A multi-modal and multi-objective trip planner provides users with various multi-modal options optimised on objectives that they prefer (cheapest, fastest, safest, etc) and has a potential to reduce congestion on both a temporal and spatial scale. The computation of multi-modal and multi-objective trips is a complicated mathematical problem, as it must integrate and utilize a diverse range of large data sets, including both road network information and public transport schedules, as well as optimising for a number of competing objectives, where fully optimising for one objective, such as travel time, can adversely affect other objectives, such as cost. The relationship between these objectives can also be quite subjective, as their priorities will vary from user to user. This paper will first outline the various data requirements and formats that are needed for the multi-modal multi-objective trip planner to operate, including static information about the physical infrastructure within Brisbane as well as real-time and historical data to predict traffic flow on the road network and the status of public transport. It will then present information on the graph data structures representing the road and public transport networks within Brisbane that are used in the trip planner to calculate optimal routes. This will allow for an investigation into the various shortest path algorithms that have been researched over the last few decades, and provide a foundation for the construction of the multi-modal multi-objective trip planner by the development of innovative new algorithms that can operate the large diverse data sets and competing objectives.
[1]
Karsten Weihe,et al.
Dijkstra's algorithm on-line: an empirical case study from public railroad transport
,
2000,
JEAL.
[2]
Ciyun Lin,et al.
Multimodal Traffic Information Service System with K-Multimodal Shortest Path Algorithm
,
2009,
2009 Second International Conference on Intelligent Computation Technology and Automation.
[3]
Hani S. Mahmassani,et al.
A note on least time path computation considering delays and prohibitions for intersection movements
,
1996
.
[4]
Gerth Stølting Brodal,et al.
Time-dependent Networks as Models to Achieve Fast Exact Time-table Queries
,
2004,
ATMOS.
[5]
Andrés L. Medaglia,et al.
Labeling algorithm for the shortest path problem with turn prohibitions with application to large-scale road networks
,
2008,
Ann. Oper. Res..
[6]
K. Nachtigall.
Time depending shortest-path problems with applications to railway networks
,
1995
.
[7]
Christos D. Zaroliagis,et al.
Efficient models for timetable information in public transportation systems
,
2008,
JEAL.
[8]
Ronald F. Kirby,et al.
The minimum route problem for networks with turn penalties and prohibitions
,
1969
.