Numerical simulation and analysis of aerodynamic drag on a subsonic train in evacuated tube transportation

The aerodynamic drag on a train running in an evacuated tube varies with tube air pressure, train speed and shape, as well as blockage ratio. This paper uses numerical simulations to study the effects of different factors on the aerodynamic drag of a train running at subsonic speed in an evacuated tube. Firstly, we present the assumption of a steady state, two dimensional, incompressible viscous flow with lubricity wall conditions. Subsequently, based on the Navier-Stokes equation and the k-c turbulent models, we calculate the aerodynamic drag imposed on the column train with a 3-meter diameter running under different pressure and blockage ratio conditions in an evacuated tube transporta- tion （ETT） system. The simulation is performed with FLUENT 6.3 software package. An analyses of the simulation re- sults suggest that the blockage ratio for ETT should be in the range of 0.25-0.7, and the tube internal diameter in the range of 2-4 m, with the feasible vacuum pressure in the range of 1-10 000 Pa for the future subsonic ETT trains.

Study on the pattern of train arrival headway time in high-speed railway

Purpose-The design goal for the tracking interval of high-speed railway trains in China is 3 min,but it is difficult to achieve,and it is widely believed that it is mainly limited by the tracking interval of train arrivals.If the train arrival tracking interval can be compressed,it will be beneficial for China's high-speed railway to achieve a 3-min train tracking interval.The goal of this article is to study how to compress the train arrival tracking interval.Design/methodologylapproach-By simulating the process of dense train groups arriving at the station and stopping,the headway between train arrivals at the station was calculated,and the pattern of train arrival headway was obtained,changing the traditional understanding that the train arrival headway is considered the main factor limiting the headway of trains.Findings-When the running speed of trains is high,the headway between trains is short,the length of the station approach throat area is considerable and frequent train arrivals at the station,the arrival headway for the first group or several groups of trains will exceed the headway,but the subsequent sets of trains will havea headway equal to the arrival headway.This convergence characteristic is obtained by appropriately increasing the running time.Originality/value-According to this pattern,there is no need to overly emphasize the impact of train arrival headway on the headway.This plays an important role in compressing train headway and improving high-speedrailwaycapacity.

Role of mountains and rivers in the formation of logistics enterprises'spatial pattern in the central urban areas of Chongqing

The complex landforms of a Shan-shui City(Shan-shui refers to mountains and rivers)significantly impact the selection of locations for logistics enterprises.This paper takes Chongqing,one of the most typical Shan-shui Cities in China,as the research object,and adopts spatial analysis methods and a mediating effect model,to explore the role of mountains and rivers in the formation of logistics enterprises’spatial pattern on the street scale.The study results reveal that 90% of the logistics enterprises in the central urban areas of Chongqing are located in the low-altitude area below 353m above sea level,and distributed in a north-south direction along the mountains,as a result of blockage by mountain ranges,such as those of Zhongliang Mountain and Tongluo Mountain.More than 70% of the logistics enterprises are located less than 5 km from either the Yangtze River or Jialing River,spreading along the rivers.In addition,more than half of the logistics enterprises in commercial and financial,and residential land are located within the urban core area,while 80.83% of the logistics enterprises located in warehousing land and industrial land are concentrated in the urban expansion area.In areas with high land prices,the negative effect of altitude on logistics enterprise agglomeration is weakened,while the promotion effect of river proximity on logistics enterprise agglomeration is enhanced.In the urban core area with the advantage of low altitude and proximity to the Jialing and Yangtze Rivers,the role of mountains and rivers on logistics enterprises is not apparent;in contrast,in the urban expansion area with more complex landforms,land price can be an effective means for the government to macro-manage the spatial pattern of logistics enterprises in a Shan-shui City.

Relationship between Logistics Firm Size and Business Diversification: An Empirical Study of Chongqing, Area in China

This paper aims to explore the relationship between logistics firm size and business diversification, especially in micro- and small-sized enterprises, and assess the diversification process of Chongqing logistics enterprises using new data source. It is intended to contribute to theoretical research on diversification in the emerging logistics industry based on resource-based view (RBV) and factor production theory. We innovatively establish the logistics business classification system, then measure the degree and direction of diversification of logistics enterprises based on mean narrow-spectrum diversification-based product count methods. The empirically shows that there are a growing number of highly-diversified logistics enterprises in general, with an average annual increase of 14.23%. Especially after 2008, small and medium-sized enterprises developed towards related diversification while micro-sized enterprises came to the opposite conclusion. For them, creating unrelated diversified business is a crucial way to generate a competitive advantage. Unlike the conclusions derived from other industries, small-sized logistics enterprises are prone to try a new business, both related and unrelated, for better survival and performance. And changes in the firm size of medium and large-sized enterprises have little impact on the development of the business diversification.

