Modal choice models applied to interregional or international freight transportation network models are often based on rather coarse origin-destination matrices, containing annual transported tonnages between (sub)reg...Modal choice models applied to interregional or international freight transportation network models are often based on rather coarse origin-destination matrices, containing annual transported tonnages between (sub)regions, for instance. Generally, only basic (sometimes constructed) independent variables (transportation costs or transit times) are used because other variables such as shipment sizes, service frequencies, etc. are not available. Using origin-destination matrices and an assignment model, it is also possible to compute spatial accessibility measures that can further be used as additional explanatory variables. Indeed, several published studies have identified network accessibility as an important element in the mode-choice decision. This paper also shows that the inclusion of an accessibility measure in the utility functions of a logit model substantially improves the performance of a transportation network model, both in the modal choice and the assignment levels of the classical four-step model. Consequently, the assignment of the estimated modal demands results in more accurate estimated traffic on the networks. The model presented in this paper is to be considered as a proof of concept because its workflow should further be streamlined to make it easily useable by modelers.展开更多
Strategic transportation network models are often used as support tools in the framework of decisions to be taken at the policy level, such as the Trans-European Network projects. These models are mostly setup using a...Strategic transportation network models are often used as support tools in the framework of decisions to be taken at the policy level, such as the Trans-European Network projects. These models are mostly setup using aggregated or limited data. If their calibration is regularly mentioned in the literature, their validation is barely discussed. In this paper, several modal choice model specifications that make only use of explanatory variables available at the network level are described and applied to a large scale case. A validation exercise is performed at three levels of aggregation. The paper is designed from a strategic transport planning perspective, and does not present new modal choice formulations or assignment procedures. Its main added value is the focus on calibration and validation considerations. Despite the limited explanatory information used, the global performance of the best models can be considered as satisfactory. However, the quality of the models varies from mode to mode, the use of railway transport being the most difficult to predict without more specific input.展开更多
This paper develops a model for analyzing the potential of longer and heavier vehicles (LHVs) related to pre- and post-haulage in the intermodal rail-road transport chain (IRT). The paper considers the combined econom...This paper develops a model for analyzing the potential of longer and heavier vehicles (LHVs) related to pre- and post-haulage in the intermodal rail-road transport chain (IRT). The paper considers the combined economic and emission costs among three different transport networks including intermodal rail-road transport with current Swedish regulatory framework for trucks, intermodal rail-road transport with LHVs, and direct-road transport. The objective is to analyse the potential of high-capacity transport associated with pre- and post-haulage for enhancing the competitiveness of intermodal transport from a full-costs perspective. The model developed is applied to a Swedish context and case study. Research findings reveal that the break-even of the IRT compared to the direct road transport could be significantly lowered, which suggests the LHVs contribute to exploring the market of IRT over smaller flows.展开更多
This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors.Th...This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors.The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and Convolutional Long Short Term Memory Neural Network(ConvLSTM)to predict short-term taxi travel demand.The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components,capturing sequence characteristics at different time scales and frequencies.Based on the sample entropy value of components,secondary processing of more complex sequence components after decomposition is employed to reduce the cumulative prediction error of component sequences and improve prediction efficiency.On this basis,considering the correlation between the spatiotemporal trends of short-term taxi traffic,a ConvLSTM neural network model with Long Short Term Memory(LSTM)time series processing ability and Convolutional Neural Networks(CNN)spatial feature processing ability is constructed to predict the travel demand for urban taxis.The combined prediction model is tested on a taxi travel demand dataset in a certain area of Beijing.The results show that the CEEMDAN-ConvLSTM prediction model outperforms the LSTM,Autoregressive Integrated Moving Average model(ARIMA),CNN,and ConvLSTM benchmark models in terms of Symmetric Mean Absolute Percentage Error(SMAPE),Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and R2 metrics.Notably,the SMAPE metric exhibits a remarkable decline of 21.03%with the utilization of our proposed model.These results confirm that our study provides a highly accurate and valid model for taxi travel demand forecasting.展开更多
为深入推进货物运输“公转铁”,铁路运输企业需掌握竞争方式同口径可比价格,并充分理解货运方式选择行为。本文提出一种不同装载和运输方式之间的运价转换方法,解决了采用RP(Revealed Preference)数据进行离散选择建模时备选项属性数据...为深入推进货物运输“公转铁”,铁路运输企业需掌握竞争方式同口径可比价格,并充分理解货运方式选择行为。本文提出一种不同装载和运输方式之间的运价转换方法,解决了采用RP(Revealed Preference)数据进行离散选择建模时备选项属性数据缺失的问题。通过改进的PPS(Probability Proportionate to Size Sampling)方法,有效组合多源RP数据,构建货运方式选择行为模型。结果表明,模型能正确预测90%以上的观测值。轻货的VOT(Value of Time)相比重货更高。价格弹性的推导和计算表明,提高公路价格比降低铁路价格能使铁路分担率有更大的提升,降低当前铁路价格可以增加运输收入。当铁路价格下降到收入最大化目标的最优定价点时,不仅会带来铁路分担率、运量和收入的显著增加,还有望获得一定的碳减排效益。展开更多
文摘Modal choice models applied to interregional or international freight transportation network models are often based on rather coarse origin-destination matrices, containing annual transported tonnages between (sub)regions, for instance. Generally, only basic (sometimes constructed) independent variables (transportation costs or transit times) are used because other variables such as shipment sizes, service frequencies, etc. are not available. Using origin-destination matrices and an assignment model, it is also possible to compute spatial accessibility measures that can further be used as additional explanatory variables. Indeed, several published studies have identified network accessibility as an important element in the mode-choice decision. This paper also shows that the inclusion of an accessibility measure in the utility functions of a logit model substantially improves the performance of a transportation network model, both in the modal choice and the assignment levels of the classical four-step model. Consequently, the assignment of the estimated modal demands results in more accurate estimated traffic on the networks. The model presented in this paper is to be considered as a proof of concept because its workflow should further be streamlined to make it easily useable by modelers.
