3-4: Social Carpooling-based Road Congestion Mitigation: A Three-Level Analysis

Abstract

Different from Transportation Network Companies (taxicabs, i.e., Uber) and shared-car providers (rental cars, i.e., Zipcar), social carpooling is personalized incentives-based and smartphone-enabled peer-to-peer ridesharing. Social carpooling emerges as a community-based strategy to reduce car ownership and mitigate congestion. It facilitates the transition from solo driving to effective carpooling by matching individuals’ travel demand in space and time. Personalized incentives, such as real-time information, travel feedbacks, and monetary incentives, are leveraged to spur the change. This project investigates the impact of social carpooling on users’ travel behavior and the system’s performance. The empirical data is supported by Metropia, which is a social carpooling platform enabling ride-match. Three research tasks are planned. First, at an individual level, we will identify what factors impact the travel mode change toward social carpooling? The findings will help understand how social carpooling is varied by individuals’ income, gender, race, and other sociodemographic characteristics. Meanwhile, we will explore the effectiveness of different personalized incentive schemes the use social carpooling. Second, at an area level, we will estimate how many social carpooling trips can be generated if such programs are fully deployed by the general public? This step will generate social carpooling trip demands for all traffic analysis zones, which is the basis for the following trip distribution, mode split, and dynamic traffic assignment. Third, at the system level, we will answer how much road congestion can be mitigated with a large-scale social carpooling deployment? Statistical models, machine learning algorithms, and simulation methods, including panel data analysis, synthetic minority oversampling technique, and dynamic traffic assignment, will be applied.