FUZZY COMPREHENSIVE EVALUATION OF CONTAINER TRANSPORTATION MODES ALONG THE CHANGJIANG RIVER MAIN LINE AND ITS DELTA AREA

The Changjiang River (Yangtze) is one of the fastest growth areas of container transportation in China. Rail, road and water transportation have competed against each other for container transportation in the Chang-jiang River main line and its delta area. It is of significance to assess these different transportation modes scientifically in order to organize container transportation efficiently in this area and make decision for integral plan and construction of transportation system in this area. This paper outlines application of fuzzy comprehensive evaluation to appraise different modes of typical direction of containers. Twelve assessment indexes were decided. Membership functions were formulated. Evaluation results indicated that road transportation was optimal mode in the Changjiang River delta area, however water transportation was the primary way in the Changjiang River main line.

Spatial Accessibility to Shopping Malls in Nanjing,China:Comparative Analysis with Multiple Transportation Modes

This study attempts to evaluate the rationality of the spatial layout of shopping malls and identify the urban area with poor shopping mall accessibility,as well as to provide a reference and assist decision-making for planning.Using the case of Nanjing,China,this study developed a method to evaluate the accessibility of shopping malls by three transport modes(car traffic,public transit,and bicycle).Specially,we divide the urban space into a regular hexagonal grid and harvest the total travel time from each of 7204 hexagon centroids to each of 129 shopping malls using the Baidu Internet map.The door-to-door travel time approach is used to evaluate all travel stages(walking,waiting,transfer,and transportation)based on travel time calculations.We further divide the shopping malls into two levels(super-regional and regional)based on the Dianping App’s information and develop the indicator of accessibility to shopping malls:the number of shopping malls within tolerance time thresholds and apply the closest facility and cumulative opportunities methods to measure accessibility scores.The results show that the accessibility estimations vary greatly with transport modes.The accessibility of shopping malls presents a concentric ring trend centered on the city center under the car traffic and bicycle modes.And public transit accessibility tends to axially extend due to the topology of bus routes and metro lines.In particular,we observe that the accessibility of shopping malls in Nanjing has an uneven spatial distribution pattern,with high accessibility values in the central urban areas and lots of underserved areas in urban fringe regions.Based on the accessibility measurements,we finally map the poor accessibility area and propose corresponding implications for urban planning.

A comparison of the energy consumption and carbon emissions for different modes of transportation in open-cut coal mines

Transportation accounts for 80% of open-cut coal mine carbon emissions. With regard to the energy con- sumption and carbon emissions of transportation within an open-cut mine, this paper systematically compared the work and energy consumption of a truck and belt conveyor on a theoretical basis, and con- structed a model to calculate the energy consumption of open-cut mine transportation. Life cycle carbon emission factors and power consumption calculation model were established through a Process Analysis- Life Cycle Analysis （PA-LCA）. The following results were obtained： （1） the energy consumption of truck transportation was four to twelve times higher than that of the belt conveyor; （2） the C02 emissions from truck transportation were three to ten times higher than those of the belt conveyor; （3） with the increase in the slope angle for transportation, the ratio of truck to belt conveyor for both energy consumption and carbon emissions gradually decreased; （4） based on 2013 prices in China, the energy cost of transportation using a belt conveyor in open-cut coal mines could save 0.6-2.4 Yuan/（t kin） compared to truck transportation.

Empirical Analysis of Multiple-Criteria Decision-Making (Mcdm) Process for Freight Transportation Mode Selection

