scholarly journals Modeling Liability Data Collection Systems for Intelligent Transportation Infrastructure Using Hyperledger Fabric

2019 ◽  
pp. 137-156
Author(s):  
Luis Cintron ◽  
Scott Graham ◽  
Douglas Hodson ◽  
Barry Mullins
Keyword(s):  
Data Collection ◽  
Intelligent Transportation ◽  
Transportation Infrastructure

INTELLIGENT TRANSPORTATION SYSTEMS IN IMPROVING TRAFFIC FLOW IN TOURISM DESTINATIONS∗

2007 ◽  
Vol 13 (3) ◽  
pp. 627-636
Author(s):  
Edna Mrnjavac ◽  
Robert Marsanić
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Emergency Services ◽  
Road Traffic ◽  
Weather Conditions ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Transportation Infrastructure ◽  
Public Traffic ◽  
Tourism Destinations

The rapid growth and development of motorisation combined with relatively small investments made to improving transportation infrastructure in cities, as well as in tourism destinations, has led to serious problems in the unobstructed movement of vehicles in public traffic areas. Traffic congestion on roadways, in ferryboat ports and at state borders during the summer months and year-round lines of cars going to or returning from work are a regular presence in traffic in most urban and tourism destinations in Croatia, as well as in the rest of Europe. Intelligent transportation systems (ITS) can be implemented in urban and tourism centres, which, for example, have no opportunity for increasing the capacity of their traffic networks by constructing new, or expanding existing, transportation infrastructure, and no opportunity for increasing parking capacities. The only solution would be to optimise traffic networking by introducing intelligent technologies. Intelligent transportation systems and services represent a coupling of information and telecommunication technologies with transportation means and infrastructure to ensure greater efficiency in the mobility of people and goods. ITS implementation helps to provide better information to motorists and travellers (tourists); improve traffic and tourist flows, cargo transportation, public passenger-transportation; facilitate the work of emergency services; enable electronic traffic-related payments; enhance the security of people in road traffic; and monitor weather conditions and the environment. To motorists the system provides guidance to roads on which traffic is less intense, guidance to available parking spaces, and guidance, for example, to a good restaurant or interesting tourist attraction. his paper focuses, in particular, on ITS application in city and tourism destinations in connection with parking problems. Guiding vehicles to the closest vacant parking space helps to reduce traffic congestion, reduce the amount of time lost in searching and increase the occupancy rate of car-parks

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.

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