Operator Support in Traffic Management: A Heuristics Model and Experimental Study

The identification of problems from numeric traffic measurements is an important part of control center activities in ATMS (Advanced Traffic Management Systems). However, an information modeling process that relies solely upon ‘traditional’ quantitative data analysis does not reflect faithfully the actual methods used by human operators. In addition to common-sense knowledge and specific contextual information, operators also use various heuristics and rules-of-thumb to supplement the numerical analysis. This paper describes an experiment to examine the effectiveness of an expert system that integrates quantitative and qualitative traffic information using a human-centered knowledge system design. The system's performance was investigated using a data suite of real traffic scenarios; the statistically significant results showed that the integrated process had superior performance compared to the ‘traditional’ quantitative analysis running alone.

Download Full-text

Going with the Flow - Computer Visualization of Road Traffic Information

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/154193129303700421 ◽

1993 ◽

Vol 37 (4) ◽

pp. 353-357

Author(s):

John Murray ◽

Yili Liu

Keyword(s):

Traffic Control ◽

Traffic Management ◽

Road Traffic ◽

Human Subjects ◽

Traffic Information ◽

Color Coding ◽

Control Center ◽

Computer Visualization ◽

Vehicular Speed ◽

Real State

Advanced road traffic management systems provide numerous opportunities for the application of sophisticated computer visualization concepts. The operating staff in a traffic control center are required to assimilate large quantities of incoming data in order to determine the real state of traffic flow and congestion. Part of the incoming data relates to vehicular speed and density, and is often not subjected to sufficient pre-processing before presentation in tabular form on a video display terminal (VDT). Improvements in the format of the tabular information are therefore worthy of investigation. A traffic control simulation experiment was conducted to examine how human subjects extract information from VDT data presented in several different formats. Subjects were asked to respond to exceptional values which occurred randomly in tabular columns of frequently changing data. Their accuracy and reaction time were measured for data columns which were sorted or unsorted, and for data which was presented either numerically or color-coded. Analysis of the results suggests that both sorting and color-coding are significant in reducing response time, and that color-coding is appreciably more effective in this regard.

Download Full-text

Context-Driven Proactive Decision Support for Hybrid Teams

AI Magazine ◽

10.1609/aimag.v40i3.4810 ◽

2019 ◽

Vol 40 (3) ◽

pp. 41-57

Author(s):

Manisha Mishra ◽

Pujitha Mannaru ◽

David Sidoti ◽

Adam Bienkowski ◽

Lingyi Zhang ◽

...

Keyword(s):

Decision Support ◽

Situational Awareness ◽

Feature Space ◽

Human Interaction ◽

Superior Performance ◽

Uncertain Environments ◽

Cognitive States ◽

Contextual Elements ◽

Human Operators ◽

Smart Machine

A synergy between AI and the Internet of Things (IoT) will significantly improve sense-making, situational awareness, proactivity, and collaboration. However, the key challenge is to identify the underlying context within which humans interact with smart machines. Knowledge of the context facilitates proactive allocation among members of a human–smart machine (agent) collective that balances autonomy with human interaction, without displacing humans from their supervisory role of ensuring that the system goals are achievable. In this article, we address four research questions as a means of advancing toward proactive autonomy: how to represent the interdependencies among the key elements of a hybrid team; how to rapidly identify and characterize critical contextual elements that require adaptation over time; how to allocate system tasks among machines and agents for superior performance; and how to enhance the performance of machine counterparts to provide intelligent and proactive courses of action while considering the cognitive states of human operators. The answers to these four questions help us to illustrate the integration of AI and IoT applied to the maritime domain, where we define context as an evolving multidimensional feature space for heterogeneous search, routing, and resource allocation in uncertain environments via proactive decision support systems.

Download Full-text

Privacy-Preserving Smart Road-Pricing System with Trustworthiness Evaluation in VANETs

Sensors ◽

10.3390/s21113658 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3658

Author(s):

Qingfeng Zhu ◽

Sai Ji ◽

Jian Shen ◽

Yongjun Ren

Keyword(s):

Traffic Control ◽

Traffic Management ◽

Location Privacy ◽

Vehicular Ad Hoc Networks ◽

Intelligent Transportation System ◽

Smart Cities ◽

Security Analysis ◽

Geographical Location ◽

Experimental Simulation ◽

Control Center

With the advanced development of the intelligent transportation system, vehicular ad hoc networks have been observed as an excellent technology for the development of intelligent traffic management in smart cities. Recently, researchers and industries have paid great attention to the smart road-tolling system. However, it is still a challenging task to ensure geographical location privacy of vehicles and prevent improper behavior of drivers at the same time. In this paper, a reliable road-tolling system with trustworthiness evaluation is proposed, which guarantees that vehicle location privacy is secure and prevents malicious vehicles from tolling violations at the same time. Vehicle route privacy information is encrypted and uploaded to nearby roadside units, which then forward it to the traffic control center for tolling. The traffic control center can compare data collected by roadside units and video surveillance cameras to analyze whether malicious vehicles have behaved incorrectly. Moreover, a trustworthiness evaluation is applied to comprehensively evaluate the multiple attributes of the vehicle to prevent improper behavior. Finally, security analysis and experimental simulation results show that the proposed scheme has better robustness compared with existing approaches.

