Intelligent Interactive Multimedia Systems and Services

Cloud computing has become so popular that most sensitive data are hosted on the cloud. This fast-growing paradigm has brought along many problems, including the security and integrity of the data, where users rely entirely on the providers to secure their data. This paper investigates the use of the pattern fragmentation to split data into chunks before storing it in the cloud, by comparing the performance on two different cloud providers. In addition, it proposes a novel approach combining a pattern fragmentation technique with a NoSQL database, to organize and manage the chunks. Our research has indicated that there is a trade-off on the performance when using a database. Any slight difference on a big data environment is always important, however, this cost is compensated by having the data organized and managed. The use of random pattern fragmentation has great potential, as it adds a layer of protection on the data without using as much resources, contrary to using encryption.

Word representations are mathematical items capturing a word’s meaning and its grammatical properties in a machine-readable way. They map each word into equivalence classes including words sharing similar properties. Word representations can be obtained automatically by using unsupervised learning algorithms that rely on the distributional hypothesis, stating that the meaning of a word is strictly connected to its context in terms of surrounding words. This assessed notion of context has been recently reconsidered in order to include both distributional and morphological features of a word in terms of characters co-occurrence. This approach has evidenced very promising results, especially in NLP tasks, e.g, POS Tagging, where the representation of the so-called Out of Vocabulary (OOV) words represents a partially solved issue. This work is intended to face the problem of representing OOV words for a POS Tagging task, contextualized to the Italian language. Potential benefits and drawbacks of adopting a Bidirectional Long Short Term Memory (bi-LSTM) fed with a joint character and word embeddings representation to perform POS Tagging also considering OOV words have been investigated. Furthermore, experiments have been performed and discussed by estimating qualitative and quantitative indicators, and, thus, suggesting some possible future direction of the investigation.

In recent years, virtual reality technologies have been improving in terms of resolution, convenience and portability, fostering their adoption in real life applications. The Vive Controllers and Leap Motion are two of the most commonly used low-cost input devices for interactions in virtual environments. This paper discusses their differences in terms of interaction design, and presents the results of a user study focusing on manipulation tasks, namely Walking box and blocks, Block tower and Numbered cubes tasks, taking into account both quantitative and qualitative observations. The experimental findings show a general preference for the Vive Controllers, but also highlight that further work is needed to simplify complex tasks.

The exploitation of Cloud infrastructure in Big Data management is appealing because of costs reductions and potentiality of storage, network and computing resources. The Cloud can consistently reduce the cost of analysis of data from different sources, opening analytics to big storages in a multi-cloud environment. Anyway, creating and executing this kind of service is very complex since different resources have to be provisioned and coordinated depending on users’ needs. Orchestration is a solution to this problem, but it requires proper languages and methodologies for automatic composition and execution. In this work we propose a methodology for composition of services used for analyses of different Big Data sources: in particular an Orchestration language is reported able to describe composite services and resources in a multi-cloud environment.

Cyber-attacks represent a serious threat to public authorities and their agencies are an attractive target for hackers. The public sector as a whole collects lots of data on its citizens, but that data is often kept on vulnerable systems. Especially for Local Public Administrations (LPAs), protection against cyber-attacks is an extremely relevant issue due to outdated technologies and budget constraints. Furthermore, the General Data Protection Regulation (GDPR) poses many constraints/limitations on the data usage when “special type of data” is processed. In this paper the approach of the EU project COMPACT (H2020) is presented and the solutions used to guarantee the data privacy during the real time monitoring performed by the COMPACT security tools are highlighted.

In this paper, we propose a novel data model for Multimedia Social Networks, i.e. particular social media networks that combine information on users belonging to one or more social communities together with the content that is generated and used within the related environments. The proposed model relies on the hypergraph data structure to capture and represent in a simple way all the different kinds of relationships that are typical of social media networks, and in particular among users and multimedia content. We also introduce some user and multimedia ranking functions to enable different applications. Finally, some experiments concerning effectiveness of the approach for supporting relevant information retrieval activities are reported and discussed.

