On-demand last mile transportation: Real-time route optimization with Location Intelligence` is a complex problem that requires advanced algorithms and techniques to solve efficiently. The traditional last mile transportation problem is a vehicle routing problem (VRP), which is a combinatorial optimization problem known for its complexity from an algorithmic design point of view as well as computationally. On the other hand, real-time on-demand transportation problems are smaller problems because there is less information available but a response is needed immediately. The main challenge in solving this problem is to calculate all the required costs and design an algorithm that can find a near-optimal solution in a matter of seconds. Traditional logistics solutions cannot be adapted to on-demand transportation problems due to their complexity. To overcome this, spatial data science can make a difference by building data models that simulate existing conditions providing insights on existing constraints inefficient assignments and much more. Two initial approaches to solve the problem are the greedy algorithm and the batch assignment algorithm. The greedy algorithm searches for the nearest idle rider and assigns the order to that rider, while the batch assignment algorithm postpones the order assignment and runs the algorithm every n minutes. The batch assignment algorithm improves the assignments by having more information available at each iteration of the problem. To further improve the assignments, broadening the information available at each iteration is essential, such as considering what could happen in the following 30 to 60 minutes or even more time depending on the length of the services. Other improvements that would lead to more efficient and higher quality assignments include optimization criteria, such as time, utilization, customer and rider experience, matching the quality of the customer in terms of the frequency of use of the service and the quality of the rider based on the customer’s opinions and efficiency in delivery, combining orders, and creating larger batches by postponing the assignment as much as possible.