Directions API Research Articles

A travel time matrix data set for the Helsinki region 2023 that is sensitive to time, mode and interpersonal differences, and uses open data and novel open-source software

Travel times between different locations form the basis for most contemporary measures of spatial accessibility. Travel times allow to estimate the potential for interaction between people and places, and is therefore a vital measure for understanding the functioning, sustainability, and equity of cities. Here, we provide an open travel time matrix dataset that describes travel times between the centroids of all cells in a grid (N = 13,132) covering the metropolitan area of Helsinki, Finland. The travel times recorded in the dataset follow a door-to-door approach that provides comparable travel times for walking, cycling, public transport and car journeys, including all legs of each trip by each mode, such as the walk to a bus stop, or the search for a parking spot. We used the r5py Python package, that we developed specifically for this computation. The data are sensitive to diurnal variations and to variations between people (e.g. slow and fast walking speed). We validated the data against the Google Directions API and present use cases from a planning practice. The five key principles that guided the data set design and production – comparability, simplicity, reproducibility, transferability, and sensitivity to temporal and interpersonal variations – ensure that urban and transport planners, business and researchers alike can use the data in a wide range of applications.

Enhanced Ridesharing Using Directions API and Geolocation

Abstract: In the ever-evolving realm of urban transportation, the fusion of technological advancements and ubiquitous mobile connectivity has sparked a paradigm shift in how cities are navi- gated. This shift has led to innovative approaches in ridesharing and crowdsourcing, offering promising solutions to tackle traffic congestion, reduce emissions, and prioritize travel needs in urbanareas. Unlike traditional carpooling methods, where arrange-ments are made in advance, ridesharing in smart cities operates on real-time matching of drivers and riders, with priority given to factors such as destination and purpose.This study delves into the intricate dynamics of ridesharing and crowdsourcing for smart city transportation, investigating the complex interplay between technology and social factors.Additionally, it considers aspects of security, policy, and pricing strategies to ensure a seamlessand equitable experience for all stakeholders.Drawing insights from comprehensive case studies and practical implementations, this exploration highlights the advantages of ridesharing and crowdsourcing, including the reduction of carbon dioxide emis- sions and the prioritization of travel needs based on purpose. It offers guidance for stakeholders navigating the complexities and embracing the transformative potential of these innovative solutions in shaping the future of urban mobility.

Measuring geographic access to emergency obstetric care: a comparison of travel time estimates modelled using Google Maps Directions API and AccessMod in three Nigerian conurbations.

Google Maps Directions Application Programming Interface (the API) and AccessMod tools are increasingly being used to estimate travel time to healthcare. However, no formal comparison of estimates from the tools has been conducted. We modelled and compared median travel time (MTT) to comprehensive emergency obstetric care (CEmOC) using both tools in three Nigerian conurbations (Kano, Port-Harcourt, and Lagos). We compiled spatial layers of CEmOC healthcare facilities, road network, elevation, and land cover and used a least-cost path algorithm within AccessMod to estimate MTT to the nearest CEmOC facility. Comparable MTT estimates were extracted using the API for peak and non-peak travel scenarios. We investigated the relationship between MTT estimates generated by both tools at raster celllevel (0.6 km resolution). We also aggregated the raster cell estimates to generate administratively relevant ward-level MTT. We compared ward-level estimates and identified wards within the same conurbation falling into different 15-minute incremental categories (<15/15-30/30-45/45-60/+60). Of the 189, 101 and 375 wards, 72.0%, 72.3% and 90.1% were categorised in the same 15- minute category in Kano, Port-Harcourt, and Lagos, respectively. Concordance decreased in wards with longer MTT. AccessMod MTT were longer than the API's in areas with ≥45min. At the raster cell-level, MTT had a strong positive correlation (≥0.8) in all conurbations. Adjusted R2 from a linear model (0.624-0.723) was high, increasing marginally in a piecewise linear model (0.677-0.807). In conclusion, at <45-minutes, ward-level estimates from the API and AccessMod are marginally different, however, at longer travel times substantial differences exist, which are amenable to conversion factors.