Drones have been considered for use in various fields according to the performance improvement and the price down of devices. They are expected for some applications: disaster relief, farm field, security field, transportation field, etc. Some companies will employ the autopilot system for their business. However, they have to switch to manual operation in case of emergency due to the autopilot safety is not guaranteed. Therefore, a pilot must connect with the drone continuously by the network for remote monitoring. Cellular network systems are the candidate networks for remote monitoring. However, typical design of cellular networks does not assume user equipment devices in the air because antennas of cellular networks are usually aimed downward to reduce inter-cell interference. This means that drones may fly out a communication area of cellular networks. Therefore, business drones must communicate with some cellular networks to keep continuous communication. However, IP-based application will disconnect due to change of cellular networks. As a result, practical business drones’ operations require a continuous communication mechanism. This paper proposes a continuous remote control architecture for drone operations to improve safety of the autopilot function. The proposed architecture employs NTMobile technology as a seamless mobility protocol supporting continuous communication. Additionally, it also employs IP-based remote control application to control drones remotely. The evaluation system can acquire sensor information and exchange control information continuously when drones switch access networks. The proposed architecture can be a fundamental framework to realize a wide area drone operation service.

Information technology has been focused to estimate growth degree of plants in agriculture. This paper focuses on leaf temperature that changes according to the activity of photosynthesis. Infrared cameras are a major method to measure leaf temperature in conventional methods. However, the expensive device price causes difficulty to install many sensors in practical fields. Infrared radiation sensors are new candidate device to estimate growth state by measuring leaf temperature. Since the price of infrared radiation sensors is inexpensive, we can install a lot of sensors into fields. Additionally, the consumed power of infrared radiation sensors is relatively small comparing to Infrared cameras. These features of infrared radiation sensors are appropriate for sensor networks working with a battery. This paper proposes a field sensor network to measure growth state of plants by infrared radiation sensors. Our goal is to realize a practical and inexpensive sensor network system with typical system on chip (SoC). Therefore, we employ a reasonable price SoC supporting IEEE 802.15.4 standard to design a unique device with various sensors. In order to realize multi-hop communication with low-power consumption, we propose a routing and media access control mechanisms for the developed system. The media access control technology realizes periodic sleep operation of all devices to enable long-term operation of the system. The routing control technology can construct a multi-hop network with the minimum number of hops. The experimental results demonstrated that the development system works in the practical fields.

This paper proposes an approach to detect mistaken foldings in computer-aided origami by using a single top-view camera. The position of the origami paper is continually tracked by matching the shape of the paper with the state model of the folding process. Further, the change of the shape is also grasped, and the folder’s mistake is pointed out online. In this paper, first, we define the change ratio of the shape of the paper to measure the progress of foldings. Next, a criterion expressing the degree of difference from the correct shape is introduced to detect the mistake. Finally, threshold measurements for those criteria are investigated. Several experimental results showed the validity of our approach.

This paper proposes an algorithm for deploying sink nodes in outdoor wireless sensor networks focusing on smart meters for electricity or gas in a residential area. In this situation, the location of nodes is pre-determined and the nodes cannot be deployed freely. This algorithm calculates the number of required sink nodes and selects the appropriate nodes in order to decrease operational costs of the wireless sensor networks. Positional information on meters in a real residential area was used for experiments. Our algorithm calculated an optimal number of sink nodes.

A time session variability between the enrollment data and the recognized data degrades speaker recognition performance. Hence, the time session variability is one of the most important issues in the speaker recognition technology. In this paper, we propose a robust speaker recognition method for the time session variability. The proposed method estimates a time session variability subspace. Then, the proposed method carries out the speaker recognition in the orthogonal complement of the time session variability subspace. In addition, we incorporate a linear discriminant analysis method into the proposed method. In order to evaluate the proposed method, we conducted a speaker identification experiment. Experimental results show that the proposed method improves speaker identification performance of baseline.

