Online Map Services Research Articles

OnMapGaze and GraphGazeD: A Gaze Dataset and a Graph-Based Metric for Modeling Visual Perception Differences in Cartographic Backgrounds Used in Online Map Services

OnMapGaze and GraphGazeD: A Gaze Dataset and a Graph-Based Metric for Modeling Visual Perception Differences in Cartographic Backgrounds Used in Online Map Services

Enhanced Deep Neural Networks for Traffic Speed Forecasting Regarding Sustainable Traffic Management Using Probe Data from Registered Transport Vehicles on Multilane Roads

Early forecasting of vehicle flow speeds is crucial for sustainable traffic development and establishing Traffic Speed Forecasting (TSF) systems for each country. While online mapping services offer significant benefits, dependence on them hampers the development of domestic alternative platforms, impeding sustainable traffic management and posing security risks. There is an urgent need for research to explore sustainable solutions, such as leveraging Global Positioning System (GPS) probe data, to support transportation management in urban areas effectively. Despite their vast potential, GPS probe data often present challenges, particularly in urban areas, including interference signals and missing data. This paper addresses these challenges by proposing a process for handling anomalous and missing GPS signals from probe vehicles on parallel multilane roads in Vietnam. Additionally, the paper investigates the effectiveness of techniques such as Particle Swarm Optimization Long Short-Term Memory (PSO-LSTM) and Genetic Algorithm Long Short-Term Memory (GA-LSTM) in enhancing LSTM networks for TSF using GPS data. Through empirical analysis, this paper demonstrates the efficacy of PSO-LSTM and GA-LSTM compared to existing methods and the state-of-the-art LSTM approach. Performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Median Absolute Error (MDAE) validate the proposed models, providing insights into their forecasting accuracy. The paper also offers a comprehensive process for handling GPS outlier data and applying GA and PSO algorithms to enhance LSTM network quality in TSF, enabling researchers to streamline calculations and improve supposed model efficiency in similar contexts.

Digital identification and pattern recognition capabilities using machine learning methods, navigation systems, and video surveillance

The objects of the study are unmanned vehicles and branches of the bridge of the city of Kyiv (Ukraine), which connects the Great Ring Road, Zhytomyr Highway and Peremogy Avenue. The built routes were analyzed using the technology of recognition of road signs, people and vehicles. The important problem of this research is to analyze the possibilities of detecting obstacles by an unmanned vehicle using pattern recognition, which combines the methods of machine communication, navigation and real-time video surveillance. Based on the study, the results of detecting and avoiding obstacles on the road, where a study was conducted to investigate the main reasons that can cause time delays (traffic jams, weather conditions, accidents). The results of planning and navigation are obtained to determine the appropriate road route, which allows detecting and eliminating obstacles on the road, as well as building a map plan of the route in advance using online map services (Google Maps). It is shown that recognition of road signs (based on the classification using a road sign map consisting of 7 categories), people and vehicles minimizes the occurrence of road accidents, traffic jams and time delays. To recognize the images of road signs, people and vehicles, we studied the road sections connecting to the branched bridge. Thus, the authors have reviewed and analyzed the digital capabilities of pattern identification and recognition using machine learning methods, navigation and video surveillance systems, where the safety of vehicles with detection of road signs and obstacles on the way is of great importance. The results obtained can complement the possibilities of using unmanned vehicles to avoid obstacles and road accidents based on a trained pattern recognition system. This system, using convolutional neural networks and video surveillance navigation systems, will be able to provide the driver and the people around it with safe driving conditions.

Assessment of Farmer Characteristics and Design Preferences on Location-based Pest and Disease Reporting Service for Digital Agriculture: A Preliminary Analysis

