Carpooling Algorithm with the Common Departure

Ridesharing is becoming a popular mode of transportation with profound effects on the industry. We propose and investigate a novel carpooling with the common departure (CCD) problem that is to find optimal matching planning with minimal global travel cost. This problem applies to carpool among employees in large companies. This type of functionality holds the potential to bring significant benefits to society and the environment, such as reducing energy consumption and reducing traffic congestions. Although there are many ridesharing services for passengers and drivers, there is less research work on carpooling with common departure, especially when considering the global optimal aspects. To this end, a Three-level Matching Strategy for solving the CCD problem is established, including destination grid-based allocation, driving route and grid-based allocation, and passenger supplement strategy. The purpose is to reduce the detour distance traveled by the driver when serving passengers. Meanwhile, the probability of passengers traveling individual is reduced, so that the global travel cost is minimized. It can be seen that the Three-level Matching Strategy is an effective algorithm for the CCD problem. Finally, we conduct extensive experiments on real road networks and show that the Three-level Matching Strategy can save 66.7% of global travel costs compared with the non-matching method. Simultaneously, the average system response time was 2.65 min when the experimental data nodes density was 16% and show that our methods achieve satisfactory performance.