Asistobe provides a public transportation planning tool that integrates diverse mobility data sources, including transit, micro-mobility, and demographic information, to accurately forecast future transport demand. The platform enables cities to optimize their transport networks, enhancing efficiency and reducing operational costs by utilizing AI-driven insights based on real-world travel patterns.
Funding
$570K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Cities often struggle to efficiently plan and optimize public transportation networks due to the complexity of integrating diverse mobility data sources and accurately forecasting future transport demand. Traditional methods rely on broad assumptions or costly consultants, making it difficult to adapt to evolving transport scenarios and optimize resource allocation. This can lead to increased operational costs, reduced service quality, and a larger carbon footprint.
Solution
Asistobe offers a public transportation planning platform that leverages AI and machine learning to provide cities with a deep understanding of real transport demand. The platform integrates various data sources, including public transit data, micro-mobility data, demographics, and mobile network data, to uncover actual movement patterns. By analyzing this data, Asistobe's algorithms forecast transportation demand with high precision, enabling transport planners to strategically optimize their networks, reduce operational costs, and enhance efficiency. The platform allows for the creation of real-world scenarios and provides insights to tailor public transportation services to the actual needs of travelers, ensuring strategies are rooted in the most current information.
Target Audience
Asistobe primarily targets transport planners and public transport providers in cities seeking to optimize their networks, reduce operational costs, and improve service efficiency.
Features
- Integration of diverse mobility data sources (public transit, micro-mobility, demographics, mobile network data)
- AI and machine learning algorithms for accurate transportation demand forecasting (85-90% precision)
- Scenario planning to simulate real-world events (e.g., concerts, new hospitals) and their impact on transport demand
- Network optimization to reduce operational costs and carbon footprint
- Identification of optimal trip frequencies based on demand, operational costs, and service constraints
- Analysis of the effectiveness of transportation strategies and adaptation to evolving transport scenarios