APIASO: A Novel API Call Obfuscation Technique Based on Address Space Obscurity

API calls are programming interfaces used by applications. When it is difficult for an analyst to perform a direct reverse analysis of a program, the API provides an important basis for analyzing the behavior and functionality of the program. API address spaces are essential for analysts to identify API call information, and therefore API call obfuscation is used as a protection strategy to prevent analysts from obtaining call information from API address spaces. API call obfuscation avoids direct API calls and aims to create a more complex API calling process. Unfortunately, current API call obfuscation methods are not effective in preventing analysts from obtaining usable information from the API address space. To solve this issue, in this paper, we propose an API call obfuscation model based on address space obscurity. The key functions within the API are encrypted and moved to the user code space for execution. This breaks the relationship between the API and its address space, making it impossible for analysts to obtain address information about a known API from the API address space. In our experiments, we developed an archetypical compiler-level API call obfuscation system to automate the obfuscation of input source code into an obfuscated file. The results show that our approach can thwart existing API deobfuscation techniques and is highly resistant to various open-source dynamic analysis platforms. Compared to other obfuscation techniques, our scheme improves API address space obscurity by more than two times, the detection rate of deobfuscation techniques such as Scylla, etc. is zero, and the increase in obfuscation overhead is not more than 20%. The above results show that APIASO has better obfuscation effect and practicability.

A flexible and scalable single-level framework for OD matrix inference using IoT data

This study proposes a flexible and scalable single-level framework for origin–destination matrix (ODM) inference using data from IoT (Internet of Things) and other sources. The framework allows the analyst to integrate information from multiple data sources, while controlling for differences in data quality across sources. We assess the effectiveness of the framework through a real-world experiment in Greater Adelaide (GA), Australia. We infer car OD flows within the region using four separate data sources: site-level traffic counts from loop detectors, vehicle trajectories recorded by roadside Bluetooth sensors, partial OD flows based on data from in to vehicle navigation systems, and journey-to-work OD data collected by the Australian Census. We compare our OD inferences with those from the current version of the Strategic Adelaide Model (SAM), calibrated using data from traditional household travel surveys. We find remarkable consistency between our inferences and those from SAM, despite differences in input data and methodologies. For example, for the morning peak period, we predict the total number of trips made within GA to be equal to 556,000, while the corresponding prediction from SAM is equal to 484,000. The two predictions are within 15 per cent of each other. When we compare the spatial distribution of trips, in terms of origins and destinations, we find that our inferred OD matrix has an 86 per cent cosine similarity to the corresponding SAM matrix. Travel time predictions based on our OD inferences correspond closely to those predicted by the Google Directions API, lending further confidence in our inferences. In summary, our results show that the proposed framework can produce highly comparable ODM, trip production and trip attraction patterns to those inferred from traditional household travel survey-based transportation demand modelling methods.

Analysis in the field of volume-delay function research

One of the key parts of the travel demand model is the correct determination of the performance of the road system. Performance is partially determined by the correct choice of volume-delay functions (VDFs) and their input parameters. Worldwide, transport modellers have a wide range of VDFs to choose from, but research is needed to select the one that provides the most accurate estimates for specific conditions. The problem is that road conditions and traffic conditions are specific to each country, so assuming that we can use VDFs and their parameters based on traffic data from another country can lead to errors. For this reason, there is a need for research on VDFs in the context of a particular country's traffic conditions.The main objective of this paper is to provide knowledge and practices that can be used as a basis for future research. This paper discusses the most commonly used VDFs for links, their parameterisation and the types of data required for calibration, and approaches to their collection. This paper also aims to introduce crowdsourced real-time data as a possible new source of traffic data, with a focus on traffic data from the Google Maps Directions API. The results of this paper will be used in future research to select and parameterise the best-fitting VDFs for specific types of road facilities and traffic conditions within the national transport model of the Slovak Republic.

