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Currently, signal control mode is the main control method of urban road intersections. Given that the traffic efficiency of road intersections is mainly affected by signal timing schemes, it is important to optimize signal timing at road intersections. Therefore, signal timing optimization methods of urban road intersections are explored in this work. When optimizing the timing of the signal at the intersections, the selection of optimization targets play an important role. At present, there are multiple objectives considered while designing signal timing scheme, including capacity, delays, and automobile exhaust. However, from the perspective of the traveler, they are more concerned about their own delay while passing intersections. In this work, we propose a novel multi-objective signal timing optimization model with goals of per capita delay, vehicle emissions, and intersection capacity. Considering the problem characteristics of the target problem, a meta-heuristic algorithm combining difference operator, which is based on Particle Swarm Optimization Algorithm, is developed. To test the validity of proposed approach, we applied it to real-world intersection signal timing problems in China. The results show that the optimized signal timing scheme obtained by the proposed algorithm is better than the realistic one. Also, the effectiveness of the developed algorithm is demonstrated by comparing it with other efficient algorithms.

The layout of functional areas has a great impact on the implementing of a logistics park. Existing layout models with multiple planning objectives and influencing factors are too complex to be solved. This article puts forward a two-stage layout method for functional areas in logistics parks combining a layer-by-layer analysis process and a multi-objective programming model. Base on the rational setting of functional areas, the optimization of operational processes and the quantification of the interrelations between functional areas, some green logistic goals are achieved in two stages. In the first stage, the method aims at maximizing the comprehensive relationship among functional areas and obtaining the layout structure by the layer-by-layer analysis process. In the second stage, a multi-objective planning model is established to get the location and shape of each functional area, with the goals of minimizing the material handling costs and maximizing the spatial utilization rate. A genetic algorithm is designed to solve the model. Finally, an example is put forward to verify the feasibility of the two-stage layout method, and the comparison with the previous general model is presented. This article provides a new method for the layout planning of function areas in green logistics parks.

The location problem of logistics park is the basis of logistics network planning. The establishment of conventional logistics park location planning model requires consideration of multiple factors, including position relationships between site selection points and demand points, economic cost, service efficiency, and consumer satisfaction. “Green development concept” gradually leads the trend of social and economic development in the contemporary world. The green logistics park emerges as the requirement of era. There are obvious differences between the site selection rules and the location demand of the green logistics park with the conventional logistics park. On the basis of analyzing the evaluation indexes of green logistics park and the factors affecting the site selection, this article builds a single-objective decision model based on the comprehensive cost. The model sets transportation cost and environment cost as the objective function. In addition, according to the constraints of location and quantity of the proposed logistics park, the model is divided into four parts to refine, and the solving process based on hybrid particle algorithm and alternate location allocation heuristic algorithm is given. The model and algorithm are feasible by analyzing the numerical example. Finally, the green logistics park site selection scheme is designed. The location model that considers the single comprehensive cost target provides a simple and feasible decision method for the green logistics park location.

This article proposes a machine learning–based travel mode detection method using urban residents’ travel routes as the data source, collected via smartphone global positioning system modules. A data-driven machine learning strategy was chosen in the model construction. This study performed data cleaning and mining on over 4400 pieces of urban resident travel records containing several millions of global positioning system tracking points. Series of characteristic values of speed, travel distance, and direction are calculated, which reflect the travel mode of smartphone holders. In travel mode identification, first, the transition regions of travel segments of different travel modes are effectively distinguished; then, continuous tracking points for single-mode travel are connected into single-mode travel segments. The travel mode of the surveyed subjects is identified based on the calculated features of average speed, average acceleration, and average change of direction within each single-mode segment. The random forest method is chosen as the basis model to classify travel mode. Three-quarters of the travel records were used to construct the random forest classifier, and the detection accuracy of the established model for the remaining ¼ of the travel record reached 94.4%. The proposed method uses massive smartphone global positioning system tracking points as the basis; the detection results are consistent with manually collected prompted recall survey records.

Product style characteristics have a large impact on product function. Making an objective and precise assessment of style characteristic has become an increasing importance to improve the production efficiency and reduce environmental pollution. This work proposes a framework built by analytic hierarchy process and technique for order preference by similarity to ideal solution methods, that is, analytical hierarchy process and technique for order performance by similarity to ideal solution, to evaluate automotive style design alternatives’ performance, together with automotive style design characteristics. Analytic hierarchy process is applied to obtain weights of the performance, and technique for order preference by similarity to ideal solution is adopted to rank the design alternatives. A case study is illustrated to test and verify the proposed method. Simultaneously, sensitivity analysis is provided to monitor the robustness of this method. The results show that it provides an effective and feasible method for evaluation of automotive style design alternatives’ performance.