It is said that the most cause of traffic accidents is the lack of confirming the safety. Visual information from both eyes is one of the important factors for safe driving. In this paper, we collect eye-gaze data of drivers who watch a driving video, and try to develop a model of their eye movements to identify factors to enhance their safety. For the purpose of modeling, we adopted a recurrent neural network and Long Short-Term Memory (LSTM) to the collected eye-gaze data because the LSTM is able to deal with a time-series data such as the eye-gaze data. Moreover, we performed an experiment to evaluate the identification accuracy of drivers. The results indicated that the driver’s intention and habit can be approximated partially by the trained network, but it was insufficient to identify a personal driver for practical use.

The educational/learning research field has counted up over 60 years since “Educational Technology” had been established as one of new research frontiers. For 60 years, the research viewpoints have been shifted from teaching-specific functions to learning-based functions, owing to the enormous evolution of information technology. This shift has enforced to evolve research issues with smart teaching/learning support systems. This research field is generally characterized as one of field-fusion types of existing related research fields, and may be often said that the original discussion points are not sharply recognized. In this paper, we survey our current educational/learning research viewpoints and re-consider innovative research topics over the accumulated results with a view to attaining the advanced educational/learning paradigm or framework as one step-up subject in the next age.

In this research report we present the current state of the transition from traditional, internal combustion engines vehicles, to electrical vehicles. The main characteristic of this transition is that new generation cars are matching the cost and performance of traditional petrol cars. Transition to electric vehicles is driven by the environmental sustainability, in the first place, economy, government policies, inherent automotive industry dynamics and consumer preferences. Transition is presented from global perspective in addition to specificities in Australian context. The conclusion is that such transition is a major disruption affecting the whole economy. It is characterized by the convergence of mobility and energy what can bring significant benefits to the entire society.

Piecewise linear vibration isolator system is one of the development to introduce dual rate stiffness and damping. There are several vibrating behavior if the system is designed properly. Analytical treatment of the system determines some difficulties such as jump. This investigation indicates that such strong nonlinear systems have a new phenomenon called Frequency Island in their frequency response plot. Frequency Island is a possible isolated frequency response that the vibrating system may jump into the island and stays there until the excitation frequency moves out of the range of the island. In this student existence, appearance, growing and disappearing of frequency island will be studied and examined. Frequency Island corresponds to large amplitude vibration for certain range of system parameters and considered as a dangerous phenomena in real system. As a result, understanding its appearance will help designers and engineers to design the system to avoid Frequency Island.

One of the core features of social robotics system is a physical interaction between humans and humanoid robots. This provides additional challenges, both from safety and usability prospectives. When dealing with human-robot interaction, human safety has the highest priority. While in industrial environment we have robot cells to protect humans, in social robotics, that we consider, physical contact is possible, as well as other interactions, with consequences that might be in psychological areas. For example, the conversation with children might have different requirements in comparison to the conversation with adults, the behavioural assumptions might be different, etc. This paper summarises the core results of a project on social robotics system, where an autonomous humanoid robot guides visitors through a lab tour. The results of our work were implemented on the humanoid PAL REEM robot. The implementation includes a web-application to support the management of robot-guided tours. The application also provides recommendations for the users as well as allows for a visual analysis of historical data on the tours.

Saliency detection aims to segment the object of interest from the rest of the scene. While there has been a big number of saliency detection methods in still images, video saliency is in its early stages. In this paper, we propose a two stages video saliency detection method using local saliency estimation and global homogeneity refinement. Starting from a patch, the problem of saliency detection is modeled as a growing region which starts from a patch with high saliency information to the background. Local saliency is measured by combining spatial priors presented by local surrounding contrast with temporal information issued from the motion estimation feature. Temporal and spatial information are fused and then used to label each patch as foreground and background patches and produce the final saliency maps. Finally, Global homogeneity refinement is used to refine the saliency results by evaluating the foreground and background probabilities ratio propagated from the patches. Experiments have proved that the proposed method outperforms state-of-the-art methods over two benchmark datasets.

