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.

Smart Traffic Management for Collision Avoidance Using V2V

2021 ◽  
Vol 11 (1) ◽  
pp. 01-07
Author(s):  
V. Naren Thiruvalar ◽  
E. Vimal
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Emergency Situation ◽  
Transportation Systems ◽  
Pollution Monitoring ◽  
Inappropriate Use ◽  
V2v Communication ◽  
Use Of Resources ◽  
Smart Traffic

The main objective of this project is to connect the vehicles together and avoid accidents by using V2V Communication. The vehicles are to be connected together by means of DSRC algorithm which is used for transceiving alert messages among the connected vehicles, in case of any emergency situation such as accidents. The Vehicle-to-Vehicle (V2V) and Vehicle-to- Infrastructure (V2I) technologies are specific cases of IoT and key enablers for Intelligent Transportation Systems (ITS). V2V and V2I have been widely used to solve different problems associated with transportation in cities, in which the most important is traffic congestion. A high percentage of congestion is usually presented by the inappropriate use of resources in vehicular infrastructure. In addition, the integration of traffic congestion in decision making for vehicular traffic is a challenge due to its high dynamic behaviour. An increase in the infrastructure growth is a possible solution but turns out to be costly in terms of both time and effort. Various applications that target transport efficiency could make use of the vast information collected by vehicles: safety, traffic management, pollution monitoring, tourist information, etc.

scholarly journals IoT based Real Time Traffic Monitoring System Using M/G/1 Queuing Research

2019 ◽  
Vol 8 (6) ◽  
pp. 3818-3822
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Queuing Theory ◽  
Traffic Monitoring ◽  
Transportation Systems ◽  
Theory Approach ◽  
The Road ◽  
Traffic Conditions ◽  
Performance Framework ◽  
Real Time Traffic

Vehicular Traffic crowding is paramount worry in urban cities. The use of technologies like Intelligent Transportation systems and Internet of Things can solve the problem of traffic congestion to some extent. The paper analyses the traffic conditions on a particular urban highway using queuing theory approach. It researches on performance framework such as time for waiting and queue length. The results can provide significant analysis to predict traffic congestion during peak hours. A congestion controlling action can be generated to utilize the road capacity fully during peak hours by using these results

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

Road Traffic Congestion (TraCo) Estimation Using Multi-Layer Continuous Virtual Loop (MCVL)

2021 ◽  
Vol 17 (2) ◽  
pp. 46-71
Author(s):  
Manipriya Sankaranarayanan ◽  
Mala C. ◽  
Samson Mathew
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Road Traffic ◽  
Vehicle Detection ◽  
Transportation Systems ◽  
Traffic Information ◽  
Traffic Characteristics ◽  
Traffic Video ◽  
Virtual Loop

Any road traffic management application of intelligent transportation systems (ITS) requires traffic characteristics data such as vehicle density, speed, etc. This paper proposes a robust and novel vehicle detection framework known as multi-layer continuous virtual loop (MCVL) that uses computer vision techniques on road traffic video to estimate traffic characteristics. Estimations of traffic data such as speed, area occupancy and an exclusive spatial feature named as corner detail value (CDV) acquired using MCVL are proposed. Further, the estimation of traffic congestion (TraCo) level using these parameters is also presented. The performances of the entire framework and TraCo estimation are evaluated using several benchmark traffic video datasets and the results are presented. The results show that the improved accuracy in vehicle detection process using MCVL subsequently improves the precision of TraCo estimation. This also means that the proposed framework is well suited to applications that need traffic characteristics to update their traffic information system in real time.

scholarly journals The Study on In-City Capacity Affected by Pedestrian Crossing

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Chang-jiang Zheng ◽  
Rui He ◽  
Xia Wan ◽  
Chen Wang
Keyword(s):  
Traffic Congestion ◽  
Traffic Accident ◽  
Motor Vehicle ◽  
Road Traffic ◽  
The Road ◽  
Simulation Evaluation ◽  
Road Capacity ◽  
Pedestrian Crossing ◽  
Road Traffic Congestion

