Real-Time Route Planning and Online Order Dispatch for Bus-Booking Platforms

2019 ◽  
pp. 748-763
Author(s):  
Hao Zhou ◽  
Yucen Gao ◽  
Xiaofeng Gao ◽  
Guihai Chen
Keyword(s):  
Real Time ◽  
Route Planning

Design and development of a navigation system for agricultural aerial spraying

2017 ◽  
Vol 8 (2) ◽  
pp. 870-875
Author(s):  
M. J. Zhang ◽  
R. R. Zhang ◽  
G. Xu ◽  
L. P. Chen
Keyword(s):  
Real Time ◽  
Navigation System ◽  
High Efficiency ◽  
Route Planning ◽  
Utilization Rate ◽  
Tablet Pc ◽  
Design And Development ◽  
Heavy Workload ◽  
Aerial Spraying ◽  
Time Evaluation

Problems in the process of manned agricultural aerial spraying, such as heavy workload in route planning, overlaps or omissions in spraying seriously reduce the efficiency of spraying and utilization rate of pesticides. This paper presents the design and development of a navigation system for manned agricultural aerial spraying based on an industrial tablet PC. This system provides three key functions: route planning, spraying navigation and real-time evaluation of spraying quality. The test and application results show that this system has high efficiency in route planning, and the average coverage rate of spraying could reach as high as 96%. Its application effect is remarkable, and as a result, this system can meet the demand of manned agricultural aerial spraying in route planning and navigation.

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 Real-time urban regional route planning model for connected vehicles based on V2X communication

2020 ◽  
Vol 13 (1) ◽  
pp. 517-538 ◽  
Author(s):  
Pangwei Wang ◽  
Hui Deng ◽  
Juan Zhang ◽  
Mingfang Zhang
Keyword(s):  
Land Use ◽  
Travel Time ◽  
Real Time ◽  
Land Use Planning ◽  
Route Planning ◽  
Urban Traffic ◽  
Urban Transport ◽  
Connected Vehicles ◽  
Planning Model ◽  
Optimal Route

Advancement in the novel technology of connected vehicles has presented opportunities and challenges for smart urban transport and land use. To improve the capacity of urban transport and optimize land-use planning, a novel real-time regional route planning model based on vehicle to X communication (V2X) is presented in this paper. First, considering the traffic signal timing and phase information collected by V2X, road section resistance values are calculated dynamically based on real-time vehicular driving data. Second, according to the topology structure of the current regional road network, all predicted routes are listed based on the Dijkstra algorithm. Third, the predicted travel time of each alternative route is calculated, while the predicted route with the least travel time is selected as the optimal route. Finally, we design the test scenario with different traffic saturation levels and collect 150 sets of data to analyze the feasibility of the proposed method. The numerical results have shown that the average travel times calculated by the proposed optimal route are 8.97 seconds, 12.54 seconds, and 21.85 seconds, which are much shorter than the results of traditional navigation routes. This proposed model can be further applied to the whole urban traffic network and contribute to a greater transport and land-use efficiency in the future.

scholarly journals Robust algorithm for real-time route planning

2000 ◽  
Vol 36 (3) ◽  
pp. 869-878 ◽  
Author(s):  
R.J. Szczerba ◽  
P. Galkowski ◽  
I.S. Glicktein ◽  
N. Ternullo
Keyword(s):  
Real Time ◽  
Route Planning ◽  
Robust Algorithm

MISSION-ADAPTABLE ROUTE PLANNING IN UNCERTAIN AND ADVERSARIAL ENVIRONMENT

2006 ◽  
Vol 15 (05) ◽  
pp. 803-821 ◽  
Author(s):  
PING YAN ◽  
MINGYUE DING ◽  
CHANGWEN ZHENG
Keyword(s):  
Real Time ◽  
A Priori ◽  
Route Planning ◽  
Optimal Solution ◽  
Dijkstra Algorithm ◽  
Solution Quality ◽  
Model Complex ◽  
Local Methods ◽  
Domain Specific Knowledge ◽  
Planning Algorithm

In this paper, the route-planning problems of Unmanned Aerial Vehicle (UAV) in uncertain and adversarial environment are addressed, including not only single-mission route planning in known a priori environment, but also the route replanning in partially known and mission-changeable environments. A mission-adaptable hybrid route-planning algorithm based on flight roadmap is proposed, which combines existing global and local methods (Dijkstra algorithm, SAS and D*) into a two-level framework. The environment information and constraints for UAV are integrated into the procedure of building flight roadmap and searching for routes. The route-planning algorithm utilizes domain-specific knowledge and operates in real time with near-optimal solution quality, which is important to uncertain and adversarial environment. Other planners do not provide all of the functionality, namely real-time planning and replanning, near-optimal solution quality, and the ability to model complex 3D constraints.

scholarly journals Real-Time Route Search by Locations

2020 ◽  
Vol 34 (01) ◽  
pp. 574-581
Author(s):  
Lisi Chen ◽  
Shuo Shang ◽  
Tao Guo
Keyword(s):  
Real Time ◽  
Data Streams ◽  
High Efficiency ◽  
Route Planning ◽  
Real Data ◽  
Location Based Services ◽  
Good Time ◽  
Efficient Route ◽  
Route Search ◽  
Insight Into

With the proliferation of GPS-based data (e.g., routes and trajectories), it is of great importance to enable the functionality of real-time route search and recommendations. We define and study a novel Continuous Route-Search-by-Location (C-RSL) problem to enable real-time route search by locations for a large number of users over route data streams. Given a set of C-RSL queries where each query q contains a set of places q.O to visit and a threshold q.θ, we continuously feed each query q with routes that has similarity to q.O no less than q.θ. We also extend our proposal to support top-k C-RSL problem where each query continuously maintains k most similar routes. The C-RSL problem targets a variety of applications, including real-time route planning, ridesharing, and other location-based services that have real-time demand. To enable efficient route matching on a large number of C-RSL queries, we develop novel parallel route matching algorithms with good time complexity. Extensive experiments with real data offer insight into the performance of our algorithms, indicating that our proposal is capable of achieving high efficiency and scalability.

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.

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