Internets of Things (IoT) applications and services have spread and are rapidly being deployed in the information services of the healthcare and financial industries, etc. However, the previous paper suggested that the current IoT services were individually developed, therefore, the open platform and architecture for the above IoT services of the healthcare industries should be deemed necessary. An open healthcare platform is expected to promote and implement the digital IT applications for healthcare communities efficiently. In this paper, we suggest that the open platform for healthcare related IoT services will be proposed and verified by the research initiative named “Open Healthcare Platform 2030 – OHP2030”. In addition, the vision for the OHP2030 research initiative is expressed.

Internets of Things (IoT) and Big Data applications and services have spread and are rapidly being deployed in the information services of the healthcare and financial industries, etc. However, the previous paper suggested that the current IoT services were individually developed, therefore, the open platform and architecture for the above IoT services of the healthcare industries should be deemed necessary, while the Big Data applications prevail in healthcare industry gradually. An open healthcare platform is expected to promote and implement the digital IT applications for healthcare communities efficiently. In this paper, we suggest that various IoT and Big Data applications will be designed and verified while the open platform for healthcare related IoT services should be proposed and verified by the research initiative named “Open Healthcare Platform 2030 – OHP2030”. In addition, the vision paper for enabling Digital Healthcare applications in the above OHP2030 research initiative is explained.

The demand on the innovation of enterprise services are rapidly increased. There are various modeling methods that can be applied to design digital transformation of services. However, there are few visual modeling methods to design innovations. Without consistent visual modeling methods, it is difficult to integrate service innovations and digital transformations.In this paper, we propose a Model Based Jobs Theory (MBJT) which can visually model the Jobs Theory of Christensen. Moreover, we examine the applicability of MBJT by designing an e-Healthcare service.

Fully convolutional network has predicted multiple class dense outputs in CT image labels and obtained significant improvements in segmentation tasks. In this paper, we present a joint multi-path fully convolutional network (MFCN) with random forests (RF) architecture for abdominal organs segmentation automatically. First, in coarse segmentation step, three FCNs are trained respectively with three orthogonal directions which consider contextual and spatial information of fusion layers adequately. In classification step, using features extracted from different layers of network and normalizing them to mean value as supervoxel representation to train RF. This allows the computation of supervoxel at each orientation achieve high efficiency. Finally, we aggregate the results of MFCN and RF on voxel-wise and perform conditional random fields (CRF) focuses on smoothing borders of fine segmentation regions. We exceeds the state-of-the-art methods and get achievable DSC values for our work is 90.1%, 88.4%, 88.0%, 88.6% represent liver, right and left kidney, spleen respectively.

Organ segmentation is one of the most fundamental and challenging task in computer aided diagnosis (CAD) systems, and segmenting liver from 3D medical data becomes one of the hot research topics in medical analysis field. Graph cut algorithms have been successfully applied to medical image segmentation of different organs for 3D volume data which not only leads to very large-scale graph due to the same node number as voxel number. Slice by Slice liver segmentation method is one of the technique that normally used to solve the memory usage. However, the computation times are increased and reduce the accuracy. In this paper we propose an interactive organ segmentation using fully convolutional networks. The network will perform slice by slice which only 1 slice of seed points in each volume. To validate effectiveness and efficiency of our proposed method, we conduct experiments on 20 CT volumes, focus on liver organ and most of which have tumors inside of the liver, and abnormal deformed shape of liver. Our method can segment with 0.95401 dice accuracy with better than stage-of-the-art methods.

In this paper, we present a novel touchless interaction system for visualization of hepatic anatomical models in surgery. Real-time visualization is important in surgery, particularly during the operation. However, it often faces the challenge of efficiently reviewing the patient’s 3D anatomy model while maintaining a sterile field. The touchless technology is an attractive and potential solution to address the above problem. We use a Microsoft Kinect sensor as input device to produce depth images for extracting a hand without markers. Based on this representation, a deep convolutional neural network is used to recognize various hand gestures. Experimental results demonstrate that our system can significantly improve the response time while achieve almost same accuracy compared with the previous researches.

