Multi-Objective Route Planning for Aircraft Taxiing Under Different Traffic Conflict Types

2021 ◽  
pp. 1-19
Author(s):  
Ming Zhang ◽  
Sihan Liu ◽  
Huiying Li
Keyword(s):  
Route Planning ◽  
Multi Objective ◽  
Traffic Conflict ◽  
Conflict Types

scholarly journals A multi-objective route planning model based on genetic algorithm for cuboid surfaces

Automatika ◽  
2018 ◽  
Vol 59 (1) ◽  
pp. 120-130 ◽  
Author(s):  
Gonca Ozmen Koca ◽  
Sengul Dogan ◽  
Hicran Yilmaz
Keyword(s):  
Genetic Algorithm ◽  
Route Planning ◽  
Planning Model ◽  
Multi Objective ◽  
Model Based

scholarly journals Interactive Multi-objective Route Planning for Sightseeing on Time-expanded Networks under Various Conditions

2013 ◽  
Vol 22 ◽  
pp. 221-230 ◽  
Author(s):  
Takashi Hasuike ◽  
Hideki Katagiri ◽  
Hiroe Tsubaki ◽  
Hiroshi Tsuda
Keyword(s):  
Route Planning ◽  
Multi Objective ◽  
Time Expanded Networks

scholarly journals TRAFFIC CONFLICT IDENTIFICATION OF E-BIKES AT SIGNALIZED INTERSECTIONS

Transport ◽  
2020 ◽  
Vol 0 (0) ◽  
pp. 1-14
Author(s):  
Zhaowei Qu ◽  
Yuhong Gao ◽  
Xianmin Song ◽  
Yingji Xia ◽  
Lin Ma ◽  
...  
Keyword(s):  
Motor Vehicle ◽  
Space Time ◽  
Spatial Proximity ◽  
Time To Collision ◽  
Traffic Conflicts ◽  
Traffic Conflict ◽  
Discriminant Analyses ◽  
Change Trend ◽  
Conflict Identification ◽  
Conflict Types

The increase of e-bikes has raised traffic conflict concerns over past decade. Numerous conflict indicators are applied to measure traffic conflicts by detecting differences in temporal or spatial proximity between users. However, for traffic environment with plenty of e-bikes, these separate space-time approaching indicators may not be applicable. Thus, this study aims to propose a multi-variable conflict indicator and build a conflict identification method for e-bikes moving in the same direction. In particular, by analysing the conflict characteristics from e-bikes trajectories, a multi-variable conflict indicator utilizing change of forecast post encroachment time, change of relative speed and change of distance is derived. Mathematical statistics and cluster discriminant analyses are applied to identify types of conflict, including conflict existence identification and conflict severity identification. The experimental results show: in mixed traffic environments with many e-bikes, compared with time-to-collision and deceleration, accuracy of identifying e-bike conflict types based on proposed method is the highest and can reach more than 90%; that is, multi-variable indicator based on time and space are more suitable for identifying e-bike conflicts than separate space-time approaching indicators. Furthermore, setting of dividing strip between motor vehicle and non-motorized vehicle has significant influence on number and change trend of conflict types. The proposed method can not only provide a theoretical basis and technical support for automated conflict detection in mixed transportation, but also give the safety optimization sequence of e-bikes at different types of intersections.

scholarly journals Energy efficient route planning for electric vehicles with special consideration of the topography and battery lifetime

2020 ◽  
Vol 13 (8) ◽  
pp. 1705-1726
Author(s):  
Theresia Perger ◽  
Hans Auer
Keyword(s):  
Energy Consumption ◽  
San Francisco ◽  
Electric Vehicles ◽  
Optimization Problem ◽  
Route Planning ◽  
Planning System ◽  
Decision Variable ◽  
Multi Objective Optimization ◽  
Battery Lifetime ◽  
Multi Objective

Abstract In contrast to conventional routing systems, which determine the shortest distance or the fastest path to a destination, this work designs a route planning specifically for electric vehicles by finding an energy-optimal solution while simultaneously considering stress on the battery. After finding a physical model of the energy consumption of the electric vehicle including heating, air conditioning, and other additional loads, the street network is modeled as a network with nodes and weighted edges in order to apply a shortest path algorithm that finds the route with the smallest edge costs. A variation of the Bellman-Ford algorithm, the Yen algorithm, is modified such that battery constraints can be included. Thus, the modified Yen algorithm helps solving a multi-objective optimization problem with three optimization variables representing the energy consumption with (vehicle reaching the destination with the highest state of charge possible), the journey time, and the cyclic lifetime of the battery (minimizing the number of charging/discharging cycles by minimizing the amount of energy consumed or regenerated). For the optimization problem, weights are assigned to each variable in order to put emphasis on one or the other. The route planning system is tested for a suburban area in Austria and for the city of San Francisco, CA. Topography has a strong influence on energy consumption and battery operation and therefore the choice of route. The algorithm finds different results considering different preferences, putting weights on the decision variable of the multi-objective optimization. Also, the tests are conducted for different outside temperatures and weather conditions, as well as for different vehicle types.

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