Abstract
Bus line timetabling is a part of the tactical planning phase which consists of
the following stages: (i) frequency settings; (ii) timetable design; (iii) vehicle
and crew scheduling ([7], [3], [1]).
Timetables are usually determined with the objective of minimizing pas-
senger waiting times at stops [4]. Several studies have considered also the min-
imization of the waiting times of passengers at transfer stops as an additional
metric for reducing the total travel time of passengers.
The problem of timetable synchronization has been addressed by [2], [6],
[11], [10] with the objective of reducing the waiting time of passengers at the
transfer stops while maintaining even dispatching headways among the daily
trips. Most works in the literature have decoupled the timetabling synchro-
nization from the other tactical planning problems, except the work of [12]
that tried to minimize also the total number of required vehicles and the total
deadheading time of all daily trips. This was achieved by solving each objective
separately, using bi-level programming where the number of the required ve-
hicles was solved rst and the total transfer time of passengers was minimized
using a heuristic algorithm at the second stage.
the following stages: (i) frequency settings; (ii) timetable design; (iii) vehicle
and crew scheduling ([7], [3], [1]).
Timetables are usually determined with the objective of minimizing pas-
senger waiting times at stops [4]. Several studies have considered also the min-
imization of the waiting times of passengers at transfer stops as an additional
metric for reducing the total travel time of passengers.
The problem of timetable synchronization has been addressed by [2], [6],
[11], [10] with the objective of reducing the waiting time of passengers at the
transfer stops while maintaining even dispatching headways among the daily
trips. Most works in the literature have decoupled the timetabling synchro-
nization from the other tactical planning problems, except the work of [12]
that tried to minimize also the total number of required vehicles and the total
deadheading time of all daily trips. This was achieved by solving each objective
separately, using bi-level programming where the number of the required ve-
hicles was solved rst and the total transfer time of passengers was minimized
using a heuristic algorithm at the second stage.
Original language | English |
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Title of host publication | hEART 2018: 7th Symposium of the European Association for Research in Transportation, 5-7 September, Athens, Greece |
Number of pages | 6 |
Publication status | Published - 2018 |
Event | hEART 2018: 7th Symposium of the European Association for Research in Transportation - , Greece, Athens, Greece Duration: 5 Sept 2018 → 7 Sept 2018 Conference number: 7 https://www.events.tum.de/frontend/index.php?folder_id=877 |
Conference
Conference | hEART 2018 |
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Abbreviated title | hEART 2018 |
Country/Territory | Greece |
City | Athens |
Period | 5/09/18 → 7/09/18 |
Internet address |