Intelligent Handover Algorithm for Vehicle-to-Network Communications With Double-Deep Q-Learning

For vehicle-to-network communications, handover (HO) management enables vehicles to maintain the connection with the network while transiting through coverage areas of different base stations (BSs). However, the high mobility of vehicles means shorter connection periods with each BS that leads to frequent HOs, hence raises the necessity for optimal HO decision making for high quality infotainment services. Machine learning is capable of capturing underlying pattern via data driven methods to find optimal solutions to complex problems, and much learning-based HO optimization research has been conducted focusing on specific network setups. However, attention still needs to be paid to the actual deployment aspect and standardized datasets or simulation environments for evaluation. This paper proposes a deep reinforcement learning-based HO algorithm using the input parameters that are configurable in the existing measurement report of cellular networks. The performance of the proposed algorithm is evaluated using the well-known network simulator ns-3 with its official LTE module. A realistic network setup in the city center of Glasgow (U.K.) is configured with vehicle trajectories generated by the routes mobility model using Google Maps Directions API. Evaluation results reveal that the proposed algorithm significantly outperforms the A3 RSRP baseline with an average of 25.72% packet loss reduction per HO, suggesting significant improvement in quality of service of phone call and video streaming, etc. The proposed algorithm also has a small implementation cost compared to some state-of-the-art and should be deployed by a software update to a local BS controller.

Design and Development of Video-Conferencing System with Sign Language Detection and Voice Recognition using Machine Learning Techniques

Communication is the process of sending and receiving messages through verbal or non-verbal means, including speech or oral methods, writing or gestures and behaviour. In this modern and fast-paced world communication is now easier and more accessible to everyone whether it is audio or video communication. But this comes with some challenges, when we talk about a country or country like India, people speak a thousand languages and learning all languages is not possible, so with the help of voice to a text recognition system, supported by mechanical learning it is possible to transform human language and actions into an internationally accepted language like English. Thus it can be understood by all. Language is a correspondence medium. It's hard to speak with individuals who talk an assortment of dialects. Individuals with incapacities and others with extraordinary requirements Text correspondence is trying for individuals with dyslexia. Address right now, text and interpretation applications are accessible disconnected substances are accessible This is a report about a program that takes sound and changes it to message, as well as some other language, eminently Indian, is utilized to interpret their material dialects. It utilizes a direct Translator API. Speaking with the Deaf (deaf/mute) is a primary task in our society today; this may be due to the truth that their way of communique (symptoms or gestures) requires an interpreter at all times. The conversion of symbols into text may be executed by the use of system mastering algorithms. This project pursuits to construct video conference systems that can guide signal language discovery and translation. Powerful and advanced machine gaining knowledge of fashions with the right statistics could be used to obtain excessive efficiency and accuracy.

Integrating SAP SD with Third-Party Applications for Enhanced EDI and IDOC Communication

This research paper explores the integration of SAP Sales and Distribution (SD) module with third-party applications to enhance Electronic Data Interchange (EDI) and Intermediate Document (IDOC) communication. As businesses increasingly rely on seamless data exchange for efficient operations, the ability to integrate SAP SD with external systems becomes crucial for optimizing supply chain processes. The study examines current challenges faced by organizations in achieving effective EDI and IDOC communication, including data inconsistency, interoperability issues, and delays in processing transactions. Through a comprehensive analysis of integration methodologies, including middleware solutions and direct API connections, the paper identifies best practices for facilitating real-time data exchange between SAP SD and third-party applications. Case studies of successful implementations demonstrate the positive impact on order processing speed, accuracy, and overall customer satisfaction. Additionally, the research highlights the role of emerging technologies, such as cloud computing and machine learning, in streamlining integration efforts and enhancing the reliability of EDI transactions. By providing actionable insights and practical recommendations, this study aims to equip organizations with the knowledge needed to leverage SAP SD integration effectively, thereby improving operational efficiency and fostering stronger partnerships with suppliers and customers. Ultimately, the findings underscore the importance of robust EDI and IDOC communication in achieving a competitive advantage in today’s dynamic business environment.