Remote consultation and diagnosis platform has been widespread in market for group diagnosis, education and etc. It provides convenience for people enjoying superior quality of medical service. However, most of platforms cannot meet increasing demands in practice. The most important reason is that they have troubles of navigating in complicated Internet condition. In general, remote rendering is preferred to support data transmission, by which server renders images locally, captures screenshot and delivers them to clients synchronously. As a result of neighborhood DICOM slices serving a high similarity, following frame is compressed according to the difference with last one. Even though necessary demand of bandwidth has been reduced a lot, remaining volume proves too large especially remote 3D volume rendering. In this paper, we proposed a novel method bit difference compression transmission. Compared with traditional algorithm, it redesigns a new data structure, which gives more significances to bit. Consequently, demands of network fall into a desirable amount. Specifically, we improved solution of 3D images sharing and collaboration. Instead of terrible screenshots, synchronization of cameras in clients has been introduced. With a minimal cost our platform realized the cumbersome process. Collected data from experiments demonstrated our proposed methods have an obvious superiority and run robust in different environment of Internet.

Telemedicine is an important diagnostic auxiliary tool. This field has recently begun a period of explosive growth. In this paper, we combine mobile devices and image processing algorithms to develop a real-time collaborative image processing telemedicine platform for mobile devices. This C/S mode platform is based on C++, which is mainly implemented by VTK and ITK. In addition to implementing image transmission, 3D visualization and remote rendering, we focus on paranasal sinus CT and adopt automatic medical image segmentation function using the DRLSE algorithm. Besides, collaboration function ensures that users can process images in real time using mobile devices, which benefits communication between medical experts. Through testing, the platform is proved to be able to maintain stable bandwidth demand even in crowded network. According to the current research, this is the first platform to combine paranasal sinus CT image analysis with telemedicine. Therefore, our platform outperforms conventional teleradiology platform in functional completeness. Our platform helps radiologists and medical specialists to make correct diagnoses.

To automate the process of flatfoot finite element (FE) modeling, a software plug-in was developed and introduced in this paper. The plug-in was written in Python and based on the Abaqus Scripting Interface. It consists of three modules: script data, GUI (graphic user interface), and script command. The plug-in is integrated into Abaqus/CAE and can be easily adopted to reduce modeling time and efforts. The detailed procedures regarding FE modeling were automated by the proposed plug-in, and the users only have to determine and pick the corresponding nodes to represent the origin and insertion of ligaments, plantar fascias, and other small tissues of interests. By applying the proposed plug-in, the complicated modeling procedure can be simplified and sped up, and the users’ workload can be dramatically alleviated.

Computer Graphics technology enables a three-dimensional representation of object’s shape and inner structure. It is widely used in the field of visualization and simulation such as computer-aided design, scientific visualization, and medical simulation. Recent studies on implicit surface generation from shape measured three-dimensional point cloud data provide precise and refined surface visualization for complex objects from buildings and tangible heritages to the internal structure of the human body. However, to understand and analyze the structural characteristics of complex shapes, conventional methods, which visualize the whole object with one criterion, could not produce satisfactory results. A more comprehensive visualization method that extracts and highlights the edges and feature regions of a complex object is desired. In this paper, we propose a fused visualization method that extracts and highlights the shape characteristics of three-dimensional volume data of the human body. For the implicit surface generation, volume stochastic process sampling method is applied. The surface curvature is then calculated by projecting the mathematically well-defined curvature information at a point on the iso-surface to its tangent plane. The high curvature area is extracted as the feature region and transparently fused with the original volume data. The proposed method, which realizes three-dimensional transparent fusion of feature-highlighted iso-surface visualization and volume visualization, comprehensively visualizes global structure of the target medical data as well as emphasizes the structural characteristics in the feature region.

