Intelligent Transportation Management System Research Articles

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.

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.

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.

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.

스마트폰을 활용한 실시간 화물추적 및 지능형 수.배송 관리시스템

In these days, mobile technology such as smart phone and GPS have an effect on business processes of many companies especially a transportation company. The purpose of this paper is to present the development processes of real time vehicles tracking and intelligent TMS (Transportation Management System) using smart phone applications. The objective of this study is two-fold. The first is to redesign business process of the transportation company. Using BPR (business process re-engineering), we analyze current processes to find opportunities for improvement redefining processes after adopting mobile technology precisely. The second is to develop the real time vehicles tracking and intelligent TMS. Proposed system consists of four parts : (1) intelligent TMS(web system) (2) real time vehicle tracking application for TMS (3) real time tracking application for customer (4) salesman supporting application. Developed system was tested at the transportation company and was found to be an useful system.

A City Intelligent Transportation Management Command System

With the rapid development of China’s urbanization, the traffic become much more heavy in the city, transportation management problem becomes increasingly serious and city intelligent transportation management command system (CITMCS) has become one of the urgent research and solving subjects of ITMS (intelligent transportation management system). This paper presents a city intelligent transportation management command system. It is an integrated intelligence system of the transportation detection, control, management, decision making, command and communication. It can control the transportation facilities conveniently, provides useful information for transportation user, and ensures travelers to go through the road conveniently and safely. The system will have an extensive prospect of application and development. The paper firstly introduces the concept of the CITMCS, then presents the basic composition and function structure diagram of CITMCS based on information processing and information share, and finally gives the analysis of the relative key technique in system construction process.

Optimization models for assessing the peak capacity utilization of intelligent transportation systems

With limited economic and physical resources, it is not feasible to continually expand transportation infrastructure to adequately support the rapid growth in its usage. This is especially true for traffic coordination systems where the expansion of road infrastructure has not been able to keep pace with the increasing number of vehicles, thereby resulting in congestion and delays. Hence, in addition to striving for the construction of new roads, it is imperative to develop new intelligent transportation management and coordination systems. The effectiveness of a new technique can be evaluated by comparing it with the optimal capacity utilization. If this comparison indicates that substantial improvements are possible, then the cost of developing and deploying an intelligent traffic system can be justified. Moreover, developing an optimization model can also help in capacity planning. For instance, at a given level of demand, if the optimal solution worsens significantly, this implies that no amount of intelligent strategies can handle this demand, and expanding the infrastructure would be the only alternative. In this paper, we demonstrate these concepts through a case study of scheduling vehicles on a grid of intersecting roads. We develop two optimization models namely, the mixed integer programming model and the space–time network flow model, and show that the latter model is substantially more effective. Moreover, we prove that the problem is strongly NP-hard and develop two polynomial-time heuristics. The heuristic solutions are then compared with the optimal capacity utilization obtained using the space–time network model. We also present important managerial implications.

Event-driven architecture for decision support in traffic management systems

Decision support systems for traffic management systems have to cope with a high volume of events continuously generated by sensors. Conventional software architectures do not explicitly target the efficient processing of continuous event streams. Recently, event-driven architectures (EDA) have been proposed as a new paradigm for event-based applications. In this paper we propose a reference architecture for event-driven traffic management systems, which enables the analysis and processing of complex event streams in real-time and is therefore well-suited for decision support in sensor-based traffic control systems. We will illustrate our approach in the domain of road traffic management. In particular, we will report on the redesign of an intelligent transportation management system (ITMS) prototype for the high-capacity road network in Bilbao, Spain.

Benefit Evaluation Framework of Intelligent Transportation Systems

This study proposes a practical framework to assess the evaluable societal profitability of Intelligent Transportation Systems (ITS) projects, which are widely implemented in many cities of China. This work aims to estimate the whole economic benefit into an outcome figure which received great care from government authorities and transportation engineers. The initiative of proved framework is to maintain both the practicability in terms of data accessibility in societal reality and scalability in terms of its convincingness and academic value. A case study of assessing intelligent transportation management and control system in Beijing is conducted to further illustrate the proposed methodology. The investment for ITS can be enlarged more than 20 times, which shows significant “leverage effect” of ITS investment, and establishing intelligent transportation systems is an effective way to resolve the conflicts between sharply increasing motor vehicle amount and limited city land resources.

Optimization Models for Assessing the Peak Capacity Utilization of Intelligent Transportation Systems

Due to economic and physical resource constraints, it is not feasible to continually expand transportation infrastructures to adequately support the rapid growth in the usages of these infrastructures. This is especially true for traffic coordination systems where the expansion of the road infrastructure has not been able to keep pace with the increasing number of vehicles, thereby resulting in congestion and delays on the roads as well as in substantial increase in pollution. Hence, in addition to striving for the construction of new roads, it is imperative to develop new intelligent transportation management and coordination systems that can effectively enable the existing infrastructure to be used more efficiently. However, for a deployed solution to be practical and cost-effective, it needs to be provably superior to current methods. The effectiveness of a given technique can be evaluated by comparing it with the optimal capacity utilization. If this comparison indicates that substantial improvements are possible, then the cost of developing and deploying an intelligent traffic system can be justified. Moreover, developing an optimization model can also help in capacity planning. For instance, at a given level of demand, if the optimal solution worsens significantly, this implies that no amount of intelligent strategies can handle this demand, and expanding the infrastructure would be the only alternative. In this paper, we demonstrate these concepts through a case study of scheduling vehicles on a grid of intersecting roads. We develop two optimization models namely, the mixed integer programming model and the space-time network flow model for this problem, and show that the latter model is substantially more effective. Moreover, we prove the strong NP-hard property of this problem and develop two polynomial-time heuristic solutions, which are evaluated and compared with the optimal capacity utilization obtained using the space-time network model. In addition, we also present important implications for the managers.