Modeling Departure Time Choice of Car Commuters in Dhaka, Bangladesh

Dhaka, one of the fastest-growing megacities in the world, faces severe traffic congestion leading to a loss of 3.2 million business hours per day. While peak-spreading policies hold the promise to reduce the traffic congestion levels, the absence of comprehensive data sources makes it extremely challenging to develop econometric models of departure time choices for Dhaka. This motivates this paper, which develops advanced discrete choice models of departure time choice of car commuters using secondary data sources and quantifies how level-of-service attributes (e.g., travel time), socio-demographic characteristics (e.g., type of job, income, etc.), and situational constraints (e.g., schedule delay) affect their choices. The trip diary data of commuters making home-to-work and work-to-home trips by personal car/ride-hailing services (957 and 934 respectively) have been used in this regard. Given the discrepancy between the stated travel times and those extracted using the Google Directions API, a sub-model is developed first to derive more reliable estimates of travel time throughout the day. A mixed multinomial logit model and a simple multinomial logit model are developed for outbound and return trip, respectively, to capture the heterogeneity associated with different departure time choice of car commuters. Estimation results indicate that the choices are significantly affected by travel time, schedule delay, and socio-demographic factors. The influence of type of job on preferred departure time (PDT) has been estimated using two different distributions of PDT for office employees and self-employed people (Johnson’s SB distribution and truncated normal respectively). The proposed framework could be useful in other developing countries with similar data issues.

An integrated analysis of housing and transit affordability in the Chicago metropolitan area

An integrated analysis of housing and transport affordability provides comprehensive insights into the affordability of different locations in a city. By focusing on transit‐based workers, who constitute a significant portion of commuters but are understudied in the affordability literature, this study proposes a new, integrated method for estimating housing and transit (H + T) affordability in the Chicago Metropolitan Area using census data and data from the Google Maps Directions API. Methodologically, the study contributes to the literature by proposing and implementing a new method that estimates H + T affordability in an integrated manner based on census data and data from the Google Maps Directions API across three types of housing occupants (i.e., owners with/without a mortgage and renters). Empirically, the results indicate varying H + T affordability distribution patterns among different types of housing occupants and provide a comprehensive picture of housing and transport affordability in the study area without incurring prohibitive time and cost of data collection. The study indicates that policy‐makers should consider expanding the existing concessionary fare scheme to include low‐income residents and establishing affordable housing programmes that subsidise not only mortgage/rent but also miscellaneous costs related to housing (such as utilities) in order to improve the overall H + T affordability for different types of housing occupants.

Machine Learning Framework for the Estimation of Average Speed in Rural Road Networks with OpenStreetMap Data

Average speed information, which is essential for routing applications, is often missing in the freely available OpenStreetMap (OSM) road network. In this contribution, we propose an estimation framework, including different machine learning (ML) models that estimate rural roads’ average speed based on current road information in OSM. We rely on three datasets covering two regions in Chile and Australia. Google Directions API data serves as reference data. An appropriate estimation framework is presented, which involves supervised ML models, unsupervised clustering, and dimensionality reduction to generate new input features. The regression performance of each model with different input feature modes is evaluated on each dataset. The best performing model results in a coefficient of determination R2=80.43%, which is significantly better than previous approaches relying on domain-knowledge. Overall, the potential of the ML-based estimation framework to estimate the average speed with OSM road network data is demonstrated. This ML-based approach is data-driven and does not require any domain knowledge. In the future, we intend to focus on the generalization ability of the estimation framework concerning its application in different regions worldwide. The implementation of our estimation framework for an exemplary dataset is provided on GitHub.

