Dongye Sun

In view of the problems existing in the path planning algorithms of unmanned vehicles, such as low search efficiency, slow convergence speed and easy to fall into the local optimal. Based on the characteristics of route planning for unmanned vehicles, this paper introduces Q-Learning into the traditional ant colony algorithm to enhance the learning ability of the algorithm in dynamic environment, so as to improve the overall efficiency of route search. By mapping pheromones into Q values in Q-learning, rapid search in complex environments is realized, and a collection-free path satisfying constraints is quickly found. The results of case analysis show that compared with the traditional ant colony algorithm and the improved ant colony algorithm considering reward and punishment factors, the improved ant colony algorithm based on Q-Learning can effectively reduce the number of iterations, shorten the path optimization time and path length and other performance indicators, and has many advantages in jumping out of the local optimal, improving the global search ability and improving the convergence speed, and has good adaptability and robustness in complex environments. It ensures the safety and stability of unmanned vehicles in complex environments.

Crash risk analysis and prediction are considered the premise of highway traffic safety control, which directly affects the accuracy and effectiveness of traffic safety decisions. A highway traffic crash risk prediction method considering temporal correlation characteristics is proposed in this research. Firstly, the case-control sample analysis method is used to extract 6 time series sample data composed of crash traffic flow data and corresponding non-crash traffic flow data for crash risk analysis and prediction. Secondly, the multiparameter fusion clustering analysis method is used to indicate that the sample data of different time series have different effects on the crash risk. Then, the random forest model is used to screen several traffic flow variables that affect the highway crash risk. Thereafter, the downstream mean speed (ASD1D2), the upstream mean occupancy (AOU1U2), and the speed difference (DSU1D1) on the nearest detector were determined as the explanatory variables of the crash risk prediction model. Finally, based on the three variables, the dynamic Bayesian network model for highway traffic crash risk prediction is proposed. The overall prediction accuracy of this model is 84.9%, the crash prediction accuracy is 60.8%, and the non-crash prediction accuracy is 92.3%. Also, the prediction results show that the dynamic Bayesian model has better prediction effect than the static Bayesian model for the same sample data.

Traffic status recognition and classification is an important prerequisite for traffic management and control. Based on the idea of weight optimal, a weighted fuzzy c-means clustering method for improving the accuracy of traffic classification is proposed in this study to ease traffic congestion. First, since there are many indexes that affect the traffic flow state classification, three commonly used indexes namely, volume, speed and occupancy are chosen as the main parameters for the traffic flow state classification in this paper. Second, in order to quantitatively analyze the influence degree of different traffic flow parameters on traffic flow state division, based on the principle of weight optimization, the objective function of weight optimization is established. Then the weight of each attribute index is obtained by using the branch and bound algorithm. Finally, since the traditional fuzzy c-means clustering method will not consider the influence of different traffic flow parameter weights on the traffic flow state classification results, the classification effect needs to be further improved. A fuzzy weighted c-means classification method which uses weighted Euclidean distance instead of Euclidean distance is proposed to classify the traffic flow states. Based on the same traffic flow data sample on the same road section, the traffic state classification results with different methods show that it is helpful to improve the traffic flow state classification accuracy by weighting the clustering index. Because the influence of different parameters on the traffic flow state classification is considered in the process of clustering, it is more conducive to improve the classification accuracy. Moreover, it can provide more accurate classification information for traffic control and decision making.

In order to quantitatively analyze the influence of different traffic conditions on highway crash risk, a method of crash risk assessment based on traffic safety state division is proposed in this paper. Firstly, the highway crash data and corresponding traffic data of upstream and downstream are extracted and processed by using the matched case-control method to exclude the influence of other factors on the model. Secondly, considering the weight of traffic volume, speed and occupancy, a multi-parameter fusion cluster method is applied to divide traffic safety state. In addition, the quantitative relationship between different traffic states and highway crash risk is analyzed by using Bayesian conditional logistic regression model. Finally, the results of case study show that different traffic safety conditions are in different crash risk levels. The highway traffic management department can improve the safety risk management level by focusing on the prevention and control of high-risk traffic safety conditions.

