An important problem in the assessment of reliability benefits of transport projects is that link level improvements must be translated to network level, so that they can be economically valued based on users' trips from origins to destinations. For intermodal transport, shipments follow a chain with more than one mode. Generally, this requires aggregation of travel time distributions that are not additive. We propose an approach that estimates the change in transport time reliability of an intermodal transport chain based on the changes for links of that chain. We demonstrate the framework of reliability assessment for a case study of network improvement for rail-truck intermodal transport in China. Also, we demonstrate the application in a cost-benefit analysis context with user valuations of transport reliabilities from the case at hand. The application leads to the result that projects for the renovation and expansion of the transshipment terminal perform better compared with project that improve rail haulage speed. Another finding is that the effect of reliability improvement projects can be super-additive at network level. In comparison with traditional methods, we conclude that the proposed method can better estimate transport time reliability benefits when the distribution of link travel times is highly skewed. Also, it opens new possibilities for further research for measuring correlated reliability measures within networks and for performing network resilience analysis.

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Increasing the mode share of railway in hinterland leg containers transportation requires a better understanding about the effects of critical factors on shippers’ mode choices. This paper focuses on the effects of travel time reliability (TTR) and commodity characteristics on freight mode choice. A two-stage survey is conducted in the Yiwu-Port of Ningbo corridor, China, to collect shippers’ preference data. Five model specifications are estimated using these data. Estimation results of generic parameters indicate that significant interaction effects between commodity characteristics and travel time exist. The value of generic reliability was then calculated and the effects of commodity characteristics was quantified. In addition, mode-specific values of reliability are estimated. Remarkable differences are found in the mode-specific value of reliability for different modes. Also, the effects of mode-specific value of reliability on the demand forecasting were investigated. Results imply that the mode share of railway will be underestimated if the mode-specific value of reliability is neglected, especially when travel time of railway transportation is reliable. Therefore, it is recommended that the mode-specific willingness-to-pay should be considered in railway demand forecasting and project appraisals.

For cities, the problem of “difficult parking and chaotic parking” increases carbon emissions and reduces quality of life. Accurately and efficiently predicting the availability of vacant parking spaces (VPSs) can help motorists reduce the time spent looking for a parking space and reduce greenhouse gas pollution. This paper proposes a deep learning model called DWT-ConvGRU-BRC to predict the future availability of VPSs in multiple parking lots. The model first uses a discrete wavelet transform (DWT) to denoise the historical parking data and then extracts the temporal correlation of the parking lots themselves and the spatial correlation between different parking lots using a convolutional gated recurrent unit network (ConvGRU) while using a BN-ReLU-Conv (1 × 1) module to further improve the propagation and reuse of features in the prediction process. In addition, the model uses availability, temperature, humidity, wind speed, weekdays, and weekends as inputs to improve the accuracy of the forecasts. The model performance is evaluated through a case study of 11 parking lots in Santa Monica. The DWT-ConvGRU-BRC model outperforms the LSTM and GRU baseline methods, with an average testing MAPE of 2.12% when predicting multiple parking lot occupancies over the subsequent 60 min.