scholarly journals Travel Time Prediction on Un-Monitored Roads: A Spatial Factorization Machine Based Approach (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13855-13856 ◽  
Author(s):  
Lile Li ◽  
Wei Liu
Keyword(s):  
Traffic Congestion ◽  
Route Planning ◽  
Traffic Monitoring ◽  
Time Of Arrival ◽  
Traffic Data ◽  
Travel Time Prediction ◽  
Large Cities ◽  
Time Prediction ◽  
Real Time Traffic ◽  
Factorization Machine

Real-time traffic monitoring is one of the most important factors for route planning and estimated time of arrival (ETA). Many major roads in large cities are installed with live traffic monitoring systems, inferring the current traffic congestion status and ETAs to other locations. However, there are also many other roads, especially small roads and paths, that are not monitored. Yet, live traffic status on such un-monitored small roads can play a non-negligible role in personalized route planning and re-routing when road incident happens. How to estimate the traffic status on such un-monitored roads is thus a valuable problem to be addressed. In this paper, we propose a model called Spatial Factorization Machines (SFM) to address this problem. A major advantage of the SFM model is that it incorporates physical distances and structures of road networks into the estimation of traffic status on un-monitored roads. Our experiments on real world traffic data demonstrate that the SFM model significantly outperforms other existing models on ETA of un-monitored roads.

scholarly journals A Freeway Travel Time Prediction Method Based on An XGBoost Model

2021 ◽  
Vol 13 (15) ◽  
pp. 8577
Author(s):  
Zhen Chen ◽  
Wei Fan
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
Model Performance ◽  
Route Planning ◽  
Prediction Method ◽  
Gradient Boosting ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Vehicle Data ◽  
Comparison Results

Travel time prediction plays a significant role in the traffic data analysis field as it helps in route planning and reducing traffic congestion. In this study, an XGBoost model is employed to predict freeway travel time using probe vehicle data. The effects of different parameters on model performance are investigated and discussed. The optimized model outputs are then compared with another well-known model (i.e., Gradient Boosting model). The comparison results indicate that the XGBoost model has considerable advantages in terms of both prediction accuracy and efficiency. The developed model and analysis results can greatly help the decision makers plan, operate, and manage a more efficient highway system.

Travel Time Prediction of Road Network Based on Multi-Source Data Fusion

2012 ◽  
Vol 490-495 ◽  
pp. 850-854
Author(s):  
Wen Ting Liu
Keyword(s):  
Data Fusion ◽  
Travel Time ◽  
Road Network ◽  
Traffic Data ◽  
Traffic Patterns ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Real Time Traffic ◽  
Source Data ◽  
Two Phases

This paper is concerned with the task of travel time pre-diction of urban roadway. For improving the travel time predication ac-curacy, a travel time predication model based multi-source data fusion is proposed. The prediction procedure is divided into two phases, the estimation phase and the prediction phase The method is combined the historical traffic patterns with real-time traffic data as a linear. The resulting model is tested with realistic traffic data, and is found to perform well.

scholarly journals Traffic congestion prediction based on Estimated Time of Arrival

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0238200
Author(s):  
Noureen Zafar ◽  
Irfan Ul Haq
Keyword(s):  
Traffic Congestion ◽  
Rapid Expansion ◽  
Time Of Arrival ◽  
Random Forest Classification ◽  
Traffic Forecasting ◽  
Real Time Traffic ◽  
Forest Classification ◽  
Time Period ◽  
Congestion Prediction ◽  
Google Map Api

With the rapid expansion of sensor technologies and wireless network infrastructure, research and development of traffic associated applications, such as real-time traffic maps, on-demand travel route reference and traffic forecasting are gaining much more attention than ever before. In this paper, we elaborate on our traffic prediction application, which is based on traffic data collected through Google Map API. Our application is a desktop-based application that predicts traffic congestion state using Estimated Time of Arrival (ETA). In addition to ETA, the prediction system takes into account various features such as weather, time period, special conditions, holidays, etc. The label of the classifier is identified as one of the five traffic states i.e. smooth, slightly congested, congested, highly congested or blockage. The results demonstrate that the random forest classification algorithm has the highest prediction accuracy of 92 percent followed by XGBoost and KNN respectively.

Managing Spatial Graph Dependencies in Large Volumes of Traffic Data for Travel-Time Prediction

2016 ◽  
Vol 17 (6) ◽  
pp. 1678-1687 ◽  
Author(s):  
Athanasios Salamanis ◽  
Dionysios D. Kehagias ◽  
Christos K. Filelis-Papadopoulos ◽  
Dimitrios Tzovaras ◽  
George A. Gravvanis
Keyword(s):  
Travel Time ◽  
Traffic Data ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Spatial Graph

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

Open Threads-Enabled Mesh Networks in Vehicles for Real-Time Traffic Monitoring

2020 ◽  
pp. 120-143
Author(s):  
Parul Choudhary ◽  
Rakesh kumar Dwivedi ◽  
Umang Singh
Keyword(s):  
Traffic Congestion ◽  
Communication Systems ◽  
Mesh Networks ◽  
Traffic Monitoring ◽  
Mesh Network ◽  
Real Time Traffic ◽  
Innovative Solutions ◽  
Efficient Communication ◽  
Adaptive Solution ◽  
Inter Communication

The exponential increase of traffic on roads has led to numerous disastrous consequences. These issues demand an adaptive solution that ensures road safety and decreases the traffic congestion on roads. New paradigms such as Cloud computing and internet of things are aiding in achievement of the inter-communication among the vehicles on road. VANETs are designed to provide effective and efficient communication systems to develop innovative solutions but are restricted due to mobility constraints. This chapter proposes an IP-based novel framework composed of open threads integrated with VANETs exchanging information to create a mesh network among vehicles. This novel Open Threads-based infrastructure can help in achieving a more economical, efficient, safer, and sustainable world of transportation which is safer and greener. This chapter also discusses and compares various thread-enabled microcontrollers by different vendors that can be utilized to create a mesh network.

scholarly journals Traffic Management as a Service: The Traffic Flow Pattern Classification Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Carlos T. Calafate ◽  
David Soler ◽  
Juan-Carlos Cano ◽  
Pietro Manzoni
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Intelligent Transportation System ◽  
Route Planning ◽  
Classification Problem ◽  
Time Dependent ◽  
Traffic Pattern ◽  
High Data ◽  
Real Time Traffic

Intelligent Transportation System (ITS) technologies can be implemented to reduce both fuel consumption and the associated emission of greenhouse gases. However, such systems require intelligent and effective route planning solutions to reduce travel time and promote stable traveling speeds. To achieve such goal these systems should account for both estimated and real-time traffic congestion states, but obtaining reliable traffic congestion estimations for all the streets/avenues in a city for the different times of the day, for every day in a year, is a complex task. Modeling such a tremendous amount of data can be time-consuming and, additionally, centralized computation of optimal routes based on such time-dependencies has very high data processing requirements. In this paper we approach this problem through a heuristic to considerably reduce the modeling effort while maintaining the benefits of time-dependent traffic congestion modeling. In particular, we propose grouping streets by taking into account real traces describing the daily traffic pattern. The effectiveness of this heuristic is assessed for the city of Valencia, Spain, and the results obtained show that it is possible to reduce the required number of daily traffic flow patterns by a factor of 4210 while maintaining the essence of time-dependent modeling requirements.

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