Dynamic vanpool services: Passenger preferences, operations modeling and simulation-based quantification of impacts

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In this project, we propose to investigate fundamental problems associated with dynamic vanpooling from the perspectives of passengers, operators, as well as policy makers. Specifically, we want to (i) quantify passengers preference toward various service attributes of dynamic vanpooling such as pick-up delay, circuity of the routes, and fare; (ii) build a simulation platform that can allow us to quantitatively evaluate the performance of dynamic vanpooling and its social impacts; (iii) develop a high-performance scheduling algorithm, which is a core technology for dynamic vanpooling. It shall be capable of handling stochasticity in both demand and travel time, and can coordinate a fleet of vehicles in megacities like Shanghai; and (iv) assess the social and environmental impacts of dynamic vanpooling and identify ways to integrate it with existing travel modes to serve the mobility needs of the residents. The outcome of the proposed project shall allow us to develop a thorough understanding of the preferences of the passengers, the operational challenges of dynamic vanpooling, and its impact to building greener and more sustainable cities.The overall methodological approach for the project combines four interrelated work-packages (WPs). The first work-package covers the estimation of passenger preference models, that are necessary for the downstream tasks of this research project. In particular, we collect stated preference (smartphone-based survey data based on a specialized electronic questionnaire) and revealed preference data from Panda Bus, and couple them with data from sensors on the smartphones and the vehicles. We then use the outputs of this work-package as inputs to work-packages 2 and 3. In WP2 we extend the state-of-the-art in traffic modeling and simulation systems, so that they can capture dynamic vanpooling services. In WP3, we incorporate the outputs of WP1 and develop algorithms for optimal scheduling of the service vehicles, in a way that maximizes the service quality for a given fleet size. In both work-packages 2 and 3, we explicitly deal with demand and supply uncertainty. In WP4, we use the output of work-packages 2 and 3 and develop a simulation-based sensitivity analysis framework for the network-wide analysis of the impacts of the vanpooling service. The simulation-based sensitivity analysis framework is preferred over the analysis of impacts on a small number of different networks, as its results can be generalized into relationships between each impact and each of the model inputs. For example, the output of our model will be a specific formula linking the demand level, the type of network, and the traffic mix with each type of impact level. Thus, the results will be useable by any type of vanpool service, and not restricted to the specific systems under analysis.