Improving transit representation in travel forecasting models

加拿大和北美的一项主要倡议是在政府预算的限制下为旅行者提供更好的公共交通。因此，有必要评估过境基础设施的新投资和过境业务的变化对吸引（乘客）和运营效率的影响。估计这些影响的一种方法是通过旅行预测模型。这些广泛使用的模型预测未来的旅行模式和系统性能，假设所有的旅行者都选择成本最低的方式（例如乘坐公共汽车、有轨电车或私家车）到达目的地。然而，最常用的模型通常不适合分析公交系统，因为它们不能准确地表示乘坐公交出行的成本。大多数模型假设交通运行（例如公交车之间的分钟数）是固定的，但事实可能并非如此。与需求增加而拥堵的高速公路系统不同，随着需求的增加，公交系统实际上对乘客更有吸引力，因为公交服务的频率可以随着需求的增加而增加，这意味着更短的等待时间和总旅行时间。当然，公交运营商对每条线路上的服务频率是有限制的，必须努力平衡他们的服务与整个系统的需求。然而，随着乘客数量的增加，收入也会增加，运输机构可能会在个别线路或新地区增加服务。传统的模型没有捕捉到这些动态。拟议的研究计划将开发数学方法来表示交通频率，客流量和财政限制之间的动态关系。这里生成的模型可以作为传统旅行预测模型的一部分或与之结合使用。这项研究将为地区、城市和交通机构带来更强大的交通建模能力，使政府能够投入稀缺的资源，以实现与更大的交通客流量相关的经济、社会和环境目标。

A major initiative throughout Canada and North America is to provide travelers with better public transportation within the limits of government budgets.  As such, there is a need to evaluate the impacts of both new investments in transit infrastructure and changes in transit operations on passenger attraction (ridership) and operating efficiency.  One method for estimating these impacts is via travel forecasting models.  These widely-used models predict future travel patterns and system performance assuming all travelers choose the lowest cost means (for example by bus, streetcar, or private car) to arrive at their destination.  The most commonly used models, however, are typically ill-suited for analyzing transit systems because they do not accurately represent the costs of traveling by transit.  Most models assume that transit operations (for example the minutes between buses) are fixed which may not be the case.  Unlike highway systems that become congested as demand increases, transit systems actually become more attractive to passengers as demand increases, because transit frequency of service can increase with demand, meaning shorter wait times and total travel times.  Naturally, transit operators have limits on how frequent the service can be on individual lines and must attempt to balance their service to demand throughout the system.  However, as ridership increases, revenues also increase and the transit agency may be able to increase its service along individual lines or in new areas.  These dynamics are not captured by conventional models.The proposed research program will develop mathematical methods to represent the dynamic relationship among transit frequency, ridership, and fiscal constraints.  The models generated here can be used as part of, or in conjunction with, traditional travel forecasting models.  The research developed will lead to much more robust transit modeling capabilities for regions, cities and transit agencies, allowing governments to invest scarce resources in order to achieve the economic, social and environmental goals associated with greater transit ridership.