A Roadway Safety Sustainable Approach: Modeling for Real-Time Traffic Crash with Limited Data and Its Reliability Verification

With the development of freeway system informatization, it is easier to obtain the traffic flow data of freeway, which are widely used to study the relationship between traffic flow state and traffic safety. However, as the development degree of the freeway system is different in different regions, the sample size of traffic data collected in some regions is insufficient, and the precision of data is relatively low. In order to study the influence of limited data on the real-time freeway traffic crash risk modeling, three data sets including high precision data, small sample data, and low precision data were considered. Firstly, Bayesian Logistic regression was used to identify and predict the risk of three data sets. Secondly, based on the Bayesian updating method, the migration test towards high and low precision data sets was established. Finally, the applicability of machine learning and statistical methods to low precision data set was compared. The results show that the prediction performance of Bayesian Logistic regression improves with the increasing of sample size. Bayesian Logistic regression can identify various significant risk factors when data sets are of different precision. Comparatively, the prediction performance of the support vector machine is better than that of Bayesian Logistic. In addition, Bayesian updating method can improve the prediction performance of the transplanted model.

Vessel Navigation Behavior Analysis and Multiple-Trajectory Prediction Model Based on AIS Data

With the increasing application and utility of automatic identification systems (AISs), large volumes of AIS data are collected to record vessel navigation. In recent years, the prediction of vessel trajectories has become one of the hottest research issues. In contrast to existing studies, most researchers have focused on the single-trajectory prediction of vessels. This article proposes a multiple-trajectory prediction model and makes two main contributions. First, we propose a novel method of trajectory feature representation that uses a hierarchical clustering algorithm to analyze and extract the vessel navigation behavior for multiple trajectories. Compared with the classic methods, e.g., Douglas–Peucker (DP) and least-squares cubic spline curve approximation (LCSCA) algorithms, the mean loss of trajectory features extracted by our method is approximately 0.005, and it is reduced by 50% and 30% compared to the DP and LCSCA algorithms, respectively. Second, we design an integrated model for simultaneous prediction of multiple trajectories using the proposed features and employ the long short-term memory (LSTM)-based neural network and recurrent neural network (RNN) to pursue this time series task. Furthermore, the comparative experiments prove that the mean value and standard deviation of root mean squared error (RMSE) using the LSTM are 4% and 14% lower than those using the RNN, respectively.

Research on the Optimization of Cross-Border Logistics Paths of the “Belt and Road” in the Inland Regions

Taking logistics time, logistics cost, and carbon emissions as optimization objectives, air transportation is included in the cross-border logistics paths optimization of multimodal transportation. Considering the scale effect of transportation, a multiobjective optimization model of cross-border logistics paths including road, water, railway, and air is constructed. The problem of cross-border logistics paths along the “Belt and Road” regions for cities in inland is studied via the NSGA-II method. The research results show that Chengdu and Xi’an should bear a large number of cross-border air transportation and be constructed as the national airport-type logistics hub. The foreign destinations of cross-border air transportation are distributed in different regions, mainly in Eastern Europe and Eastern Central Europe. The optimization result shows that if there is a 1-fold increase in logistics cost, the logistics time can reduce by 1.37 folds after the cross-border air transportation joins in the model. Such a result has effectively guided the transition from cross-border water transportation to cross-border air transportation.

Dynamic Optimization of Bus Line Schedule in Commuter Corridor Based on Bus IC Card Data