In Ghana, freight transport is growing continuously every year due to its location and business processes. However, road transport carries 86% of freight despite its numerous negative impacts. Hence, the government has invested in rail transport, with 70% of its capacity is for freight transport in her busy freight transport corridor (eastern transport regions of Ghana). Thus, awareness of criteria considered when deciding on freight transport becomes vital. Hence, this study aims to improve the understanding of the factors of freight transport mode selection in Ghana from the decision-making process by identifying criteria that affect their decisions on mode transportation. The combination of Fuzzy AHP and Topsis is used to find the weights and suggest suitable alternatives for the decision-makers in the Eastern transport regions of Ghana. The result of this study shows that the criteria to consider when selecting freight transport mode in the regions are prioritized in other of Transport cost (0.6544), transport Time factors (0.2562), reliability, and flexibility (0.0605), and security, Risk of damage and lose factors (0.0287). Additionally, the suitable mode(s) of transportation in the stated corridor is owned truck carrier transport compared to the railroad, road-barge, and Contracted Carrier, thus, in descending order. The results provide organizations to prioritize these factors when deciding to select freight transport mode. At the same time, the government must remove some inputs that result in high transport costs, enforce policies, and invest in the appropriate mode.

Scale properties of turbulent transport and coherent structure in stably stratified flows

The empirical mode decomposition （EMD） is used to study the scale properties of turbulent transport and coherent structures based on velocity and temperature time series in stably stratified turbulence. The analysis is focused on the scale properties of intermittency and coherent structures in different modes and the contributions of energy-contained coherent structures to turbulent scalar counter-gradient transport （CGT）. It is inferred that the velocity intermittency is scattered to more modes with the development of the stratified flow, and the intermittency is enhanced by the vertical stratification, especially in small scales. The anisotropy of the field is presented due to different time scales of coherent structures of streamwise and vertical velocities. There is global counter-gradient heat transport close to the turbulence-generated grid, and there is local counter-gradient heat transport at certain modes in different positions. Coherent structures play a principal role in the turbulent vertical transport of temperature.

Hanoi Public TransportmTransformation by Management Using Action Research and Behavior Setting Theory

After the economic reforms in Vietnam, the number of motorbikes surged while public transport lost its passengers. No funds for investment available TRAMOC （Transport Management and Operation Centre）, the Transport Management and Operation Center started the experiment of transforming Hanoi Public Transport by management based on action research, introducing some interventions, which had shown to be effective in Europe. Phase I of the experimental approach was carried out with the smallest company that operated Line 32. The number of daily passengers surged from 1,700 to 8,000. In Phase II, the experiment was extended to the whole net. In 2001, there were 35,000 passengers per day, in 2010, there were 1 million; this is an increase of 3,000%. The result surpassed by far the expectations. The key for understanding the surprising results is the mode choice. 53% of the users are riders by choice; they have access to a car or motorbike. Simulation of the decisions as rational choice based on time needed for trips was proved to be quite accurate. Behavior was analyzed in the frame of behavior setting theory, which brings together urban structure and the design of the transport system. Success with introducing public transport needs a self-reliant leadership, which works with people in their real life situation. An urban transport system is part of the organized behavior of the people, who make use of the technical opportunities offered.

Discrete Choice Models and Artificial Intelligence Techniques for Predicting the Determinants of Transport Mode Choice--A Systematic Review

Forecasting travel demand requires a grasp of individual decision-making behavior.However,transport mode choice(TMC)is determined by personal and contextual factors that vary from person to person.Numerous characteristics have a substantial impact on travel behavior(TB),which makes it important to take into account while studying transport options.Traditional statistical techniques frequently presume linear correlations,but real-world data rarely follows these presumptions,which may make it harder to grasp the complex interactions.Thorough systematic review was conducted to examine how machine learning(ML)approaches might successfully capture nonlinear correlations that conventional methods may ignore to overcome such challenges.An in-depth analysis of discrete choice models(DCM)and several ML algorithms,datasets,model validation strategies,and tuning techniques employed in previous research is carried out in the present study.Besides,the current review also summarizes DCM and ML models to predict TMC and recognize the determinants of TB in an urban area for different transport modes.The two primary goals of our study are to establish the present conceptual frameworks for the factors influencing the TMC for daily activities and to pinpoint methodological issues and limitations in previous research.With a total of 39 studies,our findings shed important light on the significance of considering factors that influence the TMC.The adjusted kernel algorithms and hyperparameter-optimized ML algorithms outperform the typical ML algorithms.RF(random forest),SVM(support vector machine),ANN(artificial neural network),and interpretable ML algorithms are the most widely used ML algorithms for the prediction of TMC where RF achieved an R2 of 0.95 and SVM achieved an accuracy of 93.18%;however,the adjusted kernel enhanced the accuracy of SVM 99.81%which shows that the interpretable algorithms outperformed the typical algorithms.The sensitivity analysis indicates that the most significant parameters influencing TMC are the age,total trip time,and the number of drivers.

