Combining Reinforcement Learning With Genetic Algorithm for Many-To-Many Route Optimization of Autonomous Vehicles

S Yoo, H Kim, J Lee - IEEE Access, 2024 - ieeexplore.ieee.org
This study introduces an approach for route optimization of many-to-many Demand-
Responsive Transport (DRT) services. In contrast to conventional fixed-route transit systems …

[HTML][HTML] Real-Time Dynamic Route Generation Algorithm for Demand-Responsive Driverless Transit Operation (DRDTO) Applied to Corridors to Consider U-Turns

H Kim, S Yoo, J Lee, B Baek, J Shin - Journal of Korean Society of …, 2022 - jkst.or.kr
Recently, autonomous vehicle is receiving much attention in various sectors including
transportation and public transportation. This study presents an adaptive routing algorithm …

[HTML][HTML] Assessment of the DRT system based on an optimal routing strategy

J Kim - Sustainability, 2020 - mdpi.com
Demand responsive transport (DRT) is operated according to flexible routes, dispatch
intervals, and dynamic demand, is attracting a lot of attention. The biggest characteristic of …

[PDF][PDF] Efficient Operation of Demand-Responsive Transport (DRT) Systems: Active Requests Rejection

C Lu, S Tiwari, N Nassir, K Nagel - 2024 - svn.vsp.tu-berlin.de
Demand-responsive transport (DRT) is getting more attention in the public sectors. In DRT
systems operated by public sectors, providing a good and equal service to the public is more …

Reinforcement Learning Based Route And Stop Planning For Autonomous Vehicle Shuttle Service

S Mahmud, H Shen, YNZ Foutz… - 2022 IEEE 19th …, 2022 - ieeexplore.ieee.org
AV shuttles can be a complement to the existing bus services to fill the gap between the
mobility demand of a city population and the supply of the existing bus services. In contrast …

A scalable heuristic algorithm for demand responsive transportation for first mile transit

T Perera, A Prakash… - 2017 IEEE 21st …, 2017 - ieeexplore.ieee.org
First/last mile transit using public transport has consistently been a bottleneck for commuters
due to the relatively higher time spent in these legs when compared to the overall journey …

[HTML][HTML] Population Game-Assisted Multi-Agent Reinforcement Learning Method for Dynamic Multi-Vehicle Route Selection

L Yan, Y Cai - Electronics, 2024 - mdpi.com
To address urban traffic congestion, researchers have made various efforts to mitigate
issues such as prolonged travel time, fuel wastage, and pollutant emissions. These efforts …

Machine learning guided optimization for demand responsive transport systems

L Zigrand, P Alizadeh, E Traversi… - … European Conference on …, 2021 - Springer
Most of the time, objective functions used for solving static combinatorial optimization
problems cannot deal efficiently with their real-time counterparts. It is notably the case of …

Integrate, Not Compete! On Potential Integration of Demand Responsive Transport Into Public Transport Network

S Dytckov, JA Persson… - 2023 IEEE 26th …, 2023 - ieeexplore.ieee.org
On-demand transport services are often envisioned as stand-alone modes or as a
replacement for conventional public transport modes. This leads to a comparison of service …

Optimal reinforcement learning and probabilistic-risk-based path planning and following of autonomous vehicles with obstacle avoidance

H Taghavifar, L Taghavifar, C Hu… - Proceedings of the …, 2024 - journals.sagepub.com
In this paper, a novel algorithm is proposed for the motion planning and path following
automated cars with the incorporation of a collision avoidance strategy. This approach is …