Road traffic injury is currently the leading cause of death among children and young adults aged 5–29 years all over the world. Measures must be taken to avoid accidents and promote the sustainability of road safety. The current study aimed to identify risk factors that are significantly associated with the severity in crash accidents; therefore, traffic crashes could be reduced, and the sustainable safety level of roadways could be improved. The Apriori algorithm is carried out to mine the significant association rules between the severity of the crash accidents and the factors influencing the occurrence of crash accidents. Compared to previous studies, the current study included the variables more comprehensively, including environment, management, and the state of drivers and vehicles. The data for the current study comes from the Wisconsin Transportation crash database that contains information on all reported crashes in Wisconsin in the year 2016. The results indicate that male drivers aged 16–29 are more inclined to be involved in crashes on roadways with no physical separation. Additionally, fatal crashes are more likely to occur in towns while property damage crashes are more likely to occur in the city. The findings can help government to make efficient policies on road safety improvement.

A reasonable taxi fleet size has a significant impact on the satisfaction of urban traffic demand, the alleviation of urban traffic congestion, and the stability of taxi business groups. Most existing studies measure the overall scale by using macro indices, and few studies are from the micro level. To meet the transportation demand for taxis, mitigating the mismatch between taxi supply and demand, this research proposes an urban taxi fleet size calculating model based on GPS tracking data. Firstly, on the basis of road network segmentation, the probability model of a passenger taxi-taking a road section as a unit is built to evaluate the difficulty of taxi-taking on a road section. Furthermore, a user queuing model is built for the “difficult to take a taxi” road section in the peak period, and the service mileage required by potential taxi users is calculated. After that, a transportation capacity measurement model is built to estimate the number of taxis required in different time periods, Finally, the income constraint model is used to explain the impact of different vehicle fleet sizes on the income of taxi groups, so as to provide a reference for the determination of the final fleet size. The model is applied to data from Xi’an. The calculation results are based on data from May 2014, and show that the scale of taxi demand is about 654–2237, and after considering the impact of different fleet size increases on income, when the income variation index is limited to 0.10, i.e., the decrease of drivers’ income will not exceed 10%, an increase of 1286 taxis will be able to meet 66% of the unmet demand in the peak period. The conclusion indicates that the model can effectively calculate the required fleet size and formulate the constraint solutions. This method provided can be considered as a support for formulating the regulation strategy of an urban taxi fleet size.

Passenger transport safety evaluation plays a very important role in the development of China’s railway. In order to obtain the optimal indicator weight in railway passenger transport safety evaluation, firstly, fuzzy theory and analytic hierarchy pr

Based on the idea of maximizing variances, a weight optimization method is proposed in this research to improve railway transportation safety evaluation. Firstly, the main evaluation indicators that can reflect the safety of railway transportation are selected as the independent variables. Secondly, in order to avoid the influence of experts’ empowerment on the evaluation results of railway transport safety, fuzzy set theory is introduced to generate the variation range of the weights of each evaluation index, which is used as the constraint of weight optimization model. Then, the weight optimization model for railway transportation safety performance measurement is established based on the principle of maximum variance. The structure of the optimization model shows the characteristics of the quadratic programming model. Therefore, the optimal weight is calculated by using the branch bounded algorithm, which is one of the quadratic programming model solution algorithms. Finally, the empirical analysis of the safety performance measurement for 18 railway bureaus shows that using the optimized index weight for safety performance measurement can not only make full use of prior information but also ensure that 18 railway bureaus can be distinguished to the maximum extent.

According to the analysis of the bidding and tendering for Chinese railway engineering construction projects in the past 10 years, the strong reciprocity phenomenon between the owners and contractors that accepted and maintained the specific cooperation is discovered in this study. Based on the existing supporting theory of cooperation behavior in the field of brain science, the theory of strong reciprocity is proposed to analyze the game and cooperation mechanism between the owner and the contractor under the bounded rationality. Moreover, the evolutionary game model for the relationship between the owners and contractors of railway engineering projects is established in this research. Simultaneously, the numerical simulation analysis for the influence of different parameter variables on the evolution of the model is conducted. Finally, the results of the study show that when they anticipating the cooperation of the counterparty, the owner and the contractor will respond positively to cooperation. On the contrary, the owners and contractors or third-party regulatory agencies will punish uncooperative behaviors that disrupt cooperation rules. The research results about the cooperative behavior between the owner and the contractor coincides with the interpretation of cooperation in the field of brain science.