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.

Road–rail container intermodal transportation is considered a solution to reduce the share of truck transportation in China. A modal shift from truck to intermodal alternatives requires a better understanding of freight mode choice behavior and improved estimations of the value of service attributes. This paper focuses on the effects of distance and reliability on the value of time (VOT). An adaptive experiment is conducted on potential customers of intermodal transportation in the Yangtze River Delta area, China. Multinomial logit (binary logit) and mixed logit models are estimated for eight specifications. The results show that shipper characteristics, commodity characteristics, and shipment characteristics significantly influence the mode choice behavior. Specifications with an interaction term between the logarithm of distance and transportation time perform better. The VOT of short-distance transportation is higher than that of long-distance transportation. The rate of VOT reduction decreases with increasing distance. In addition, incorporating the reliability variable in model specifications leads to a more homogeneous random parameter distribution of time and a lower VOT. This study helps intermodal operators to optimize product and design pricing strategies. Moreover, the proposed measures help to promote the modal shift from truck to road–rail container intermodal transportation.

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Designing efficient strategies to adjust freight transportation mode structure requires in-depth understanding of shippers’ mode choice behavior. This paper presents an empirical study to investigate preference heterogeneity in value of reliability (VOR) for hinterland leg transportation mode choice. A stated preference survey using a D-efficient design approach is carried out in the corridor from Yiwu to the port of Ningbo to collect data on shippers’ behavior. Two model specifications including the Base Model and the Heterogeneous Model are developed to analyze these data. Mixed logit is applied to estimate the parameters of models. The estimation results of the base model reveal significant preference heterogeneity in shippers’ VOR. We then calculate the mean and variation of overall VOR. In addition, the potential factors leading to heterogeneity in VOR are identified. Results imply that commodity characteristics including shipment size, value, and weight could partially explain the shippers’ heterogeneity in VOR. Based on these factors, eight sub-groups of container shippers are obtained, and the mode shares of railway under different levels of railway reliability are estimated for each sub-group. Results show that improvement in the level of reliability is important to increase the mode share of rail, especially in the sub-group where shipments are light and of high value. The findings of this paper can be used for demand forecasting and transportation policy making.

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