scholarly journals The Road Reservation Scheme in Emergency Situation for Intelligent Transportation Systems

2011 ◽  
Vol 36 (11B) ◽  
pp. 1346-1356
Author(s):  
Jae-Bong Yoo ◽  
Chan-Young Park
Keyword(s):  
Intelligent Transportation Systems ◽  
Emergency Situation ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
The Road

Potential Using Vehicle to Vehicle Communication Based on Wireless Fidelity (Wi-Fi) for Supporting Intelligent Transportation Systems (ITS)

2021 ◽  
pp. 61-76
Author(s):  
إسراء عصام بن موسى ◽  
عبدالسلام صالح الراشدي
Keyword(s):  
Intelligent Transportation Systems ◽  
Ad Hoc ◽  
Real Life ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
The Road ◽  
Vehicle Communication ◽  
Vehicle To Vehicle ◽  
Vehicle To Vehicle Communication ◽  
Wireless Fidelity

Vehicular Ad-hoc Network (VANET) becomes one of the most popular modern technologies these days, due to its contribution to the development and modernization of Intelligent Transportation Systems (ITS). The primary goal of these networks is to provide safety and comfort for drivers and passengers in roads. There are many types of VANET that are used in ITS, in this paper, we particularly focus on the Vehicle to Vehicle communication (V2V), which each vehicle can exchange information to inform drivers of other vehicles about the current state of the road flow, in the event of any emergency to avoid accidents, and reduce congestion on roads. We proposed V2V using Wi-Fi (wireless fidelity); the reason of its unique characteristics that distinguish it from other types. There are many difficulties and the challenges in implementing most types of V2V, and the reason is due to the lack of devices and equipment needed for real implementation. To prove the possibility of applying this type in real life, we made a prototype contains a modified toy car, a 12-volt power supply, sensors, visual, audible alarm, a visual “LED” devices, and finally a 12-volt DC relay unit. As a conclusion, the proposed implementation in spite of minimal requirements and use simple equipment, we have achieved the most important main objectives of the paper: preventing vehicles from collision, early warning, and avoiding congestion on the roads.

scholarly journals REDUCING THE TIME TO GET EMERGENCY ASSISTANCE FOR ACCIDENT VEHICLES ON THE ROAD THROUGH AN INTELLIGENT TRANSPORTATION SYSTEM

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 827-832
Author(s):  
A. H. Nourbakhsh ◽  
M. R. Delavar ◽  
M. Jadidi ◽  
B. Moshiri
Keyword(s):  
Intelligent Transportation Systems ◽  
Intelligent Transportation System ◽  
Intelligent System ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Dijkstra Algorithm ◽  
Notification System ◽  
Smart Transportation ◽  
The Road ◽  
On The Road

Abstract. Intelligent Transportation Systems (ITS) is one of the main components of a smart city. ITS have several purposes including the increase of the safety and comfort of the passengers and the reduction of the road accidents. ITS can enhance safety in three modes before, within and after the collision by preventing accident via assistive system, sensing the collision situation and calculating the time of the collision and providing the emergency response in a timely manner. The main objective of this paper is related to the smart transportation services which can be provided at the time of the collision and after the accident. After the accident, it takes several minutes to hours for the person to contact the emergency department. If an accident takes place for a vehicle in a remote area, this time increases and that may cause the loss of life. In addition, determination of the exact location of the accident is difficult by the emergency centres. That leads to the possibility of erroneous responder act in dispatching the rescue team from the nearest hospital. A new assistive intelligent system is designed in this regard that includes both software and hardware units. Hardware unit is used as an On-Board Unit (OBU), which consists of GPS, GPRS and gyroscope modules. Once OBU detects the accident, a notification system designed and connected to OBU will sent an alarm to the server. The distance to the nearest emergency center is calculated using Dijkstra algorithm. Then the server sends a request for assistance to the nearest emergency centre. The proposed system is developed and tested at local laboratory conditions. The results show that this system can reduce Ambulance Arrival Time (AAT). The preliminary results and architecture of the system have been presented. The inclination angle determined by the proposed system along with the car position identified by the installed GPS sensor assists the crash/accident warning part of the system to send a help request to the nearest road emergency centre. These results verified that the probability of having a remote and smart car crash/accident decision support system using the proposed system has been improved compared to that of the existing systems.

