TY - CHAP
T1 - AI Approaches on Urban Public Transport Routing
AU - Guo, Rongge
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024/5/16
Y1 - 2024/5/16
N2 - Artificial intelligence (AI) is an innovative concept that can provide potentials to overcome the challenges of operation in public transport (PT) systems. The AI applications in the PT operation offer the opportunities to alleviate traffic congestion and enhance the accessibility, mobility, and reliability of services with a more efficient and effective PT system. One of the key areas that AI is beneficial is in optimization of network route design. Examples of AI approaches that are finding their way under fuel and electric vehicle conditions include genetic algorithm (GA), simulated annealing (SA), etc. The more promising application of AI techniques requires a well-understood knowledge of multiple data sets and features of different PT services, including conventional buses and demand-responsive transit systems, especially when dealing with travel demands fluctuating in time and space. Moreover, the emerging development in connected and autonomous vehicles (CAVs) is leading a rapid improvement in flexibility, punctuality, vehicle safety, and transit priority. The purpose of this chapter is to review AI approaches applied on public transport operation, especially on routing. The overview concludes by addressing the issues and challenges of AI applications in PT operation.
AB - Artificial intelligence (AI) is an innovative concept that can provide potentials to overcome the challenges of operation in public transport (PT) systems. The AI applications in the PT operation offer the opportunities to alleviate traffic congestion and enhance the accessibility, mobility, and reliability of services with a more efficient and effective PT system. One of the key areas that AI is beneficial is in optimization of network route design. Examples of AI approaches that are finding their way under fuel and electric vehicle conditions include genetic algorithm (GA), simulated annealing (SA), etc. The more promising application of AI techniques requires a well-understood knowledge of multiple data sets and features of different PT services, including conventional buses and demand-responsive transit systems, especially when dealing with travel demands fluctuating in time and space. Moreover, the emerging development in connected and autonomous vehicles (CAVs) is leading a rapid improvement in flexibility, punctuality, vehicle safety, and transit priority. The purpose of this chapter is to review AI approaches applied on public transport operation, especially on routing. The overview concludes by addressing the issues and challenges of AI applications in PT operation.
KW - Artificial intelligence (AI)
KW - public transport (PT) systems
KW - traffic congestion
KW - Connected and autonomous vehicles
KW - Transit network design problem
KW - Public transport systems
KW - Artificial intelligence
UR - https://doi.org/10.1007/978-3-031-55044-7
UR - http://www.scopus.com/inward/record.url?scp=85194545594&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-55044-7_8
DO - 10.1007/978-3-031-55044-7_8
M3 - Chapter
SN - 9783031550430
SN - 9783031550461
T3 - Wireless Networks
SP - 111
EP - 130
BT - Deception in Autonomous Transport Systems
A2 - Parkinson, Simon
A2 - Nikitas, Alexandros
A2 - Vallati, Mauro
PB - Springer, Cham
ER -
