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Hubris or humility? Accuracy issues for the next 50 years of travel demand modeling

机译：傲慢还是谦卑？未来50年旅行需求建模的准确性问题

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This study reviews the 50-year history of travel demand forecasting models, concentrating on their accuracy and relevance for public decision-making. Only a few studies of model accuracy have been performed, but they find that the likely inaccuracy in the 20-year forecast of major road projects is ±30 % at minimum, with some estimates as high as ±40–50 % over even shorter time horizons. There is a significant tendency to over-estimate traffic and underestimate costs, particularly for toll roads. Forecasts of transit costs and ridership are even more uncertain and also significantly optimistic. The greatest knowledge gap in US travel demand modeling is the unknown accuracy of US urban road traffic forecasts. Modeling weaknesses leading to these problems (non-behavioral content, inaccuracy of inputs and key assumptions, policy insensitivity, and excessive complexity) are identified. In addition, the institutional and political environments that encourage optimism bias and low risk assessment in forecasts are also reviewed. Major institutional factors, particularly low local funding matches and competitive grants, confound scenario modeling efforts and dampen the hope that technical modeling improvements alone can improve forecasting accuracy. The fundamental problems are not technical but institutional: high non-local funding shares for large projects warp local perceptions of project benefit versus costs, leading to both input errors and political pressure to fund projects. To deal with these issues, the paper outlines two different approaches. The first, termed ‘hubris’, proposes a multi-decade effort to substantially improve model forecasting accuracy over time by monitoring performance and improving data, methods and understanding of travel, but also by deliberately modifying the institutional arrangements that lead to optimism bias. The second, termed ‘humility’, proposes to openly quantify and recognize the inherent uncertainty in travel demand forecasts and deliberately reduce their influence on project decision-making. However to be successful either approach would require monitoring and reporting accuracy, standards for modeling and forecasting, greater model transparency, educational initiatives, coordinated research, strengthened ethics and reduction of non-local funding ratios so that localities have more at stake.
机译：这项研究回顾了旅行需求预测模型的50年历史，重点研究了其准确性和与公共决策的相关性。仅进行了少量的模型精度研究，但他们发现，大型道路项目20年预测中的误差可能至少为±30％，而在更短的时间内，某些估计甚至高达±40-50％。视野。特别是对于收费公路，存在明显高估交通和低估成本的趋势。对运输成本和载客量的预测更加不确定，而且也非常乐观。美国旅行需求建模中最大的知识鸿沟是美国城市道路交通预测的准确性未知。确定了导致这些问题的建模弱点（非行为内容，输入和关键假设的不准确性，政策不敏感以及过于复杂）。此外，还审查了鼓励乐观偏见和预测中的低风险评估的制度和政治环境。主要的制度因素，特别是本地资金匹配和竞争性赠款较低，使情景建模工作变得混乱，并削弱了仅靠技术建模改进可以提高预测准确性的希望。根本问题不是技术问题，而是体制问题：大型项目在非本地资金中所占比例很高，这扭曲了当地对项目收益与成本的看法，从而导致投入错误和为项目提供资金的政治压力。为了解决这些问题，本文概述了两种不同的方法。第一个被称为“ hubris”，它提出了数十年的努力，旨在通过监视性能，改进数据，方法和对旅行的理解，以及通过故意修改导致乐观主义偏见的制度安排，来随着时间的推移大幅提高模型的预测准确性。第二种方法称为“谦卑”，它建议公开量化和认识旅行需求预测中固有的不确定性，并有意减少其对项目决策的影响。然而，要想成功，这两种方法都需要监测和报告准确性，建模和预测标准，更大的模型透明度，教育举措，协调的研究，加强道德操守和降低非本地资金比率，以便使本地风险更大。

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