scholarly journals Detection of Road Traffic Anomalies Based on Computational Data Science

Author(s):  
Jamal Raiyn

Abstract The development of 5G has enabled the autonomous vehicles (AVs) to have full control over all functions. The AV acts autonomously and collects travel data based on various smart devices and sensors, with the goal of enabling it to operate under its own power. However, the collected data is affected by several sources that degrade the forecasting accuracy. To manage large amounts of traffic data in different formats, a computational data science approach (CDS) is proposed. The computational data science scheme introduced to detect anomalies in traffic data that negatively affect traffic efficiency. The combination of data science and advanced artificial intelligence techniques, such as deep leaning provides higher degree of data anomalies detection which leads to reduce traffic congestion and vehicular queuing. The main contribution of the CDS approach is summarized in detection of the factors that caused data anomalies early to avoid long- term traffic congestions. Moreover, CDS indicated a promoting results in various road traffic scenarios.

2021 ◽  
Vol 13 (23) ◽  
pp. 13068
Author(s):  
Akbar Ali ◽  
Nasir Ayub ◽  
Muhammad Shiraz ◽  
Niamat Ullah ◽  
Abdullah Gani ◽  
...  

The population is increasing rapidly, due to which the number of vehicles has increased, but the transportation system has not yet developed as development occurred in technologies. Currently, the lowest capacity and old infrastructure of roads do not support the amount of vehicles flow which cause traffic congestion. The purpose of this survey is to present the literature and propose such a realistic traffic efficiency model to collect vehicular traffic data without roadside sensor deployment and manage traffic dynamically. Today’s urban traffic congestion is one of the core problems to be solved by such a traffic management scheme. Due to traffic congestion, static control systems may stop emergency vehicles during congestion. In daily routine, there are two-time slots in which the traffic is at peak level, which causes traffic congestion to occur in an urban transportation environment. Traffic congestion mostly occurs in peak hours from 8 a.m. to 10 a.m. when people go to offices and students go to educational institutes and when they come back home from 4 p.m. to 8 p.m. The main purpose of this survey is to provide a taxonomy of different traffic management schemes for avoiding traffic congestion. The available literature categorized and classified traffic congestion in urban areas by devising a taxonomy based on the model type, sensor technology, data gathering techniques, selected road infrastructure, traffic flow model, and result verification approaches. Consider the existing urban traffic management schemes to avoid congestion and to provide an alternate path, and lay the foundation for further research based on the IoT using a Mobile crowd sensing-based traffic congestion control model. Mobile crowdsensing has attracted increasing attention in traffic prediction. In mobile crowdsensing, the vehicular traffic data are collected at a very low cost without any special sensor network infrastructure deployment. Mobile crowdsensing is very popular because it can transmit information faster, collect vehicle traffic data at a very low cost by using motorists’ smartphone or GPS vehicular embedded sensor, and it is easy to install, requires no special network deployment, has less maintenance, is compact, and is cheaper compared to other network options.


This research presents the logistics management information system (LMIS) for the supply chain of lychee products of Phayao Province, Thailand. The main aim of this research is to develop a management application for Phayao’s agricultures to improve their competitive abilities on Chinese markets by utilizing a prediction method for traffic congestion based on both real-time and anticipated road traffic. The loss of productivity caused by traffic congestion has become a huge and increasingly heavy burden on Phayao farmers. Therefore, the prediction of urban road network traffic flow and the rapid and accurate evaluation of traffic congestion is of great significance to solve this problem. By using traffic data obtained by distance, road conditions, transportation safety, traffic density, and customs clearance, the local farmers in Phayao can deliver lychee products on time and reduce the loss of high emissions and environmental pollution caused by traffic congestion effectively.


2021 ◽  
Vol 7 (5) ◽  
pp. 4672-4681
Author(s):  
Shuai Lai ◽  
Jinfeng Wu

Objectives: The transportation problem of Linjiao transit passage in Lhasa City from the perspective of traffic sociology is studied. Methods: Firstly, the research history and current situation of experts in the field of traffic congestion prediction are studied. The common parameters and models of congestion prediction are analyzed. Results: Combined with the complexity of road traffic structure and the possession of a large number of high-dimensional traffic data records, the use of a prediction model is finally determined based on RNN-RBM deep learning network. Through the research and analysis of all-day road traffic flow data, accurate judgment and prediction of traffic congestion status are made. Conclusion: In this paper, the role of the RNN model on the time axis and the state judgment of the RBM network are used to predict the traffic congestion based on the characterization of the congestion sequence.


2021 ◽  
Vol 22 (9) ◽  
pp. 1247-1259
Author(s):  
Iftikhar Ahmad ◽  
Rafidah Md Noor ◽  
Zaheed Ahmed ◽  
Umm-e-Habiba ◽  
Naveed Akram ◽  
...  

AbstractHeterogeneous vehicular clustering integrates multiple types of communication networks to work efficiently for various vehicular applications. One popular form of heterogeneous network is the integration of long-term evolution (LTE) and dedicated short-range communication. The heterogeneity of such a network infrastructure and the non-cooperation involved in sharing cost/data are potential problems to solve. A vehicular clustering framework is one solution to these problems, but the framework should be formally verified and validated before being deployed in the real world. To solve these issues, first, we present a heterogeneous framework, named destination and interest-aware clustering, for vehicular clustering that integrates vehicular ad hoc networks with the LTE network for improving road traffic efficiency. Then, we specify a model system of the proposed framework. The model is formally verified to evaluate its performance at the functional level using a model checking technique. To evaluate the performance of the proposed framework at the micro-level, a heterogeneous simulation environment is created by integrating state-of-the-art tools. The comparison of the simulation results with those of other known approaches shows that our proposed framework performs better.