User interface design is critical for ensuring that agricultural technology is accessible and usable for farmers. Usability and accessibility for farmers can boost crop yields and lower losses. Therefore, it is crucial to understand the preferences and needs of farmers, particularly in terms of user interface design. This study examines the preferences of chili farmers for user interface elements in reporting chili pests and diseases and investigates how farmer characteristics (i.e. age categories and educational backgrounds) influence these preferences. Data was collected from a group of chili farmers in Batu Pahat using a survey instrument and field experimental tasks that list several user interface elements according to the tasks given in relation to disease and pest attack reporting information for chili crops. The study includes user interface-related tasks for online map services such as Google Maps and Waze, which aim to report the location of attacks. The study’s findings underscore the significance of user interface elements in shaping user experiences. Google Maps emerged as the favored navigation service, highlighting the importance of a straightforward and user-friendly interface. The study suggests that enhancing user knowledge of lesser-known functions can lead to higher satisfaction and loyalty. In agricultural applications, visual cues and intuitive design, exemplified by the “Picture Radio Button” and “Horizontal Date” elements, were well-received. Overall, this study highlights the importance of considering farmers’ demographic characteristics in designing user interfaces for agricultural technology. By doing so, agricultural technology can be made more accessible and usable for farmers, ultimately leading to more effective management of chili pests and diseases. The findings of this research will yield advantages by aiding farmers, specifically in Malaysia, in transitioning into intelligent farmers, aligning with the objectives of the Shared Prosperity Vision 2030 and the realization of smart agriculture in Malaysia. Furthermore, this endeavor is consistent with the Food and Agriculture Organization of the United Nations (FAO) initiative aimed at digitizing farmers, encompassing smallholder farmers as well.

Developing a mapping procedure for urban forests using online map services and Sentinel-2A images

Accurate area statistics at the local government level are important for the scientific management of urban forests. A more consistent and robust mapping of urban forests can enable a comparison of the size and quality management guidelines at major cities in Asia. However, few administrative statistics have employed these scientific techniques. Therefore, a cover map of urban forests focusing on their types is needed for the quantitative and qualitative estimation and evaluation of urban forests. This study presents an approach for mapping urban forest cover for 2021 using Sentinel remote sensing images and aerial photographs from commercial maps. Cover maps of urban forests at a city scale were developed for three pilot study areas in Korea’s capital area. Labeling was performed for deciduous forests, coniferous forests, grasslands, wetlands, bare land, croplands, urban areas, and water bodies. The land cover classification was performed using machine learning models, including random forest (RF), support vector machines (SVM), and gradient tree boost classifier (GBT), and a comparison was made among them. All three methods performed well in the three study areas; the RF model was simple to use, and the SVM was better at detecting forest connections. The proposed mapping method, which focuses on forest types in cities, can be used to compare the area and connection of urban forests in cities.

Online Destinations Map using Google Maps API Based on the Private Database

Online mapping applications have undergone a radical change as a result of the widespread use of the Internet,and an interested in the developing of the online mapping services due to the use of the Google Maps Application Programming Interface (API), Yahoo Maps API, and ESRI ArcGIS API, as well as the support for maps by operating systems on tablets and smartphones. One of the most helpful technologies for identifying locations and providing information about roads is online mapping. Although these applications are simple to use, they have a limited amount of the data that may be inaccurate, and the mapping technology does not have sophisticated or detailed characteristics of the required places. As a result, developers and programmers use maps as a platform to create features that are tailored to the needs of the user by using various programming languages. The goal of this paper is to develop the Google API by connecting it to a local, private database that houses data that is regularly gathered and developed. It also aims to create opportunities that make it easier for clients to access information stored on particular servers that can provide a variety of locations and details in order to meet their needs.

Health Risks Forecast of Regional Air Pollution on Allergic Rhinitis: High-Resolution City-Scale Simulations in Changchun, China

Accurate assessments of exposure to urban air pollution with higher traffic emissions and its health risks still face several challenges, such as intensive computation of air pollution modeling and the limited availability of personal activity data. The macroscopic health effects can be transmitted to the whole population for personal prevention via air quality health index (AQHI), but the possibility risk index of the specific allergic diseases is still lacking. This interdisciplinary study aims at evaluating the forecasted results of high-resolution air quality with updated traffic emissions and accessing the potential impacts of outdoor pollution on morbidity of rhinitis for urban residents. A high-resolution modelling system (1 km × 1 km) containing the online traffic emission model (VEIN), meteorological and air quality model (WRF-CHIMERE) and the health impact module was developed. A new health index of Potential Morbidity Risk Index (PMRI) was further established using higher resolution health risk coefficients of major air pollutants on allergic rhinitis, and different methods (with/without considering population distributions) targeting different user groups (residents, hospitals and health administrations) were calculated and analyzed. Operational forecasted results of hourly PMRI can be further combined with online map services to serve as an effective tool for patients with allergic rhinitis to arrange their daily activities so as to avoid acute exacerbation. The forecasted PMRIs accessible to the public will also be beneficial for the public health administrations in planning the medical resource and improving the outpatient efficiency.