Experimental analysis of decision-making for demand chasing in order behaviors

The traditional newsvendor model assumes that a decision-maker is risk-neutral. However, in actuality, a decisionmaker's order behavior is often influenced by waste-averse preference and stockout-averse preference. We extend the newsvendor model with consideration of averse preferences to investigate how the decision results of the previous period impact the order behavior of the current period, and design an inventory decision-making behavior experiment. Results from the study demonstrate that the order behavior of both a group and an individual exhibits a demand chasing phenomenon, and the former is more significant. Through the interval estimation of the decision maker's order quantity, by the maximum likelihood method we find that the stockout-averse preference has an effect on the decision-making when the prior period is insufficient, causing the current period order quantity larger than the expected profit-maximizing order quantity. In a similar way, waste-averse preference has an effect on decision-making when the prior period is surplus, resulting in the current period order quantity smaller than the expected profit-maximizing order quantity. Finally, we investigate the formation mechanism of demand chasing phenomenon from the perspective of the averse preferences, and propose that this phenomenon is a decision maker's cognitive reaction to stochastic demand environment.

Vehicular emissions and concentrations in school zones: A case study

Recent research has revealed that human exposure to air pollutants such as CO, NO_x, and particulates can lead to respiratory diseases, especially among school-age children. Towards understanding such health impacts, this work estimates local-scale vehicular emissions and concentrations near a highway traffic network, where a school zone is located in. In the case study, VISSIM traffic micro-simulation is used to estimate the source of vehicular emissions at each roadway segment. The local-scale emission sources are then used as inputs to the California line source dispersion model(CALINE4) to estimate concentrations across the study area. To justify the local-scale emissions modeling approach, the simulation experiment is conducted under various traffic conditions. Different meteorological conditions are considered for emission dispersion. The work reveals that emission concentrations are usually higher at locations closer to the congested segments, freeway ramps and major arterial intersections. Compared to the macroscopic estimation(i.e. using network-average emission factors), the results show significantly different emission patterns when the local-scale emission modeling approach is used. In particular, it is found that the macroscopic approach over-estimates emission concentrations at freeways and under-estimations are observed at arterials and local streets. The results of the study can be used to compare to the US environmental protection agency(EPA) standards or any other air quality standard to further identify health risk in a fine-grained manner.

An experimental analysis on the turnover time of railway freight transportation based on the impact strength of factors

Based on the factors impact strength model(FISM), we studied on calculation formulas of influence strength and key elements of FISM, and analyzed the turnover time of railway freight transportation of China. The results show that wagon transfer time is the most critical factor among the three subjective factors of wagons turnover time. The FISM based analysis of wagon transfer time show that the wagon turnover time is significantly correlated with transit time with resorting. Among the seven factors of detention time of transit time with resorting, the time of waiting to departing, converging, and waiting to break-up are key factors, while the time of make-up, break-up, arrival and departure are general factors. We carried out one empirical research based on the data of Baoji East Railway Station in 2015. The results of empirical research and FISM are consistent completely.

Entropy measures of type-2 intuitionistic fuzzy sets and type-2 triangular intuitionistic trapezodial fuzzy sets

In order to measure the uncertain information of a type- 2 intuitionistic fuzzy set （T21FS）, an entropy measure of T21FS is presented by using the constructive principles. The proposed entropy measure is also proved to satisfy all of the constructive principles. Further, a novel concept of the type-2 triangular in- tuitionistic trapezoidal fuzzy set （T2TITrFS） is developed, and a geometric interpretation of the T2TITrFS is given to comprehend it completely or correctly in a more intuitive way. To deal with a more general uncertain complex system, the constructive principles of an entropy measure of T2TITrFS are therefore proposed on the basis of the axiomatic definition of the type-2 intuitionisic fuzzy entropy measure. This paper elicits a formula of type-2 triangular intuitionistic trapezoidal fuzzy entropy and verifies that it does sa- tisfy the constructive principles. Two examples are given to show the efficiency of the proposed entropy of T2TITrFS in describing the uncertainty of the type-2 intuitionistic fuzzy information and illustrate its application in type-2 triangular intuitionistic trapezodial fuzzy decision making problems.