Download Full-text

The TravTek Traffic Management Center and Traffic Information Network

10.4271/912822 ◽

1991 ◽

Cited By ~ 4

Author(s):

Robert L. Rupert

Keyword(s):

Traffic Management ◽

Traffic Information ◽

Information Network

Download Full-text

Mobile Road Traffic Management System Using Weighted Sensor

International Journal of Interactive Mobile Technologies (iJIM) ◽

10.3991/ijim.v11i5.6745 ◽

2017 ◽

Vol 11 (5) ◽

pp. 147 ◽

Cited By ~ 2

Author(s):

Solomon Adegbenro Akinboro ◽

Johnson A Adeyiga ◽

Adebayo Omotosho ◽

Akinwale O Akinwumi

Keyword(s):

Real Time ◽

Traffic Control ◽

Urban Areas ◽

Traffic Management ◽

Management System ◽

Road Traffic ◽

Traffic Information ◽

Traffic Light ◽

Real Time Traffic ◽

Traffic Management System

Vehicular traffic is continuously increasing around the world, especially in urban areas, and the resulting congestion has become a major concern to automobile users. The popular static electric traffic light controlling system can no longer sufficiently manage the traffic volume in large cities where real time traffic control is paramount to deciding best route. The proposed mobile traffic management system provides users with traffic information on congested roads using weighted sensors. A prototype of the system was implemented using Java SE Development Kit 8 and Google map. The model was simulated and the performance was assessed using response time, delay and throughput. Results showed that, mobile devices are capable of assisting road users’ in faster decision making by providing real-time traffic information and recommending alternative routes.

Download Full-text

MODEL FOR THE RELIABILITY, AVAILABILITY AND SAFETY OF THE CONTROL CENTERS OF THE VESSEL TRAFFIC MANAGEMENT AND INFORMATION SYSTEMS

PROMET - Traffic&Transportation ◽

10.7307/ptt.v25i4.1088 ◽

2013 ◽

Vol 25 (4) ◽

pp. 395-403

Author(s):

Pančo Ristov

Keyword(s):

Information Systems ◽

Traffic Management ◽

Block Diagram ◽

System Failure ◽

Control Center ◽

Quality Of Control ◽

Maritime Traffic ◽

Critical Components ◽

Quantitative Indicators

The quality of Vessel Traffic Management and Information Systems depends on the quality of all subsystems, in particular the quality of control centers. The most commonly used quantitative indicators of the control centers' quality are: reliability, availability, safety, and system failure. Therefore, a block diagram of reliability and the model for reliability / availability (Markov model) have been created in this paper and a detailed analysis and calculation of the quantitative indicators of critical components (servers) of the control center have been performed. The quality functioning of the control centers will enable gathering, processing, storing and dissemination of timely, safe, and reliable data and information to the services in charge of monitoring and management of maritime traffic.

Download Full-text

Ordinal synchronization mark sequence and its steganography for a multi-link network covert channel

PLoS ONE ◽

10.1371/journal.pone.0252813 ◽

2021 ◽

Vol 16 (6) ◽

pp. e0252813

Author(s):

Songyin Fu ◽

Rangding Wang ◽

Li Dong ◽

Diqun Yan

Keyword(s):

Hash Function ◽

High Capacity ◽

Superior Performance ◽

Covert Channel ◽

Correlation Test ◽

Switching Patterns ◽

Finite Set ◽

Set Up ◽

Network Covert Channel ◽

Real Traffic

A multi-link network covert channel (MLCC) such as Cloak exhibits a high capacity and robustness and can achieve lossless modulation of the protocol data units. However, the mechanism of Cloak involving an arrangement of packets over the links (APL) is limited by its passive synchronization schemes, which results in intermittent obstructions in transmitting APL packets and anomalous link switching patterns. In this work, we propose a novel ordinal synchronization mark sequence (OSMS) for a Cloak framework based MLCC to ensure that the marked APL packets are orderly distinguishable. Specifically, a unidirectional function is used to generate the OSMS randomly before realizing covert modulation. Subsequently, we formulate the generation relation of the marks according to their order and embed each mark into the APL packets by using a one-way hash function such that the mark cannot be cracked during the transmission of the APL packet. Finally, we set up a retrieval function of the finite set at the covert receiver to extract the marks and determine their orders, and the APL packets are reorganized to realize covert demodulation. The results of experiments performed on real traffic indicated that the MLCC embedded with OSMS could avoid the passive synchronization schemes and exhibited superior performance in terms of reliability, throughput, and undetectability compared with the renowned Cloak method, especially under a malicious network interference scenario. Furthermore, our approach could effectively resist the inter-link correlation test, which are highly effective in testing the Cloak framework.

