AI-Driven Spatio-Temporal Prediction Model for Taxi Demand Prediction
While ride-hailing market share has been growing steadily since 2015, taxis are still in high demand in certain geographies due to stricter regulations on ride-hailing. Yet, supply-demand mismatches still persist due to poor taxi positioning. On the other hand, hotspot maps provide only current demand snapshots without forecasting future passenger needs or guiding supply responses. Similarly, ride-hailing surge pricing mechanisms fail to optimise fleet movement, providing only the current state of passenger demand.
AI-Based Dynamic Route Optimization And Driver Job Recommendation Tool
Same-day logistics players are crucial for the growth of eCommerce, particularly in urban areas. With eCommerce platforms facilitating continuous sales from multiple vendors, the logistics challenge has intensified. Logistics operators must pick up items from various decentralized locations and deliver them, handle dynamically generated delivery orders, and optimize order delivery for a win-win scenario among stakeholders (customers, eCommerce platform providers, vendors, logistics providers, and riders). In this industry, route optimization and rider dispatch is essential to increase earning potential for riders and reduce carbon footprint.
Collaborative Urban Delivery Optimisation (CUDO)
Consumer trends have had a significant impact on urban logistics. One key trend is the rise of e-commerce and online shopping, which has led to an exponential increase in package deliveries to urban areas. This surge in demand has put pressure on logistics service providers (LSPs) to develop more efficient and responsive last-mile delivery solutions.