Risk Evaluation on Logistics Finance of Agricultural Products Based on Fuzzy-AHP Model

The development of logistics finance business for agricultural products is the best way to realize the common interests of logistics enterprises,small and medium-sized agricultural product enterprises and financial institutions,which will contribute to the development of China's new socialist rural economy and the construction of " two-oriented society". As the agricultural products have some special attributes,it's easy to create the risk in carrying out the logistics finance business. The paper constructs a risk evaluation indicator system for logistics finance of agricultural product,and uses the Fuzzy-AHP model to evaluate. The results show that the comprehensive risk level is normal risk,which shows that third-party logistics enterprises can carry out the logistics financial business of agricultural products,but the risks from logistics enterprises and agricultural collateral need to be prevented.

Numerical Simulation of the Mixing and Hydrodynamics of Asphalt and Rubber in a Stirred Tank

Computational fluid dynamics(CFD)has been used to analyze the mixing process of Asphalt and Rubber(AR)in a stirred tank with a six flat-blade disc turbine(Rushton),a down-pumping 45°pitched-blade turbine(PBTD-6)and a down-pumping propeller(TXL).The two-phase(solid-liquid)flow in the considered stirred tank has been modelled in the framework of an Eulerian-Eulerian approach,a laminar-flow assumption and a multi-reference frame strategy.The following effects have been investigated:The influence of the impeller speed,impeller type,crumb rubber(CR)particle diameter and initial CR particle loading on the quality of the CR particle’s suspension.The outcomes of the numerical simulations have been found to be in good agreement with the experimental results.The most important findings can be summarized as follows.The flow velocity on the lower part of the stirred tank,induced by the Rushton impeller,is the highest for the three considered mixing systems at the same impeller speed.The suspension quality of CR particles increases with the impeller speed.A larger particle diameter and a higher initial CR particle loading lead to an uneven distribution of particles.The degree of homogeneity in the Rushton impeller system is better than that of the PBTD-6 impeller system and the TXL impeller system at the same impeller speed,while the suspension uniformity of the TXL impeller system is the best possible one for the same power consumption.

Identification of crash hotspots using kernel density estimation and kriging methods:a comparison

This paper presents a study aimed at comparing the outcome of two geostatistical-based approaches, namely kernel density estimation （KDE） and kriging, for identifying crash hotspots in a road network. Aiming at locating high-risk locations for potential intervention, hotspot identification is an integral component of any comprehensive road safety management programs. A case study was conducted with historical crash data collected between 2003 and 2007 in the Hennepin County of Min- nesota, U.S. The two methods were evaluated on the basis of a prediction accuracy index （PAI） and a comparison in hotspot ranking. It was found that, based on the PAI measure, the kriging method outperformed the KDE method in its ability to detect hotspots, for all four tested groups of crash data with different times of day. Further- more, the lists of hotspots identified by the two methods were found to be moderately different, indicating the im- portance of selecting the right geostatistical method for hotspot identification. Notwithstanding the fact that the comparison study presented herein is limited to one case study, the findings have shown the promising perspective of the kriging technique for road safety analysis.

Public priorities and consumer preferences for selected attributes of automated vehicles

Public opinion and consumer preferences are among the various constraints on the rollout of automated cars, as they will affect the decision-making of both automotive industry actors and public-sector regulators. This study contributes to the growing body of the literature regarding this issue, through a moderate-scale survey （n = 370） that incorporated both prioritization/attitudinal questions （regarding public opinion） and a stated-prefer- ence module （to identify consumer preferences）. The sur- vey protocol includes a stated-preference approach to investigate consumers＇ preferences for the possibility of very high rates of speed in automated cars on long-distance journeys. We found separately identifiable effects for average travel speeds （manifested as journey duration） and maximum travel speed in the stated-preference scenarios. In the ＇prioritization＇ component of the survey, respondents ranked having the ＇highest possible level of safety＇ as the single most important benefit that they would like auto- mated cars to deliver, ahead of benefits such as being able to performing activities while traveling or having traffic congestion reduced. This result has consequences for the car-following distances that are programmed into the control algorithms of automated cars. Documenting this finding is important, as decisions must be made in the near future by driving-algorithm designers, public-sector regu- lators, and ultimately the judiciary regarding the guidelines for acceptable automated driving-behavior instructions.

