scholarly journals Development of Rewarding System for Solving Traffic Congestion in Saudi Arabia

TEM Journal ◽  
2020 ◽  
pp. 951-958
Author(s):  
Fatmah Yousef Assiri
Keyword(s):  
Air Pollution ◽  
Saudi Arabia ◽  
Travel Time ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Recommendation System ◽  
Historical Data ◽  
Traffic Information ◽  
Departure Time

Traffic congestion impacts economics, health, and productivity. Traffic congestion is increasing in the urban areas and negatively impacts people in these areas due to air pollution, accidents, delays in travel time, and more. In Saudi Arabia, more than 500,000 people have died or been injured because of traffic. To improve this problem, we developed a rewarding system that encourages people to drive during off-peak hours. In addition, the system provides recommendations for a preferred departure time in order to avoid traffic congestion. Recommended departure time is based on historical data. This system will be used to create dataset of drivers and traffic information in order to build an intelligent recommendation system

scholarly journals Overview of Intelligent Transport System (ITS) and Its Applications

2021 ◽  
Author(s):  
Purnendu S M Tripathi ◽  
Ambuj Kumar ◽  
Ashok Chandra
Keyword(s):  
Air Pollution ◽  
Radio Frequency ◽  
Transport System ◽  
Urban Areas ◽  
Road Traffic ◽  
Traffic Information ◽  
Intelligent Transport System ◽  
Comprehensive Overview ◽  
Frequency Bands ◽  
Intelligent Transport

Since last decades world, predominantly urban areas, is experiencing huge voluminous road traffic growth, resulting in heavy congestion, air pollution, accidents, and poor efficiency.  Many people every day are the victims of this poor management of tremendous traffic. Since many years, there had been some automation in managing the traffic namely Electronic Toll Collection (ETC), Electronic parking payment, normal traffic information etc. However, there are little efforts for making the system more advanced. Recently, several kinds of research are being launched by many countries to develop Intelligent Transport System (ITS), with the objectives to minimize congestion, ensure better safety, reduce air pollution etc. ITS are planned to establish robust communication between vehicle to vehicle (V2V), vehicle to pedestrian (V2P), vehicle to infrastructure (V2I), and vehicle to network (V2N). Initially, for communication links ITS, deploys Wi-Fi network, but because of limited capacity and huge requirement, some links use 5.8 GHz radio frequency for such purposes. IEEE, International Telecommunications Union (ITU) and other advanced research organisations are studying 700 MHz band and mm frequency bands for advanced ITS. ITS is poised to use Information & Communication Technology (ICT) networks for such purposes. ITU has established Study Groups/study questions for addressing ITS issues. The World Radio Conference (WRC-2019) has made a Recommendation 208 regarding harmonization of frequency bands for ITS applications. This paper presents a comprehensive overview of ITS, its applications and analysis etc. The radio frequency spectrum aspects and role of 5 G in ITS are also described in detail.  

scholarly journals Effect of Traffic Information on Travel Time of Medium-distance Trips: A Case Study in Tehran

2018 ◽  
Vol 30 (3) ◽  
pp. 281-291 ◽  
Author(s):  
Roozbeh Mohammadi ◽  
Amir Golroo ◽  
Mahdieh Hasani
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Traffic Congestion ◽  
Information Dissemination ◽  
Traffic Information ◽  
Real Time Traffic ◽  
Trip Distance ◽  
Traffic Information Dissemination ◽  
Time Reduction

In populated cities with high traffic congestion, traffic information may play a key role in choosing the fastest route between origins and destinations, thus saving travel time. Several research studies investigated the effect of traffic information on travel time. However, little attention has been given to the effect of traffic information on travel time according to trip distance. This paper aims to investigate the relation between real-time traffic information dissemination and travel time reduction for medium-distance trips. To examine this relation, a methodology is applied to compare travel times of two types of vehicle, with and without traffic information, travelling between an origin and a destination employing probe vehicles. A real case study in the metropolitan city of Tehran, the capital of Iran, is applied to test the methodology. There is no significant statistical evidence to prove that traffic information would have a significant impact on travel time reduction in a medium-distance trip according to the case study.

