Carsharing is growing rapidly in popularity worldwide. When the vehicles involved are Battery Electric Vehicles (BEV), carsharing has been proven to remarkably contribute to easing energy and environment crises. In this study, individuals’ acceptance to carsharing in China was measured from three aspects: carsharing mode choice behavior, highest acceptable price to use carsharing, and willingness to forgo car purchases. The data were collected by a web-based survey. The hierarchical tree-based regression (HTBR) method was applied to explore the effects of potential influencing factors on individuals’ acceptance, and some interesting findings were obtained: participants who know about carsharing were more likely to use carsharing, pay higher prices and forgo car purchases; the most competitive trip purpose and trip distance for choosing carsharing were, respectively, business activities and 11–20 km; most participants (47.1%) were willing to pay 1–2 Yuan per minute to use carsharing, and males or participants with higher income-level could accept higher price; and when car purchase restrain policy (CPRP) was carried out in a city or the urban public transport service level (UPTSL) was high, participants were more willing to forgo car purchases. Based on the above findings, corresponding policies were proposed to provide guidance for successful establishment of carsharing in China.
Understanding the public transportation users' preferences to long-distance travel modes would contribute to reasonable developing policies and resource allocation. This paper aims to explore the influencing mechanism of potential factors on the long-distance travel mode choice. A survey was conducted to collect the data. The analysis of variance (ANOVA) approach was applied to analyze the correlation relationship between potential factors and travel mode choice behavior. The results showed that, except gender, service demand for safety and departure time, all of the other factors significantly influenced the travel mode choice behavior. Specifically, passengers with higher education level and income level were more likely to choose high-speed railway (HSR) and plane; passengers caring about travel expense were more likely to choose ordinary train, whereas plane and HSR may be chosen more by passengers caring more about comfort, punctuality and efficiency; the more passengers were satisfied with travel modes' service performance, the more they would be likely to choose them; the most competitive distance ranges for coach, ordinary train, HSR and plane were below 500 km, 500-1000 km, 500-1500 km and over 1500 km, respectively. Besides, the structural equation modeling (SEM) technique was applied to investigate the influencing mechanism of factors on the long-distance travel mode choice. The results revealed that travel distance was the most significant variable directly influencing passengers' mode choices, followed by the service demand, performance evaluation, and personal attributes. Furthermore, personal attributes were verified to have an indirect effect on travel mode choice behavior by significantly affecting the service demand and performance evaluation.
Compared with traditional freight transportation, intermodal freight transportation is more competitive which can combine the advantages of different transportation modes. As a consequence, operational research on intermodal freight transportation has received more attention and developed rapidly, but it is still a young research field. In this paper, a stochastic intermodal service network design problem is introduced in a sea-rail transportation system, which considers stochastic travel time, stochastic transfer time, and stochastic container demand. Given candidate train and ship services, we develop a two-stage chance constrained programming model for this problem with the objective of minimising the expected total cost. The first stage allows for the selection of operated services, while the second stage focuses on the determination of intermodal container routes where capacity and on-time delivery chance constraints are presented. A hybrid heuristic algorithm, incorporating sample average approximation and ant colony optimisation, is employed to solve this model. The proposed model is applied to a realistic intermodal sea-rail network, which demonstrates the performance of the model and algorithm as well as the influence of stochasticity on transportation plans. Hence, the proposed methodology can improve effectively the performance of intermodal service network design scheme under stochastic conditions and provide managerial insights for decision-makers.
As one of main challenge for carriers, empty container repositioning is subject to various uncertain factors in practice, which causes more operation costs. At the same time, the movements of empty containers can result in air pollution because of the CO2 emission, which has a negative impact on sustainable development. To incorporate environmental and stochastic characteristics of container shipping, in this paper, an empty container repositioning problem, taking into account CO2 emission, stochastic demand, and supply, is introduced in a sea–rail intermodal transportation system. This problem is formulated as a chance-constrained nonlinear integer programming model minimising the expected value of total weighted cost. A sample average approximation method is applied to convert this model into its deterministic equivalents, which is then solved by the proposed two-phase tabu search algorithm. A numerical example is studied to conclude that the stochastic demand and supply lead to more repositioning and CO2 emission-related cost. Total cost, inventory cost, and leasing cost increase with the variabilities of uncertain parameters. We also found that the total cost and other component costs are strongly dependent on the weights of repositioning cost and CO2 emission-related cost. Additionally, the sensitivity analysis is conducted on unit leasing cost.
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