Intelligent Transportation Management Research Articles

Urban Human Mobility

Human mobility is a multidisciplinary field of physics and computer science and has drawn a lot of attentions in recent years. Some representative models and prediction approaches have been proposed for modeling and predicting human mobility. However, multi-source heterogeneous data from handheld terminals, GPS, and social media, provides a new driving force for exploring urban human mobility patterns from a quantitative and microscopic perspective. The studies of human mobility modeling and prediction play a vital role in a series of applications such as urban planning, epidemic control, location-based services, and intelligent transportation management. In this survey, we review human mobility models based on a human-centric angle in a datadriven context. Specifically, we characterize human mobility patterns from individual, collective, and hybrid levels. Meanwhile, we survey human mobility prediction methods from four aspects and then describe recent development respectively. Finally, we discuss some open issues that provide a helpful reference for researchers' future direction. This review not only lays a solid foundation for beginners who want to acquire a quick understanding of human mobility but also provides helpful information for researchers on how to develop a unified human mobility model.

Analysis and Prediction of Regional Mobility Patterns of Bus Travellers Using Smart Card Data and Points of Interest Data

Mobility patterns at region level can provide more macroscopic and intuitive knowledge on how people gather in or depart from the region. However, the analysis and prediction of regional mobility patterns have yet to be effectively addressed. In light of this, using smart card data (SCD) and points of interest (POI) data, a multi-step methodology which integrates the inner-restricted fuzzy C-means clustering, nonnegative tensor factorization and artificial neural network are proposed and implemented in this paper. It overcomes the difficulties in region division, pattern extraction, and prediction. The bus SCD and POI data in Beijing city are utilized for proving the usefulness of the methodology. The regional mobility patterns of bus travellers in Beijing city are extracted from the third-order tensors involving 1110 regions, 34 time slots, and 7 days of the week. The analyzed results show that the proposed methodology has a good performance on predicting the regional mobility patterns based on the regional properties. Furthermore, by considering both of the regional boarding and alighting patterns, the predictions of the regional aggregation pattern can also be achieved. These research achievements can not only provide a deep insight on the human mobility patterns at region level, but also support the evidence-based and forward-looking urban planning and intelligent transportation management.

The benefits of National Intelligent Transportation Management Centre (NITMC) establishment in Malaysia

Malaysia has been embracing Intelligent Transportation System (ITS) technology and has started the effort by establishing a few traffic management centres (TMC) which are operated by different government agencies for transportation data collection. For examples, Kuala Lumpur City Hall (DBKL) has been operating Integrated Transport Information System (ITIS) where traffic data is collected in Kuala Lumpur and Klang Valley area., In addition, Malaysian Highway Authority (MHA) has TMCs at all tolled highways to collect and manage traffic data and Land Public Transport Land Public Transport Agency (APAD) is in charge for public transport management including bus and light rail train. Due to the operation and maintenance of these centres are maintained by multiple agencies separately, transportation data from them are not coordinated and integrated. Therefore, the in-silo working culture causes difficulty in data collection and analysis for historical data record, real-time information and future prediction. In conclusion, National Intelligent Transport Management Centre (NITMC) is important as the centre that will coordinate, integrate, collect, analyse and disseminate these data from multiple agencies from government and private agencies. The data analysed in NITMC is expected to benefit all relevant stakeholders including the public by providing up-to-date and reliable data to them.

Semantic Modelling of Ship Behavior in Harbor Based on Ontology and Dynamic Bayesian Network

Recognizing ship behavior is important for maritime situation awareness and intelligent transportation management. Some scholars extracted ship behaviors from massive trajectory data by statistical analysis. However, the meaning of the behaviors, i.e., semantic meanings of behaviors and their relationships, are not explicit. Ship behaviors are affected by navigational area and traffic rules, so their meanings can be obtained only in specific maritime situations. The work establishes the semantic model of ship behavior (SMSB) to represent and reason the meaning of the behaviors. Firstly, a semantic network is built based on maritime traffic rules and good seamanship. The corresponding detection methods are then proposed to identify basic ship behaviors in various maritime scenes, including dock, anchorage, traffic lane, and general scenes. After that, dynamic Bayesian network (DBN) is used to reason potential ship behaviors. Finally, trajectory annotation and semantic query of the model are validated in the different scenes of harbor. The basic behaviors and potential behaviors in all typical scenes of any harbor can be obtained accurately and expressed conveniently using the proposed model. The model facilitates the ships behavior research, contributing to the semantic trajectory analysis.

