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

在这个项目中，我们建议从乘客、运营商和政策制定者的角度来研究与动态拼车相关的基本问题。具体地说，我们希望(I)量化乘客对动态拼车的各种服务属性的偏好，如上车延误、路线迂回和票价；(Ii)建立一个仿真平台，使我们能够定量评估动态拼车的性能及其社会影响；(Iii)开发一种高性能的调度算法，这是动态拼车的核心技术。它应能够处理需求和出行时间的随机性，并能够协调上海等特大城市的车队；以及(Iv)评估动态拼车对社会和环境的影响，并确定将其与现有出行方式相结合的方法，以满足居民的出行需求。拟议项目的结果将使我们能够深入了解乘客的偏好、动态拼车的运营挑战及其对建设更环保和更可持续的城市的影响。项目的总体方法方法包括四个相互关联的工作包(WPS)。第一个工作包包括对乘客偏好模型的估计，这是本研究项目下游任务所必需的。特别是，我们从熊猫巴士收集声明的偏好(基于智能手机的调查数据，基于专门的电子问卷)和揭示的偏好数据，并将它们与智能手机和车辆上的传感器数据相结合。然后，我们使用这个工作包的输出作为工作包2和3的输入。在WP2中，我们扩展了流量建模和仿真系统中的最新技术，以便它们能够捕获动态的VANPOLING服务。在WP3中，我们结合了WP1的输出，并开发了服务车辆的优化调度算法，以最大化给定车队规模的服务质量。在两个工作包2和3中，我们都明确地处理了需求和供应的不确定性。在WP4中，我们使用工作包2和3的输出，并开发了一个基于模拟的敏感度分析框架，用于在网络范围内分析VANPOLING服务的影响。基于模拟的敏感性分析框架优于对少数不同网络的影响分析，因为其结果可以概括为每个影响和每个模型输入之间的关系。例如，我们的模型的输出将是一个特定的公式，将需求水平、网络类型和流量组合与每种类型的影响水平联系起来。因此，结果将可用于任何类型的VANPOOL服务，而不限于正在分析的特定系统。

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.