文摘Strategic transportation network models are often used as support tools in the framework of decisions to be taken at the policy level, such as the Trans-European Network projects. These models are mostly setup using aggregated or limited data. If their calibration is regularly mentioned in the literature, their validation is barely discussed. In this paper, several modal choice model specifications that make only use of explanatory variables available at the network level are described and applied to a large scale case. A validation exercise is performed at three levels of aggregation. The paper is designed from a strategic transport planning perspective, and does not present new modal choice formulations or assignment procedures. Its main added value is the focus on calibration and validation considerations. Despite the limited explanatory information used, the global performance of the best models can be considered as satisfactory. However, the quality of the models varies from mode to mode, the use of railway transport being the most difficult to predict without more specific input.
文摘This paper develops a model for analyzing the potential of longer and heavier vehicles (LHVs) related to pre- and post-haulage in the intermodal rail-road transport chain (IRT). The paper considers the combined economic and emission costs among three different transport networks including intermodal rail-road transport with current Swedish regulatory framework for trucks, intermodal rail-road transport with LHVs, and direct-road transport. The objective is to analyse the potential of high-capacity transport associated with pre- and post-haulage for enhancing the competitiveness of intermodal transport from a full-costs perspective. The model developed is applied to a Swedish context and case study. Research findings reveal that the break-even of the IRT compared to the direct road transport could be significantly lowered, which suggests the LHVs contribute to exploring the market of IRT over smaller flows.
基金supported by the Surface Project of the National Natural Science Foundation of China(No.71273024)the Fundamental Research Funds for the Central Universities of China(2021YJS080).
文摘This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors.The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and Convolutional Long Short Term Memory Neural Network(ConvLSTM)to predict short-term taxi travel demand.The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components,capturing sequence characteristics at different time scales and frequencies.Based on the sample entropy value of components,secondary processing of more complex sequence components after decomposition is employed to reduce the cumulative prediction error of component sequences and improve prediction efficiency.On this basis,considering the correlation between the spatiotemporal trends of short-term taxi traffic,a ConvLSTM neural network model with Long Short Term Memory(LSTM)time series processing ability and Convolutional Neural Networks(CNN)spatial feature processing ability is constructed to predict the travel demand for urban taxis.The combined prediction model is tested on a taxi travel demand dataset in a certain area of Beijing.The results show that the CEEMDAN-ConvLSTM prediction model outperforms the LSTM,Autoregressive Integrated Moving Average model(ARIMA),CNN,and ConvLSTM benchmark models in terms of Symmetric Mean Absolute Percentage Error(SMAPE),Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and R2 metrics.Notably,the SMAPE metric exhibits a remarkable decline of 21.03%with the utilization of our proposed model.These results confirm that our study provides a highly accurate and valid model for taxi travel demand forecasting.
文摘为深入推进货物运输“公转铁”,铁路运输企业需掌握竞争方式同口径可比价格,并充分理解货运方式选择行为。本文提出一种不同装载和运输方式之间的运价转换方法,解决了采用RP(Revealed Preference)数据进行离散选择建模时备选项属性数据缺失的问题。通过改进的PPS(Probability Proportionate to Size Sampling)方法,有效组合多源RP数据,构建货运方式选择行为模型。结果表明,模型能正确预测90%以上的观测值。轻货的VOT(Value of Time)相比重货更高。价格弹性的推导和计算表明,提高公路价格比降低铁路价格能使铁路分担率有更大的提升,降低当前铁路价格可以增加运输收入。当铁路价格下降到收入最大化目标的最优定价点时,不仅会带来铁路分担率、运量和收入的显著增加,还有望获得一定的碳减排效益。
