Multimodal transport is a process of effectively moving cargoes in a single container by combining land transport (road or rail) and maritime or river transport (vessel or barge) in one transport chain. However, cold chain logistics (CCL), as a special while major kind of cargo delivery, has not been incorporated with this beneficial combination. In order to realize efficient delivery of cold chain foods (CCF), in this study, the characteristics of multimodal and CCL are analyzed and integrated to select the optimal logistics path. In establishing the path-selection model, customer satisfaction is introduced, which is reflected by arrival punctuality and the quality of CCF. An improved particle swarm optimization algorithm (IPSO) is introduced to address the model and is proven to retain a fast convergence rate and achieve outstanding solving accuracy through the experimental study. Sensitivity analysis is also conducted to present the impact of railway speed and cost variation on path selection. Results show that compared with highway transport, railway transport is preferable to the medium and long distance. The influence of railway speed improvement is more striking than cost reduction in motivating decision makers to choose railway transport mode in logistics operations.

This paper proposes a route choice analytic method that embeds cumulative prospect theory in evolutionary game theory to analyze how the drivers adjust their route choice behaviors under the influence of the traffic information. A simulated network with two alternative routes and one variable message sign is built to illustrate the analytic method. We assume that the drivers in the transportation system are bounded rational, and the traffic information they receive is incomplete. An evolutionary game model is constructed to describe the evolutionary process of the drivers’ route choice decision-making behaviors. Here we conclude that the traffic information plays an important role in the route choice behavior. The driver’s route decision-making process develops towards different evolutionary stable states in accordance with different transportation situations. The analysis results also demonstrate that employing cumulative prospect theory and evolutionary game theory to study the driver’s route choice behavior is effective. This analytic method provides an academic support and suggestion for the traffic guidance system, and may optimize the travel efficiency to a certain extent.

In transportation planning works, critical links identification is helpful to evaluate the vulnerable parts of the designed network schemes. A new capacity-based network robustness index is presented for identifying critical links and evaluating the transportation system performance. It uses the change of the total network capacity as an evaluation measure. The advanced practical network capacity model is employed to estimate the throughput of transportation system. A sensitivity analysis based algorithm is also developed to solve the practical capacity model efficiently. The capacity-based network robustness index identifies the links which play critical roles in the transportation capacity of the whole network. The capacity-based network robustness index is an effective supplementary to the existing network evaluation indices. The experiments demonstrate the differences between the capacity-based network robustness index and the efficiency-based network robustness index. The capacity-based network robustness index can be used as a practical measure in the situations where the network robustness index is not significant.

This article aims to develop a new link criticality indictor based on the network capacity concept of a multimodal transportation network. Model formulation and solution algorithm are developed for this multimodal transportation network capacity problem. To improve the efficiency, a sensitivity-based approximation approach is also developed to avoid the need to repeatedly solve the bi-level network capacity problem for each disrupted link as in the typical network scanning approach. Numerical experiments are conducted and compared to the traditional efficiency-based criticality indicator. The findings reveal the proposed network capacity-based indicator indeed is more useful than the efficiency-based indicator when the link capacity limit is violated in the degraded network.

Transportation network capacity is an important indicator from measuring the performance of a transportation system. Previous studies on the capacity of multimodal networks have not considered transfers between different transportation modes. In fact, because of the diversified development of the urban transportation system, travelers are no longer limited to a single transportation mode in daily travel. This paper thus proposes an improved network capacity model considering intermodal transportation in urban multimodal transportation systems. The proposed network capacity model is formulated as a bi-level programming problem, in which the lower-level model is a combined modal split and traffic assignment (CMSTA) model. The CMSTA model consists of a cross-nested logit (CNL) in the phase of mode split to account for intermodal transportation and a path-size logit (PSL) in the phase of traffic assignment to account for route overlapping. Also, we consider the flow interaction of different modes (e.g., car and bus) when they share the same links. The Barzilai–Borwein step size method is used to efficiently solve the lower-level CMSTA model (i.e., the CNL-PSL model) of which the objective function is complicated. The numerical results demonstrate the advantages and features of the proposed model. The model is also applied to a real network case to assess the network capacity of the multimodal transportation system.

With the development of the advanced Intelligent Transportation System (ITS) in modern cities, it is of great significance to upgrade the forecasting methods for travel demand with the impact of ITS. The widespread use of ITS clearly changes the urban travelers’ behavior at present, in which case it is difficult for the conventional four-step travel demand forecasting model to have good performance. In this study, we apply the combined distribution and assignment (CDA) model to forecasting travel demand for modern urban transportation, in which travelers may choose the destination and path simultaneously. Furthermore, we present a new solution algorithm for solving the CDA model. With the network representation method that converts the CDA model into a standard traffic assignment problem (TAP), we develop a new path-based algorithm based on the gradient projection (GP) algorithm to solve the converted CDA model. The new solution algorithm is designed to find a more accurate solution compared with the widely used algorithm, the Evans’ two-stage algorithm. Two road networks, Sioux Falls and Chicago Sketch, are used to verify the performance of the new algorithm. Also, we conduct some experiments on the Sioux Falls network to illustrate several applications of the CDA model in consideration of the influences of ITS.