Injury severity analysis： comparison of multilevel logistic regression models and effects of collision data aggregation

This paper describes an empirical study aiming at identifying the main differences between different logistic regression models and collision data aggregation methods that are commonly applied in road safety literature for modeling collision severity. In particular, the research compares three popular multilevel logistic models （i.e., sequential binary logit models, ordered logit models, and multinomial logit models） as well as three data aggregation methods （i.e., occupant based, vehicle based, and collision based）. Six years of collision data （2001-2006） from 31 highway routes from across the province of Ontario, Canada were used for this analysis. It was found that a multilevel multinomial logit model has the best fit to the data than the other two models while the results obtained from occupant-based data are more reliable than those from vehicle- and collision-based data. More importantly, while generally consistent in terms of factors that were found to be significant between different models and data aggregation methods, the effect size of each factor differ sub- stantially, which could have significant implications forevaluating the effects of different safety-related policies and countermeasures.

ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model

The ChatGPT,a lite and conversational variant of Generative Pretrained Transformer 4(GPT-4)developed by OpenAI,is one of the milestone Large Language Models(LLMs)with billions of parameters.LLMs have stirred up much interest among researchers and practitioners in their impressive skills in natural language processing tasks,which profoundly impact various fields.This paper mainly discusses the future applications of LLMs in dentistry.We introduce two primary LLM deployment methods in dentistry,including automated dental diagnosis and cross-modal dental diagnosis,and examine their potential applications.Especially,equipped with a cross-modal encoder,a single LLM can manage multi-source data and conduct advanced natural language reasoning to perform complex clinical operations.We also present cases to demonstrate the potential of a fully automatic Multi-Modal LLM AI system for dentistry clinical application.While LLMs offer significant potential benefits,the challenges,such as data privacy,data quality,and model bias,need further study.Overall,LLMs have the potential to revolutionize dental diagnosis and treatment,which indicates a promising avenue for clinical application and research in dentistry.

Reliability-based congestion pricing model under endogenous equilibrated market penetration and compliance rate of ATIS

A reliability-based stochastic system optimum congestion pricing(SSOCP) model with endogenous market penetration and compliance rate in an advanced traveler information systems(ATIS) environment was proposed. All travelers were divided into two classes. The first guided travelers were referred to as the equipped travelers who follow ATIS advice, while the second unguided travelers were referred to as the unequipped travelers and the equipped travelers who do not follow the ATIS advice(also referred to as non-complied travelers). Travelers were assumed to take travel time, congestion pricing, and travel time reliability into account when making travel route choice decisions. In order to arrive at on time, travelers needed to allow for a safety margin to their trip.The market penetration of ATIS was determined by a continuous increasing function of the information benefit, and the ATIS compliance rate of equipped travelers was given as the probability of the actually experienced travel costs of guided travelers less than or equal to those of unguided travelers. The analysis results could enhance our understanding of the effect of travel demand level and travel time reliability confidence level on the ATIS market penetration and compliance rate; and the effect of travel time perception variation of guided and unguided travelers on the mean travel cost savings(MTCS) of the equipped travelers, the ATIS market penetration, compliance rate, and the total network effective travel time(TNETT).

Survival Analysis of Logistics Service Providers: An Empirical Study of Chengdu, Area in China

This paper worked on a sample of 6791 logistics establishments registered in Chengdu, China over the period 1984-2016 to understand the survival status of logistics service providers (LSPs) by non-parametric Kaplan-Meier estimation, together with Cox proportional hazard regression model, to identify factors affecting the failure of LSPs. In particular, it studies the interaction effect between LSPs’ size and entry timing and location. The empirical results show that: 1) Regarding the survival time, 1365 of the 6791 sample LSPs exited from the market by 2017. The exit rate is 20.1%, and the average life of the 6791 LSPs is about 6 years. 2) The survival of LSPs depends on their typology, ownership structure. And there is no significant difference in the probability of survival for both independent LSPs and logistics branches after controlling the effects of other variables. 3) Location and entry timing also play an important role in the survival of small-scale LSPs, but these factors cannot explain large-scale LSPs’ failure.

