Next-Generation Decision-Support Tool for Urban Transportation Planning: Accommodating on-Demand Mobility and Automated/Connected Automated Vehicle Systems

在基于互联网/智能手机的按需服务和自动车辆(AV)技术的推动下，即将到来的交通革命将彻底改变我们规划、维护和运营城市交通基础设施的方式。然而，我们既没有全面了解这些技术对城市交通系统的影响，也没有适当的决策支持工具来帮助制定可持续系统所需的公共政策。 音响系统将影响出行行为，提供新的选择，同时可能淘汰旧的选择。对私家车的需求可能会增加，特别是在那些不会开车的人中，例如儿童、老年人。这可以改善机动性和社会包容性，但代价是更多地依赖汽车，或许还会导致当前城市交通形式的消亡。然而，当今最好的交通需求模型无法做出可靠的预测。他们使用家庭旅行调查来获取数据。这类调查收集了城市形态和交通技术的当前/现有背景下的数据，因此这些模型的数学规范是基于传统交通系统的需求-供应相互作用。随着汽车、按需移动服务的广泛应用以及我们对交通需求的变化，目前的城市形态、交通系统和供需互动的背景在未来可能不再有效。因此，迫切需要更新当前最先进的出行需求模型(城市交通决策支持系统)，这是本项目所设想的。该项目将开发的下一代决策支持系统将基于基于消费者行为的旅行选择模型和通过设计的实验收集的准确的旅行需求数据(AV和按需移动性的影响)。这些模型可以预测使用城市交通系统的潜在/被抑制的需求，因为后者由于自动化程度的提高和协调/连接的运营方式而发展。 我在旅行调查方法、数据融合和旅行需求建模以及培训学生方面有丰富的经验，这些学生在公共、非营利性和营利性组织对城市交通的需求很高。该项目将对18名学生进行交通规划、旅行需求建模、旅行调查方法、统计分析和软件开发方面的培训。该工具(决策支持系统)将在以证据为基础的交通规划和政策制定方面开辟新天地，同时认识到未来的不确定性。它将帮助城市规划者、交通规划者、交通工程师和公共政策制定者为即将到来的时代制定交通和土地利用政策，提供关于城市经济、环境可持续发展和社会公平的准确数据。

The upcoming transport revolutiondriven by internet/smartphone-based on-demand services and automated vehicle (AV) technologieswill completely change the way we plan, maintain and operate urban transportation infrastructures. Yet, we have neither a comprehensive understanding of the impacts of these technologies on urban transportation systems nor an appropriate decision-support tool to help develop the public policies necessary for sustainable systems. AV systems will affect travel behaviour, presenting new choices while possibly eliminating old ones. The demand for private cars may increase, particularly among those incapable of driving, e.g. children, seniors. This can improve mobility and social inclusion but at the cost of greater reliance on the automobile and, perhaps, the demise of current forms of urban transit. However, today's best transportation demand models cannot make reliable predictions. They use household travel surveys for data. Such surveys collect data that are of current/existing contexts of urban form and transportation technologies, and so the mathematical specifications of these models are based on the demand-supply interactions of conventional transportation systems. Current contexts of urban form, transportation systems and demand-supply interactions may not remain valid for the future with widespread applications of AV, on-demand mobility services and our changed demands for transportation. So, there is a dire need of updating the current state-of-the art of travel demand models (a decision-support system for urban transportation) and this is envisioned in this project. The next-generation decision-support system to be developed in this project will be based on consumer behaviour-based travel choice models and on accurate travel demand data collected through designed experiments (of impacts of AV and on-demand mobility). These models can predict latent/suppressed demands for using urban transportation systems as the latter evolve due to increasing automation and a coordinated/connected approach to operations. I have extensive experience in travel survey methods, data fusion and travel demand modelling as well as in training students, who are in high demand at public, non-profit and for-profit organizations in urban transportation. The project will train 18 students on transportation planning, travel demand modelling, travel survey methods, statistical analysis and software development. The tool (decision-support system) will break new ground on evidence-based transportation planning and policy development while recognizing future uncertainty. It will help urban planners, transportation planners, traffic engineer, and public policy-makers develop transportation and land-use policies for the impending era with accurate data on the urban economy, environmental sustainability and social equity.