The aim of this study was to explore the bus operating state of the city bus passenger corridor, taking the minimum bus operating cost and passenger travel cost as the objective function, taking passenger flow demand and operating income as the constraint, and considering the average speed change of the bus line in the bus corridor at different times. This paper proposes a dynamic optimization model of bus route schedule based on bus Integrated Circuit Card (IC Card) data. The optimization variable is the departure frequency of the candidate lines. To solve the model, a dynamic departure interval optimization method based on improved Genetic Algorithm (GA) was designed under different decision preferences. The method includes the calibration of generalized cost functions for passengers and bus companies and grasps the characteristics of bus operating speed changes and the design of departure strategies under different decision preferences. The validity and applicability of the proposed method are verified by a numerical example. We mainly carried out the following work: (1) Dynamic analysis of the time dimension of the bus departure interval takes into account the changes in passenger time characteristics during peak periods. (2) Seven schemes of weight ratio of passenger waiting time cost and bus operation cost were designed, and the departure intervals with different benefit orientations of passengers and operators were discussed, respectively, so as to select the corresponding departure schemes for decision makers under different decision preferences. The results show that (1) the total cost of the 7 different weighting schemes is lower than the actual value by 6.90% to 18.20%; (2) when decision makers need to bias the weight to the bus company, the weight ratio α : β between passengers and bus company is 0.25 : 0.75 which works best. The frequency of departures has been reduced by 6, and at the same time, the total optimized cost is reduced by 18.2%; (3) when decision makers need to bias the weight to the passengers, the weight ratio α : β between the passengers and bus company is 0.75 : 0.25 which works best. The frequency of departures has been increased by 19, and at the same time, the total optimized cost is reduced by 17.7%; and (4) when decision makers consider passengers and bus companies equally, the weight ratio α : β between passengers and bus companies is 0.5 : 0.5, the optimization cost is the closest to the actual cost, the optimization cost is reduced by 6.9%, and the frequency of departures has been increased by 5. The results show that the model in this paper provides a new idea for the information mining of bus routes in the research based on the bus IC Card data and provides an effective tool for the management of different operation decision preferences.

College Students’ Choice Behavior of Electric Two-Wheeled Vehicle

Many countries have made great efforts to boost the use of electric vehicles in recent years; for example, advanced countries including Norway and the Netherlands in Europe and the United States have enhanced people’s willingness to use electric vehicles by means of appropriate subsidies and suppression of private vehicles. In Asia, Taiwan has been promoting the policy of replacing traditional fuel two-wheeled vehicles (FTWVs) with electric two-wheeled vehicles (ETWVs) and strengthening the policy by means of replacing a large number of old FTWVs and subsidizing the purchase of ETWVs. This study took college students as the subjects, as they were the first potential group to buy ETWVs, and their concept of environmental sustainability can be shaped for cultivating vehicle use habits. This study applies a questionnaire to probe into the ETWV usage preferences of college students and explores the significant factors affecting college students’ purchase of ETWVs. This study uses a mixed logit (MXL) model for estimation. The results of model estimation show that those who are younger, have higher income, have good experience in using ETWVs, and are in user-friendly external traffic environments, are more inclined to choose ETWVs. In the future, government units can formulate policies to promote ETWVs according to the characteristics of different relevant factors.

Influence of Landscape Design on Driving Behavior Based on Slope Illusion

In a continuous downhill section of a mountain highway, factors such as road alignment, roadside environment, and other visual characteristics will impact the slope illusion drivers experience and engage in unsafe driving behaviors. To improve the negative consequences of slope illusion and driving safety in continuous downhill sections, the effects of plant spacing, height, roadside distance, and color on driving behavior were all studied by simulating the plant landscape in a virtual environment. A driving simulator and UC-win/road software were used to conduct an indoor driving simulation experiment, and parameters such as speed and lateral position offset were used as the evaluation indices of driving stability to reflect the driver’s speed perception ability with subjective equivalent speeds. The results show that a plant landscape with appropriate plant spacing, height, roadside separation, and color is conducive to improving driving stability. Furthermore, a landscape with a height of 3 m, spacing of 10 m, roadside spacing of 0.75 m, and appropriate color matching can enhance the slope perception ability and speed perception ability of drivers, which is conducive to improving the driving safety of continuous downhill sections.

Automobile Industry under China’s Carbon Peaking and Carbon Neutrality Goals: Challenges, Opportunities, and Coping Strategies

China has already committed to peaking carbon dioxide emissions by 2030 and achieving carbon neutrality by 2060 (referred to as the 30·60 Target), which has brought both daunting challenges and great opportunities to the automobile industry in China. However, there is still a lack of comprehensive and in-depth studies on the challenges, paths, and strategies for reducing carbon emissions to fulfill the 30·60 Target in automobile industries. Therefore, this paper proposes low-carbon development strategies for China’s automobile industry. This study’s method is to integrate the results from different literature to summarize the status, challenges, opportunities, and refine the coping strategies for carbon emission of the automobile industry. The results indicated that the paths for achieving the 30·60 Target include joint carbon emission reduction by upstream and downstream enterprises inside the industry. It also needs cross-industry and cross-sector coordinated decarbonization outside the industry. Meanwhile, the low-carbon policy and regulation system should be established to provide a direct driving force and fundamental guarantee for the low-carbon development of China’s automobile industry.