Multiple Uses of Big Data for Model Validation and Express Lanes Traffic Forecasts

This paper highlights a variety of uses for Big Data when developing project forecasts and model validations. In most travel models, validation often refers to estimating model volumes close to the observed highway counts. While this is an established practice for producing reasonable confidence in the model, these statistics are often not sufficient to build confidence in the project forecast. This is especially true for investment-grade level traffic and revenue forecasts for projects involving congestion pricing. This paper explores the application of Big Data to validate subarea models in multiple dimensions: subarea district-to-district origin-destination (O-D) flows; corridor segment-to-segment O-D flows; and trip length distribution by O-D types for the I-4 Ultimate Express Lanes Study. Additionally, the paper reviews historical O-D flows to determine the peak seasonal flow and appropriate O-D data to use in model validation and seed tables for Origin-Destination Matrix Estimation (ODME). In addition to model validation, the expanded Big Data O-D trips were assigned to multiple paths for each O-D pair built via Google Directions API to study the eligible corridor trips and the alternative corridors competing with the project. Furthermore, spatiotemporal distributions from Big Data were used to develop time-dependent trip tables for the Dynamic Traffic Assignment (DTA) model. Several international tourist attractions located along the I-4 Ultimate Corridor serve high visitor and weekend traffic, and Big Data was used to analyze and develop a weekend distribution model. Each of these modules involves some form of observed data, all coming from one source, “Big Data.”

Comparison study between direct api 20e® identification of gram-negative bacilli from positive blood cultures and bd phoenix ™ identification performed from the colonies

Comparison study between direct api 20e® identification of gram-negative bacilli from positive blood cultures and bd phoenix ™ identification performed from the colonies

Analyzing door-to-door travel times through mobile phone data

A strategic objective of the European transport policy is the so-called 4-h door-to-door target, according to which, by 2050, 90% of travelers within Europe should be able to complete their journey, door-to-door, within 4 h. However, information on door-to-door travel times is scarce and difficult to obtain, which make it difficult to assess the level of accomplishment of this ambitious target. In this paper, we extract door-to-door travel times based on the analysis of opportunistically collected data generated by mobile phones. Anonymized mobile phone records are combined with data from the Google Maps Directions API to reconstruct the different legs of the trip and estimate the travel times for the door-to-kerb, kerb-to-gate, gate-to-gate, gate-to-kerb and kerb-to-door segments. The travel times of these legs have been extracted for different scenarios, all of them focusing on the Adolfo Suarez Madrid-Barajas airport, with the aim of exploring their influence on total door-to-door travel time. Results show that the methodology presented is able to measure door-to-door travel times and that these travel times measured are far from the 4-h door-to-door target. We finish by outlining future research directions.

Mitigating Location Privacy Attacks on Mobile Devices using Dynamic App Sandboxing

Abstract We present the design, implementation and evaluation of a system, called MATRIX, developed to protect the privacy of mobile device users from location inference and sensor side-channel attacks. MATRIX gives users control and visibility over location and sensor (e.g., Accelerometers and Gyroscopes) accesses by mobile apps. It implements a PrivoScope service that audits all location and sensor accesses by apps on the device and generates real-time notifications and graphs for visualizing these accesses; and a Synthetic Location service to enable users to provide obfuscated or synthetic location trajectories or sensor traces to apps they find useful, but do not trust with their private information. The services are designed to be extensible and easy for users, hiding all of the underlying complexity from them. MATRIX also implements a Location Provider component that generates realistic privacy-preserving synthetic identities and trajectories for users by incorporating traffic information using historical data from Google Maps Directions API, and accelerations using statistical information from user driving experiments. These mobility patterns are generated by modeling/solving user schedule using a randomized linear program and modeling/solving for user driving behavior using a quadratic program. We extensively evaluated MATRIX using user studies, popular location-driven apps and machine learning techniques, and demonstrate that it is portable to most Android devices globally, is reliable, has low-overhead, and generates synthetic trajectories that are difficult to differentiate from real mobility trajectories by an adversary.