Continuous trajectory similarity search with result diversification

With the continued increasing availability of spatio-temporal data from GPS-equipped devices, online map services, and a variety of location-based social media, trajectory similarity search has become a fundamental operation in location-based data analytics. It is of great importance to enable real-time search of trajectories that satisfy users’ personalized requirements. For the purpose, we study the Diversified Continuous Trajectory Similarity Search (DCTSS) problem. The DCTSS problem aims to process a large number of Continuous Location Set (CLS) queries over a stream of trajectory data while taking result diversity of each query into consideration. To answer the DCTSS problem, we develop a Diversity-Aware Trajectory Publish/Subscribe (DAT-PS) framework, which takes both trajectory data streams and CLS queries as input and performs query-trajectory matching between CLS queries in the query collection and trajectories over the trajectory data stream. Our experimental results on two real-life datasets show that our proposed DAT-PS framework is capable of demonstrating substantial superiority regarding both efficiency and scalability compared against baselines.

Semi-MapGen: Translation of Remote Sensing Image Into Map via Semisupervised Adversarial Learning

Online maps play an essential role in modern life. The convenience of acquiring remote sensing images provides reliable geographic information sources for the compilation of online maps. Some existing works have used the idea of domain mapping to translate remote sensing images into maps directly, which is of great prospect for application. However, many of the current remote sensing image-to-map translation works are performed in an unsupervised manner that would lead to problems such as distortion and local detail inaccuracy. Although the fully-supervised method is effective, it requires plenty of paired as well as matched data for training. Paired remote sensing images and maps with consistent spatial locations can be easily accessed through online map services, whereas many pairs of samples in which some geographic element information is not accurately and completely matched. Supervised learning-based translation models are often confused by these unmatched data. Accurate and complete matched data has to be selected deliberately by humans, and the manual selection process is time-consuming and laborious, which brings new challenges. Therefore, we propose a novel remote sensing image-to-map translation model named Semi-MapGen based on semi-supervised generative adversarial networks (GAN), which requires only a small set of accurate and complete matched data and plenty of unpaired data. In this model, we apply a knowledge extension-based learning strategy that can improve the accuracy of translated maps. In addition, we design Expansion loss and Channel-wise loss to learn the information from massive unpaired data in an unsupervised manner. Qualitative and quantitative experiment results on three datasets demonstrate that the proposed model outperforms state-of-the-art semi-supervised and supervised methods.

Interpretable machine learning models for detecting fine-grained transport modes by multi-source data

<abstract> <p>Analysis of transport mode choice is crucial in transportation planning and optimization. Traditionally, the transport mode of individuals is detected by discrete choice models (DCMs), which rely on data regarding individual and household attributes. Using these attribute data raises privacy concerns and limits the applicability of the model. Meanwhile, the detection results of DCMs may be biased, despite providing insight into the impact of variables. The machine learning models are more effective for mode detection, but most models need more interpretability. In this study, an interpretable machine learning model is developed to detect the transport modes of individuals. The mobility features of individuals, which introduce the velocity and acceleration of the center of mass (COM) are innovatively considered in the detection model. These mobility features are combined with multi-source data, including land use mix, GDP, population and online map service data as detection features. Using the travel survey data from Nanjing, China in 2015, the effects of different machine learning models on fine-grained detection performance are investigated. The results indicate that the deep forest model presents the best detection performance and achieves an accuracy of 0.82 in the test dataset, demonstrating the effectiveness of the proposed detection model. Furthermore, t-distributed stochastic neighbor embedding (t-SNE) and ablation experiments are conducted to overcome the non-interpretability issue of the machine learning models. The results show that the mobility features of individuals are the most critical features for improving detection performance. This study is essential for improving the structure of transport modes and maintaining low-carbon and sustainable development in urban traffic systems.</p> </abstract>

