A Graph-Based Framework for Real-Time Vulnerability Assessment of Road Networks

2018 ◽  
Author(s):  
Angelo Furno ◽  
Nour-Eddin El Faouzi ◽  
Rajesh Sharma ◽  
Valerio Cammarota ◽  
Eugenio Zimeo
Keyword(s):  
Real Time ◽  
Vulnerability Assessment ◽  
Road Networks

scholarly journals To re-route, or not to re-route: Impact of real-time re-routing in urban road networks

2021 ◽  
pp. 1-15
Author(s):  
Amine M. Falek ◽  
Antoine Gallais ◽  
Cristel Pelsser ◽  
Sebastien Julien ◽  
Fabrice Theoleyre
Keyword(s):  
Real Time ◽  
Road Networks ◽  
Urban Road

Pedestrian Navigation Services: Challenges and Current Trends

GEOMATICA ◽  
2013 ◽  
Vol 67 (4) ◽  
pp. 259-271 ◽  
Author(s):  
Hassan A. Karimi ◽  
Ming Jiang ◽  
Rui Zhu
Keyword(s):  
Real Time ◽  
Road Networks ◽  
Vehicle Navigation ◽  
Main Campus ◽  
Pedestrian Navigation ◽  
Current Trends ◽  
Major Shortcoming ◽  
The University

With the success and popularity of vehicle navigation services, the demand for Pedestrian Navigation Services (PNS) has increased in recent years. PNS, while overlap in functionality with vehicle navigation services, must be designed specifically for the wayfinding and navigational needs and preferences of pedestrians. One major shortcoming of most existing PNS in outdoors is that they utilize and provide services based on road networks, resulting in PNS that do not effectively and properly track pedestrians as they usually walk on sidewalks, which have more segments and are narrower than roads. Challenges in building PNS include constructing appropriate sidewalk networks, continually tracking users in real time on sidewalks without interruption, and providing personalized routes as well as directions. In this paper, these challenges are highlighted and current trends in PNS, for both outdoors and indoors, are discussed and analyzed. A prototype PNS designed for the University of Pittsburg’s main campus sidewalk network (PNS-Pitt) is also discussed.

scholarly journals RealTE: Real-time Trajectory Estimation Over Large-Scale Road Networks

2022 ◽  
Author(s):  
Qibin Zhou ◽  
Qingang Su ◽  
Dingyu Yang
Keyword(s):  
Travel Time ◽  
Real Time ◽  
High Speed ◽  
Road Network ◽  
Large Scale ◽  
Road Networks ◽  
Trajectory Data ◽  
Real Time Traffic ◽  
Histogram Estimation ◽  
Traffic Estimation

Real-time traffic estimation focuses on predicting the travel time of one travel path, which is capable of helping drivers selecting an appropriate or favor path. Statistical analysis or neural network approaches have been explored to predict the travel time on a massive volume of traffic data. These methods need to be updated when the traffic varies frequently, which incurs tremendous overhead. We build a system RealTER⁢e⁢a⁢l⁢T⁢E, implemented on a popular and open source streaming system StormS⁢t⁢o⁢r⁢m to quickly deal with high speed trajectory data. In RealTER⁢e⁢a⁢l⁢T⁢E, we propose a locality-sensitive partition and deployment algorithm for a large road network. A histogram estimation approach is adopted to predict the traffic. This approach is general and able to be incremental updated in parallel. Extensive experiments are conducted on six real road networks and the results illustrate RealTE achieves higher throughput and lower prediction error than existing methods. The runtime of a traffic estimation is less than 11 seconds over a large road network and it takes only 619619 microseconds for model updates.

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