Compressed-Domain Highway Vehicle Counting by Spatial and Temporal Regression

Counting on-road vehicles in the highway is fundamental for intelligent transportation management. This paper presents the first highway vehicle counting method in compressed domain, aiming at achieving comparable estimation performance with the pixel-domain methods. Counting in compressed domain is rather challenging due to limited information about vehicles and large variance in vehicle numbers. To address this problem, we develop new low-level features to mitigate the challenge from insufficient information in compressed videos. The new proposed features can be easily extracted from the coding-related metadata. Then, we propose a hierarchical classification-based regression (HCR) model to estimate the number of vehicles from the compressed-domain low-level features for individual frame. HCR hierarchically divides the traffic scenes into different cases according to the density of vehicles such that the large variance of traffic scenes can be effectively captured. Beside the spatial regression in each frame, we propose a locally temporal regression model to further refine the counting results, which exploits the continuous variation characteristics of the traffic flow. We extensively evaluate the proposed method on real highway surveillance videos. The experimental results consistently show that the proposed method is very competitive compared with the pixel-domain methods, which can reach similar performance with much lower computational cost.

Management system project design of urban intelligent transportation

Focused on the pressure of transportation resource and the increasing traffic demand, this paper starts from the classification of urban transportation system, making use of communication technology, information technology and computer technology, etc., analyzing the feasibility of the project based on urban intelligent transportation management system, and putting forward the study and design of urban intelligent transportation management system, pointing out the improvements on intelligent processing of transportation information, systematic control theory, application and performance of urban intelligent transportation management system.

Notice of Retraction: Intelligent Transportation System in Macao Based on Deep Self-Coding Learning

As the basic content of the construction of the intelligent city, the application of intelligent transportation system plays an important role in improving the quality and safety of modern urban traffic operation. Intelligent transportation system combines information technology, data communication technology, electronic sensing technology, global positioning technology, geographic information system technology, computer processing technology, and system engineering technology together reasonably. The intelligent transportation system is a kind of real-time, accurate, efficient, and intelligent transportation management system. In this paper, we introduce the deep code learning technique and apply it to the Macao intelligent system. At the same time, we combine the deep belief network model and support vector regression classifier as the prediction model, and use the deep belief network model to learn traffic flow characteristics.

Combining weather condition data to predict traffic flow: a GRU‐based deep learning approach

Traffic flow prediction is an essential component of the intelligent transportation management system. This study applies gated recurrent neural network to predict urban traffic flow considering weather conditions. Running results show that, under the review of weather influences, their method improves predictive accuracy and also decreases the prediction error rate. To their best knowledge, this is the first time that traffic flow is predicted in urban freeways in this particular way. This study examines it with respect to extensive weather influence under gated recurrent unit-based deep learning framework.

Mining Precise-Positioning Episode Rules from Event Sequences

Episode Rule Mining is a popular framework for discovering sequential rules from event sequential data. However, traditional episode rule mining methods only tell that the consequent event is likely to happen within a given time interval after the occurrence of the antecedent events. As a result, they cannot satisfy the requirement of many time sensitive applications, such as program security trading and intelligent transportation management due to the lack of fine-grained response time. In this study, we come up with the concept of fixed-gap episode to address this problem. A fixed-gap episode consists of an ordered set of events where the elapsed time between any two consecutive events is a constant. Based on this concept, we formulate the problem of mining precise-positioning episode rules in which the occurrence time of each event in the consequent is clearly specified. In addition, we develop a trie-based data structure to mine such precise-positioning episode rules with several pruning strategies incorporated for improving the performance as well as reducing memory consumption. Experimental results on real datasets show the superiority of our proposed algorithms.

Internet of Things (IoT) in high-risk Environment, Health and Safety (EHS) industries: A comprehensive review

The rise of ubiquitous systems is sustained by the development and progressive adoption of the Internet of Things (IoT) devices and their enabling technologies. IoT has been shown to have significant potential in high-risk Environment, Health, and Safety (EHS) industries. In these industries, human lives are at stake and IoT-based applications are primed to offer safe, reliable, and efficient solutions due to their ability to operate at a fine granular level and provide rich low-level information. We review existing published research on IoT-based applications in high-risk EHS industries with specific emphasis on healthcare industry, food supply chain (FSC), mining and energy industries (oil & gas and nuclear), intelligent transportation (e.g., connected vehicles), and building & infrastructure management for emergency response operations until 2016. We also highlight IoT-related challenges and proposed solutions in high risk EHS industries. We then conclude by presenting research challenges and expected trends for IoT in these industries.

