<p>Low vehicle occupancy rates in metropolitan areas contribute to traffic congestion, fuel consumption, and greenhouse gas emissions. To address these challenges, this study proposes a peer-to-peer (P2P) ridesharing model without predetermined drivers, enabling grouping of up to four passengers. The model improves matching efficiency with a column generation (CG) based heuristic approach to selectively form multi-passenger groups. To better capture user preferences, the model incorporates the value of time (VoT) into the cost function, accounting for dissatisfaction caused by additional travel time in ridesharing trips. Although VoT is widely used in transportation modeling, it is often neglected in ridesharing contexts, failing to represent the disutility of shared travel experience. Validated with Chicago taxi data, the model demonstrates a strong network effect: higher participation leads to improved matching rates and greater cost and distance savings. A sensitivity analysis under various cost structures shows that VoT has a more pronounced effect in low-fare scenarios, particularly when the distance rate is low. For instance, under a $0 base fare and $0.10/km distance rate, increasing VoT from $0/min to $1/min reduces cost savings by 20% and lowers average occupancy from 2 to 1.5 passengers per vehicle-kilometer. These findings highlight the importance of incorporating VoT in ridesharing models to ensure practicality and user satisfaction. The proposed model offers a scalable and policy-relevant tool to enhance vehicle occupancy, reduce travel distances, and support more sustainable urban transportation systems.</p>