Du-Bus: A Realtime Bus Waiting Time Estimation System Based On Multi-Source Data

Realtime bus waiting time information is of great importance to the intelligent public transportation system and is beneficial for improving user satisfaction by online map services. While there are limited realtime bus waiting time services in a city, because of the expensive cost of sensor deployment and sophisticated traffic conditions. To address the above problem, we propose Du-Bus, a multi-source data fusion based system, which estimates the realtime bus waiting time based on approximating the realtime locations of buses without GPS sensors, by a variety of urban datasets, including historical bus trip data reported by a limited number of GPS equipped buses, transportation network data, traffic condition data, user mobility data, and temporal data. Du-Bus approximates the realtime locations of buses without GPS sensors by jointly modeling the bus timetable and the bus realtime travel time, which can be estimated by a variety of data sources. Specifically, we first propose a BiLSTM based end-to-end model for each bus route to estimate the bus departure interval and generate the corresponding departure timetable. Then, we estimate the travel time for each individual bus via a deep neural network component by incorporating the traffic conditions, geolocation, and map query information. Finally, we estimate the bus waiting time for arbitrary stations in the city by jointly modeling the estimated bus departure timetable and travel time. We evaluate our system on two real-world datasets, and the results verify the effectiveness of Du-Bus compared with historical average based and headway based methods. Since early 2019, Du-Bus has been deployed on Baidu Maps, one of the world’s largest map services, servicing over 20 major cities in China.

Evaluating the accessibility of urban parks and waterfronts through online map services: A case study of Shaoxing, China

Regular access to natural environment has many physical and mental health benefits for urban residents. This study was to evaluate the accessibility of urban parks and waterfronts in a Chinese canal city at the household level. Shaoxing, a typical canal town in the Yangtze River Delta region of China, was selected as the case study because of its abundant but underused natural resources. The study had measured the shortest travel routes from individual households to parks and waterfronts using data from the online map service and intensive fieldwork. The results showed that only 22% of Shaoxing residents live within a 500 m walking distance to parks, but scenario analysis showed if the canals were well used, almost all the residents would have access to natural environment within 15 min’ walk. Thus, the route-based accessibility evaluation method developed in this research offers a fine-grained understanding of household inequality in access to natural environments. It not only provides specific recommendations for planning intervention to improve the accessibility of natural resources in Shaoxing, but also contributes to the advancement of accessibility measures for planning practice. This route-based measure makes it possible to combine other accessibility measures of the travel routes such as sidewalk qualities in future research. The simplicity of this method means that it can be used to evaluate accessibility to other public facilities at the household level to develop walkable neighborhoods in cities.

Machine-Learning Approaches to Identify Travel Modes Using Smartphone-Assisted Survey and Map Application Programming Interface

Travel mode choice prediction is essential for transportation planning and travel demand prediction. One of the conventional travel survey methods is collecting data over landline telephones, which lacks efficiency because of financial and time resource needs. In this regard, smartphone-assisted travel surveys can be applied to overcome the mentioned deficiencies. Smartphone-assisted travel surveys allow respondents to record GPS data, travel purpose, and travel mode via an application, simplifying the survey process. With various sensors equipped, the precision of data is ensured. Based on the survey results, varied approaches have been seen to travel mode identification. For this study, a travel survey was conducted in Hangzhou, China, supported by the smartphone application TraceRecord integrated with online mapping services. Several steps were undertaken to recognize different kinds of travel modes. First, preprocessing was adopted to screen out defective logs. With the employment of A-Map Application Programming Interface (API), trajectory segmentation was substantially boosted. Then, separately, features related to velocity, acceleration, and heading were extracted from the survey data. To achieve better accuracy and efficacy, two classification algorithms—support vector machine (SVM) and gradient boosting decision tree (GBDT)—were applied to model the travel mode identification problem. Compared with the SVM, GBDT produced a higher prediction accuracy of 90.16%. Further analysis was implemented based on the results of the GBDT model, and velocity-related features contributed the most to the identification problem. The study explores the possibility of applying travel mode recognition in real-world conditions and discusses further mining of the survey data.

