Evaluating Sustainability Impacts of Intelligent Carpooling System among Single Occupancy Vehicle Commuters

Evaluating Sustainability Impacts of Intelligent Carpooling System among Single Occupancy Vehicle Commuters

Community-based carpooling has the significant potential to alleviate traffic congestion and reduce the transportation carbon footprint. This is especially true during the morning peak period, because most of the trips are regular work-related commutes with repetitive origin-destination and start and end time patterns, thereby increasing the likelihood for pre-identifying carpool opportunities. Previous studies at Georgia Tech involving collection of on-road vehicle occupancy data revealed that during the peak periods, over 80% of vehicles are in ‘drive alone’ or single occupancy vehicle (SOV) mode. Other studies have identified that barriers deterring people from carpooling include demographic, communication, and economic barriers, among others. With the recent advancements in information technology and the wide proliferation of GPS enabled smartphones, a privacy-protected communication mechanism can be developed to seamlessly enable instantaneous formation of carpools and thereby release the hidden demands of carpooling trips.

The purpose of this study is to develop an analytical framework for assignment of individual vehicular trips into viable carpooling pairs under a constrained set of reasonable restrictions, such as temporal and spatial bounds/limits, user preferences, etc. Current studies at Georgia Tech based on Atlanta Regional Commission’s (ARC’s) Activity-based Model (ABM) show that even under strict temporal and spatial restrictions, about 18% of individual vehicular trips are still amenable to carpooling from home to employment centers. This proposed study aims to formalize a framework for the determination of the trip by extending the constraint parameters beyond basic spatial and temporal constraints. The research also proposes the use of a stochastic simulation environment for system reliability analysis. Overall, the study aims to contribute towards guidance on operational strategies to achieve environmental and economic sustainability for future intelligent carpooling systems in a complex urban traffic environment.

For evaluating systemic sustainability of the carpool system, the study will adopt a bi-pronged approach consisting of: (1) evaluation of economic benefits for users; (2) evaluation of the reliability of carpooling system in the presence of a sudden unanticipated demand (e.g., a driver cannot drive due to vehicle failure or otherwise, thus creating a “pop-up carpool demand”). Systemic performance will be mapped to the link-level transportation network to allow for the estimation of the potential benefits of traffic operations in terms of energy savings and emissions reduction using the MOVES model.

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