Reliability analysis of stochastic park-and-ride network

Park-and-ride （P＆R） facilities can alleviate the traffic burden in central urban areas by enabling car drivers to park at the perimeter of congested areas and continue their journeys with public transportation （e.g., metro and bus rapid transit）. Whether a P＆R scheme is successful depends on its attractiveness to car users. This paper presents anevaluation method for the reliability analysis of P＆R mode. Two indices, P＆R reliability and mode reliability, are in- troduced to represent the reliabilities of a transfer point and an entire trip, respectively. Then, a systematic reliability analysis is conducted for a stochastic P＆R network, where travelers can complete their journeys via two options： auto mode or P＆R mode. A variational inequality （VI） model is proposed and solved by a heuristic solution algorithm. Nu- merical results show that the P＆R facility reliability is significantly influenced by the capacity of parking facilities, the dispatching frequency of the connecting metro, and the metro fare. In addition, a higher level of total demand in the network has significant negative impacts on P＆R mode＇s attractiveness compared to auto mode.

Dynamic train dwell time forecasting:a hybrid approach to address the influence of passenger flow fluctuations

Train timetables and operations are defined by the train running time in sections,dwell time at stations,and headways between trains.Accurate estimation of these factors is essential to decision-making for train delay reduction,train dispatching,and station capacity estimation.In the present study,we aim to propose a train dwell time model based on an averaging mechanism and dynamic updating to address the challenges in the train dwell time prediction problem(e.g.,dynamics over time,heavy-tailed distribution of data,and spatiotemporal relationships of factors)for real-time train dispatching.The averaging mechanism in the present study is based on multiple state-of-the-art base predictors,enabling the proposed model to integrate the advantages of the base predictors in addressing the challenges in terms of data attributes and data distributions.Then,considering the influence of passenger flow on train dwell time,we use a dynamic updating method based on exponential smoothing to improve the performance of the proposed method by considering the real-time passenger amount fluctuations(e.g.,passenger soars in peak hours or passenger plunges during regular periods).We conduct experiments with the train operation data and passenger flow data from the Chinese high-speed railway line.The results show that due to the advantages over the base predictors,the averaging mechanism can more accurately predict the dwell time at stations than its counterparts for different prediction horizons regarding predictive errors and variances.Further,the experimental results show that dynamic smoothing can significantly improve the accuracy of the proposed model during passenger amount changes,i.e.,15.4%and 15.5%corresponding to the mean absolute error and root mean square error,respectively.Based on the proposed predictor,a feature importance analysis shows that the planned dwell time and arrival delay are the two most important factors to dwell time.However,planned time has positive influences,whereas arrival delay has negative influences.

Adaptive signal control and coordination for urban traffic control in a connected vehicle environment: A review

Existing signal control systems for urban traffic are usually based on traffic flow data from fixed location detectors.Because of rapid advances in emerging vehicular communication,connected vehicle(CV)-based signal control demonstrates significant improvements over existing conventional signal control systems.Though various CV-based signal control systems have been investigated in the past decades,these approaches still have many issues and drawbacks to overcome.We summarize typical components and structures of these existing CV-based urban traffic signal control systems and digest several important issues from the summarized vital concepts.Last,future research directions are discussed with some suggestions.We hope this survey can facilitate the connected and automated vehicle and transportation research community to efficiently approach next-generation urban traffic signal control methods and systems.

A Heterogeneous Information Fusion Method for Maritime Radar and AIS Based on D-S Evidence Theory

Maritime radar and automatic identification systems (AIS), which are essential auxiliary equipment for navigation safety in the shipping industry, have played significant roles in maritime safety supervision. However, in practical applications, the information obtained by a single device is limited, and it is necessary to integrate the information of maritime radar and AIS messages to achieve better recognition effects. In this study, the D-S evidence theory is used to fusion the two kinds of heterogeneous information: maritime radar images and AIS messages. Firstly, the radar image and AIS message are processed to get the targets of interest in the same coordinate system. Then, the coordinate position and heading of targets are chosen as the indicators for judging target similarity. Finally, a piece of D-S evidence theory based on the information fusion method is proposed to match the radar target and the AIS target of the same ship. Particularly, the effectiveness of the proposed method has been validated and evaluated through several experiments, which proves that such a method is practical in maritime safety supervision.