Telecommunications- and Information Technology–Inspired Analyses: Review of an Intelligent Transportation Systems Experience

2017 ◽  
Vol 2658 (1) ◽  
pp. 44-55 ◽  
Author(s):  
Yupo Chan
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Real Time ◽  
Traffic Management ◽  
Transportation Systems ◽  
Lessons Learned ◽  
Connected Vehicles ◽  
Ongoing Research ◽  
Real Time Information ◽  
Time Information

This paper reviews both the author’s experience with managing highway network traffic on a real-time basis and the ongoing research into harnessing the potential of telecommunications and information technology (IT). On the basis of the lessons learned, this paper speculates about how telecommunications and IT capabilities can respond to current and future developments in traffic management. Issues arising from disruptive telecommunications technologies include the ready availability of real-time information, the crowdsourcing of information, the challenges of big data, and the need for information quality. Issues arising from transportation technologies include autonomous vehicles and connected vehicles and new taxi-like car- and bikesharing. Illustrations are drawn from the following core functions of a traffic management center: ( a) detecting and resolving an incident (possibly through crowdsourcing), ( b) monitoring and forecasting traffic (possibly through connected vehicles serving as sensors), ( c) advising motorists about routing alternatives (possibly through real-time information), and ( d) configuring traffic control strategies and tactics (possibly though big data). The conclusion drawn is that agility is the key to success in an ever-evolving technological scene. The solid guiding principle remains innovative and rigorous analytical procedures that build on the state of the art in the field, including both hard and soft technologies. The biggest modeling and simulation challenge remains the unknown, including such rapidly emerging trends as the Internet of things and the smart city.

Research and Implementation of the Smart Home System Based on Internet of Things Environment

2014 ◽  
Vol 513-517 ◽  
pp. 1915-1918
Author(s):  
Heng Wang ◽  
Bi Geng Zheng
Keyword(s):  
Information Technology ◽  
Wireless Sensor Network ◽  
Internet Of Things ◽  
Real Time ◽  
Sensor Network ◽  
Smart Home ◽  
Wireless Sensor ◽  
Internet Of Things Environment ◽  
Real Time Information ◽  
Time Information

As one of the freshest technologies nowadays, the development of Internet of Things is attracting more and more concerns. Internet of Things is able to connect all the items to Internet via information technology such as RFID and Wireless Sensor Network, in order to realize intelligent identification and management. It is supposed in Internet of Things environments, satisfactory services can be provided through any devices or any networks, whenever it is demanded. It makes that not only PC device but also other small devices with intelligence can be connected to the same network. As a result, It is much more convenient for people to obtain real-time information and then to take corresponding actions.

scholarly journals Converting the existing disease surveillance from a paper-based to an electronic-based system using District Health Information System (DHIS-2) for real-time information: The Lebanese experience

2021 ◽  
Author(s):  
dalal Ali youssef
Keyword(s):  
Real Time ◽  
Disease Surveillance ◽  
Lessons Learned ◽  
Surveillance Systems ◽  
Medical Centers ◽  
Before And After ◽  
Reported Data ◽  
Real Time Information ◽  
Time Information ◽  
Roll Out

Abstract Introduction:The Ministry of Public Health in Lebanon is in the process of converting the surveillance reporting from a cumbersome paper-based system to a web-based electronic platform (DHIS-2) to have real-time information for early detection of alerts and outbreaks and for initiating a prompt response.Objectives:This paper aimed to document the Lebanese experience in implementing DHIS-2 for the disease surveillance system. It also targets to assess the improvement of reporting rates and timeliness of the reported data and to disclose the encountered challenges and opportunities. MethodologyThis is a retrospective description of processes involved in the implementation of the DHIS-2 tool in Lebanon. Initially, it was piloted for the school-based surveillance in 2014; then its use was extended in May 2017 to cover other specific surveillance systems. This included all surveillance programs collecting aggregate data from hospitals, medical centers, dispensaries, or laboratories at the first stage. As part of the national roll-out process, the online application was developed. The customized aggregated-based datasets, organization units, user accounts, specific and generic dashboards were generated. More than 80 training sessions were conducted throughout the country targeting 1290 end-users including health officers at the national and provincial levels, focal persons working in all public and private hospitals, laboratories, and medical centers as well. Completeness and timeliness of reported data were compared before and after the implementation of DHIS-2. Challenges and lessons learned during the roll-out process are listed.ResultsFor laboratory-based surveillance, completeness of reporting increased from 70.8% in May to 89.6% in October. Timeliness has improved from 25% to 74%. For medical centers, an improvement of 8.1% for completeness and 9.4% in timeliness was recorded before and after training sessions. For zero reporting, completeness remains the same (88%) and timeliness has improved from 74% to 87%. The main challenges faced during the implementation of DHIS-2 were mainly infrastructural and system-related in addition to poor internet connectivity and limited workforce and frequent changes to DHIS-2 versions.ConclusionImplementation of DHIS-2 improved timeliness and completeness for aggregated data reporting. Continued on-site support, monitoring, and system enhancement are needed to improve the performance of DHIS-2.

