scholarly journals Research and Application of the Beijing Road Traffic Prediction System

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Ruimin Li ◽  
Hongliang Ma ◽  
Huapu Lu ◽  
Min Guo
Keyword(s):  
Traffic Management ◽  
Web Application ◽  
Prediction Models ◽  
Road Traffic ◽  
Traffic Prediction ◽  
Accurate Estimation ◽  
Prediction System ◽  
Traveler Information ◽  
Level Data ◽  
Function Modules

As an important part of the urban Advanced Traffic Management Systems (ATMS) and Advanced Traveler Information Systems (ATIS), short-term road traffic prediction system has received special attention in recent decades. The success of ATMS and ATIS technology deployment is heavily dependent on the availability of timely and accurate estimation or prediction of prevailing and emerging traffic conditions. We studied a real-time road traffic prediction system developed for Beijing based on various traffic detection systems. The logical architecture of the system was presented, including raw data level, data processing and calculation level, and application level. Four key function servers were introduced, namely, the database server, calculation server, Geographic Information System (GIS) server, and web application server. The functions, function modules, and the data flow of the proposed traffic prediction system were analyzed, and subsequently prediction models used in this system are described. Finally, the prediction performance of the system in practice was analyzed. The application of the system in Beijing indicated that the proposed and developed system was feasible, robust, and reliable in practice.

Statistical Evaluation of Interstate 4 Traffic Prediction System

2003 ◽  
Vol 1856 (1) ◽  
pp. 16-24 ◽  
Author(s):  
Sherif Ishak ◽  
Haitham Al-Deek
Keyword(s):  
Traffic Management ◽  
Traffic Prediction ◽  
Model Parameters ◽  
Prediction Errors ◽  
Prediction System ◽  
Traveler Information ◽  
Planning Stage ◽  
Traffic Conditions ◽  
Wide Range ◽  
Prediction Systems

Short-term traffic prediction systems have received considerable attention in the past few years as a means to support advanced traveler information and traffic management systems. Predictive information allows transportation system users to make better trip decisions at the pretrip planning stage and en route. A comprehensive statistical analysis of the traffic prediction system performance implemented on the 40-mi corridor of Interstate 4 in Orlando, Florida, is presented. The system was evaluated under a wide range of traffic conditions and various model parameters. The prediction performance in terms of prediction errors was examined with both link-based and path-based approaches.

Highway Traffic Prediction Model for Transportation and Accident Management System

2020 ◽  
pp. 38-43
Author(s):  
Abdulhaq Abildtrup ◽  
Iben Charlotte Alminde
Keyword(s):  
Road Traffic ◽  
Weighted Average ◽  
Semantic Interpretation ◽  
Traffic Prediction ◽  
Prediction System ◽  
Highway Traffic ◽  
Time Data ◽  
Traffic Condition ◽  
The Public ◽  
Alert Message

In this emerging world, peoples are running behind the time and wasted their time in travelling. Drastic increase in population results in rapid increase of number of vehicles. A semantic based road traffic model is proposed to predict the traffic and to inform the public about the current traffic condition to all persons who belongs to the same lane. Real time data is acquired from Ultrasonic, PIR sensor and camera. Proposed system uses the vehicle count, distance between the vehicles and speed of the vehicle from both sensors and camera and it applies semantic interpretation of those data uses moving weighted average model to predict the traffic condition. To have time efficient prediction, the work is experimented in Apache Spark which will reduce disk latency when compared to Hadoop. Prediction result is sent it as alert message to the public as a location-based messages. So, public will receive message even they don’t have smart phone. Therefore, the traffic prediction system results are more helpful in goods transportation and accident prediction system etc.

scholarly journals Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction

2021 ◽  
Vol 11 (13) ◽  
pp. 6030
Author(s):  
Daljeet Singh ◽  
Antonella B. Francavilla ◽  
Simona Mancini ◽  
Claudio Guarnaccia
Keyword(s):  
Machine Learning ◽  
Linear Models ◽  
Prediction Models ◽  
Road Traffic ◽  
Traffic Noise ◽  
Machine Learning Techniques ◽  
Coefficient Of Determination ◽  
Road Traffic Noise ◽  
Noise Prediction ◽  
Volume Percentage

A vehicular road traffic noise prediction methodology based on machine learning techniques has been presented. The road traffic parameters that have been considered are traffic volume, percentage of heavy vehicles, honking occurrences and the equivalent continuous sound pressure level. Leq A method to include the honking effect in the traffic noise prediction has been illustrated. The techniques that have been used for the prediction of traffic noise are decision trees, random forests, generalized linear models and artificial neural networks. The results obtained by using these methods have been compared on the basis of mean square error, correlation coefficient, coefficient of determination and accuracy. It has been observed that honking is an important parameter and contributes to the overall traffic noise, especially in congested Indian road traffic conditions. The effects of honking noise on the human health cannot be ignored and it should be included as a parameter in the future traffic noise prediction models.

scholarly journals Extracting Road Traffic Volume in the City before and during covid-19 through Video Remote Sensing