Download Full-text

First-Break Picking Classification Models Using Recurrent Neural Network

10.2118/204862-ms ◽

2021 ◽

Author(s):

Mohammed Ayub ◽

SanLinn Kaka

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Neural Network ◽

Contextual Information ◽

Classification Model ◽

Superior Performance ◽

Learning Models ◽

Neural Network Models ◽

Minimum Number ◽

Machine Learning Models

Abstract Manual first-break picking from a large volume of seismic data is extremely tedious and costly. Deployment of machine learning models makes the process fast and cost effective. However, these machine learning models require high representative and effective features for accurate automatic picking. Therefore, First- Break (FB) picking classification model that uses effective minimum number of features and promises performance efficiency is proposed. The variants of Recurrent Neural Networks (RNNs) such as Long ShortTerm Memory (LSTM) and Gated Recurrent Unit (GRU) can retain contextual information from long previous time steps. We deploy this advantage for FB picking as seismic traces are amplitude values of vibration along the time-axis. We use behavioral fluctuation of amplitude as input features for LSTM and GRU. The models are trained on noisy data and tested for generalization on original traces not seen during the training and validation process. In order to analyze the real-time suitability, the performance is benchmarked using accuracy, F1-measure and three other established metrics. We have trained two RNN models and two deep Neural Network models for FB classification using only amplitude values as features. Both LSTM and GRU have the accuracy and F1-measure with a score of 94.20%. With the same features, Convolutional Neural Network (CNN) has an accuracy of 93.58% and F1-score of 93.63%. Again, Deep Neural Network (DNN) model has scores of 92.83% and 92.59% as accuracy and F1-measure, respectively. From the pexperiment results, we see significant superior performance of LSTM and GRU to CNN and DNN when used the same features. For robustness of LSTM and GRU models, the performance is compared with DNN model that is trained using nine features derived from seismic traces and observed that the performance superiority of RNN models. Therefore, it is safe to conclude that RNN models (LSTM and GRU) are capable of classifying the FB events efficiently even by using a minimum number of features that are not computationally expensive. The novelty of our work is the capability of automatic FB classification with the RNN models that incorporate contextual behavioral information without the need for sophisticated feature extraction or engineering techniques that in turn can help in reducing the cost and fostering classification model robust and faster.

Download Full-text

Research of Manufacturing Information Recommendation Model Based on Pattern Mining

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.102-104.813 ◽

2010 ◽

Vol 102-104 ◽

pp. 813-816

Author(s):

D.S. Liu ◽

Chun Hua Ju ◽

Hao Tian

Keyword(s):

Information Systems ◽

Pattern Mining ◽

Current Situation ◽

Information Modeling ◽

Access Pattern ◽

Web Environment ◽

Modeling Process ◽

Manufacturing Information ◽

Information Recommendation ◽

Sequence Behavior

Existing enterprises information systems seldom take different requirement tendency of different personnel into consideration. The idea of Manufacturing Information Active Recommendation was put forward to transfer proper information to proper personnel correctly and timely. The demand sequence behavior access pattern tree was constructed based on the different requirement of user's identity, location and business needs in Web environment manufacturing information. Historical situation similar to current situation could be sorted by its value, and the Behavior could be determined and output based on the association between the highly similar historical situations. Finally, an example was provided to demonstrate the effectiveness of the information modeling process frequent sequence behavior access pattern tree and the model.

Download Full-text

Improving Driver Assistance in Intelligent Transportation Systems: An Agent-Based Evidential Reasoning Approach

Journal of Advanced Transportation ◽

10.1155/2020/4607858 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14 ◽

Cited By ~ 2

Author(s):

M. Benalla ◽

B. Achchab ◽

H. Hrimech

Keyword(s):

Intelligent Transportation Systems ◽

Contextual Information ◽

Sensory Information ◽

Intelligent Transportation ◽

Transportation Systems ◽

Evidential Reasoning ◽

Traffic Information ◽

Driver Assistance ◽

Agent Based ◽

Reasoning System

Providing accurate real-time traffic information is an inherent problem for intelligent transportation systems (ITS). In order to improve the knowledge base of advanced driver assistance systems (ADAS), ITS are strongly concerned with data fusion techniques of all kinds of sensors deployed over the traffic network. Driver assistance is devoid of a comprehensive evidential reasoning system on contextual information, more specifically when a combination involves inside and outside sensory information of the driving environment. In this paper, we propose a novel agent-based evidential reasoning system using contextual information. Based on a series of information handling techniques, specifically, the belief functions theory and heuristic inference operations to achieve a consensus about daily driving activity in automatically inferring. That is quite different from other existing proposals, as it deals jointly with the driving behavior and the driving environment conditions. A case study including various scenarios of experiments is introduced to estimate behavioral information based on synthetic data for prediction, prescription, and policy analysis. Our experiments show promising, thought-provoking results encouraging further research.

Download Full-text