Taxi-sharing is admittedly a promising solution to reduce travel costs and mitigate congestion. However, sharing a ride with other passengers may impose long detours. Taxi-sharing can be more beneficial if the vehicle miles traveled for the detours can be reduced when serving additional passengers. In this study, we propose incorporating alternative meeting points in taxi-sharing routing to further boost efficiency by eliminating unnecessary detours and improving the chances of passengers being matched. Unlike traditional taxi-sharing systems, passengers are not necessarily picked up at their original location in the proposed system. Instead, they are serviced within a walkable distance of their origin (meeting points). This can be especially beneficial in heavily congested road networks in dense urban areas.There are several challenges in the real-world implementation of a ride-sharing system with alternative pick-up points in a dense high-demand network. The first challenge is to find appropriate passenger matches that may share a ride. The second challenge is to effectively find a set of specific locations as the potential alternative pick-up points that are likely to reduce total travel times. Once possible matches and the corresponding set of candidate pickup points are selected, the last challenge is to obtain optimal/near-optimal routes to satisfy the passengers’ demand with a reasonable computational time. In this study, we formulate a mixed integer programming model for the ride-sharing problem with alternative pick-up points and introduce strategies to cope with these challenges in a real-world setting. We use the New York City taxi demand data that may sometimes have hundreds of demands per minute in a relatively small geographical area. We first introduce a decomposition procedure to break down such a large-scale problem into smaller subproblems by pre-matching groups of passengers that are more likely to share a ride. We then create an initial set of candidate locations for all pick-up points in each group. Then, different strategies are proposed to reduce the set of candidate locations into smaller subsets, including alternative locations with higher potentials of constructing better routes. The experimental results show that incorporating alternative pick-up points results in significant savings in total travel times, total wait times, and the number of vehicles used compared to a baseline ride-sharing system that picks up each passenger exactly from their requested location. Finally, given the high computational cost of the optimization problem, we propose a genetic algorithm to find near-optimal solutions for the formulated problem, and we show that the proposed algorithm achieves solutions that are very close to the optimal solutions, with significantly lower computational times. We design specific operations to implement basic components of the genetic algorithm in the context of our algorithm. This includes strategies to diversify and create random sets of feasible solutions (individuals), select the fittest individuals in each generation, and create new generations through mutations and ordered cross-over such that the new individuals can inherit from their parents while still representing a feasible solution.