Wei Guan

Study on berth planning problem in a container seaport: Using an integrated programming approach

This paper provides an integrated solution for two common processes in a container seaport: namely, the Berth Allocation Process (BAP) and the Crane Scheduling Process (QCSP). These are formulated through a Bi-Level Programming (BLP) approach, which is used to characterize the highly interrelated relationships between the two processes and simultaneously, identify an integrated solution for both. The upper-level problem (BAP) is termed NP-hard as its computational complexity increases exponentially with the number of incoming ships for mooring, while the lower-level problem (QCSP) is a mixed-integer linear program. A revised genetic algorithm and a branch-and-bound method (B&B) are then applied for the solutions of upper and lower level problems, respectively.

Synchronizing Public Transport Transfers by Using Intervehicle Communication Scheme: Case Study

Synchronized transfers in public transport (PT) networks play an important role in reducing transfer walking time, increasing PT network connectivity, and improving PT reliability and the attractiveness of the PT service. However, because of the dynamic, stochastic, and uncertain nature of traffic, planned synchronized PT transfers do not always materialize. Missed connections frustrate the PT passengers and reduce potential new users. This research proposed an intervehicle communication (IVC)-based scheme to optimize the synchronization of planned transfers in PT networks. A semidecentralized control strategy was developed for the IVC systems to make the optimization a parallel process. Two operational tactics, changing vehicle speed and holding vehicles at transfer points, were used in the optimization with real-time vehicle speed and location information. A distance-based dynamic speed-adjustment model was developed for updating vehicle running speed under the fixed single-point encounter scenario and flexible road-segment encounter scenario. The impact of the proposed IVC scheme on the total number of direct transfers and the total passenger travel time (TPTT) was investigated with a case study of a PT network from Beijing. Results showed that by applying the proposed methodology, the number of direct transfers was considerably increased by 1,100%, and the TPTT was significantly reduced by 13.2%.

Mapping to Cells: A Simple Method to Extract Traffic Dynamics from Probe Vehicle Data

In the era of big data, mining data instead of collecting data are a new challenge for researchers and engineers. In the field of transportation, extracting traffic dynamics from widely existing probe vehicle data is meaningful both in theory and practice. Therefore, this article proposes a simple mapping-to-cells method to construct a spatiotemporal traffic diagram for a freeway network. The method partitions a network region into small square cells and represents a real network inside the region by using the cells. After determining the traffic flow direction pertaining to each cell, the spatiotemporal traffic diagram colored according to traffic speed can be well constructed. By taking the urban freeway in Beijing, China, as a case study, the mapping-to-cells method is validated, and the advantages of the method are demonstrated. The method is simple because it is completely based on the data themselves and without the aid of any additional tool such as Geographic Information System software or a digital map. The method is efficient because it is based on discrete space-space and time-space homogeneous cells that allow us to match the probe data through basic operations of arithmetic. The method helps us understand more about traffic congestion from the probe data, and then aids in carrying out various transportation researches and applications.

A Nonlinear Pairwise Swapping Dynamics to Model the Selfish Rerouting Evolutionary Game

In this paper, a nonlinear revision protocol is proposed and embedded into the traffic evolution equation of the classical proportional-switch adjustment process (PAP), developing the present nonlinear pairwise swapping dynamics (NPSD) to describe the selfish rerouting evolutionary game. It is demonstrated that i) NPSD and PAP require the same amount of network information acquisition in the route-swaps, ii) NPSD is able to prevent the over-swapping deficiency under a plausible behavior description; iii) NPSD can maintain the solution invariance, which makes the trial and error process to identify a feasible step-length in a NPSD-based swapping algorithm is unnecessary, and iv) NPSD is a rational behavior swapping process and the continuous-time NPSD is globally convergent. Using the day-to-day NPSD, a numerical example is conducted to explore the effects of the reaction sensitivity on traffic evolution and characterize the convergence of discrete-time NPSD.

A game-theoretical approach for modeling competitions in a maritime supply chain

ABSTRACT To accommodate the structural changes in logistics, competitions along a maritime supply chain are more intensive than before. This paper adopts a two-stage noncooperative game-theoretical approach to model the horizontal and vertical interactions among liners and ports. In the first stage, Bertrand game is applied to model the interactions between two liners. When the shipping line decides which port to call, a multinomial Logit model is applied. In the second stage, Bertrand game is applied to model the competitions between two ports. Nash equilibrium is derived by solving the Bertrand games. A numerical example is provided as a case study.