DEVELOPMENT OF AN ELECTRIC BICYCLE FOR A SHARING SYSTEM IN PRAGUE

By means of a development of an e-bike sharing system the Electromobility Project wants to provide an alternative way of comfortable transportation for students and staff of the CTU, primarily to commute between different campuses. The research for this project contains at least three different fields of study, namely electric vehicle and docking station development, intelligent transport systems and management and economics of transportation and telecommunication. After briefly stating general requirements for the sharing system, this paper focuses on the development of the electric bicycle. First an ideal bike design is defined. Then necessary motor power and battery capacity are calculated by estimating characteristics of cycling in Prague. A prototype was developed by converting a normal bicycle to an electric bicycle. Being equipped with devices for e-bike monitoring, controlling and data recording for a post trip analysis, this prototype is also intended to have an educational value for future students in the project. Results consist of an electrical bicycle configuration that matches the requirements and a sketch of an ideal e-bike for this project.

Urban Mobility Analysis With Mobile Network Data: A Visual Analytics Approach

Urban planning and intelligent transportation management are facing key challenges in today’s ever more urbanized world. Providing the right tools to city planners is crucial to cope with these challenges. Data collected from citizens’ mobile communication can be used as the foundation for such tools. These kinds of data can facilitate various analysis tasks, such as the extraction of human movement patterns or determining the urban dynamics of a city. City planners can closely monitor such patterns based on which strategic decisions can be taken to improve a city’s infrastructure. In this paper, we introduce a novel visual analytics approach for pattern exploration and search in global system for mobile communications mobile networks. We define geospatial and matrix representations of data, which can be interactively navigated. The approach integrates data visualization with suitable data analysis algorithms, allowing to spatially and temporally compare mobile usage, identify regularities, as well as anomalies in daily mobility patterns across regions and user groups. As an extension to our visual analytics approach, we further introduce space-time prisms with uncertain markers to visually analyze the uncertainty of urban mobility patterns.

Substantiation of sustainable transport infrastructure engineering in Baku and Baku agglomeration

Research object - transport infrastructure of Baku and Baku agglomeration (BA). Research subject - environement-relatedmodes of transportation in Baku andBA, world practices of ecofriendly mobility choices. Research target - form the rationale for tramway and bicycle network development across Baku from ecological point of view. Research methods and methodology - sociological survey, mathematical methods, cartographical and climatic analysis, SWOT analysis, description. Data base - dataof Intelligent Transport Management Center, survey data on popular websites, normative legal base andstatistics reports оf foreign government agencies. Results and approbation - reasults of research could be used for strategy realisation focused on development of sustainable transporation optionsacross ВА. Projected suggestions - initial phase of tramway network (TN) development in Baku intented to construct tramway linealong Baku sea-front. Ongoing project works of railways connecting Baku with Heydar Aliyev International Airport and touristic centers Shakhdag and Gabala will be set in operation in the future.

이산 슬라이딩모드 제어를 이용한 램프 미터링 제어

Ramp metering is one of the most efficient and widely used control methods for an intelligent transportation management system on a freeway. Its objective is to control and upgrade freeway traffic by regulating the number of vehicles entering the freeway entrance ramp, in such a way that not only the alleviation of the congestion but also the smoothing of the traffic flow around the desired density level can be achieved for the maintenance of the maximum mainline throughput. When the cycle of the signal detection is larger than that of the system process, the density tracking problem needs to be considered in the form of the discrete-time system. Therefore, a discrete-time sliding mode control method is proposed for the ramp metering problem in the presence of both input constraint in the on-ramp and exogenous disturbance in the off-ramp considering the random behavior of the driver. Simulations were performed using a validated second-order macroscopic traffic flow model in Matlab environment and the simulation results indicate that proposed control method can achieve better performance than previously well-known ALINEA strategy in the sense that mainstream flow throughput is maximized and congestion is alleviated even in the presence of input constraint and exogenous disturbance.

Towards connecting people, locations and real-world events in a cellular network

The success of personal mobile communication technologies has led an emerging expansion of the telecommunication infrastructure but also to an explosion to mobile broadband data traffic as more and more people completely rely on their mobile devices, either for work or entertainment. The continuously interaction of their mobile devices with the mobile network infrastructure creates digital traces that can be easily logged by the network operators. These digital traces can be further used, apart from billing and resource management, for large-scale population monitoring using mobile traffic analysis. They could be integrated into intelligent systems that could help at detecting exceptional events such as riots, protests or even at disaster preventions with minimal costs and improve people safety and security, or even save lives. In this paper we study the use of fully anonymized and highly aggregate cellular network data, like Call Detail Records (CDRs) to analyze the telecommunication traffic and connect people, locations and events. The results show that by analyzing the CDR data exceptional spatio-temporal patterns of mobile data can be correlated to real-world events. For example, high user network activity was mapped to religious festivals, such as Ramadan, Le Grand Magal de Touba and the Tivaouane Maouloud festival. During the Ramadan period it was noticed that the communication pattern doubled during the night with a slow start during the morning and along the day. Furthermore, a peak increase in the number of voice calls and voice calls duration in the area of Kafoutine was mapped to the Casamance Conflict in the area which resulted in four deaths. Thus, these observations could be further used to develop an intelligent system that detects exceptional events in real-time from CDRs data monitoring. Such system could be used in intelligent transportation management, urban planning, emergency situations, network resource allocation and performance optimization, etc.