Super-resolution techniques have been widely used in fields such as television, aerospace imaging, and medical imaging. In medical imaging, X-rays commonly have low resolution and a significant amount of noise, because radiation levels are minimized to maintain patient safety. So, we proposed a novel super-resolution method for X-ray images, and a novel measurement algorithm for treatment of rheumatoid arthritis (RA) using X-ray images generated by our proposed super-resolution method. However, in our proposed system, there are several operations to do by doctors manually, and it is hard for them. By utilizing image recognition technology, it is possible to extract joint images from X-ray images automatically. In this paper, we will discuss an algorithm to extract joint images from X-ray images automatically. Experimental results show that correct joint images will be obtained for our proposed method. Therefore, our proposed measurement algorithm is effective for RA medical examinations.

Sulphur oxide (SOx) emissions impose a serious health threat to the residents and a substantial cost to the local environment. In many countries and regions, ocean-going vessels are mandated to use low-sulphur fuel when docking at emission control areas. Recently, drones have been identified as an efficient way to detect non-compliance of ships, as they offer the advantage of covering a wide range of surveillance areas. To date, the managerial perspective of the deployment of a fleet of drones to inspect air pollution from ships has not been addressed yet. In this paper, we propose a modelling framework of drone deployment. It contains three components: drone scheduling at the operational level, drone assignment at the tactical level and drone base station location at the strategic level.

This paper proposes a novel approach, called dynamic integrated slack-based measure (DISBM) modeling approach, to evaluating multi-period non-radial slacks of non-storable production characterized with carry-over activities. The proposed modeling approach incorporates conventional dynamic slack-based measure (DSBM) technical efficiency and service effectiveness into data envelopment analysis (DEA) modeling such that multi-period input excesses, output shortages and consumption gaps can be simultaneously determined. Some important properties of the proposed DISBM modeling are explored. A case study on the efficiency and effectiveness of Taiwan’s intercity bus transport during 2007–2010 is presented. The results indicate that the proposed DISBM modeling is superior to conventional DSBM modeling in terms of benchmarking power, and that the non-radial slacks associated with input, output and consumption variables do provide rational information to rectify the inefficient and/or ineffective units throughout the production process.

Urban rail transit demand analysis and forecasting is an essential prerequisite for daily operations and management. This paper categorizes the proposed demand forecasting methods, and focuses on traditional models, statistical models and machine learning approaches, according to their features and fields. Especially, influential and widely-used methods including the four-stage model, land use models, time series methods, Logit regression, Artificial Neural Networks (ANNs) and other referring methods are all taken into discussion.

Shared-parking lot brings utilization improvement, but also has its disadvantage compared with traditional parking lot while they are competing for public users. In the market including both shared-parking lot and traditional parking lot, parking lot operators need to know how to deal with parking price to be competitive in the market. The Hotelling model is applied in this paper to study the product differentiation of traditional parking lot and shared-parking lot, with some equilibrium analyses to figure out equilibrium parking prices of both parking lots while considering their competition in the market. Two points of indifferent consumers exist in the competition of the traditional parking lot and the shared- parking lot.

Value of travel time (VOTT) is a fundamental index in transportation economics, which represents the trip cost that the travelers are willing to pay for saving their travel time, working as a communicator between money and time. VOTT is of great importance in urban transportation planning and traffic forecasting as VOTT of travelers will impact on their traveling mode, frequency and routes. VOTT is calculated by estimating the ratio of the utility function on travel time and cost. The model is specified through logit regression analysis. In this paper, a stated preference survey on the VOTT of travelers is conducted in Nanjing, China. We adopt mixed logit model, which is a flexible model that allows the parameters to vary across the population and is not restricted by the IIA property, to perform the regression, and analyze the distribution of VOTT.

