Using Louisiana’s Interstate system, this paper aims to demonstrate how data can be used to evaluate freight movement reliability, economy, and safety of truck freight operations to improve decision-making. Data main...Using Louisiana’s Interstate system, this paper aims to demonstrate how data can be used to evaluate freight movement reliability, economy, and safety of truck freight operations to improve decision-making. Data mainly from the National Performance Management Research Data Set (NPMRDS) and the Louisiana Crash Database were used to analyze Truck Travel Time Reliability Index, commercial vehicle User Delay Costs, and commercial vehicle safety. The results indicate that while Louisiana’s Interstate system remained reliable over the years, some segments were found to be unreliable, which were annually less than 12% of the state’s Interstate system mileage. The User Delay Costs by commercial vehicles on these unreliable segments were, on average, 65.45% of the User Delay Cost by all vehicles on the Interstate highway system between 2016 and 2019, 53.10% between 2020 and 2021, and 70.36% in 2022, which are considerably high. These disproportionate ratios indicate the economic impact of the unreliability of the Interstate system on commercial vehicle operations. Additionally, though the annual crash frequencies remained relatively constant, an increasing proportion of commercial vehicles are involved in crashes, with segments (mileposts) that have high crash frequencies seeming to correspond with locations with recurring congestion on the Interstate highway system. The study highlights the potential of using data to identify areas that need improvement in transportation systems to support better decision-making.展开更多
为解决网络货运平台价格预测不准确导致的成交率下降问题,提出基于Shingling检索的表格先验数据拟合网络(tabular prior-data fitted network,TabPFN)的局部上下文学习(local context learning with TabPFN based on shingling retrieva...为解决网络货运平台价格预测不准确导致的成交率下降问题,提出基于Shingling检索的表格先验数据拟合网络(tabular prior-data fitted network,TabPFN)的局部上下文学习(local context learning with TabPFN based on shingling retrieval,ShinglingPFN)模型。首先,该模型运用w-Shingling检索算法,从历史订单数据中匹配出与预测订单最相似的订单,构建局部关联的上下文数据。然后,加载并初始化预训练的TabPFN模型实例,将筛选出的订单数据输入模型,让TabPFN基于这些上下文信息学习货运特征与运费的关联模式。最后,输出该货运样本的运费预测结果。结果表明,ShinglingPFN模型相比随机森林(random forest,RF)模型减少了30.98%的平均绝对误差(mean absolute error,MAE)。通过全局敏感性分析,进一步增强了模型的可解释性。ShinglingPFN模型可为平台优化定价策略提供决策支撑。展开更多
This research study explores the use of an innovative freight tour-based approach to model truck trips as an alternative to the conventional trip-based approach. The tour-based approach is more realistic as it capture...This research study explores the use of an innovative freight tour-based approach to model truck trips as an alternative to the conventional trip-based approach. The tour-based approach is more realistic as it captures the intermediate stops of each truck and reflects the implications of those stops on vehicle miles traveled (VMT). The paper describes the truck tour-based model concept, and presents the framework of a truck tour-based travel demand forecasting approach. As a case study, Global Positioning System (GPS) truck data are used to determine origin, destination, and truck stops for trucks moving within the Birmingham, Alabama region. Such information is then utilized to model truck movements within the study region as individual truck tours. The tour-based model is ran, and the resulting performance measures are contrasted to those obtained from the conventional trip-based planning model used by the Regional Planning Commission of Greater Birmingham (RPCGB). This case study demonstrates the feasibility of using a tour-based freight demand forecasting model as an alternative to the conventional 4-step process currently used to estimate truck trips in the Birmingham region. The results and lessons learned from the Birmingham case study are expected to improve truck movement modeling practices in the region and advance the accuracy of truck travel demand forecasting models at other locations in the future.展开更多
文摘Using Louisiana’s Interstate system, this paper aims to demonstrate how data can be used to evaluate freight movement reliability, economy, and safety of truck freight operations to improve decision-making. Data mainly from the National Performance Management Research Data Set (NPMRDS) and the Louisiana Crash Database were used to analyze Truck Travel Time Reliability Index, commercial vehicle User Delay Costs, and commercial vehicle safety. The results indicate that while Louisiana’s Interstate system remained reliable over the years, some segments were found to be unreliable, which were annually less than 12% of the state’s Interstate system mileage. The User Delay Costs by commercial vehicles on these unreliable segments were, on average, 65.45% of the User Delay Cost by all vehicles on the Interstate highway system between 2016 and 2019, 53.10% between 2020 and 2021, and 70.36% in 2022, which are considerably high. These disproportionate ratios indicate the economic impact of the unreliability of the Interstate system on commercial vehicle operations. Additionally, though the annual crash frequencies remained relatively constant, an increasing proportion of commercial vehicles are involved in crashes, with segments (mileposts) that have high crash frequencies seeming to correspond with locations with recurring congestion on the Interstate highway system. The study highlights the potential of using data to identify areas that need improvement in transportation systems to support better decision-making.
文摘为解决网络货运平台价格预测不准确导致的成交率下降问题,提出基于Shingling检索的表格先验数据拟合网络(tabular prior-data fitted network,TabPFN)的局部上下文学习(local context learning with TabPFN based on shingling retrieval,ShinglingPFN)模型。首先,该模型运用w-Shingling检索算法,从历史订单数据中匹配出与预测订单最相似的订单,构建局部关联的上下文数据。然后,加载并初始化预训练的TabPFN模型实例,将筛选出的订单数据输入模型,让TabPFN基于这些上下文信息学习货运特征与运费的关联模式。最后,输出该货运样本的运费预测结果。结果表明,ShinglingPFN模型相比随机森林(random forest,RF)模型减少了30.98%的平均绝对误差(mean absolute error,MAE)。通过全局敏感性分析,进一步增强了模型的可解释性。ShinglingPFN模型可为平台优化定价策略提供决策支撑。
文摘This research study explores the use of an innovative freight tour-based approach to model truck trips as an alternative to the conventional trip-based approach. The tour-based approach is more realistic as it captures the intermediate stops of each truck and reflects the implications of those stops on vehicle miles traveled (VMT). The paper describes the truck tour-based model concept, and presents the framework of a truck tour-based travel demand forecasting approach. As a case study, Global Positioning System (GPS) truck data are used to determine origin, destination, and truck stops for trucks moving within the Birmingham, Alabama region. Such information is then utilized to model truck movements within the study region as individual truck tours. The tour-based model is ran, and the resulting performance measures are contrasted to those obtained from the conventional trip-based planning model used by the Regional Planning Commission of Greater Birmingham (RPCGB). This case study demonstrates the feasibility of using a tour-based freight demand forecasting model as an alternative to the conventional 4-step process currently used to estimate truck trips in the Birmingham region. The results and lessons learned from the Birmingham case study are expected to improve truck movement modeling practices in the region and advance the accuracy of truck travel demand forecasting models at other locations in the future.
