A multi-objective optimization model of urban passenger transportation structure under low-carbon orientation considering participating subjects.

The optimization of urban passenger transport structure can effectively save energy and reduce the carbon emission of transport. However, the carbon emission and energy consumption generated by the construction of transport projects are worthy of attention. In this paper, a multi-objective optimization model of urban passenger transport structure oriented to low carbon is proposed considering passenger, operator, and construction.

Analysis of EEG Characteristics of Drivers and Driving Safety in Undersea Tunnel.

To study the influence of the driving environment of an undersea tunnel on driver EEG (electroencephalography) characteristics and driving safety, a real vehicle experiment was performed in the Qingdao Jiaozhou Bay Tunnel. The experimental data of the drivers' real vehicle experiment were collected using an illuminance meter, EEG instrument, video recorder and other experimental equipment. The undersea tunnel is divided into different areas, and the distribution law of driving environment characteristics, EEG characteristics and vehicle speed characteristics is analyzed.

Road screening and distribution route multi-objective robust optimization for hazardous materials based on neural network and genetic algorithm.

Route optimization of hazardous materials transportation is one of the basic steps in ensuring the safety of hazardous materials transportation. The optimization scheme may be a security risk if road screening is not completed before the distribution route is optimized. For road screening issues of hazardous materials transportation, a road screening algorithm of hazardous materials transportation is built based on genetic algorithm and Levenberg-Marquardt neural network (GA-LM-NN) by analyzing 15 attributes data of each road network section.

Distribution path robust optimization of electric vehicle with multiple distribution centers.

To identify electrical vehicle (EV) distribution paths with high robustness, insensitivity to uncertainty factors, and detailed road-by-road schemes, optimization of the distribution path problem of EV with multiple distribution centers and considering the charging facilities is necessary. With the minimum transport time as the goal, a robust optimization model of EV distribution path with adjustable robustness is established based on Bertsimas' theory of robust discrete optimization. An enhanced three-segment genetic algorithm is also developed to solve the model, such that the optimal distribution scheme initially contains all road-by-road path data using the three-segment mixed coding and decoding method.

A method for determining the number of traffic conflict points between vehicles at major-minor highway intersections.

Background: Traffic conflict points are the areas formed by conflict movements in the traffic flow. The numbers and distributions of different types of traffic conflict points are often used to evaluate access management designs and the safety performance of intersections. Customarily, determining the number of different types of traffic conflict points is based on the manual drawing method, which is troublesome and makes using computerized procedures for calculation of the number of traffic conflict points difficult.