Xinchang Wang

Quantitative Risk Assessment Modeling for Nonhomogeneous Urban Road Tunnels

Urban road tunnels provide an increasingly cost-effective engineering solution, especially in compact cities like Singapore. For some urban road tunnels, tunnel characteristics such as tunnel configurations, geometries, provisions of tunnel electrical and mechanical systems, traffic volumes, etc. may vary from one section to another. These urban road tunnels that have characterized nonuniform parameters are referred to as nonhomogeneous urban road tunnels. In this study, a novel quantitative risk assessment (QRA) model is proposed for nonhomogeneous urban road tunnels because the existing QRA models for road tunnels are inapplicable to assess the risks in these road tunnels. This model uses a tunnel segmentation principle whereby a nonhomogeneous urban road tunnel is divided into various homogenous sections. Individual risk for road tunnel sections as well as the integrated risk indices for the entire road tunnel is defined. The article then proceeds to develop a new QRA model for each of the homogeneous sections. Compared to the existing QRA models for road tunnels, this section-based model incorporates one additional top event-toxic gases due to traffic congestion-and employs the Poisson regression method to estimate the vehicle accident frequencies of tunnel sections. This article further illustrates an aggregated QRA model for nonhomogeneous urban tunnels by integrating the section-based QRA models. Finally, a case study in Singapore is carried out.

Impact of Land Bridge on Port Market Area: Model Development and Scenario Analysis

A land bridge as an intermodal freight transport mode seamlessly integrates long-haul rail and short-haul truck services to provide transcontinental delivery of containers that can be alternatively transported by maritime transport mode. To evaluate the impact of the Myanmar–China international land bridge on the Shanghai, China, port, this paper first proposes a novel concept of the probability-based port market area to measure the service area of a port by assuming that costs and times of the intermodal freight transport routs are the normal distributed random variables. The analytical expression of the probit-based market area of the Shanghai port is then derived for two competing transport routes to the Dubai port: one involves the Myanmar–China land bridge and the other uses the direct port-to-port maritime link. The probability-based market area curves in relation to transport cost and time of the Shanghai port are established for two operational scenarios of the Myanmar–China land bridge: (a) current state of operation and (b) a possible future state of operation with efficiency similar to that of the U.S. continental land bridge operation. Each of these curves possesses one branch of a hyperbola curve representing a contour of the impact level of the land bridge on the Shanghai port. Differences of the impact contours of the two scenarios provide an indication of the market area of the Shanghai port that will be affected by changes in the efficiency of the land bridge operation.

Dynamic Server Assignment With Task-Dependent Server Synergy

We study tandem queueing systems with finite buffers in which servers work more efficiently in teams than on their own and the synergy among collaborating servers can be task-dependent. Our goal is to determine the dynamic server assignment policy that maximizes the long-run average throughput. When each server works with the same ability at each task that she/he is assigned to, we show that any nonidling policy where all servers work in teams of two or more at all times is optimal. On the other hand, when the server abilities are task-dependent, we show that for Markovian systems with two stations and two servers, depending on the synergy among the servers, the optimal policy either assigns the two servers to different stations when possible, or lets them work in a team at all times. Finally, for larger Markovian systems, we provide sufficient conditions that guarantee that the optimal policy has all servers working together at all times.

Utility-Based Estimation of Probabilistic Port Hinterland for Networks of Intermodal Freight Transportation

The inland area served by a particular port is known as the port hinterland. The probability—within a certain range [α1, α2], where parameters α1, α2 ∈ [0, 1]—that shippers will use that port to transport containers to a given destination is referred to as the ports probabilistic hinterland. The probabilistic port hinterland is a key performance index that reflects the attractiveness and competitiveness of a port, which is determined by a shippers decision of whether to choose a route passing through the port. The random utility of an intermodal route is defined as a summation of transportation cost and transport time multiplied by the value of time (VOT) perceived by shippers. The random transshipment time per container incurred at a transfer terminal on an intermodal route is then derived by modeling the transshipment process as an M[X]/G/1 queue due to containers arriving in batches. According to the utility maximization principle for shippers faced with intermodal route choice, a mathematical expression of the utility-based probabilistic port hinterland is presented. An algorithm based on Monte Carlo simulation is proposed for finding the probabilistic port hinterland. A case study is performed to estimate the probabilistic hinterland of the port of Shanghai, China, and to analyze the impact of the handling capacity at the Shanghai port and VOT on the port hinterland.

Optimal price decisions for joint ventures between port operators and shipping lines under the congestion effect

Abstract The congestion effect refers to the phenomenon that more customers choosing to use the same facility reduces the facility’s utility. This work addresses the optimal pricing problem for a firm operating a joint-venture terminal under the congestion effect. The firm is formed between a port terminal operator and a shipping line, thus being able to provide a bundle of ocean transportation and port terminal solutions to cargo suppliers. The objective is to determine the optimal prices charged to cargo suppliers to maximize the total profit of the firm. First, we develop a tractable flow-based optimization model that uses a fixed-point equation to capture the interaction between the congestion effect and cargo suppliers’ choice. Second, we characterize the optimal solution for a variety of cases, including the single origin-destination case, partially homogeneous case that includes the fully homogeneous case as a special case, and heterogeneous case. Third, we evaluate the profit loss incurred by ignoring the congestion effect with numerical studies. Moreover, we propose one-variable and two-variable search methods for the partially homogeneous and heterogeneous cases, respectively. We learn that the firm should quote the same price to all cargo suppliers under the fully homogeneous case. However, this is not necessarily optimal under the partially homogeneous or heterogeneous cases. The profit loss incurred by neglecting the congestion effect can be significant and increases as cargo suppliers become less tolerant of congestion.