2018 ◽  
pp. 1758-1772
Author(s):  
Aditya R. Raikwar ◽  
Rahul R. Sadawarte ◽  
Rishikesh G. More ◽  
Rutuja S. Gunjal ◽  
Parikshit N. Mahalle ◽  
...  

The need of faster life has caused the exponential growth in No. of vehicles on streets. The adverse effects include frequent traffic congestion, less time efficiency, unnecessary fuel consumption, pollution, accidents, etc. One of most important solution for resolving these problems is efficient transportation management system. Data science introduces different techniques and tools for overcoming these problems and to improve the data quality and forecasting inferences. The proposed long-term forecasting model can predict numerical values of effective attributes for a particular day on half-hourly basis, at least 24 hours prior to the time of prediction. The proposed forecasting model for short-term analysis will be having access to data as close as 30-minute difference from the time of prediction. Our proposed solution has integrated use of Holt-Winters (HW) method along with comparability schemes for seasonal approach.


2018 ◽  
Vol 220 ◽  
pp. 02004 ◽  
Author(s):  
Anton Agafonov ◽  
Aleksandr Borodinov

Autonomous vehicle development is one of many trends that will affect future transport demands and planning needs. Autonomous vehicles management in the context of an intelligent transportation system could significantly reduce the traffic congestion level and decrease the overall travel time in a network. In this work, we investigate a route reservation architecture to manage road traffic within an urban area. The routing architecture decomposes road segments into time and spatial slots and for every vehicle, it makes the reservation of the appropriate slots on the road segments in the selected route. This approach allows to predict the traffic in the network and to find the shortest path more precisely. We propose to use a rerouting procedure to improve the quality of the routing approach. Experimental study of the routing architecture is conducted using microscopic traffic simulation in SUMO package.


2021 ◽  
pp. 2150257
Author(s):  
Liang Chen ◽  
Yun Zhang ◽  
Kun Li ◽  
Qiaoru Li ◽  
Qiang Zheng

The connected and automated vehicle (CAV) is regarded as an effective way to improve traffic efficiency and safety, which can utilize vehicle-to-vehicle (V2V) communication technology to obtain real-time status information from multiple preceding vehicles. In view of the car-following characteristic of CAV in a V2V communications environment, an extended car-following model AHT-FVD is proposed which takes both average headway and electronic throttle angle difference into account. The stability of this model is examined via linear stability analysis. It is found that the proposed model has a larger stability region than both the full velocity difference (FVD) model and throttle-based FVD (T-FVD) model. Namely, this AHT-FVD model can effectively stabilize traffic flow and alleviate traffic congestion in theory. Moreover, a series of numerical simulations are carried out to explore how average headway together with electronic throttle angle difference influences the stability of traffic flow. Simulation results show that increasing either the average headway weight or the electronic throttle angle difference control signal coefficients can yield higher traffic flow stability. Simulation result is highly consistent with theoretical analysis.


2020 ◽  
Vol 4 (1) ◽  
pp. 24-27
Author(s):  
Ahmad Fadzli Abd Aziz

With a gradual increase in urban road traffic volume and traffic congestion degree, how to improve and solve the current traffic pressures has been a problem requiring urgent solution. To improve the traffic congestion status in urban road intersections and heighten the road traffic efficiency, the principle of fuzzy control is employed in this paper. Moreover, the multi-phase signal traffic control of intersections is performed together with vehicle queue lengths in lanes corresponding to the key traffic flow, and a traffic signal fuzzy control system is designed. Finally, this paper compares the simulation results between the fuzzy control system designed herein and the existing fixed traffic signal control methods. The comparative test results have shown that the fuzzy control method can well better actual congestion and traffic efficiency at intersections to a greater degree than the fixed timing method does.


2017 ◽  
Vol 8 (2) ◽  
pp. 38-50 ◽  
Author(s):  
Aditya R. Raikwar ◽  
Rahul R. Sadawarte ◽  
Rishikesh G. More ◽  
Rutuja S. Gunjal ◽  
Parikshit N. Mahalle ◽  
...  

The need of faster life has caused the exponential growth in No. of vehicles on streets. The adverse effects include frequent traffic congestion, less time efficiency, unnecessary fuel consumption, pollution, accidents, etc. One of most important solution for resolving these problems is efficient transportation management system. Data science introduces different techniques and tools for overcoming these problems and to improve the data quality and forecasting inferences. The proposed long-term forecasting model can predict numerical values of effective attributes for a particular day on half-hourly basis, at least 24 hours prior to the time of prediction. The proposed forecasting model for short-term analysis will be having access to data as close as 30-minute difference from the time of prediction. Our proposed solution has integrated use of Holt-Winters (HW) method along with comparability schemes for seasonal approach.


Urban Science ◽  
2018 ◽  
Vol 2 (2) ◽  
pp. 33 ◽  
Author(s):  
David Metz

An important problem for surface transport is road traffic congestion, which is ubiquitous and difficult to mitigate. Accordingly, a question for policymakers is the possible impact on congestion of autonomous vehicles. It seems likely that the main impact of vehicle automation will not be seen until driverless vehicles are sufficiently safe for use amid general traffic on urban streets. Shared use driverless vehicles could reduce the cost of taxis and a wider range of public transport vehicles could be economic. Individually owned autonomous vehicles would have the ability to travel unoccupied and may need to be regulated where this might add to congestion. It is possible that autonomous vehicles could provide mobility services at lower cost and wider scope, such that private car use in urban areas could decline and congestion reduce. City authorities should be alert to these possibilities in developing transport policy.


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