[Spatio-temporal Heterogeneity of Factors Influencing Transportation Carbon Emissions in Provinces Along the Belt and Road].

The administrative units of 17 provinces （autonomous regions and municipalities directly under the Central Government） along the "Belt and Road" were selected as basic spatial units to calculate the provincial traffic carbon emissions along the "Belt and Road" from 2000 to 2021. On the basis of analyzing the spatial and temporal characteristics of traffic carbon emissions by using the spatial autocorrelation method, the spatial and temporal heterogeneity of influencing factors of traffic carbon emissions was explored by combining a fixed-effect regression model and geographic detector. The results show that： ① The provincial traffic carbon emissions along the "Belt and Road" had significant spatial positive correlation, and the overall trend was upward. Additionally, the cluster evolution of high and low values of traffic carbon emissions presented the characteristics of polarization in space. The high value cluster area was mainly distributed in the open leading area, and the low value cluster area was mainly distributed in the core area of the silk road. ② Opening-up level and vehicle ownership were the positive driving factors of carbon emissions from transportation, whereas energy intensity, transportation structure, industry development scale, and government intervention were the negative driving factors. ③ Energy intensity and transportation structure were the main driving factors for the spatial variation of transportation carbon emissions, and most of them would produce nonlinear enhancement when they were spatially superimposed with other factors, that is, there was strong synergy among driving factors. The results showed that the provincial traffic carbon emissions along the "Belt and Road" were affected by the surrounding areas, the influence degree was increasing, and there was synergy between the key driving factors of traffic carbon emissions. Therefore, it is suggested that the provinces along the "Belt and Road" should fully consider the spatial and temporal heterogeneity of traffic carbon emission influencing factors and formulate differentiated traffic carbon emission reduction policies.

Impact of Helmet-Wearing Policy on E-Bike Safety Riding Behavior: A Bivariate Ordered Probit Analysis in Ningbo, China.

At present, Chinese authorities are launching a campaign to convince riders of electric bicycles (e-bikes) and scooters to wear helmets. To explore the effectiveness of this new helmet policy on e-bike cycling behavior and improve existing e-bike management, this study investigates the related statistical distribution characteristics, such as demographic information, travel information, cycling behavior information and riders' subjective attitude information. The behavioral data of 1048 e-bike riders related to helmet policy were collected by a questionnaire survey in Ningbo, China. A bivariate ordered probit (BOP) model was employed to account for the unobserved heterogeneity. The marginal effects of contributory factors were calculated to quantify their impacts, and the results show that the BOP model can explain the common unobserved features in the helmet policy and cycling behavior of e-bike riders, and that good safety habits stem from long-term safety education and training. The BOP model results show that whether wearing a helmet, using an e-bike after 19:00, and sunny days are factors that affect the helmet wearing rate. Helmet wearing, evenings during rush hour, and picking up children are some of the factors that affect e-bike accident rates. Furthermore, there is a remarkable negative correlation between the helmet wearing rate and e-bike accident rate. Based on these results, some interventions are discussed to increase the helmet usage of e-bike riders in Ningbo, China.

Pre-location for temporary distribution station of urban emergency materials considering priority under COVID-19: A case study of Wuhan City, China.

In order to avoid the huge hidden dangers caused by emergencies, it is particularly vital to make a reasonable pre-location and layout of emergency logistics facilities. A multi-objective pre-location model of temporary distribution station for emergency materials was built, which considered the problems of information shortage and uncertain demand after the incident with minimum time, maximum time satisfaction, minimum delivery cost and psychological trauma to the masses. The priority of candidate points was solved by comprehensive evaluation methods, the nominal demand of served points was estimated by triangular fuzzy number theory (TFN), and the location model was solved by non-dominated sorting genetic algorithm. In addition, the optimal schemes without priority and considering it were compared and analyzed, the practicability of the model is verified by concrete examples. The results show the time and cost reduction of 7.754% and 25.651%, an increment of total satisfaction value of the scheme considering location priority. Therefore, the model and algorithm provide theoretical support and practical ideas for solving the location problem, which can better complete the task of the location problem for temporary distribution stations of urban emergency materials.

Epidemic spread simulation in an area with a high-density crowd using a SEIR-based model.