scholarly journals Driver’s Drowsiness Detection Based on Behavioural Changes using ResNet

2019 ◽  
Vol 8 (3) ◽  
pp. 5708-5712
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Motor Vehicles ◽  
Steering Wheel ◽  
Road Accidents ◽  
Drowsiness Detection ◽  
Behavioural Changes ◽  
The Road

Recently there has been growing interest in intelligent transportation system because the road accidents become biggest problems of mankind and the casualties of accident also increases rapidly every year. The casualties are very often witnessed in heavy and light motor vehicles. Moreover, the accidents occur mainly due to carelessness and drowsy feeling of the driver. Intelligent transportation systems use deep learning mechanism to detect drowsiness of the driver and alert the same to driver. It results in reduction of accidents. The driver’s behaviour during drowsiness is detected by three types of approaches. One approach deploys the sensors in steering wheel and accelerator of the vehicle and analyzes the signal sent by the sensors to detect the drowsiness. Second approach focuses on measuring the heart rate, pulse rate and brain signals etc to predict the drowsiness. Third approach uses the facial expression of the driver such as blinking rate of eye, eye closure and yawning etc. The cause for most of the road accidents is driver’s drowsiness. Therefore, in this paper, the behavioural changes of driver is accounted to detect the drowsiness of the driver. Eye movement and yawning are two behavioural changes of driver is considered in this paper. There are many CNN based deep learning architectures such AlexNet, VGGNet, ResNet. In this paper, we propose the drowsiness detection using ResNet because this method works on the principle of passing the output to the next la. The performance of proposed mechanism detects the drowsiness of the driver better than AlexNet and VGGNet.

scholarly journals Enhancement of Link Stability and Connectivity in Vehicular Ad hoc Networks

2021 ◽  
Vol 12 (1S) ◽  
pp. 266-271
Author(s):  
Kishor N. Tayade, Et. al.
Keyword(s):  
Ad Hoc Networks ◽  
Intelligent Transportation Systems ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Routing Algorithm ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Mobile Nodes ◽  
The Road ◽  
Hoc Networks

Vehicular Ad hoc Networks is a promising sub-group of MANET. VANET is deployed on the highways, where the vehicles are mobile nodes. Safety and intelligent transportation are important VANET applications that require appropriate communication among vehicles, in particular routing technology. VANETs generally inherit their common features from MANETs where vehicles operate in a collaborative and dispersed way for promoting contact among vehicles and with network infrastructure like the Road Side Units (RSU) for enhanced traffic experience. In view of the fast growth of Intelligent Transportation Systems (ITS), VANETs has attracted considerable interest in this decade. VANET suffer from a major problem of link failure due to dynamic mobility of vehicles. In this paper we proposed a position based routing algorithm to identify stable path, this will improve the routing by decreasing overhead and interrupting the number of links. Link Expiration Time (LET) is used to provide the stable link, the link with the longest LET is considered as the most stable link. The multicast Ad-hoc On-demand Distance Vector (MAODV) is proposed to avoid the link breakages by using a link with longest LET.  Data loss is reduced by avoiding link breakages and enhance throughput by reducing the communication delay.

