Estimation of Travel Time in the City Based on Intelligent Transportation System Traffic Data with the Use of Neural Networks

2016 ◽  
pp. 85-95 ◽  
Author(s):  
Piotr Ciskowski ◽  
Adrianna Janik ◽  
Marek Bazan ◽  
Krzysztof Halawa ◽  
Tomasz Janiczek ◽  
...  
Keyword(s):  
Neural Networks ◽  
Travel Time ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Traffic Data ◽  
The City

scholarly journals Granular Computing in Intelligent Transportation: An Exploratory Study

2015 ◽  
Vol 15 (6) ◽  
pp. 122-134 ◽  
Author(s):  
Seng Dewen ◽  
Cheng Xinhong ◽  
Chen Jing ◽  
Fang Xujian
Keyword(s):  
Exploratory Study ◽  
Granular Computing ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Urban Transport ◽  
Traffic Situation ◽  
Traffic Data ◽  
Time Data ◽  
Data Processing Methods

Abstract With the continuous development of the cities, the traffic situation has gradually become a topic of concern. The concept of an intelligent transportation system has been proposed and developed. In the field of intelligent transportation, the traffic data has gradually increased. People have higher demands to real time data. The traditional data processing methods and tools have become unable to meet the needs of urban transport development. In this paper we analyzed the basic theory of granular computing, the methods, technology and current situation of granular computing. Besides, we discussed the hot issues of granular computing in an intelligent transportation system. Finally, granular computing in the development of intelligent transportation fields was also discussed.

Simulation of Efficient Vehicle Dispatching and Routing for the Application in Intelligent Transportation System

2014 ◽  
Vol 598 ◽  
pp. 709-713
Author(s):  
Yen Wen Chen ◽  
Chang Wu Chen ◽  
Xin Chang Chen ◽  
Addison Y.S. Su
Keyword(s):  
Travel Time ◽  
Intelligent Transportation System ◽  
Transportation Cost ◽  
Specific Area ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Vehicle Dispatching ◽  
Simulation Results ◽  
Safety Considerations

This paper provides the simulation for a designed vehicle dispatching and routing system for safe and efficient vehicle transportation. The objective of the simulation is to verify the performance of the cooperative dispatching and routing decisions for vehicles within a specific area consisting of streets and several intersections. In addition to computing a specific routing path for the arrival vehicle based on its destination and safety considerations, the proposed scheme provides a detour mechanism under the transportation cost constraint. This paper also points out the existence of hidden conflicting path problems in the dispatching approach and proposes a backtracking approach to solve this problem at the route decision phase so that the travel time can be guaranteed. The simulation results show that the proposed scheme can effectively assign the vehicles with proper routes and without any collisions with good performance.

scholarly journals Deep Intelligent Prediction Network: A Novel Deep Learning Based Prediction Model on Spatiotemporal Characteristics and Location Based Services for Big Data Driven Intelligent Transportation System

2019 ◽  
Vol 8 (11) ◽  
pp. 845-849
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Recommendation System ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Transportation Systems ◽  
Location Based Services ◽  
Traffic Data ◽  
Spatiotemporal Characteristics

The concept of big Data for intelligent transportation system has been employed for traffic management on dealing with dynamic traffic environments. Big data analytics helps to cope with large amount of storage and computing resources required to use mass traffic data effectively. However these traditional solutions brings us unprecedented opportunities to manage transportation data but it is inefficient for building the next-generation intelligent transportation systems as Traffic data exploring in velocity and volume on various characteristics. In this article, a new deep intelligent prediction network has been introduced that is hierarchical and operates with spatiotemporal characteristics and location based service on utilizing the Sensor and GPS data of the vehicle in the real time. The proposed model employs deep learning architecture to predict potential road clusters for passengers. It is injected as recommendation system to passenger in terms of mobile apps and hardware equipment employment on the vehicle incorporating location based services models to seek available parking slots, traffic free roads and shortest path for reach destination and other services in the specified path etc. The underlying the traffic data is classified into clusters with extracting set of features on it. The deep behavioural network processes the traffic data in terms of spatiotemporal characteristics to generate the traffic forecasting information, vehicle detection, autonomous driving and driving behaviours. In addition, markov model is embedded to discover the hidden features .The experimental results demonstrates that proposed approaches achieves better results against state of art approaches on the performance measures named as precision, execution time, feasibility and efficiency.