Urban green spaces accessibility in two European cities:Vilnius(Lithuania)and Coimbra(Portugal)

Urban green spaces(UGS)are essential for human well-being,contributing towards quality of life and promoting social interactions and inclusion.Accessibility to recreational UGS is key to human well-being in urban areas.However,there is a lack of works focused on recreational UGS accessibility in cities from different countries,using different types of transport.In this work,we studied the accessibility to recreational UGS in Vilnius(Lithuania)and Coimbra(Portugal)through four transport modes(foot,bike,bus and car)within 5-and 10-minute distance.The results showed important differences between cities.Vilnius had higher accessibility to recreational UGS than Coimbra in all transport modes.In both cities,the accessibility was highest by car and lowest by bus.Also,the percentage of population covered by 5-and 10-minute distance UGS accessibility in Vilnius is higher than in Coimbra,despite the lower population density in Vilnius.A large number of recreational UGS,their geographic distribution,the dispersed population pattern,and the location of several urbanizations near recreational UGS increased the inhabitant’s accessibility to recreational UGS in Vilnius.The differences in accessibility between cities were mainly attributed to the large number of recreational UGS and higher road network density,cycleways and footways density,and bus lines in Vilnius,compared to Coimbra.This work identified the areas where recreational UGS accessibility is difficult in using different transport modes and need to be improved in study areas.This is essential to support urban planners and increase cities’well-being.The findings of this work will be important for the management of the studied urban areas.

Can a polycentric structure affect travel behaviour?A comparison of Melbourne, Australia and Riyadh, Saudi Arabia

This study models the impact of the shift from a monocentric private-car-oriented city to polycentric public-transport-oriented city. Metropolitan areas have suffered traffic problems in particular increase in travel time and travel distance. Urban expansion, population growth and road network development have led to urban sprawl in monocentric cities. In many monocentric cities, travel time and distance has steadily increased and is only expected to increase in the future. Excessive travel leads to several problems such as air pollution, noise, congestion, reduction in productive time, greenhouse emissions, and increased stress and accident rates. This study examines the interaction of land use and travel. A model was developed and calibrated to Melbourne and Riyadh conditions and used for scenario analysis. This model included two parts： a spatial model and a transport model. The scenario analysis included variations of residential and activity distribution, as well as conditions of public transport service.

Risk assessment of hazardous materials transportation:A review of research progress in the last thirty years

In recent years,hazardous materials transportation accidents occur frequently,causing huge casualties and property losses.Risk assessment of hazardous materials transportation has been constantly investigated by researchers and decision-makers.In order to study the research progress of risk assessment of hazardous materials transportation,so as to better reveal relevant problems.This review systematically analyzes the existing literature,from 1991 to 2020,on risk assessment models and methods of hazardous materials transportation,and segregates the relevant studies based on unimodal and intermodal transportation systems.Additionally,an example is considered to analyze the characteristics of each risk assessment model of unimodal transportation,and a novel detailed classification is proposed for the risk assessment problems.The results indicate that the research on the risk assessment model of road transportation of hazardous materials is comprehensive,whereas that based on the railway and intermodal transportations is deficient.In comparison with the traditional risk assessment model,the conditional value-at-risk model presents a flexible decision-making framework for decision-makers and controls risk preference between risk neutrality and risk aversion.Owing to the difficulty in achieving door-to-door railway transportation and the increased risks associated with the intermodal transportation,the conclusion that the risk of railway and multimodal transportation of hazardous materials is lower than that of road transportation is not universal.Transport operators and governments can combine the advantages of different transportation modes,such as low risk,low cost,high flexibility,and high reliability,to achieve public safety and improve the competence of the system.Based on the proposed classification,this review identifies the existing trends and research gaps and summarizes future research directions.