Traffic Signal Control for a Single Intersection-Based Intelligent Transportation System

2020 ◽  
pp. 159-180
Author(s):  
Nouha Rida ◽  
Mohammed Ouadoud ◽  
Abderrahim Hasbi
Keyword(s):  
Real Time ◽  
Waiting Time ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Traffic Information ◽  
Traffic Signal Control ◽  
Traffic Light ◽  
The Road ◽  
Real Time Traffic

Traffic optimization at an intersection, using real-time traffic information, presents an important focus of research into intelligent transportation systems. Several studies have proposed adaptive traffic lights control, which concentrates on determining green light length and sequence of the phases for each cycle in accordance with the real-time traffic detected. In order to minimize the waiting time at the intersection, the authors propose an intelligent traffic light using the information collected by a wireless sensors network installed in the road. The proposed algorithm is essentially based on two parameters: the waiting time in each lane and the length of its queue. The simulations show that the algorithm applied at a network of intersections improves significantly the average waiting time, queue length, fuel consumption, and CO2 emissions.

scholarly journals Automatic Extraction of Structural and Non-Structural Road Edges from Mobile Laser Scanning Data

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5030 ◽  
Author(s):  
Yang ◽  
Liu ◽  
Jiang ◽  
Xu ◽  
Sheng ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Laser Scanning ◽  
Large Scale ◽  
Auxiliary Information ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Road Maintenance ◽  
The Road ◽  
Large Scale Data ◽  
Road Width

Accurate road information is important for applications involving road maintenance, intelligent transportation, and road network updates. Mobile laser scanning (MLS) can effectively extract road information. However, accurately extracting road edges based on large-scale data for complex road conditions, including both structural and non-structural road types, remains difficult. In this study, a robust method to automatically extract structural and non-structural road edges based on a topological network of laser points between adjacent scan lines and auxiliary surfaces is proposed. The extraction of road and curb points was achieved mainly from the roughness of the extracted surface, without considering traditional thresholds (e.g., height jump, slope, and density). Five large-scale road datasets, containing different types of road curbs and complex road scenes, were used to evaluate the practicality, stability, and validity of the proposed method via qualitative and quantitative analyses. Measured values of the correctness, completeness, and quality of extracted road edges were over 95.5%, 91.7%, and 90.9%, respectively. These results confirm that the proposed method can extract road edges from large-scale MLS datasets without the need for auxiliary information on intensity, image, or geographic data. The proposed method is effective regardless of whether the road width is fixed, the road is regular, and the existence of pedestrians and vehicles. Most importantly, the proposed method provides a valuable solution for road edge extraction that is useful for road authorities when developing intelligent transportation systems, such as those required by self-driving vehicles.

SPEED OF LAST VEHICLE FEEDBACK STRATEGY IN INTELLIGENT TRANSPORTATION SYSTEMS

2011 ◽  
Vol 22 (07) ◽  
pp. 745-753 ◽  
Author(s):  
BOKUI CHEN ◽  
MENGSU CHEN ◽  
ZILING ZHANG ◽  
YANBO XIE ◽  
BINGHONG WANG
Keyword(s):  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Feedback Strategy ◽  
Traffic Jam ◽  
The Road ◽  
Effective Strategies ◽  
Average Flux ◽  
Traffic Systems ◽  
Core Part

Traffic jam has become a big problem in the development of economy. How to effectively improve the road capacity is becoming the key problem in the research of traffic flow. As the core part of the next generation intelligent transportation systems, the feedback strategy has attracted much attention. In recent years, researchers have proposed many effective strategies. In this paper, a strategy called speed of last vehicle feedback strategy is introduced, and simulated in a two-route scenario with one exit. Result shows that compared with other strategies, this strategy has certain advantages on average flux — a criteria describing traffic capacity of traffic systems. More importantly, the implementation of this strategy is very simple.

scholarly journals Detection of Road Traffic Congestion Using V2V Communication Based on IoT

2021 ◽  
pp. 335-345
Author(s):  
Zainab A. Abood ◽  
Hazeem B. Taher ◽  
Rana F. Ghani
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
New Technologies ◽  
Road Traffic ◽  
Liquid Crystal Display ◽  
Emergency Situation ◽  
Transportation Systems ◽  
Raspberry Pi ◽  
The Road ◽  
Road Traffic Congestion