Quantifying the Greenhouse Gas Emissions of Local Collection-and-Delivery Points for Last-Mile Deliveries

Concerns about the impacts of failed first-time home deliveries on road transport and the environment are growing because of the potential for additional vehicle trips for carriers and consumers. Local collection-and-delivery points (CDPs), at which consumers can collect their failed home deliveries, have emerged as a viable solution. On the basis of two databases of households from across Winchester and West Sussex in the United Kingdom and responses from nine major carriers, this paper quantifies greenhouse gas (GHG) emissions from carrier and consumer trips related to the conventional delivery method, in which the carrier makes redelivery attempts when a delivery fails, and appraises the environmental benefits of CDP networks for handling delivery failures. The results suggest that most GHG emissions associated with handling failed home deliveries are generated by the carrier. The share of GHG emissions generated from consumers increases as the proportion of failed first-time home deliveries increases. A range of CDPs (supermarkets, railway stations, and post offices) was found to reduce the environmental impacts of failed home deliveries. A CDP network would reduce GHG emissions most effectively when (a) 30% or more of householders who experienced a failed first-time home delivery travel to the carriers depot to retrieve goods, (b) the proportion of failed first-time home deliveries is significant, and (c) “local collect” post offices are used as CDPs. The study has practical and managerial implications for retailers and carriers about ways to improve home delivery services by identifying consumer home shopping behaviors and promoting more convenient and environmentally friendly delivery strategies.

Bus Arrival Time Prediction Based on the k-Nearest Neighbor Method

In this paper, a modified k-nearest neighbor (k-NN) method integrating the cluster analysis and principal component analysis is applied to bus arrival time (BAT) prediction using historical bus GPS data. The methodology of the k-NN method based on processed GPS data is presented. To validate the introduced k-NN approach, empirical analysis based on data collected from the bus No. 16 of Beijing public transport holdings, Ltd. (BPT) is performed. The results show that the k-NN method has lower prediction error than the ANN model and is more powerful in the BAT prediction.

Optimal Synchronized Transfers in Schedule-Based Public Transport Networks Using Online Operational Tactics

Synchronized transfers in schedule-based public transport (PT) networks are used to reduce interroute or intermodal passenger transfer waiting time and provide a well-connected service. However, in practice, synchronized transfers do not always materialize because of some stochastic and uncertain factors, such as traffic disturbances and disruptions, fluctuations in passenger demand, and erroneous behavior of PT drivers. As a result, missed direct transfers not only frustrate existing users but also discourage potential passengers from using PT service. This work presents an optimization procedure based on a model predictive control (MPC) to increase the actual occurrence of synchronized transfers in schedule-based PT networks. The procedure aims to reduce the uncertainty of meetings between PT vehicles. MPC uses selected online operational tactics based on real-time data, such as skip stop, speed change, and holding. First a library of operational tactics was built to serve as a basis for the sequential receding horizon control process in the MPC. Then, an event activity network with dynamic moving elements was constructed to represent the logical process of the PT transfer synchronization problem. The MPC procedure for a real-time deployment of operational tactics was explicated. A detailed example was used as an expository device to illustrate the procedure developed, along with a real-life example from Auckland, New Zealand.

Optimal Routing Design of a Community Shuttle for Metro Stations

The optimization of a community shuttle route plays an important role in the efficient operation of public transit microcirculation. This paper develops a solution for the optimal routing design problem with the objective of minimizing the total cost, including user and supplier costs, considering passenger traffic demand and budget constraints. The optimization variables include route and headway. To calculate the user cost, a heuristic algorithm for locating stops is presented. Then the optimized headway corresponding to a given bus route is confirmed by minimizing the total cost function. In solving the problem, a depth-first search algorithm (DFS) and a genetic algorithm (GA) are both presented. By comparing the results and the CPU time of the two algorithms, this paper demonstrated that GA is reliable and more efficient than DFS. The impacts of route length and the maximum tolerable walking distance on the cost and headway are also analyzed.

Observed time-headway distribution and its implication on traffic phases

Time-headway between two subsequent vehicles in traffic flow is determined both by traffic dynamics and individual fluctuations, showing a complex behavior. Analyzing real traffic data from multiple sections of urban freeway, we show that the time-headway distributions under different velocities are fitted well with a class of distributions which can be decomposed into an exponential distributed variable plus an independent Gaussian fluctuation, where the exponential component can describe traffic states in a statistical meaning and the random fluctuation is caused by complex traffic dynamics which relates to imperfect individual driving behavior. Based on analyzing the empirical time-headway distribution, traffic flow is recognized as different phases which were observed at urban freeway previously. Moreover, high-velocity highway flow and low-velocity urban freeway flow can be described in a unified framework by shifting distribution parameters.