Decentralized Optimization of Vehicle Route Planning -- A Cross-City Comparative Study
New mobility concepts are at the forefront of research and innovation in smart cities. The introduction of connected and autonomous vehicles enables new possibilities in vehicle routing. Specifically, knowing the origin and destination of each agent in the network can allow for real-time routing of the vehicles to optimize network performance. However, this relies on individual vehicles being "altruistic" i.e., being willing to accept an alternative non-preferred route in order to achieve a network-level performance goal. In this work, we conduct a study to compare different levels of agent altruism and the resulting effect on the network-level traffic performance. Specifically, this study compares the effects of different underlying urban structures on the overall network performance, and investigates which characteristics of the network make it possible to realize routing improvements using a decentralized optimization router. The main finding is that, with increased vehicle altruism, it is possible to balance traffic flow among the links of the network. We show evidence that the decentralized optimization router is more effective with networks of high load while we study the influence of cities characteristics, in particular: networks with a higher number of nodes (intersections) or edges (roads) per unit area allow for more possible alternate routes, and thus higher potential to improve network performance.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous VehiclesSimilar Papers 제목 키워드 기반
Three Dimensional Route Planning for Multiple Unmanned Aerial Vehicles using Salp Swarm Algorithm
Route planning for multiple Unmanned Aerial Vehicles (UAVs) is a series of translation and rotational steps from a given start location to the destination goal location. The goal of the route planning problem is to deter…
Robot NavigationTranslationGreen vehicle routing problem that jointly optimizes delivery speed and routing based on the characteristics of electric vehicles
The abundance of materials and the development of the economy have led to the flourishing of the logistics industry, but have also caused certain pollution. The research on GVRP (Green vehicle routing problem) for planni…
Re-route Package Pickup and Delivery Planning with Random Demands
Recently, a higher competition in logistics business introduces new challenges to the vehicle routing problem (VRP). Re-route planning, also known as dynamic VRP, is one of the important challenges. The re-route planning…
Stochastic OptimizationA Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles
Emergency vehicles (EMVs) play a critical role in a city's response to time-critical events such as medical emergencies and fire outbreaks. The existing approaches to reduce EMV travel time employ route optimization and …
reinforcement-learningReinforcement Learning (RL)Traffic Signal ControlEMVLight: A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles
Emergency vehicles (EMVs) play a crucial role in responding to time-critical events such as medical emergencies and fire outbreaks in an urban area. The less time EMVs spend traveling through the traffic, the more likely…
reinforcement-learningReinforcement Learning (RL)Traffic Signal Control