The fundamental diagram is used to represent the graphical layout and determine the mathematical relationships among traffic flow, speed and density. Based on the observational speed-density database, the distribution of speed is scattered in any given traffic state. In order to address the stochasticity of traffic flow, a new calibration approach has been proposed to generate stochastic traffic flow fundamental diagrams. With this proposed stochastic fundamental diagram, the residual and stochasticity of the performance of calibrated fundamental diagrams can be evaluated. As previous work only shows the validation of one model, in this paper, we will use field data to validate other stochastic models. Greenshields model, Greenberg model, and Newell model are chosen to evaluate the performance of the proposed stochastic model. Results show that the proposed methodology fits field data well.

Due to the difference in operational characteristic, heavy vehicles have been viewed as a hindrance in traffic flow and capacity analysis. The emergence of passenger car equivalents (PCE) can assist traffic agencies in better understanding the impact of heavy vehicles on passenger vehicles in the mixed traffic stream, by converting a heavy vehicle of a subject class into the equivalent number of passenger cars. However, according to existing literature, most researchers have devoted to the estimation of PCE for basic freeway sections. Therefore, in this study, we explore the variation of heavy vehicle PCE for on-ramp adjacent zones under varying traffic volume. A one-lane on-ramp in Queensland, Australia, is selected for a case study and four existing PCE approaches are applied in the calculation of PCE. They are homogenization based method, time headway based method, traffic flow based method, and multiple regression method, respectively. The final PCE values are compared to those derived from VISSIM simulation model. The following conclusions are drawn: (1) homogenization based method cannot reveal the variation trend of PCE factors over traffic volume; (2) the results obtained through time headway and traffic flow based methods are more consistent with outcome from simulation model.

Road functional classification is of significance for achieving high transportation efficiency. The road network planning and design mainly depend on road functions. This concept is used in almost all countries throughout the world. This paper presents a review on the development of road functional classification systems in codes and standards in China over the past decades, whose importance received limited attention to date. Functional classification systems include the urban road functional classification system and the highway functional classification system. The review includes major components of the road functional classification, consisting of context analysis, modal accommodations and detailed categories. In addition to these concerns, some ideas for future development, especially for context definitions, functional classification concepts and criteria, and quantitative functional evaluation, are discussed.

As urban populations grow, there is a growing need for efficient and sustainable modes, such as bicycling. The shortage of bicycle demand data is a barrier to design, planning, and research efforts in bicycle transportation before. In July 2013, the New York City implements the bike-sharing system, Citi Bike, and makes their data available for analysis. Data used in this study includes the information about active stations, average bicycles available, total annual membership, maintenance issues, events of vandalism and calls and emails to system center. Through statistic description, partial correlation analysis and principle component analysis, final variables are obtained. Finally, a Poisson regression model was adopted for the analysis. The analysis results are useful for understanding the influential factors including temperatures and weathers, which reflected by seasons generally, and supplements associated with rules or policy of bike-sharing system. In addition, the inferential results of these models provide guidance on future planning of station and bike supplement.

Electric bicycle (E-bike) traffic crashes have become an important traffic safety problem in many Chinese cities. Based on the traffic crash data of E-bikes from Xintang region in Hangzhou, China, the time distribution, spatial distribution, and influencing factors for electric bicycles-related traffic crashes were analyzed, and the main factors that affect the traffic crash of electric bicycles were obtained. On this basis, a logistic model of the influencing factors on the severity of traffic crashes for electric bicycles was set up. The key factors affecting the severity of traffic crashes on electric bicycles were obtained, which provided the basis for the prevention and safety management of traffic crashes on electric bicycles.

This paper provides design and evaluation of two signal timing strategies with five cases respectively under different degree of weather condition. The main aim of this project is finding out the most suitable signal timing strategies depending on the weather condition by comparing the analysis results. The procedure mainly includes two parts in traffic signal design, which are signal timing plans design and performance analysis. Methods of design and performance analysis include Webster’s signal timing method, Akcelik’s signal timing method and HCM delay method. It is clear that the weather factor has huge impact on performance of signalized intersection. Apparently, compared to strategy B, strategy A is more suitable for bad weather condition.