scholarly journals Modeling Departure Time Choice of Car Commuters in Dhaka, Bangladesh

2021 ◽  
pp. 036119812110398
Author(s):  
Khatun Zannat ◽  
Charisma Farheen Choudhury ◽  
Stephane Hess
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
Logit Model ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Data Sources ◽  
Similar Data ◽  
Schedule Delay ◽  
Departure Time ◽  
Departure Time Choice

Dhaka, one of the fastest-growing megacities in the world, faces severe traffic congestion leading to a loss of 3.2 million business hours per day. While peak-spreading policies hold the promise to reduce the traffic congestion levels, the absence of comprehensive data sources makes it extremely challenging to develop econometric models of departure time choices for Dhaka. This motivates this paper, which develops advanced discrete choice models of departure time choice of car commuters using secondary data sources and quantifies how level-of-service attributes (e.g., travel time), socio-demographic characteristics (e.g., type of job, income, etc.), and situational constraints (e.g., schedule delay) affect their choices. The trip diary data of commuters making home-to-work and work-to-home trips by personal car/ride-hailing services (957 and 934 respectively) have been used in this regard. Given the discrepancy between the stated travel times and those extracted using the Google Directions API, a sub-model is developed first to derive more reliable estimates of travel time throughout the day. A mixed multinomial logit model and a simple multinomial logit model are developed for outbound and return trip, respectively, to capture the heterogeneity associated with different departure time choice of car commuters. Estimation results indicate that the choices are significantly affected by travel time, schedule delay, and socio-demographic factors. The influence of type of job on preferred departure time (PDT) has been estimated using two different distributions of PDT for office employees and self-employed people (Johnson’s SB distribution and truncated normal respectively). The proposed framework could be useful in other developing countries with similar data issues.

scholarly journals Density-Based Statistical Clustering: Enabling Sidefire Ultrasonic Traffic Sensing in Smart Cities

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Volker Lücken ◽  
Nils Voss ◽  
Julien Schreier ◽  
Thomas Baag ◽  
Michael Gehring ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Smart Cities ◽  
Integrated Approach ◽  
Traffic Information ◽  
Limiting Factor ◽  
Personal Mobility ◽  
Real Time Traffic ◽  
Ultrasonic Sensing

Traffic routing is a central challenge in the context of urban areas, with a direct impact on personal mobility, traffic congestion, and air pollution. In the last decade, the possibilities for traffic flow control have improved together with the corresponding management systems. However, the lack of real-time traffic flow information with a city-wide coverage is a major limiting factor for an optimum operation. Smart City concepts seek to tackle these challenges in the future by combining sensing, communications, distributed information, and actuation. This paper presents an integrated approach that combines smart street lamps with traffic sensing technology. More specifically, infrastructure-based ultrasonic sensors, which are deployed together with a street light system, are used for multilane traffic participant detection and classification. Application of these sensors in time-varying reflective environments posed an unresolved problem for many ultrasonic sensing solutions in the past and therefore widely limited the dissemination of this technology. We present a solution using an algorithmic approach that combines statistical standardization with clustering techniques from the field of unsupervised learning. By using a multilevel communication concept, centralized and decentralized traffic information fusion is possible. The evaluation is based on results from automotive test track measurements and several European real-world installations.

scholarly journals Preliminary Results of Air Pollution Status in Selected Roadsides in Jalingo, Taraba State Nigeria

2021 ◽  
Vol 5 (3) ◽  
pp. 118-123
Author(s):  
Maxwell Obia Kanu ◽  
Terkaa Victor Targema ◽  
Gideon Maumee Abednego
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Ambient Air ◽  
Acceptable Limit ◽  
Pollution Levels ◽  
The Mean ◽  
Carbon Dioxide Co2 ◽  
Mean Concentration