2021 ◽  
Vol 13 (12) ◽  
pp. 2329
Author(s):  
Elżbieta Macioszek ◽  
Agata Kurek
Keyword(s):  
Travel Behavior ◽  
Traffic Management ◽  
Road Traffic ◽  
Traffic Volume ◽  
The Road ◽  
Traffic Characteristics ◽  
Annual Average Daily Traffic ◽  
On The Road ◽  
The Impact ◽  
The City

Continuous, automatic measurements of road traffic volume allow the obtaining of information on daily, weekly or seasonal fluctuations in road traffic volume. They are the basis for calculating the annual average daily traffic volume, obtaining information about the relevant traffic volume, or calculating indicators for converting traffic volume from short-term measurements to average daily traffic volume. The covid-19 pandemic has contributed to extensive social and economic anomalies worldwide. In addition to the health consequences, the impact on travel behavior on the transport network was also sudden, extensive, and unpredictable. Changes in the transport behavior resulted in different values of traffic volume on the road and street network than before. The article presents road traffic volume analysis in the city before and during the restrictions related to covid-19. Selected traffic characteristics were compared for 2019 and 2020. This analysis made it possible to characterize the daily, weekly and annual variability of traffic volume in 2019 and 2020. Moreover, the article attempts to estimate daily traffic patterns at particular stages of the pandemic. These types of patterns were also constructed for the weeks in 2019 corresponding to these stages of the pandemic. Daily traffic volume distributions in 2020 were compared with the corresponding ones in 2019. The obtained results may be useful in terms of planning operational and strategic activities in the field of traffic management in the city and management in subsequent stages of a pandemic or subsequent pandemics.

scholarly journals Road Traffic Congestion Management Based on a Search-Allocation Approach

2017 ◽  
Vol 18 (1) ◽  
pp. 25-33 ◽  
Author(s):  
Jamal Raiyn
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Road Traffic ◽  
Smart Cities ◽  
Self Regulation ◽  
Congestion Management ◽  
Shortest Route ◽  
Road Traffic Congestion

Abstract This paper introduces a new scheme for road traffic management in smart cities, aimed at reducing road traffic congestion. The scheme is based on a combination of searching, updating, and allocation techniques (SUA). An SUA approach is proposed to reduce the processing time for forecasting the conditions of all road sections in real-time, which is typically considerable and complex. It searches for the shortest route based on historical observations, then computes travel time forecasts based on vehicular location in real-time. Using updated information, which includes travel time forecasts and accident forecasts, the vehicle is allocated the appropriate section. The novelty of the SUA scheme lies in its updating of vehicles in every time to reduce traffic congestion. Furthermore, the SUA approach supports autonomy and management by self-regulation, which recommends its use in smart cities that support internet of things (IoT) technologies.

ORGANIZATION OF ROAD TRAFFIC TAKING INTO ACCOUNT ELECTRIC ROUTE PASSENGER TRANSPORT

2021 ◽  
Vol 2 (65) ◽  
pp. 66-77
Author(s):  
Denis V. Kapsky ◽  
◽  
Sergey S. Semchenkov ◽  
Evgeny N. Kot ◽  
◽  
...  
Keyword(s):  
Traffic Management ◽  
Road Traffic ◽  
Rapid Development ◽  
Living Environment ◽  
Transport Sector ◽  
Passenger Transport ◽  
Wide Range ◽  
Practical Development ◽  
The Republic ◽  
Conducting Research

The rapid development of the transport sector leads not only to positive changes in the life of cities and towns, to an increase in convenience and comfort for residents, but also worsens the ecology and their living environment. The “profitable-safe” dilemma can be solved by the approach of environmentally oriented selection of the type of route passenger transport and decisionmaking in favor of electric route passenger transport. The article discusses the types of such transport on the example of the experience of their use in the Republic of Belarus. The classification and systematization developed by the authors are presented with subsequent recommendations for its application. On the basis of the results of the authors’ research, the issues of interaction of rail and non-rail route vehicles with an electric drive with the organization of road traffic are separately considered. The presented materials can be useful to a wide range of readers, as well as to specialists conducting research and practical development in the field of electrically powered route vehicles and traffic management.

Towards Intelligent Road Traffic Management over Weighted Large Graphs Hybrid Meta-heuristic-Based Approach

2022 ◽  
Vol 24 (3) ◽  
pp. 0-0
Keyword(s):  
Traffic Management ◽  
Large Scale ◽  
Road Traffic ◽  
Optimization Technique ◽  
Shortest Paths ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Computation Time ◽  
Exact Algorithm ◽  
On The Road

This paper introduces a new approach of hybrid meta-heuristics based optimization technique for decreasing the computation time of the shortest paths algorithm. The problem of finding the shortest paths is a combinatorial optimization problem which has been well studied from various fields. The number of vehicles on the road has increased incredibly. Therefore, traffic management has become a major problem. We study the traffic network in large scale routing problems as a field of application. The meta-heuristic we propose introduces new hybrid genetic algorithm named IOGA. The problem consists of finding the k optimal paths that minimizes a metric such as distance, time, etc. Testing was performed using an exact algorithm and meta-heuristic algorithm on random generated network instances. Experimental analyses demonstrate the efficiency of our proposed approach in terms of runtime and quality of the result. Empirical results obtained show that the proposed algorithm outperforms some of the existing technique in term of the optimal solution in every generation.

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