In general, large passenger hubs contain a variety of transportation functional areas. The facility configuration requirements of different functional areas are not the same, and a rational collocation of spatial positions and the number of functional areas will greatly improve the level of service. By analyzing the connotations of the functional area, an optimization objective function was built, based on the average expected passenger walking time, average cross-collision delays [average delay due to collisions between passengers moving in opposing directions] of passengers, and the cost of the hub, with constraint conditions on functional acreage, shape and location, as well as a model of macroscopical layout optimization of hub facilities. A variety of optimization problem-solving methods were compared, and given the features of this model, the genetic algorithm NSGA-II was selected. Finally, this paper gives an example of a three-storey high-speed rail hub, evaluates the hub’s layout scheme, and proves that the model is effective and feasible. Therefore, the research results can be used to evaluate the layout plans of passenger transfer facilities for large hubs, and provide a theoretical foundation for perfecting hub transfer facilities.

The Gold Coast Light Rail project represents a significant investment in public transportation infrastructure on the Gold Coast and is one of the key initiatives set out in City of Gold Coast Council’s Transport Strategy 2031. This paper conducts an impact analysis of Stage 2 on the peripheral transportation network. To assess the anticipated impacts, a traffic simulation was conducted in PTV Visum 16 for the base year of 2018 and forecasted for year 2031. For the base and forecasted models, a scenario was prepared both with and without Stage 2 of the system to allow a comparison of results between the variants. According to the results, it is found that Stage 2 has a positive impact on the transportation network by successfully implementing an integrated transport solution encouraging users to switch to public transport.

The vigorous development of O2O e-commerce promote the appearance of many small orders, increasing the stresses on logistics operators to carry out city distribution. However, a crowdsourcing joined to release these stresses is a new try and become more popular. This paper focuses on matching of the crowds and tasks from crowdsourcing platform for city distribution. To address exploring the impact of time, space and efficiency on the task matching in crowdsourcing platform, a bi-objective matching and differentiated pricing model creates to achieve the highest efficiency and the lowest total cost in urban distribution system. For solving the model, a two-dimensional and multi-stage roulette algorithm has been designed, with combining modeling and simulation method. The proposed method takes full use of economic development of the region where the task is located and the space-time efficient distance and space-time reachable distance. To illustrate the effectiveness and validity of the proposed method, a sample test is conducted with the actual operating data of a company in the PRD region, and the results show that 719 tasks out of 746 matching pairs are executed, the task matching rate is 89.4%, the completion rate is 86.1% and the total task price 39140 RMBs. Compared with the original matching situation, the total price increases at 3.90%, while the task completion rate is improved at 37.7%, which greatly enhance the efficiency of crowdsourcing platform member matching. The matching of the participants and tasks of the city distribution crowdsourcing platform, which combines with the measurement of differential pricing and crowds’ credibility, can be applied in this problem successfully.

In this paper, a novel two-staged pavement image processing framework is presented. The pavement images are classified into four general categories in the first stage, so that the images can be processed using category-specific algorithms in the 2nd stage. The proposed algorithm first fuses a local contrast enhanced image with a global grayscale corrected image to obtain an enhanced distressed pavement image. The enhanced image is then decomposed with a three-layer wavelet transform to obtain three texture features of the entire image including High-Amplitude Wavelet Coefficient Percentage (HAWCP), the High-Frequency Energy Percentage (HFEP), and the Standard Deviation (STD). In the meantime, an improved P-tile method is used to obtain the binary image. From the binary image, three additional shape features are extracted including the Average Area of all Connected Components (AA), the Area of the Maximum Connected Component (AM), and the Equivalent Length of the longest Connected Component (EL). Finally, a BP neural network is used to fuse both the texture and shape features sequentially to achieve the initial classification. Experimental results show that for the four types of pavement images, the proposed algorithm achieves an effective classification of the pavement distress image with the accuracy rates of 96.5%, 91.4%, 95.2% and 98.1% respectively, which are higher than those of the classification algorithm with a single-type feature.