Dynamic route planning with real-time traffic predictions

2017 ◽  
Vol 64 ◽  
pp. 258-265 ◽  
Author(s):  
Thomas Liebig ◽  
Nico Piatkowski ◽  
Christian Bockermann ◽  
Katharina Morik
Keyword(s):  
Real Time ◽  
Route Planning ◽  
Real Time Traffic ◽  
Dynamic Route

scholarly journals Accurate Real-Time Traffic Speed Estimation Using Infrastructure-Free Vehicular Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-19 ◽  
Author(s):  
Zongjian He ◽  
Buyang Cao ◽  
Yan Liu
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Vehicular Networks ◽  
Route Planning ◽  
Speed Estimation ◽  
Traffic Condition ◽  
Real Time Traffic ◽  
Traffic Trace ◽  
Traffic Speed ◽  
Traffic Speed Estimation

Real-time traffic speed is indispensable for many ITS applications, such as traffic-aware route planning and eco-driving advisory system. Existing traffic speed estimation solutions assume vehicles travel along roads using constant speed. However, this assumption does not hold due to traffic dynamicity and can potentially lead to inaccurate estimation in real world. In this paper, we propose a novel in-network traffic speed estimation approach using infrastructure-free vehicular networks. The proposed solution utilizes macroscopic traffic flow model to estimate the traffic condition. The selected model only relies on vehicle density, which is less likely to be affected by the traffic dynamicity. In addition, we also demonstrate an application of the proposed solution in real-time route planning applications. Extensive evaluations using both traffic trace based large scale simulation and testbed based implementation have been performed. The results show that our solution outperforms some existing ones in terms of accuracy and efficiency in traffic-aware route planning applications.

scholarly journals A Dynamic Route-Planning System Based on Industry 4.0 Technology

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 308
Author(s):  
Duy Nguyen Duc ◽  
Thong Tran Huu ◽  
Narameth Nananukul
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Industry 4.0 ◽  
Big Data Analytics ◽  
Route Planning ◽  
Research Area ◽  
Automated Guided Vehicles ◽  
Planning System ◽  
Routing Problem ◽  
Dynamic Route

Due to the availability of Industry 4.0 technology, the application of big data analytics to automated systems is possible. The distribution of products between warehouses or within a warehouse is an area that can benefit from automation based on Industry 4.0 technology. In this paper, the focus was on developing a dynamic route-planning system for automated guided vehicles within a warehouse. A dynamic routing problem with real-time obstacles was considered in this research. A key problem in this research area is the lack of a real-time route-planning algorithm that is suitable for the implementation on automated guided vehicles with limited computing resources. An optimization model, as well as machine learning methodologies for determining an operational route for the problem, is proposed. An internal layout of the warehouse of a large consumer product distributor was used to test the performance of the methodologies. A simulation environment based on Gazebo was developed and used for testing the implementation of the route-planning system. Computational results show that the proposed machine learning methodologies were able to generate routes with testing accuracy of up to 98% for a practical internal layout of a warehouse with 18 storage racks and 67 path segments. Managerial insights into how the machine learning configuration affects the prediction accuracy are also provided.

Perspectives on Driver Preferences for Dynamic Route Guidance Systems

1997 ◽  
Vol 1588 (1) ◽  
pp. 26-31 ◽  
Author(s):  
Joseph L. Schofer ◽  
Frank S. Koppelman ◽  
William A. Charlton
Keyword(s):  
Traffic Congestion ◽  
Intelligent Transportation System ◽  
Route Planning ◽  
Traffic Information ◽  
Route Guidance ◽  
Real Time Traffic ◽  
Operational Test ◽  
Guidance Systems ◽  
Dynamic Route ◽  
And Storage

Insights about the design of route guidance systems based on the needs and desires of drivers who are familiar with the travel network are provided. Results from the ADVANCE Intelligent Transportation System operational test, in which more than 100 drivers used vehicles equipped with dynamic route guidance systems for 2-week periods, suggest that such drivers value real-time traffic information, and they want to incorporate their own knowledge and perspectives into the development of route plans, which they expect to be superior to those prepared by the navigation computer. This suggests that future route guidance systems likely to be targeted at familiar drivers should be based on a sharing of tasks between computer and driver that takes greater advantage of driver knowledge than that considered in current designs. Specifically, the driver should be able to take more responsibility for route planning, with the computer responsible mainly for traffic congestion data acquisition, organization and storage, and evaluation of driver-defined routes.

scholarly journals RiskOracle: A Minute-Level Citywide Traffic Accident Forecasting Framework

2020 ◽  
Vol 34 (01) ◽  
pp. 1258-1265 ◽  
Author(s):  
Zhengyang Zhou ◽  
Yang Wang ◽  
Xike Xie ◽  
Lianliang Chen ◽  
Hengchang Liu
Keyword(s):  
Real Time ◽  
Emergency Response ◽  
Traffic Accident ◽  
Road Network ◽  
Route Planning ◽  
Training Sample ◽  
Dynamic Nature ◽  
Real Time Traffic ◽  
Region Selection ◽  
Real World Datasets

Real-time traffic accident forecasting is increasingly important for public safety and urban management (e.g., real-time safe route planning and emergency response deployment). Previous works on accident forecasting are often performed on hour levels, utilizing existed neural networks with static region-wise correlations taken into account. However, it is still challenging when the granularity of forecasting step improves as the highly dynamic nature of road network and inherent rareness of accident records in one training sample, which leads to biased results and zero-inflated issue. In this work, we propose a novel framework RiskOracle, to improve the prediction granularity to minute levels. Specifically, we first transform the zero-risk values in labels to fit the training network. Then, we propose the Differential Time-varying Graph neural network (DTGN) to capture the immediate changes of traffic status and dynamic inter-subregion correlations. Furthermore, we adopt multi-task and region selection schemes to highlight citywide most-likely accident subregions, bridging the gap between biased risk values and sporadic accident distribution. Extensive experiments on two real-world datasets demonstrate the effectiveness and scalability of our RiskOracle framework.

New Scheduling Algorithm Proposed for Real Time Traffic Over LTE Femto-Cell

2015 ◽  
Vol 5 (6) ◽  
pp. 383
Author(s):  
Mohammed Mahfoudi ◽  
Moulhime El Bekkali ◽  
Abdellah Najid ◽  
Mohamed El Ghazi ◽  
Said Mazer
Keyword(s):  
Real Time ◽  
Scheduling Algorithm ◽  
Real Time Traffic
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