The Poverty Indicator and Vulnerability Estimator (PIVE) project, partnered with
the Technology Innovation Challenge, Asian Development Bank (ADB), and being implemented by LocationMind, Inc., a Japanese technology company, tapped SafeTravelPH Mobility Innovations Organization as the local technical support partner in building the field survey tools for collecting poverty, disaster vulnerability, and urban transport data.
The project is expected to be completed by May 2024 and requires the extensive collaboration with potential users from the governmane, as well as other nonprofit organizations that are active in capacitating communities to address climate, mobility, and socio-economic challenges.
SafeTravelPH will support local mobility analysis and evaluation of transport policies in the
Philippines. which involves providing analytical support to assess the effectiveness of local mobility and transport policies in the Philippines, including conducting data analysis, developing and applying transport models, and producing reports and recommendations based on findings.
Also, the organization will provide assistance with the development of use-cases based on the requirements and feedback from potential users. This is entail working with stakeholders to identify and develop use-cases, conduct user research, and developing use case scenarios for estimating vulnerability, and development of policies and solutions.

Finally, SafeTravelPH will collaborate with government institutions, and advisory support for the social implementation of PIVE deliverables, and will deliver and design capacity building and training workshops for the related stakeholders, building the knowledge and skills of the potential users.
One of the innovation to applied in PIVE is the use of Mobile GPS data from various sources that collected device location data based on user consent or acceptance of terms of use and activities on different apps. This big data requires calibration and training datasets from local and ground truth data such as those being generated and collected by the SafeTravelPH mobile app. Zhang et al (2020) devised a method for this use case that can help determine travel patterns such as vehicle/commute mode, origin-destination pairs, and trip trajectories or route assignments.
Any city or intercity transport planning modeling works and government intervention such as route rationalization shall benefit from this combination of crowdsourced data and digitally co-produced ground information.
This paper can be accessed with the information below.

Haoran Zhang, Jinyu Chen, Wenjing Li, Xuan Song, Ryosuke Shibasaki, Mobile phone GPS data in urban ride-sharing: An assessment method for emission reduction potential, Applied Energy, Volume 269, 2020, 115038, ISSN 0306-2619,
Abstract: Spreading green and low-consumption transportation methods is becoming an urgent priority. Ride-sharing, which refers to the sharing ofcarjourneys so that more than one person travel in a car, and prevents the need for others to drive to a location themselves, is a critical solution to this issue. Before being introduced into one place, it needs a potential analysis. However, current studies did this kind of analysis based on home and work locations or social ties between people, which is not precise and straight enough. Few pieces of research departed from real mobility data, but uses time-consuming methodology. In this paper, we proposed an analysis framework to bridge this gap. We chose the case study of Tokyo area with over 1 million GPS travel records and trained a deep learning model to find out this potential. From the computation result, on average, nearly 26.97% of travel distance could be saved by ride-sharing, which told us that there is a significant similarity in the travel pattern of people in Tokyo and there is considerable potential of ride-sharing. Moreover, if half of the original public transit riders in our study case adopt ride-sharing, the quantity of CO2 is estimated to be reduced by 84.52%; if all of the original public transit riders in our study case adopt ride-sharing, 83.56% of CO2 emission reduction can be expected with a rebound effect because of increase of participants from public transit. Ride-sharing can not only improve the air quality of these center business districts but also alleviate some city problems like traffic congestion. We believe the analysis of the potential of ride-sharing can provide insight into the decision making of ride-sharing service providers and decision-makers.
Keywords: Ride-sharing; Deep learning; Users matching; Potential emission reduction
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