The rapid increase in vehicular activities in the past two centuries contributes vastly to air pollution levels. In as much as Social interactions and economic growth are well enhanced by vehicular transportation in many developing countries, it is unfortunate that exhausts from vehicles contribute immensely to ambient air quality especially in the urban areas. The concentrations of carbon monoxides (CO) and carbon dioxide (CO2) emissions in selected roadsides in Jalingo have been assessed. Four roads were used as sample locations where the concentration of CO2 and CO were measured using an air quality meter for four weeks. The mean concentration of CO2 and CO obtained were respectively as follows: 542.25 ppm and 7.49 ppm for the roadblock, 540.05 ppm and 5.55 ppm for Hammaruwa way, 598.81 ppm and 17.42 ppm for market road, and 463.80 ppm and 1.08 ppm for Nigerian Labour Congress (NLC) road (control). Based on the acceptable limit of CO2 (600 ppm), the Roadblock road, Hammaruwa way, and the NLC/control road are safe. Only the market road had value that exceeded the acceptable limit, and it may be attributed to high vehicular activities on the roadsides. Therefore, more alternative roads should be constructed in other to minimize traffic congestion and also, the use of nose masks should be encouraged. For the CO, all the sites are safe because they fall within the acceptable level of CO (1-70 ppm).

Departure and travel time model for the temporal distribution of morning rush-hour traffic congestion

2019 ◽  
Vol 31 (02) ◽  
pp. 2050023
Author(s):  
Sida Luo
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
Temporal Distribution ◽  
Time Estimation ◽  
Wave Model ◽  
Model Parameters ◽  
Travel Time Estimation ◽  
Time Model ◽  
Departure Time ◽  
Departure Time Choice

The chronic traffic congestion undermines the level of satisfaction within a society. This study proposes a departure time model for estimating the temporal distribution of morning rush-hour traffic congestion over urban road networks. The departure time model is developed based on the point queue model that is used for estimating travel time. First, we prove the effectiveness of the travel time model (i.e. point queue), showing that it gives the same travel time estimation as the kinematic wave model does for a road with successive bottlenecks. Then, a variant of the bottleneck model is developed accordingly, aiming to capture travelers’ departure time choice for commute trips. The proposed departure time model relaxes a traditional assumption that the last commuter experiences the free flow travel time and considers travelers’ unwillingness of late arrivals for work. Numerical experiments show that the morning rush-hour generally starts at 7:29 am and ends at 8:46 am with a traffic congestion delay index (TCDI) of 2.164 for Beijing, China. Furthermore, the estimation of rush-hour start and end time is insensitive to most model parameters including the proportion of travelers who tend to arrive at work earlier than their schedules.

scholarly journals Scalable Data Model for Traffic Congestion Avoidance in a Vehicle to Cloud Infrastructure

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5074
Author(s):  
Ioan Stan ◽  
Vasile Suciu ◽  
Rodica Potolea
Keyword(s):  
Data Structures ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Negative Impact ◽  
Range Query ◽  
Data Representation ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Traffic Information ◽  
Cloud Infrastructure

Traffic congestion experience in urban areas has negative impact on our daily lives by consuming our time and resources. Intelligent Transportation Systems can provide the necessary infrastructure to mitigate such challenges. In this paper, we propose a novel and scalable solution to model, store and control traffic data based on range query data structures (K-ary Interval Tree and K-ary Entry Point Tree) which allows data representation and handling in a way that better predicts and avoids traffic congestion in urban areas. Our experiments, validation scenarios, performance measurements and solution assessment were done on Brooklyn, New York traffic congestion simulation scenario and shown the validity, reliability, performance and scalability of the proposed solution in terms of time spent in traffic, run-time and memory usage. The experiments on the proposed data structures simulated up to 10,000 vehicles having microseconds time to access traffic information and below 1.5 s for congestion free route generation in complex scenarios. To the best of our knowledge, this is the first scalable approach that can be used to predict urban traffic and avoid congestion through range query data structure traffic modelling.

scholarly journals Air-pollution prediction in smart city, deep learning approach

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Abdellatif Bekkar ◽  
Badr Hssina ◽  
Samira Douzi ◽  
Khadija Douzi
Keyword(s):  
Air Pollution ◽  
Deep Learning ◽  
Urban Areas ◽  
Historical Data ◽  
Meteorological Data ◽  
Predictive Performance ◽  
The Past ◽  
Life Threatening ◽  
The Difference ◽  
Beijing China

