▶ Addresses a wide range of timely environment, economic and energy topics ▶ A forum to review, analyze and stimulate the development, testing and implementation of mitigation and adaptation strategies at regional, national and global scales ▶ Contributes to real-time policy analysis and development as national and international policies and agreements are discussed and promulgated ▶ 94% of authors who answered a survey reported that they would definitely publish or probably publish in the journal again

Mitigation and Adaptation Strategies for Global Change

Short-term traffic forecast is one of the essential issues in intelligent transportation system. Accurate forecast result enables commuters make appropriate travel modes, travel routes, and departure time, which is meaningful in traffic management. To promote the forecast accuracy, a feasible way is to develop a more effective approach for traffic data analysis. The availability of abundant traffic data and computation power emerge in recent years, which motivates us to improve the accuracy of short-term traffic forecast via deep learning approaches. A novel traffic forecast model based on long short-term memory (LSTM) network is proposed. Different from conventional forecast models, the proposed LSTM network considers temporal-spatial correlation in traffic system via a two-dimensional network which is composed of many memory units. A comparison with other representative forecast models validates that the proposed LSTM network can achieve a better performance.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-its.2016.0208

LSTM network: a deep learning approach for short-term traffic forecast

A survey of berth allocation and quay crane scheduling problems in container terminals

Short-term passenger demand forecasting is of great importance to the on-demand ride service platform, which can incentivize vacant cars moving from over-supply regions to over-demand regions. The spatial dependencies, temporal dependencies, and exogenous dependencies need to be considered simultaneously, however, which makes short-term passenger demand forecasting challenging. We propose a novel deep learning (DL) approach, named the fusion convolutional long short-term memory network (FCL-Net), to address these three dependencies within one end-to-end learning architecture. The model is stacked and fused by multiple convolutional long short-term memory (LSTM) layers, standard LSTM layers, and convolutional layers. The fusion of convolutional techniques and the LSTM network enables the proposed DL approach to better capture the spatio-temporal characteristics and correlations of explanatory variables. A tailored spatially aggregated random forest is employed to rank the importance of the explanatory variables. The ranking is then used for feature selection. The proposed DL approach is applied to the short-term forecasting of passenger demand under an on-demand ride service platform in Hangzhou, China. The experimental results, validated on the real-world data provided by DiDi Chuxing, show that the FCL-Net achieves the better predictive performance than traditional approaches including both classical time-series prediction models and state-of-art machine learning algorithms (e.g., artificial neural network, XGBoost, LSTM and CNN). Furthermore, the consideration of exogenous variables in addition to the passenger demand itself, such as the travel time rate, time-of-day, day-of-week, and weather conditions, is proven to be promising, since they reduce the root mean squared error (RMSE) by 48.3%. It is also interesting to find that the feature selection reduces 24.4% in the training time and leads to only the 1.8% loss in the forecasting accuracy measured by RMSE in the proposed model. This paper is one of the first DL studies to forecast the short-term passenger demand of an on-demand ride service platform by examining the spatio-temporal correlations.

Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach

A follow-up survey of berth allocation and quay crane scheduling problems in container terminals

The forecasting of short-term traffic flow is one of the key issues in the field of dynamic traffic control and management. Because of the uncertainty and nonlinearity, short-term traffic flow forecasting could be a challenging task. Artificial Neural Network (ANN) could be a good solution to this issue as it is possible to obtain a higher forecasting accuracy within relatively short time through this tool. Traditional methods for traffic flow forecasting generally based on a separated single point. However, it is found that traffic flows from adjacent intersections show a similar trend. It indicates that the vehicle accumulation and dissipation influence the traffic volumes of the adjacent intersections. This paper presents a novel method, which considers the travel flows of the adjacent intersections when forecasting the one of the middle. Computational experiments show that the proposed model is both effective and practical.

Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections

This paper proposes a novel integrated model for yard truck and yard crane scheduling problems for loading operations in container terminal. The problem is formulated as a mixed-integer programming model. Due to the computational intractability, two efficient solution methods, based on Benders’ decomposition, are developed for problem solution; namely, the general Benders’ cut-based method and the combinatorial Benders’ cut-based method. Computational experiments are conducted to evaluate the effectiveness of the proposed solution methods.

The integrated yard truck and yard crane scheduling problem: Benders’ decomposition-based methods

In this research, continuous and dynamic Berth Allocation Problem (BAP) is studied to minimize the total weighted flow time. Different from previous studies on continuous BAP, an efficient method is proposed to address the problem to identify the possible locations for next vessel in the Time-space diagram. Then two versions of Greedy Randomized Adaptive Search Procedure (GRASP) are developed to search for near optimal solutions. Both small and large scale numerical experiments are tested to examine the effectiveness of the proposed GRASPs by comparison with CPLEX and stochastic beam search, respectively.

The continuous Berth Allocation Problem: A Greedy Randomized Adaptive Search Solution

The yard truck scheduling and the storage allocation are two important decision problems affecting the efficiency of container terminal operations. This paper proposes a novel approach that integrates these two problems into a whole. The objective is to minimize the weighted sum of total delay of requests and the total travel time of yard trucks. Due to the intractability of the proposed problem, a hybrid insertion algorithm is designed for effective problem solutions. Computational experiments are conducted to examine the key factors of the problem and the performance of the proposed heuristic algorithm.

A heuristic algorithm for yard truck scheduling and storage allocation problems

To quantify the impacts of tides on seaside operations in container ports, this study reformulates the berth allocation problem by modeling their impacts on the entrance/exit of vessels into/from ports. Furthermore, to mitigate the tidal impacts, we borrow the so-called virtual arrival policy, whose potential for reducing bunker fuel consumption and vessel emissions is widely recognized by the shipping industry, and accordingly retrofit the berth allocation model. In the latter model, the state-of-the-art technique of second-order cone programming is adopted to handle the nonlinear intractability involved. We conduct extensive numerical experiments to evaluate the impacts of tides on the seaside operations in a tidal container port, and also to verify the competence of the virtual arrival policy in delivering win-win economic and environmental benefits for both the port and shipping lines. It is also intriguing to observe that the virtual arrival policy would be an applicable substitute for the costly approach of deepening the navigation channel in a tidal port.

Jin Xin Cao

Papers

Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections

The integrated yard truck and yard crane scheduling problem: Benders’ decomposition-based methods

The continuous Berth Allocation Problem: A Greedy Randomized Adaptive Search Solution

A heuristic algorithm for yard truck scheduling and storage allocation problems

Modeling the Impacts of Tides and the Virtual Arrival Policy in Berth Allocation