Multivariate approaches for the development of quality control in-situ fiber optics dissolution methods for fixed-dose combination tablets

The purpose of this research was to develop a fiber optic (FO) dissolution method for quantification of multiple actives in combination pharmaceutical tablets. FO dissolution allows direct API quantification in the vessel, obviating the need for error-prone facets of traditional dissolution methods. However, FO dissolution is potentially challenged by overlapping UV spectra, matrix effects, UV-active excipients, API interactions with excipients and media, and undissolved components attenuating the UV signal. These obstacles might render FO dissolution method development more complex than LC-end dissolution. The case study in this manuscript has the added complexity of a triple combination product (Midol), where acetaminophen, caffeine, and pyrilamine maleate exhibit similar release kinetics, share largely overlapping UV spectra and span an order of magnitude difference in concentration. Single-wavelength quantification required unique features for the actives of interest, which were not available for the formulation of interest without preprocessing. The methods employed for the quantification of actives were a partial least squares multivariate calibration and a peak area calibration, both using prepared mixtures as reference data. The selected combination tablet demonstrated collinear API release; therefore, individual quantification required a design of experiments for mixture design. The advantages of FO dissolution will be discussed in the context of the formulation under investigation. Additionally, some general guidelines will be suggested for the development of other FO methods.

New approaches to facilitate reporting of dengue cases in Taiwan

Purpose: Early detection and timely reporting of dengue cases are important strategies for the dengue prevention and control.To shorten the time between case reports submitted from medical institutions and received at public health authorities (PHA), Taiwan has established an electronic notification disease surveillance system (NDSS) since 2002. However, paper-based reporting is still preferred by most clinics. This may cause delays in implementing dengue control measures and increase the data entry workload for PHA. Frontline interviews indicated that complicated NDSS account application procedure and the lengthy reporting form are major reasons hindering clinics to use NDSS. To encourage clinics using NDSS, we proposed two approaches by providing convenient NDSS login and a data exchange API. Methods & Materials: We developed two reporting applications based on the existing NDSS system architecture, the widely-available clinic information systems(CIS), and the virtual private network (VPN) of the National Health Insurance (NHI) program. The first application allows doctors to enter NDSS accountlessly by having their RSA cards verified inside the NHI VPN. The second application allows clinics to use their CIS to report dengue cases to NDSS through a secure data exchange API and a shortened report form.To evaluate the effectiveness of the two applications, we analyzed the NDSS system logs and performed questionnaire surveys among clinics. Results: We found that doctors are more willing to use NDSS by accountless login, which saves login time and reduces login failures. Clinics without their own internet connections are more willing to report cases by NDSS via NHI VPN. Meanwhile, the direct API connection of the CIS with NDSS could reduce at least 70% of data entry workload. Conclusion: The study showed that these two new approaches effectively solved the NDSS login issue and significantly reduced time spent on data entry in clinics. The clinics’ willingness to report dengue via NDSS was improved. With decreasing paper-based reports, PHA staffs also benefited from less time spent and fewer entry errors while entering data into the NDSS. More time could be saved for dengue prevention and control measures.

Time-Dependent Intermodal A* Algorithm: Methodology and Implementation on a Large-Scale Network

This paper proposes a time-dependent intermodal A* (TDIMA*) algorithm. The algorithm works on a multimodal network with transit, walking, and vehicular network links, and finds paths for the three major modes (transit, walking, driving) and any feasible combination thereof (e.g., park-and-ride). Turn penalties on the vehicular network and progressive transfer penalties on the transit network are considered for improved realism. Moreover, upper bounds to prevent excessive waiting and walking are introduced, as well as an upper bound on driving for the park-and-ride (PNR) mode. The algorithm is validated on the large-scale Chicago Regional network using real-world trips against the Google Directions API and the Regional Transit Authority router.

Data Portability Under GDPR: Technical Challenges

Right to data portability under GDPR enables individuals to request a copy of their personal data, also for exporting it to other service providers’ platforms. While WP29 has issued a guideline, there are many questions about how to implement this right effectively. In this paper, we will focus on some of the more technical challenges as well as some of the complex tensions that ensue due to existing descriptions of what the right should entail. The prior includes matters like determining the right format and implementing possibilities for direct transmission and API’s. The latter focuses on issues like distinguishing provided (raw) from inferred (derived) data; separating data obtained under consent or contract from other data collected under different legal grounds; determining harm against other individuals’ rights and freedoms and potentially redacting personal data automatically; and the challenge of implementing portability “without hindrance” while at the same time implementing “appropriate security measures” which are mostly obstructive by nature.