Discover Trip Purposes From Cellular Network Data With Topic Modeling

The widespread use of mobile phones has generated a large amount of individual trajectory data. Such data can greatly help analyze and understand human daily travel behavior. In this paper, we use the topic modeling technique to infer trip purposes based on pseudonymized users’ trajectory data from cellular network and points of interest (POIs) from online map services. The adapted latent Dirichlet allocation method is used to model the trip generation process and then infer trip purposes behind the data. The experiments are performed on a data set of 27,732 trip records in Beijing on weekdays. Ten topics are discovered. This method can easily infer different trip purposes based on three trip attributes, i.e., trip departure time, stay duration, and POI categories for destinations, and most of the topics/trip purposes are explainable.

Measuring spatio-temporal disparity of location-based accessibility to emergency medical services.

The importance and sensitivity of the time dimension in the emergency medical service (EMS) field have been widely recognized in recent years. However, the EMS spatio-temporal disparity remains partially uncovered in traditional accessibility measurements due to EMS's context-sensitive characteristics and demand specificity. In this study, we obtained dynamic traffic conditions and realistic EMS demand from online map services and historical emergency calls-out data, and then developed three location-based spatio-temporal EMS accessibility measurements. The empirical results demonstrate that the three different spatio-temporal EMS accessibility have a complex relationship, and are beneficial for measuring the EMS spatio-temporal disparity in different scenarios with distinct competitive effects. Ultimately, we emphasized four original EMS accessibility patterns, which helped us uncover significantly well-served or underserved areas. This study contributes to optimize the layout of EMS resources by understanding the regularity of EMS spatio-temporal disparity.

Developing an Advertising Location-Based Service in Urban Area Based on Location-Awareness and Desirability Criteria

Background and Purpose: In recent years, the use of smart mobile devices due to easy access, portability, and also simpler user interface, compared to desktop computers has been met with unparalleled acceptance by Iranian users, managers, and sellers of goods. That's why application development for smart devices in Internet businesses has become essential. On the other hand, the location-awareness of such programs will make it smarter and provide targeted advertising and services to users.Methods: This study provides a model that first handles and categorizes information from some service centers and quickly exchanges data between smart media (such as a mobile, tablet) and data centers based upon online map services (Google Maps). Second, based on the components of location-based services (LBS), in order to provide effective advertising to users, the components of selecting the service center and advertising and navigation of the user to it were put on the agenda based on a loose coupling strategy.Findings: In order to have the features of recommendation, three criteria of desirability, namely the introduction of the final option based on the shortest route in the transportation network, or based on user scores or based on scores allocated by the relevant service unit class were considered. Finally, the Location Finder application was implemented under the Android operating system using the new technologies of the mobile and web-based spatial information system through the proposed model.Discussion and Conclusion: The results of the questionnaire were statistically analyzed using the frequency of the respondents and significantly showed the users' satisfaction with the program, with 55 percent rating it as a great choice for quality, and its compliance with the set goals so that it could fill part of the gap caused by the advertising software.

Impact of the COVID-19 pandemic on breast cancer screening volumes and patient screening behaviors.

PurposeIn order to facilitate targeted outreach, we sought to identify patient populations with a lower likelihood of returning for breast cancer screening after COVID-19-related imaging center closures.MethodsWeekly total screening mammograms performed throughout 2019 (baseline year) and 2020 (COVID-19-impacted year) were compared. Demographic and clinical characteristics, including age, race, ethnicity, breast density, breast cancer history, insurance status, imaging facility type used, and need for interpreter, were compared between patients imaged from March 16 to October 31 in 2019 (baseline cohort) and 2020 (COVID-19-impacted cohort). Census data and an online map service were used to impute socioeconomic variables and calculate travel times for each patient. Logistic regression was used to identify patient characteristics associated with a lower likelihood of returning for screening after COVID-19-related closures.ResultsThe year-over-year cumulative difference in screening mammogram volumes peaked in week 21, with 2962 fewer exams in the COVID-19-impacted year. By week 47, this deficit had reduced by 49.4% to 1498. A lower likelihood of returning for screening after COVID-19-related closures was independently associated with younger age (odds ratio (OR) 0.78, p < 0.001), residence in a higher poverty area (OR 0.991, p = 0.014), lack of health insurance (OR 0.65, p = 0.007), need for an interpreter (OR 0.68, p = 0.029), longer travel time (OR 0.998, p < 0.001), and utilization of mobile mammography services (OR 0.27, p < 0.001).ConclusionSeveral patient factors are associated with a lower likelihood of returning for screening mammography after COVID-19-related closures. Knowledge of these factors can guide targeted outreach to vulnerable patients to facilitate breast cancer screening.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10549-021-06252-1.