Understanding the spread of infectious diseases is an extremely essential step to preventing them. Thus, correct modeling and simulation approaches are critical for elucidating the transmission of infectious diseases and improving the control of epidemics. The primary objective of this study is to simulate the spread of communicable diseases in an urban rail transit station. Data were collected by a field investigation in the city of Ningbo, China. A SEIR-based model was developed to simulate the spread of infectious diseases in Tianyi station, considering four groups of passengers (susceptible, exposed, infected, and recovered) and a 14-day incubation period. Based on the historical data of infectious diseases, the parameters of the SEIR infectious disease model were clarified, and a sensitivity analysis of the parameters was also performed. The results showed that the contact rate (CR), infectivity (I), and average illness duration (AID) were positively correlated with the number of infections. It was also found that the length of the average incubation time (AIT) was positively correlated with the number of exposed individuals and negatively correlated with the number of infectors. These simulation results provide support for the validity and reliability of using the SEIR model in studies of the spread of epidemics and facilitate the development of effective measures to prevent and control an epidemic.

The temporal stability of factors affecting driver injury severity in run-off-road crashes: A random parameters ordered probit model with heterogeneity in the means approach.

This study presents an effort to investigate the determinants of driver injury severity in run-off-road (ROR) crashes. In order to account for unobserved heterogeneity, a random parameter ordered probit with heterogeneity in the means approach is applied. The police-reported ROR crash data that occurred from 2014 to 2017 in the state of North Carolina is used. Four injury-severity levels are defined: property damage only (PDO), possible injury (PI), non-incapacitating (N), and F/I (by merging fatal and incapacitating). The driver, crash, roadway, environmental characteristics that potentially affect the driver injury severity are explored. Besides, the temporal stability of modeling results among the four-time periods is investigated using a series of likelihood ratio tests. Significant temporal instability is found, indicating underestimating the temporal instability might result in unreliable conclusions. Estimation results demonstrate that the indicators, including male driver, alcohol, and curved roadways, increase the possibility of fatal and incapacitating injuries in the ROR crash in most of the year periods.

Design of a Network Optimization Platform for the Multivehicle Transportation of Hazardous Materials.

With economic development, the volume of hazardous materials is increasing, and the potential risks to human beings and the natural environment are expanding. Road transportation has become the main mode of transportation for hazardous materials. Because of the specific characteristics of hazardous materials, if an accident occurs in the transportation process, it often causes mass casualties, serious property and socioeconomic damage, and damage to the ecological environment. Hence, transportation is an important part of the life cycle of hazardous materials. This paper designs an optimization platform for multidestination, multiterminal, and multivehicle networks that transport hazardous materials. The logistics module in TransCAD software is used to construct this platform. By identifying the effective transportation routes considering the transportation risk, sensitive target population, and transportation time of each road section, the entropy method can be used to fuse and obtain the comprehensive impedance value of each road section. Finally, the optimal transportation network of hazardous materials was obtained by the transportation network optimization algorithm in TransCAD. The platform can display the optimal transport program with data windows, text, and maps. The research results provide a reference for relevant departments to scientifically manage the transport of hazardous materials.

Causation Analysis of Hazardous Material Road Transportation Accidents Based on the Ordered Logit Regression Model.

Understanding the influence factors and related causation of hazardous materials can improve hazardous materials drivers' safety awareness and help traffic professionals to develop effective countermeasures. This study investigates the statistical distribution characteristics, such as types of hazardous materials transportation accidents, driver properties, vehicle properties, environmental properties, road properties. In total, 343 data regarding hazardous materials accidents were collected from the chemical accident information network of China. An ordered logit regression (OLR) model is proposed to account for the unobserved heterogeneity across observations. Four independent variables, such as hazardous materials drivers' properties, vehicle properties, environmental properties, and road properties are employed based on the OLR model, an ordered multinomial logistic regression (MLR) is estimated the OLR model parameters. Both parameter estimates and odds ratio (OR) are employed to interpret the impact of influence factors on the severity of hazardous materials accidents. The model estimation results show that 10 factors such as violations, unsafe driving behaviors, vehicle faults, and so on are closely related to accidents severity of hazardous materials transportation. Furthermore, three enforcement countermeasures are proposed to prevent accidents when transporting hazardous materials.

The association between musculoskeletal disorders and driver behaviors among professional drivers in China.

The main objective of this study is to explore correlations between the severity of musculoskeletal disorders (MSDs) and aberrant driving behaviors among professional taxi drivers. Questionnaires were administered to 162 taxi drivers in a Chinese city. Drivers with more severe MSDs reported more general and dangerous error behaviors and negative moods. Interestingly, MSDs affect drivers' error behaviors through negative moods. The study also examined the effects of age, driving experience, traffic accidents, mood states, safety awareness and driving skills on aberrant driving behaviors. The results showed that age and driving experience were significant predictors of aberrant driving behaviors. Anger was a significant predictor of aggressive violations and dangerous errors. Additionally, drivers who reported higher levels of safety awareness also reported fewer aggressive violations, and drivers with higher levels of driving skills reported fewer dangerous error behaviors.