AbstractOver the past few decades, due to human activities, industrialization, and urbanization, air pollution has become a life-threatening factor in many countries around the world. Among air pollutants, Particulate Matter with a diameter of less than $$2.5 \mu m$$ 2.5 μ m ($$PM_{2.5}$$ P M 2.5 ) is a serious health problem. It causes various illnesses such as respiratory tract and cardiovascular diseases. Hence, it is necessary to accurately predict the $$PM_{2.5}$$ P M 2.5 concentrations in order to prevent the citizens from the dangerous impact of air pollution beforehand. The variation of $$PM_{2.5}$$ P M 2.5 depends on a variety of factors, such as meteorology and the concentration of other pollutants in urban areas. In this paper, we implemented a deep learning solution to predict the hourly forecast of $$PM_{2.5}$$ P M 2.5 concentration in Beijing, China, based on CNN-LSTM, with a spatial-temporal feature by combining historical data of pollutants, meteorological data, and $$PM_{2.5}$$ P M 2.5 concentration in the adjacent stations. We examined the difference in performances among Deep learning algorithms such as LSTM, Bi-LSTM, GRU, Bi-GRU, CNN, and a hybrid CNN-LSTM model. Experimental results indicate that our method “hybrid CNN-LSTM multivariate” enables more accurate predictions than all the listed traditional models and performs better in predictive performance.

scholarly journals Congestion-Preventing Routing and Charging Scheduling Mechanism for Electric Vehicles in Dense Urban Areas

2021 ◽  
Vol 50 (2) ◽  
pp. 284-307
Author(s):  
Chenn-Jung Huang ◽  
Kai-Wen Hu ◽  
Hsing-Yi Ho ◽  
Hung-Wen Chuang
Keyword(s):  
Electric Vehicles ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Road Traffic ◽  
Research Work ◽  
Critical Issue ◽  
Traffic Information ◽  
Economic Losses ◽  
Planning Strategy ◽  
Traffic Regulation

Traffic congestion in metropolitan areas all over the world has become a critical issue that governments mustdeal with effectively. Traffic congestion during rush hours causes vehicle drivers to arrive late at their destinations,resulting in significant economic losses. Although researchers have proposed solutions to the traffic congestionproblem, little research work has presented a joint route and charging planning strategy for electric vehicles(EVs) that alleviates traffic congestion problems simultaneously. Accordingly, a congestion-preventing route and charging planning mechanism for EVs is proposed in this work to tackle the complicated route and charging optimizationproblems of EVs. The route and charging planning proposed in this work analyzes the information providedby EVs, the charging points, and road traffic information simultaneously, and mediates the traffic jammingby means of a route and charging reservation mechanism. Possible occurrence of traffic congestion is detectedin advance and traffic regulation is carried out by allocating an elastic range to the traveling period for late-bookingEVs, to avoid moving during rush hours. EV owners are also encouraged to provide rideshare services forlate-booking EV users during rush hours. The simulation results reveal that the proposed work can satisfy thepreferred route and charging demands of EV users and alleviate traffic congestion effectively.

scholarly journals Evaluation of effective factors on air pollution using optimized cellular automata: A case study of Tehran

2021 ◽  
Author(s):  
Alireza Hamoudzadeh ◽  
◽  
Saeed Behzadi ◽  
Keyword(s):  
Air Pollution ◽  
Cellular Automata ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Urban Air Pollution ◽  
Urban Pollution ◽  
Air Pollutant ◽  
Great Promise ◽  
And Diffusion

Vehicles and traffic congestion have been known as the main reasons for air pollution in urban areas, and Cellular Automata (CA) holds a great promise for predicting this hazard. Urban air pollution is a complex phenomenon and many factors involve in its distribution and diffusion. In this paper, the traffic map was used as the source of the air pollutant. Also, the prediction of urban pollution has been done using different data sources such as green space, buildings, wind direction and speed. The coefficient of these factors got estimated with Genetic Algorithm, and a comparison between different modes of the model got done. With considering the effect of these factors an accuracy of 58.4% was obtained.

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