A Vehicular Cloud-Based Framework for the Intelligent Transport Management of Big Cities

An intelligent transport system (ITS) is intended to streamline the operations of vehicles, manage vehicle traffic, and help drivers with safety and other information, as well as supply convenient applications for passengers. This system is essential for tackling the problems of a big city, like traffic congestion and a lack of a communication infrastructure or traffic engineering, among other factors. With these challenges in mind, we propose a vehicular cloud architecture to assist in the management of large cities. This will create a framework to support different types of services as well as provide storage mechanisms, access, and information management which includes tools for different modes of transport not only for citizens but also for commercial vehicles and emergency services like ambulances. In addition, it will be possible to increase the capacity for abstraction to meet information needs through the use of vehicular networks and the integration of VANETs with other networks, so as to provide relevant information for the monitoring and management of an intelligent transport system.

Dynamic Vehicle Detection via the Use of Magnetic Field Sensors.

The vehicle detection process plays the key role in determining the success of intelligent transport management system solutions. The measurement of distortions of the Earth’s magnetic field using magnetic field sensors served as the basis for designing a solution aimed at vehicle detection. In accordance with the results obtained from research into process modeling and experimentally testing all the relevant hypotheses an algorithm for vehicle detection using the state criteria was proposed. Aiming to evaluate all of the possibilities, as well as pros and cons of the use of anisotropic magnetoresistance (AMR) sensors in the transport flow control process, we have performed a series of experiments with various vehicles (or different series) from several car manufacturers. A comparison of 12 selected methods, based on either the process of determining the peak signal values and their concurrence in time whilst calculating the delay, or by measuring the cross-correlation of these signals, was carried out. It was established that the relative error can be minimized via the Z component cross-correlation and Kz criterion cross-correlation methods. The average relative error of vehicle speed determination in the best case did not exceed 1.5% when the distance between sensors was set to 2 m.

Optimal supervisor synthesis for petri nets with uncontrollable transitions: A bottom-up algorithm

Petri nets are a widely used tool to model, analyze and control discrete event systems that arise from automated production, intelligent transportation, and workflow management. For a class of Petri nets with uncontrollable transitions, this paper proposes a bottom-up algorithm to transform a given generalized mutual exclusion constraint into an optimal admissible one. Based on the transformation, a design method is proposed to synthesize an optimal supervisor. Compared with the existing methods that require the computation of exponential complexity, the proposed one can obtain an optimal supervisor with polynomial complexity.

Research of Intelligent Traffic Management System

With the growing economic level and the continuous development of information network technology, as well as the continuous improvement of people's living standards, transportation demand gradually increased. Due to the current transportation system in China has been facing the problem of traffic congestion and inefficient, and cause serious pollution and energy shortages, and intelligent traffic management system is the key to solve these problems, the modern information network technology and transportation combined. This article gives a brief introduction to the development and study of the status quo of China's intelligent transportation management system, intelligent traffic management system architecture of CORBA and CORBA, and described the implementation of an intelligent traffic management system based on CORBA.

Analysis of ITMS System Impact Mechanism in Beijing Based on FD and Traffic Entropy

Although more attention has been attracted to benefit evaluation of Intelligent Transportation Systems (ITS) deployment, how ITS impact the traffic system and make great effects is little considered. As a subsystem of ITS, in this paper, Intelligent Transportation Management System (ITMS) is studied with its impact mechanism on the road traffic system. Firstly, the correlative factors between ITMS and the road traffic system are presented and 3 positive feedback chains are defined. Secondly, we introduce the theory of Fundamental Diagram (FD) and traffic system entropy to demonstrate the correlative relationship between ITMS and feedback chains. The analyzed results show that ITMS, as a negative feedback factor, has damping functions on the coupling relationship of all 3 positive feedback chains. It indicates that with its deployment in Beijing, ITMS has impacted the improvement of efficiency and safety for the road traffic system. Finally, related benefits brought by ITMS are presented corresponding to the correlative factors, and effect standards are identified for evaluating ITMS comprehensive benefits.