Transportation Mode Identification with GPS Trajectory Data and GIS Information

Global Positioning System(GPS) trajectory data can be used to infer transportation modes at certain times and locations. Such data have important applications in many transportation research fields, for instance,to detect the movement mode of travelers, calculate traffic flow in an area, and predict the traffic flow at a certain time in the future. In this paper, we propose a novel method to infer transportation modes from GPS trajectory data and Geographic Information System(GIS) information. This method is based on feature extraction and machine learning classification algorithms. While using GIS information to improve inference accuracy, we ensure that the algorithm is simple and easy to use on mobile devices. Applied to GeoLife GPS trajectory dataset, our method achieves 91.1% accuracy while inferring transportation modes, such as walking, bike, bus, car, and subway, with random forest classification algorithm. GIS features in our method improved the overall accuracy by 2.5% while raising the recall of the bus and subway transportation mode categories by 3.4% and 18.5%. We believe that many algorithms used in detecting the transportation modes from GPS trajectory data that do not utilize GIS information can improve their inference accuracy by using our GIS features, with a slight increase in the consumption of data storage and computing resources.

Review and evaluation of methods in transport mode detection based on GPS tracking data

Mobility data,based on global positioning system(GPS)tracking,have been widely used in many areas,such as analyzing travel patterns,investigating transport safety and efficiency,and evaluating travel impacts.Transport modes are essential factors in understanding mobility within the transport system.Therefore,in this study,a significant number of algorithms were tested for transport mode detection.However,no conclusive recommendations can be drawn regarding which method should be used.The evaluation of the performance of the algorithms was not discussed systematically either in current literature.This paper aims to provide an in-depth review of the methods applied in transport mode detection based on GPS tracking data.The performances of the reviewed methods are then compared and evaluated to provide guidance in choosing algorithms for transport mode detection based on GPS tracking data.The results indicate that the majority of current studies are based on a supervised learning method for transport mode detection.Many of the reviewed methods first require manual dataset labeling,which can produce major drawbacks,such as inefficiency and human errors.It was also found that deep learning approaches have the potential to deal with large amounts of unlabeled raw GPS datasets and increase the accuracy and efficiency of transport mode detection.

Seeing through a new lens: exploring the potential of city walking tour videos for urban analytics

City Walking Tour Videos(CWTVs)are a novel source of Volunteered Geographic Information providing street-level imagery through video sharing platforms such as YouTube.We demonstrate that these videos contain rich information for urban analytical applications,by conducting a mobility study.We detect transport modes with a focus on active(pedestrians and cyclists)and motorised mobility(cars,motorcyclists and trucks).We chose the City of Paris as our area of interest given the rapid expansion of the bicycle network as a response to the Covid-19 pandemic and compiled a video corpus encompassing more than 66 hours of video footage.Through the detection of street names in the video and placename containing timestamps we extracted and georeferenced 1169 locations at which we summarise the detected transport modes.Our results show high potential of CWTVs for studying urban mobility applications.We detected significant shifts in the mobility mix before and during the pandemic as well as weather effects on the volumes of pedestrians and cyclists.Combined with the observed increase in data availability over the years we suggest that CWTVs have considerable potential for other applications in the field of urban analytics.

The role of intelligent technology in the development of urban air mobility systems:A technical perspective

Urban Air Mobility(UAM)is an emerging transportation system that aims at revolutionizing urban mobility through the deployment of small electric vertical takeoff and landing(eVTOL)aircraft.The development of UAM is largely driven by advances in Intelligent Technology(IT).This review article provides an overview of the UAM system and discusses the application of IT in UAM.Major challenges facing UAM are also identified,and an outlook on the future of this promising transportation system is presented.Our main conclusions suggest that IT is a fundamental driver of UAM,enabling a range of applications such as air traffic management and autonomous drone control.However,the UAM system is facing a number of challenges,including eVTOL technology,system integration issues,and noise pollution.Despite these challenges,the future of UAM appears promising;as a disruptive transportation mode,UAM is expected to play an important role in addressing the growing demand of urban transportation in the coming decades.