Risk Riding Behaviors of Urban E-Bikes: A Literature Review.

In order to clearly understand the risky riding behaviors of electric bicycles (e-bikes) and analyze the riding characteristics, we review the research results of the e-bike risky riding behavior from three aspects: the characteristics and causes of e-bike accidents, the characteristics of users' traffic behavior, and the prevention and intervention of traffic accidents. The analysis results show that the existing research methods on risky riding behavior of e-bikes mainly involve questionnaire survey methods, structural equation models, and binary probability models. The illegal occupation of motor vehicle lanes, over-speed cycling, red-light running, and illegal manned and reverse cycling are the main risky riding behaviors seen with e-bikes. Due to the difference in physiological and psychological characteristics such as gender, age, audiovisual ability, responsiveness, patience when waiting for a red light, congregation, etc., there are differences in risky cycling behaviors of different users. Accident prevention measures, such as uniform registration of licenses, the implementation of quasi-drive systems, improvements of the riding environment, enhancements of safety awareness and training, are considered effective measures for preventing e-bike accidents and protecting the traffic safety of users. Finally, in view of the shortcomings of the current research, the authors point out three research directions that can be further explored in the future. The strong association rules between risky riding behavior and traffic accidents should be explored using big data analysis. The relationships between risk awareness, risky cycling, and traffic accidents should be studied using the scales of risk perception, risk attitude, and risk tolerance. In a variety of complex mixed scenes, the risk degree, coupling characteristics, interventions, and the coupling effects of various combination intervention measures of e-bike riding behaviors should be researched using coupling theory in the future.

Time-of-day Control Double-Order Optimization of Traffic Safety and Data-Driven Intersections.

This paper proposes a novel two-order optimization model of the division of time-of-day control segmented points of road intersection to address the limitations of the randomness of artificial experience, avoid the complex multi-factor division calculation, and optimize the traditional model over traffic safety and data-driven methods. For the first-order optimization-that is, deep optimization of the model input data-we first increase the dimension of traditional traffic flow data by data-driven and traffic safety methods, and develop a vector quantity to represent the size, direction, and time frequency with conflict point traffic of the total traffic flow at a certain intersection for a period by introducing a 3D vector of intersection traffic flow. Then, a time-series segmentation algorithm is used to recurse the distance amongst adjacent vectors to obtain the initial scheme of segmented points, and the segmentation points are finally divided by the combination of the preliminary scheme. For the second-order optimization-that is, model adaptability analysis-the traffic flow data at intersections are subjected to standardised processing by five-number summary. The different traffic flow characteristics of the intersection are categorised by the K central point clustering algorithm of big data, and an applicability analysis of each type of intersection is conducted by using an innovated piecewise point division model. The actual traffic flow data of 155 intersections in Yuecheng District, Shaoxing, China, in 2016 are tested. Four types of intersections in the tested range are evaluated separately by the innovated piecewise point division model and the traditional total flow segmentation model on the basis of Synchro 7 simulation software. It is shown that when the innovated double-order optimization model is used in the intersection according to the 'hump-type' traffic flow characteristic, its control is more accurate and efficient than that of the traditional total flow segmentation model. The total delay time is reduced by approximately 5.6%. In particular, the delay time in the near-peak-flow buffer period is significantly reduced by approximately 17%. At the same time, the traffic accident rate has also dropped significantly, effectively improving traffic safety at intersections.

Path optimization of taxi carpooling.

The problem that passengers are hard to take taxis while empty driving rate is high widely exists under the traditional taxi operation mode. The implementation of taxi carpooling mode can alleviate the problem in a certain extent. The objective of this study is to optimize the taxi carpooling path. Firstly, the taxi carpooling path optimization model with single objective and its extended model with multiple objectives are built respectively. Then, the single objective path optimization model of taxi carpooling is solved based on the improved single objective genetic algorithm, and the multiple-objective path optimization model of taxi carpooling is solved based on the improved multiple-objective genetic algorithm. Finally, a case study is carried out based on a road network with 24 nodes. The case study results show the path optimization models and algorithms of taxi carpooling proposed in the paper can quickly get the taxi carpooling path, and can increase the income of taxi driver while reduce the cost for passengers.

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. A multi-objective robust optimization model with adjustable robustness is constructed for the hazardous materials transportation problem of single distribution center to minimize transportation risk and time. A multi-objective genetic algorithm is designed to solve the problem according to the characteristics of the model. The algorithm uses an improved strategy to complete the selection operation, applies partial matching cross shift and single ortho swap methods to complete the crossover and mutation operation, and employs an exclusive method to construct Pareto optimal solutions. Studies show that the sets of hazardous materials transportation road can be found quickly through the proposed road screening algorithm based on GA-LM-NN, whereas the distribution route Pareto solutions with different levels of robustness can be found rapidly through the proposed multi-objective robust optimization model and algorithm.