scholarly journals Fast and Improved Real-Time Vehicle Anti-Tracking System

2020 ◽  
Vol 10 (17) ◽  
pp. 5928
Author(s):  
Olivier Oheka ◽  
Chunling Tu
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Tracking System ◽  
Adaboost Algorithm ◽  
Background Removal ◽  
Driving Assistance ◽  
Unique Approach ◽  
Computer Vision Systems ◽  
Real Time Information ◽  
Time Information

Safety on roads and the prevention of accidents have become major problems in the world. Intelligent cars are now a standard in the future of transportation. Drivers will benefit from the increased support for driving assistance. This means relying on the development of integrated systems that can provide real-time information to help drivers make decisions. Therefore, computer vision systems and algorithms are needed to detect and track vehicles. This helps traffic management and driving assistance. This paper focuses on developing a reliable vehicle tracking system to detect the vehicle that is following the host vehicle. The proposed system uses a unique approach consisting of a mixture of background removal techniques, Haar features in a modified Adaboost algorithm in a cascade configuration, and SURF descriptors for tracking. From the camera mounted at the rear of the host vehicle, videos are captured. The results presented in this paper demonstrate the potential and efficiency of the system.

scholarly journals Improving Performance in Trust Based Multi-Link-Rat Communication for V2x Heterogeneous Vehicular Network

2021 ◽  
pp. 458-464
Author(s):  
G. Baskaran ◽  
G. Pragathi ◽  
S. Prithika ◽  
P. Rajeswari ◽  
B. Rubasri
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Vehicular Networks ◽  
Ad Hoc ◽  
Road Traffic ◽  
Vehicle Speed ◽  
V2v Communications ◽  
Real Time Information ◽  
Traffic Detection ◽  
Time Information

The dynamic nature of vehicular networks imposes a lot of challenges in multi hop data transmission as links are vulnerable in their existence due to associated mobility of vehicles. It is very difficult to establish and maintain end-to-end connections in a vehicle ad hoc network (VANET) as a result of high vehicle speed, long inter-vehicle distance, and varying vehicle density. Here propose a distributed heterogeneous V2V communications algorithm that allows each vehicle to dynamically select the RAT that is more suitable at each point in time. Multi-link is the capability of a device to communicate using multiple wireless links simultaneously. Multi-RAT is the capability of a device to communicate using different RATs. To propose a Predictive Routing based on Markov Model (PRM) to ensure more reliable and timely data transmissions in VANETs. In the case of accident management, emergency messages may be sent to a pre-determined road rescue site upon the occurrence of an accident, such as a crash on the highway during a snow day and a car spontaneous combustion due to the stored explosives. PRM can facilitate the transmission of real-time information from vehicles to a road traffic controller for more efficient traffic management. Rather than using passive traffic detection through sensors, the real-time reports of traffic data through V2V and V2I can avoid the costs of installing and maintaining a large number of sensors.

scholarly journals Estimation of the Number of Passengers in a Bus Using Deep Learning

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2178
Author(s):  
Ya-Wen Hsu ◽  
Yen-Wei Chen ◽  
Jau-Woei Perng
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Occupancy Rate ◽  
Convolutional Autoencoder ◽  
Real Time Information ◽  
Time Information ◽  
Better Than

For the development of intelligent transportation systems, if real-time information on the number of people on buses can be obtained, it will not only help transport operators to schedule buses but also improve the convenience for passengers to schedule their travel times accordingly. This study proposes a method for estimating the number of passengers on a bus. The method is based on deep learning to estimate passenger occupancy in different scenarios. Two deep learning methods are used to accomplish this: the first is a convolutional autoencoder, mainly used to extract features from crowds of passengers and to determine the number of people in a crowd; the second is the you only look once version 3 architecture, mainly for detecting the area in which head features are clearer on a bus. The results obtained by the two methods are summed to calculate the current passenger occupancy rate of the bus. To demonstrate the algorithmic performance, experiments for estimating the number of passengers at different bus times and bus stops were performed. The results indicate that the proposed system performs better than some existing methods.

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