Intelligent Transportation Systems (ITS) have been developed to improve the efficiency and safety of road transport by using new technologies for communication. Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) are a subset of ITS widely used to solve different issues associated with transportation in cities. Road traffic congestion is still the most significant problem that causes important economic and productivity damages, as well as increasing environmental effects. This paper introduces an early traffic congestion alert system in a vehicular network, using the internet of things (IoT) and fuzzy logic, for optimizing the traffic and increasing the flow. The proposed system detects critical driving conditions, or any emergency situation blocking the road, and broadcasts remote warnings to the following vehicles. Since not all vehicles are equipped with new technologies, Liquid Crystal Display (LCD) fixed on the roads displays the alert to warn the other vehicles which have neither communication nor sensors. The system was designed with Raspberry Pi 3 Model B equipped with sensors and GPS module to emulate real-world vehicles. The results and observations collected during the experiments showed that the proposed system is able to monitor the road conditions, detect the emergency situation, and broadcast a warning message to the approaching vehicles.

scholarly journals Efficient Occluded Road Extraction from High-Resolution Remote Sensing Imagery

2021 ◽  
Vol 13 (24) ◽  
pp. 4974
Author(s):  
Dejun Feng ◽  
Xingyu Shen ◽  
Yakun Xie ◽  
Yangge Liu ◽  
Jian Wang
Keyword(s):  
Intelligent Transportation Systems ◽  
Road Network ◽  
Smart Cities ◽  
Heuristic Method ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Road Extraction ◽  
Learning Models ◽  
The Road ◽  
The Common

Road extraction is important for road network renewal, intelligent transportation systems and smart cities. This paper proposes an effective method to improve road extraction accuracy and reconstruct the broken road lines caused by ground occlusion. Firstly, an attention mechanism-based convolution neural network is established to enhance feature extraction capability. By highlighting key areas and restraining interference features, the road extraction accuracy is improved. Secondly, for the common broken road problem in the extraction results, a heuristic method based on connected domain analysis is proposed to reconstruct the road. An experiment is carried out on a benchmark dataset to prove the effectiveness of this method, and the result is compared with that of several famous deep learning models including FCN8s, SegNet, U-Net and D-Linknet. The comparison shows that this model increases the IOU value and the F1 score by 3.35–12.8% and 2.41–9.8%, respectively. Additionally, the result proves the proposed method is effective at extracting roads from occluded areas.

scholarly journals Emergency Messages Dissemination Challenges Through Connected Vehicles for Efficient Intelligent Transportation Systems: A Review

2020 ◽  
Vol 17 (4) ◽  
pp. 1304
Author(s):  
Muhammad Akram Akram Mujahid ◽  
Kamalrulnizam Bin Abu Bakar ◽  
Tasneem S.J Darwish ◽  
Fatima Zuhra ◽  
Muhammad Aamer Ejaz ◽  
...  
Keyword(s):  
Wireless Communication ◽  
Intelligent Transportation Systems ◽  
Road Safety ◽  
Performance Metrics ◽  
Heavy Traffic ◽  
Relay Node ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
The Road ◽  
On The Road

Recent growth in transport and wireless communication technologies has aided the evolution of Intelligent Transportation Systems (ITS). The ITS is based on different types of transportation modes like road, rail, ocean and aviation. Vehicular ad hoc network (VANET) is a technology that considers moving vehicles as nodes in a network to create a wireless communication network. VANET has emerged as a resourceful approach to enhance the road safety. Road safety has become a critical issue in recent years. Emergency incidents such as accidents, heavy traffic and road damages are the main causes of the inefficiency of the traffic flow. These occurrences do not only create the congestion on the road but also increase the fuel consumption and pollute the environment. Emergency messages notify the drivers about road accidents and congestions, and how to avoid the dangerous zones. This paper classifies the emergency messages schemes into three categories based on relay node, clustering and infrastructure. The capabilities and limitations of the emergency messages schemes are investigated in terms of dissemination process, message forward techniques, road awareness and performance metrics. Moreover, it highlights VANET-based challenges and open research problems to provide the solutions for a safer, more efficient and sustainable future ITS.

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