Fuel Consumption and Traffic Emissions Evaluation of Mixed Traffic Flow with Connected Automated Vehicles at Multiple Traffic Scenarios

To analyze the impact of different proportions of connected automated vehicles (CAVs) on fuel consumption and traffic emissions, this paper studies fuel consumption and traffic emissions of mixed traffic flow with CAVs at different traffic scenarios. Firstly, the car-following modes and proportional relationship of vehicles in the mixed traffic flow are analyzed. On this basis, different car-following models are applied to capture the corresponding car-following modes. Then, Virginia Tech microscopic (VT-micro) model is adopted to calculate the instantaneous fuel consumption and traffic emissions. Finally, based on three typical traffic scenarios, a basic segment with bottleneck zone, ramp of the freeway, and signalized intersection, a simulation platform is built based on Python and SUMO to obtain vehicle trajectory data, and the fuel consumption and traffic emissions in different scenarios are obtained. The results show that (1) In different traffic scenarios, the application of CAVs can reduce fuel consumption and traffic emissions. The higher the penetration rate, the more significant the reduction in fuel consumption and traffic emissions. (2) In the three typical traffic scenarios, the advantages of CAVs are more evident in the signalized intersection. When the penetration rate of CAVs is 100%, the fuel consumption and traffic emissions reduction ratio is as high as 32%. It is noteworthy that the application of CAVs in urban transportation will significantly reduce fuel consumption and traffic emissions.

An Empirical Study on the Segmentation of Potential Users of Shared Parking Spaces considering Individual Heterogeneity

Shared parking has become the most effective way to utilize existing parking resources. Little attention has been focused on drivers’ intention to use shared parking spaces in residential areas considering individual heterogeneity. To fill this gap, this paper explores the influencing factors and mechanism of shared parking use intention (SPUI) and further studies the preferences for the shared parking of different types of drivers. Firstly, based on the extended unified theory of acceptance and use of technology that includes psychological factors, personal attributes, and travel characteristics, the multiple indicator multiple cause (MIMIC) model was employed for parameter estimation and model assessment. Secondly, using MIMIC’s output results as input variables, the segmentation method of the latent class model (LCM) was adopted to explore drivers’ preferences regarding SPUI. Finally, a quantitative study was carried out through questionnaire data. The empirical results show that: (a) the extended unified theory of acceptance and use of technology has good explanatory power for SPUI. SPUI is directly affected by perceived risk (PR), behavioral habit (BH), social influence (SI), facilitating conditions (FCs), and effort expectancy (EE), while performance expectancy (PE) have no significant effect on SPUI. In addition, some factors of personal attributes and travel characteristics affect SPUI through psychological factors. (b) According to individual heterogeneity, the surveyed driver groups are divided into four segments: sensitive type (36%), conservative type (29.6%), neutral type (24.5%), and approved type (9.9%), respectively. There are significant differences in psychological observation variables such as EE, PE, FC, and SI among the four segments of drivers. According to the influence mechanism of psychological factors and preferences analysis of different types of drivers, the shared parking promotion strategy can be formulated from the aspects of management, operation, and technology.

Effectiveness Analysis on Human-Machine Information Interaction of Intelligent Highway

Different human-machine collaboration modes and driving simulation tests with the orthogonal method considered are designed for a series of typical intelligent highway landscapes. The feedback of drivers under different interaction modes is evaluated through NASA-LTX questionnaire, driving simulator, eye tracker, and electroencephalograph (EEG). This optimal interaction mode (including voice form, broadcasting timing, and frequency) of each driving assistance scene in CVI (Cooperative Vehicle Infrastructure) environment under the conditions of high and low traffic is determined from subjective and objective perspectives. In accordance with feedback of these subjects on each set scene, the voice information structure of each assistance mode plays the most important role on drivers followed by the broadcasting timing and frequency. These broadcasts which provide good effects include scenarios such as various assistance scenes at curves and an early warning timing at a long-distance trip as well as a high early warning frequency; in addition, as for an exit-tip assistance scenario, a voice mode assistance is preferred; and for various speed assistance scenes, the beep mode is better. Furthermore, it is found that, at a higher traffic level but a short-distance trip, an early warning timing is favored generally for various scenes while under a low traffic level, a long-distance early warning timing is better.