Currently, the urban road traffic congestion is serious and the traffic accident is happening at a high frequency; thus it has not satisfied the travel needs of security and affects the quality of urban trips. In order to effectively relieve the confliction of people and motor vehicle, to make sure of the safety of pedestrians crossing the road, and to improve the capacity of urban roads, this passage focuses on studying the influence of pedestrians crossing the roads on the capacity of urban roads in three pedestrian crossing approaches including freely crossing the street, uncontrolled crossing of the pedestrian crosswalk, and controlled crossing of the pedestrian crosswalk. Firstly, it confirms the general formula of the road capacity when pedestrians are crossing the road based on three preassumptions, combined with the survey data, and then constructs the empirical mathematical model of pedestrian crossing on the capacity impact. Lastly, it takes the step of case calculation and simulation evaluation and calculates errors between them, finding that the error between the model calculation and software simulation is small. The efficiency of the model is validated and improved.

scholarly journals Does Road Traffic Congestion Increase Fuel Consumption of Households in Kathmandu City?

2021 ◽  
Vol 3 (2) ◽  
pp. 67-79
Author(s):  
Raghu Bista ◽  
Surendra Paneru
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Economic Loss ◽  
Multiple Regression Model ◽  
Urban Life ◽  
Descriptive Statistics ◽  
Additional Time ◽  
The Road ◽  
Urban City ◽  
Road Traffic Congestion

The growth of vehicle and road traffic congestion is characteristics of urbanization and urban city and indicators of urban life in developing countries. In Nepal, non-economic factors and non-state factors have accelerated unexpectedly and haphazardly urbanization process, although the country was reengineered into seven provincial federal structure. In this backdrop, this paper empirically examines the growth of traffic congestion and its impact on urban households and livelihood based on 160 vehicle owners and users’ survey at six major traffic routes of two urban cities by applying mixed analytical methods (qualitative cum quantitative), descriptive statistics and multiple regression model. The descriptive statistics result of the study reveals nearly 94 percent acceptance level of vehicle owners and users about the growth of traffic congestion. Despite short distances of the road i.e. 2-4 kilometers and vehicle efficiency, the growth of traffic congestion increases 14036-liters fuel additional consumption. Per month, additional cost of fuel is estimated at 18,808 US dollars for a sum of distance i.e. 72,992 km between residence location and workplace each month. In the case of commuters, the estimation result of the study is 1188 hours of additional time loss with 6706 US dollars’ worth per month. The estimation of total economic loss is 25514 US dollars per month. Specifically, per month, economic loss of doctors and taxi drivers is 6556 US dollars but teachers and bankers have not economic loss.

scholarly journals A Peak Traffic Congestion Prediction Method Based on Bus Driving Time

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 709 ◽  
Author(s):  
Zhao Huang ◽  
Jizhe Xia ◽  
Fan Li ◽  
Zhen Li ◽  
Qingquan Li
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Short Term Memory ◽  
Road Traffic ◽  
Prediction Method ◽  
Transportation Systems ◽  
Percentage Error ◽  
Driving Time ◽  
Congestion Prediction ◽  
Congestion Index

Road traffic congestion has a large impact on travel. The accurate prediction of traffic congestion has become a hot topic in intelligent transportation systems (ITS). Recently, a variety of traffic congestion prediction methods have been proposed. However, most approaches focus on floating car data, and the prediction accuracy is often unstable due to large fluctuations in floating speed. Targeting these challenges, we propose a method of traffic congestion prediction based on bus driving time (TCP-DT) using long short-term memory (LSTM) technology. Firstly, we collected a total of 66,228 bus driving records from 50 buses for 66 working days in Guangzhou, China. Secondly, the actual and standard bus driving times were calculated by processing the buses’ GPS trajectories and bus station data. Congestion time is defined as the interval between actual and standard driving time. Thirdly, congestion time prediction based on LSTM (T-LSTM) was adopted to predict future bus congestion times. Finally, the congestion index and classification (CI-C) model was used to calculate the congestion indices and classify the level of congestion into five categories according to three classification methods. Our experimental results show that the T-LSTM model can effectively predict the congestion time of six road sections at different time periods, and the average mean absolute percentage error ( M A P E ¯ ) and root mean square error ( R M S E ¯ ) of prediction are 11.25% and 14.91 in the morning peak, and 12.3% and 14.57 in the evening peak, respectively. The TCP-DT method can effectively predict traffic congestion status and provide a driving route with the least congestion time for vehicles.

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