. Since risk zones indirectly indicate the proportional number of detection units to be deployed, for each configuration setting of the EDUs, we propose an algorithm that computes the positions for such units only on streets, in a balanced way. Furthermore, considering that EDUs are expected to report detected emergencies through a wireless connection, we have also modelled the coverage area of existing networks in a city, which is also processed according to a suitable dataset. The proposed algorithm performs a fine-grained positioning of EDUs based on the number of active networks, flexibly favouring the EDUs' connectivity requirements such as reliability, throughput, latency, and transmission costs according to the actual demands of any urban emergency management system. Experimental results with real data demonstrated the applicability of the proposed mathematical model and the associated algorithm, reinforcing its practical application for the planning and construction of smart cities.

The coronavirus disease 2019 (COVID-19) has caused significant changes in urban networks due to epidemic prevention policies (e.g., social distancing strategies) and personal concerns. Previous measurements of urban networks were mainly based on flow data or were simulated from statistical data using models (e.g., Gravity model). However, these measurements are not directly applicable to the mapping of directional urban networks during unexpected events, such as COVID-19. Since nighttime light (NTL) data offer a unique opportunity to track near real-time human activities, the radiation model, traditionally used for routine situations only, was modified to measure directional urban networks using NTL data under three scenarios: the routine scenario (before the Shanghai lockdown), the COVID-19 scenario (during the Shanghai lockdown), and the extreme scenario (without Shanghai's participation). When compared with the Baidu migration index, the modified radiation model achieved an acceptable accuracy of 0.74 under the routine scenario and 0.44 under the COVID-19 scenario. Our mapping of each scenario's urban networks in the Yangtze River Delta Region (YRDR) shows that the Shanghai lockdown reduced the urban interaction index between Shanghai and its surrounding cities. However, it led to an increase in the urban interaction index centered on the periphery cities of YRDR. Our findings suggest that urban interactions within YRDR are resilient, even under extreme scenarios. Considering the long time series and global coverage of NTL data, the proposed NTL-based urban network model can be readily updated and applied to other regions.

Since June 2022, France is equipped with cell broadcast technology which alerts individuals within a predefined area. Despite the proven effectiveness of this technology, few studies take a spatial view of cell broadcast alert at a local level. Trials carried out in France were assessed only on their technical success, without verifying the rate of reception of the message by individuals in the official alert area, or the gap between the official alert area and the actual broadcast area. This study focuses on a trial conducted in April 2023 in Cannes (France). Using a geo-located survey method and spatial analysis tools, we show how cell broadcasting is more imprecise than one might think at the local level. Reception rates depend on the telephone operators and a large and ragged edge effect is measured, which means that the message is broadcast far beyond the area defined by the authorities. A second approach was to check the reception of three cell broadcast messages sent within a 20-min interval at fixed points, which revealed the fluctuation of the broadcast area over time, making its spatial extent complex to predict. Similar works should be carried out in other urban and rural areas.

A rich volume of research has detected urban growth by quantifying the land use/land cover (LU/LC) changes based on remote sensing technologies. However, the research has limitations in identifying various formats of urban growth, particularly small-scale urban growth, such as infill development or redevelopment in urban areas, prompted by smart growth and sustainable urban development. This paper aims to design a framework for the accurate detection of residential infill development in the City of Los Angeles by employing a deep-learning method that has been increasingly applied to analyze phenomena in cities. In order to do so, this paper develops six models that reflect the variations of image classification methods, deep-learning algorithms, and estimation types. The results from the models emphasize the potential of the deep-learning models for the detection of micro-urban growth at a city scale. However, there is room for the improvement of estimation accuracy in the cases that detect some new developments and replacements as not developed parcels. The findings suggest that the performance of the models depends primarily on the articulations of the training dataset rather than the types of deep-learning algorithms. Findings from the models provide the city with insights into land use and transportation planning decision-making based on a better understanding of the spatial distribution patterns of urban growth and development.

The burgeoning availability of sensing technology and location-based data is driving the expansion of analysis of human mobility networks in science and engineering research, as well as in epidemic forecasting and mitigation, urban planning, traffic engineering, emergency response, and business development. However, studies employ datasets provided by different location-based data providers, and the extent to which the human mobility measures and results obtained from different datasets are comparable is not known. To address this gap, in this study, we examined three prominent location-based data sources—Spectus, X-Mode, and Veraset—to analyze human mobility networks across metropolitan areas at different scales: global, sub-structure, and microscopic. Dissimilar results were obtained from the three datasets, suggesting the sensitivity of network models and measures to datasets. This finding has important implications for building generalized theories of human mobility and urban dynamics based on different datasets. The findings also highlighted the need for ground-truthed human movement datasets to serve as the benchmark for testing the representativeness of human mobility datasets. Researchers and decision-makers across different fields of science and technology should recognize the sensitivity of human mobility results to dataset choice and develop procedures for ground-truthing the selected datasets in terms of representativeness of data points and transferability of results.