scholarly journals Implementasi Fasilitas Intelligent Transportation System (ITS) Di Kota Palangka Raya

2020 ◽  
Vol 8 (2) ◽  
pp. 72-78
Author(s):  
Devia Devia ◽  
Prihanika Prihanika
Keyword(s):  
Control System ◽  
Traffic Safety ◽  
Traffic Control ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Travel Speed ◽  
Transportation System ◽  
Transportation Systems ◽  
Traffic Control System ◽  
The City

The movement of people and goods is increasing in line with economic growth in society. This causes the potential for increased transportation activities in the City of Palangka Raya so it needs efforts to improve adequate transportation facilities and infrastructure. The application of technology-based Intelligent Transportation System (ITS) in Palangka Raya City is needed so that the management of the transportation system becomes more effective and efficient. This paper provides an overview of the application of ITS facilities and types in Palangka Raya City and provides recommendations for the use of new ITS facilities or optimizing existing technology so that ITS facilities can be utilized by stakeholders in traffic management and transportation systems in Palangka Raya City. Based on observations of the application of ITS in the City of Palangka Raya is applied to improve the performance of intersections and road services. The type of ITS facility is the Area Traffic Control System (ATCS), which is a vehicle traffic control system at the signal intersection to increase travel speed and travel time so that delays in travel can be minimized. It is also expected that the implementation of ITS in Palangkaraya City can also optimize the performance of public transport and traffic safety as well as the collaboration between stakeholders so that the improvement of the integrated transportation system can be well integrated.

The Improvements of Traffic Data NN Rules Based on Hypothesis Interval

2013 ◽  
Vol 347-350 ◽  
pp. 3518-3522
Author(s):  
Shi Yong Ma ◽  
Shi An ◽  
Tian Hua Song
Keyword(s):  
Data Collection ◽  
Intelligent Transportation System ◽  
Nearest Neighbor ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Traffic Data ◽  
Practical Application ◽  
Nearest Neighbor Query ◽  
Traffic Data Collection ◽  
Noise Data

In the practical application of the Intelligent Transportation System (ITS), the collected and stored data through Nearest Neighbor Query can easily be contaminated by noise data. The reason is that the sensitivity of Nearest Neighbor Rules (NN Rules) to the noise data leads to the limits of Nearest Neighbor Query's practical application. To solve this problem, by using the insensitivity of Hypothesis Interval to noise data, this thesis improves NN Rules and proposes a classification mode of traffic data collection nearest neighbor rules. When the model predicts the samples, not only the distance from the test samples to the nearest neighbor is considered, but also the degree of the class to which this nearest neighbor belongs is taken into account.

Analysis on Improved Dijkstra Algorithm in Intelligent Transportation System

2013 ◽  
Vol 385-386 ◽  
pp. 877-881
Author(s):  
Hao Yu Wang ◽  
Chao Jun Ji ◽  
Xiao Juan Ji
Keyword(s):  
Real Time ◽  
Shortest Path ◽  
Traffic Congestion ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Dijkstra Algorithm ◽  
Key Technology ◽  
The Core ◽  
The City

Intelligent transportation system (ITS) is a complicated system, and selecting the shortest path is the core of the system. In order to ease traffic congestion of the city, through solving the shortest path, the key technology in realizing the function of ITS, and based on the analysis of traditional Dijkstra algorithm, the paper puts forward the improved algorithm. The new algorithm includes two parts, that is, preprocessing and real-time pruning research, and the effect of pruning totally depends on the division of specific diagram.

Sign in / Sign up

Export Citation Format

Share Document