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.

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.

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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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Using an incentive measure to encourage people to share their private parking spaces could be an effective strategy for urban parking problems. This paper discusses an innovative mechanism of shared parking, “FlexPass,” which applies a reverse auction in which drivers propose bids in line with their individual expectations to share their idle parking spaces. The auction mechanism, hypotheses on bidding process principles, the competitive environment, and the risk-averse decisions of providers with regard to parking spaces are analysed to explore the sustainability of the economic benefits obtained for FlexPass parking spaces. A total of 216 respondents from the University of California, Berkeley, were invited to participate in bidding in an actual survey during their daily use of parking spaces. The analytical results show that operational rules based on risk aversion can enable profit-seeking with a bounded capability to obtain considerable economic benefits and release parking resources in an environment of demand competition. Particularly in some scenarios, FlexPass would sacrifice a certain monetary income to ensure the perceived benefits of parking space providers. With the improvement of people’s enthusiasm for participating in shared parking, the benefits to individuals and parking lots would be further enhanced, suggesting that our mechanism can operate sustainably over the long term. These findings are helpful for policymakers to formulate feasible shared parking policies from the perspective of monetary incentives.

Promoting the usage of sustainable commuting modes requires in-depth understanding about residents’ commuting mode choice behavior. This study presents an empirical study to investigate the relationship between the built environment and commuting mode choice using CLDS 2016 cross-city questionnaire data. Several multilevel multinomial logit models including the null model, base model, and moderating effect model are developed to analyze the effects of built environments at both city and neighborhood levels on commuting mode choice. Estimation results of the null model reveal the significant spatial heterogeneities in commuting mode choice across different cities and different neighborhoods within a specific city. We then explore the potential built environment variables yielding the spatial heterogeneity via the base model. Results show that the built environment at the city level (including the urbanization rate, number of public transportation vehicles, metro operating mileage, GDP, city population density, and road area per capita) and neighborhood level (including neighborhood population density, air quality, neighborhood location, and land use diversity) could partially explain the spatial heterogeneities in commuting mode choice. In addition, the moderating effects of these built environments on the link between commuting time and commuting mode choice are examined. Results imply that the urbanization rate and neighborhood population density moderate the effect of commuting time on choosing nonmotorized modes, while neighborhood location moderates the effect of commuting time on choosing public transit. Also, the mode shares of nonmotorized mode and public transit under different levels of commuting time are estimated in different built environment contexts. The findings of this study are expected to provide serviceable support for urban planning and transportation policy making.