Legal Evaluation for Security Exception Claims About the Cross-Border Transfer of the Korean Map: A Case of Google

Google, a U.S. company, has made repeated requests for the cross-border transfer of the Korean map. The requests have been rejected by the South Korean government, citing security concerns for satellite imagery. This article examined the legality of this rejection based on the Security Regulations on National Spatial Information of South Korea. The legal evaluation showed that the Korean government does not have the legal authority to regulate the online provision of satellite imageries by U.S. companies. Strict regulations of satellite imagery services, which are not specified in the current domestic law, create uncertainties for domestic and foreign companies providing online mapping services. This article offered legal insights on how to fundamentally address the deep-rooted stumbling block raised in the 2020 U.S. Trade Barrier Report concerning the cross-border transfer of the Korean map.

Spatiotemporal access to emergency medical services in Wuhan, China: accounting for scene and transport time intervals

BackgroundAccess as a primary indicator of Emergency Medical Service (EMS) efficiency has been widely studied over the last few decades. Most previous studies considered one-way trips, either getting ambulances to patients or transporting patients to hospitals. This research assesses spatiotemporal access to EMS at the shequ (the smallest administrative unit) level in Wuhan, China, attempting to fill a gap in literature by considering and comparing both trips in the evaluation of EMS access.MethodsTwo spatiotemporal access measures are adopted here: the proximity-based travel time obtained from online map services and the enhanced two-step floating catchment area (E-2SFCA) which is a gravity-based model. First, the travel time is calculated for the two trips involved in one EMS journey: one is from the nearest EMS station to the scene (i.e. scene time interval (STI)) and the other is from the scene to the nearest hospital (i.e. transport time interval (TTI)). Then, the predicted travel time is incorporated into the E-2SFCA model to calculate the access measure considering the availability of the service provider as well as the population in need. For both access measures, the calculation is implemented for peak hours and off-peak hours.ResultsBoth methods showed a marked decrease in EMS access during peak traffic hours, and differences in spatial patterns of ambulance and hospital access. About 73.9% of shequs can receive an ambulance or get to the nearest hospital within 10 min during off-peak periods, and this proportion decreases to about 45.5% for peak periods. Most shequs with good ambulance access but poor hospital access are in the south of the study area. In general, the central areas have better ambulance, hospital and overall access than peripheral areas, particularly during off-peak periods.ConclusionsIn addition to the impact of peak traffic periods on EMS access, we found that good ambulance access does not necessarily guarantee good hospital access nor the overall access, and vice versa.

Geovisualization Techniques of Spatial Environmental Data Using Different Visualization Tools

The appearance of online map services and frameworks (e.g., KML, QGIS) has increased the possibilities to easily, quickly and—in many cases—cost-effectively publish spatial data stored in databases. The aim of this study is to present the geovisualization of spatial databases of a Hungarian settlement on the web, using the open source webGIS system and Google application programming interfaces (APIs). The interactive point and interpolated distribution maps available online provide a detailed picture of the level of contamination, the spatial distribution of the ground water supply of the investigated settlement, and the changes which have occurred following the establishment of the sewage system. In the case of PO43− we determined that in the year before the sewage system was constructed, most of the area of the settlement could be considered contaminated, with the highest level of contamination measured in the central area of the settlement. Five years after the construction of the sewage system, the ratio of the contaminated areas had significantly decreased. In the case of NO3− we found a high level of contamination before the construction of the sewage system with a concentration increase in a North–West direction. After the establishment of the sewage system, the increase in concentration was the most intense in the central parts of the settlement, while the characteristic spatial distribution could no longer be observed. The geovisualization techniques developed are able to provide information about the different spatial data for users in a visual way, and also help to understand better the spatial information using a cognitive approach. The advantage of interactive web maps created with the technologies applied over traditional static maps is a new approach, which allows the user to manipulate the temporal and spatial data directly in the most appropriate way.