Investigating factors influencing drivers' speed selection behavior under reduced visibility conditions.

OBJECTIVE: Driving speed is a major concern for driving safety under reduced visibility conditions. Many factors affect speed selection in low visibility, but few studies have been conducted examining drivers' characteristics, particularly in China. The present study aimed to investigate the correlation between drivers' demographic information, driving ability, and speed choice in low-visibility conditions using a sample of Chinese drivers. METHODS: A self-designed driving ability scale was used to assess driving ability in reduced visibility conditions. The reliability and validity of 306 gathered questionnaires were examined in this article, and a structural equation model (SEM) was built to explore the predictors of drivers' speed selection behavior under reduced visibility conditions and to measure the relationships between various factors. RESULTS: Age and driving experience have no direct relationship to speed selection behavior in reduced visibility, but the frequency of using expressways and annual mileage are significantly related to the speed on roads that have a speed restriction of 80 or 120 km/h. Under reduced visibility conditions, driving ability has a significant effect on speed selection behavior, and driving skill (DS) is the most influential on speed selection behavior on roads with a speed limit of 120 km/h; otherwise, the effect of risk perception (RP) does not differ by speed choice on 3 roads with different speed limits. Driving speed in good weather also has a positive influence on speed selection behavior in low visibility. CONCLUSION: Driving ability is directly associated with speed selection in reduced visibility conditions, and some demographic data indirectly influence speed selection. This study provides useful recommendations for drivers' training programs to reduce casualties from accidents in low-visibility conditions.

Effects of mobile phone distraction on pedestrians' crossing behavior and visual attention allocation at a signalized intersection: An outdoor experimental study.

With the rapid growth in mobile phone use worldwide, traffic safety experts have begun to consider the impact of mobile phone distractions on pedestrian crossing safety. This study sought to investigate how mobile phone distractions (music distraction, phone conversation distraction and text distraction) affect the behavior of pedestrians while they are crossing the street. An outdoor-environment experiment was conducted among 28 college student pedestrians. Two HD videos and an eye tracker were employed to record and analyze crossing behavior and visual attention allocation. The results of the research showed that the three mobile phone distractions cause different levels of impairment to pedestrians' crossing performance, with the greatest effect from text distraction, followed by phone conversation distraction and music distraction. Pedestrians distracted by music initiate crossing later, have increased pupil diameter, and reduce their scanning frequency, fixation points and fixation times toward traffic signal area priorities. In addition to the above effects, pedestrians distracted by phone conversation cross the street more slowly, direct fewer fixation points to the right traffic area, and spend less fixation time and lower average fixation duration on the left traffic area. Moreover, pedestrians distracted by texting look left and right less often and switch, distribute and maintain less visual attention on the traffic environment. These findings may inform researchers, policy makers, and pedestrians.

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. During genetic manipulation, different interlacing and mutation operations are carried out on different chromosomes, while, during population evolution, the infeasible solution is naturally avoided. A part of the road network of Xifeng District in Qingyang City is taken as an example to test the model and the algorithm in this study, and the concrete transportation paths are utilized in the final distribution scheme. Therefore, more robust EV distribution paths with multiple distribution centers can be obtained using the robust optimization model.

Driving anger and its relationships with type A behavior patterns and trait anger: Differences between professional and non-professional drivers.

The present study examined the types of situations that caused Chinese professional and non-professional drivers to become angry and investigated the differences in driving-elicited anger, considering the influences of type A behavior pattern and trait anger between the two groups. The 20-item revised Driving Anger Scale (DAS) was used to assess a sample of 232 drivers (57% professional, 43% non-professional). The non-professional drivers reported significantly higher levels of anger than the professional drivers on the overall Driving Anger Scale (DAS) and the traffic obstructions and discourtesy subscales. In both groups, the preferred driving speeds were positively related to driving anger. Furthermore, drivers with a type A personality exhibited higher overall driving anger scores and higher anger scores in response to traffic obstructions and slow driving than drivers with a type B personality. Trait anger was significantly related to driving anger in both groups. In the non-professional group, type A behavior patterns (TABPs) and time hurry (TH) were positively correlated with anger evoked by slow driving. In the professional group, TABPs, TH and competitive hostility (CH) were positively related to driving anger, and the TABPs exerted an indirect effect on driving anger by mediating the influence of trait anger. Overall, these findings provide a theoretical basis for implementing targeted interventions for driving anger in both professional and non-professional drivers.