A cost-minimization model for bus fleet allocation featuring the tactical generation of short-turning and interlining options

Gkiotsalitis, Konstantinos; Wu, Zongxiang; Cats, Oded

doi:10.1016/j.trc.2018.11.007

Cited by 72 publications

(33 citation statements)

References 55 publications

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“…These dispatching headway bounds are determined at the frequency settings stage that precedes our problem [48] and impose the inequality constraints:…”

Section: Remarkmentioning

confidence: 99%

Robust Network-Wide Bus Scheduling With Transfer Synchronizations

Gkiotsalitis

Eikenbroek

Cats

2020

IEEE Trans. Intell. Transport. Syst.

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“…These dispatching headway bounds are determined at the frequency settings stage that precedes our problem [48] and impose the inequality constraints:…”

Section: Remarkmentioning

confidence: 99%

Robust Network-Wide Bus Scheduling With Transfer Synchronizations

Gkiotsalitis

Eikenbroek

Cats

2020

IEEE Trans. Intell. Transport. Syst.

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“…In general, the daily operations of a bus line are formed on the basis of an extensive tactical planning process that involves the planning of bus frequencies, crew and vehicle schedules (Kepaptsoglou and Karlaftis 2009;Farahani et al 2013;Gkiotsalitis and Cats 2018;Gkiotsalitis, Wu, and Cats 2019). During the tactical planning phase, the dispatching time of each bus trip is defined and the daily timetable is generated.…”

Section: Related Studiesmentioning

confidence: 99%

Multi-constrained bus holding control in time windows with branch and bound and alternating minimization

Gkiotsalitis

Cats

2019

Transportmetrica B: Transport Dynamics

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This work proposes a periodic bus holding control method where the bus holding times of all running trips are computed simultaneously within each optimization time period; thus, increasing the coordination among running buses for avoiding bus bunching. This paper considers the adverse effects of the bus holding control in the in-vehicle travel times of on-board passengers and performs holistic bus holding decisions by modelling the bus holding problem as a discrete, nonlinear, constrained optimization problem. Given the computational complexity of the bus holding problem, an alternating minimization approach is introduced for computing the optimal holding times at each optimization instance. The performance of the periodic control method is evaluated against the performance of event-based control methods using 5-month automated vehicle location and automated passenger count data from bus line 1 in Stockholm for contacting simulation-based experiments.

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“…Several other works such as Van Der Zijpp (1997); White and Wells (2002); Pan et al (2006); Zhou and Mahmassani (2006); Gonzàlez et al (2008); Zhang et al (2010); Baek et al (2010); Calabrese et al (2011b) and Shang et al (2012) have also explored the use of continuously updated data, such as Floating Car Data (FCD), Automated Vehicle Identification (AVL) data, Inductive Loop Detector (ILD) data, and cellular data to predict the mobility of people. A special category is Smart Card (SC) data that can be used for estimating the spatio-temporal density of public transport passengers (Pelletier et al, 2011;Munizaga and Palma, 2012;Sun et al, 2012;Gkiotsalitis et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

Investigating the relation between social media activities and the mobility and activity types of users

Gkiotsalitis¹

2019

Preprint

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In the past years, there has been an emerging number of studies on estimating the passenger demand in urban environments based on social media and cellular data. However, the study of the travel behavior at the individual level and the relation between social media activity and the activity/mobility patterns of users has received limited attention. To rectify this, this study examines Twitter data for unveiling the relations between geo-tagged Tweets and Twitter user sentiments, and the respective activity types performed in the real-world. In this work we try to find common patterns between users' Twitter activity and their actual mobility/activity patterns with the aim to provide some generalizations that can help to understand and model the travel behavior of users. This is achieved with the development of educated rules and probabilistic models that can predict the mobility transfers of users between different activities based solely on social media data. The validity of our generalizations is validated with the use of 4-month Twitter data from London. Only active Twitter users have been selected to study in deep the relations between social media activities/sentiments and the activity types performed in the real-world. Although our generalizations are case study-specific, they demonstrate that it is possible to extract the activity and mobility behavior of users with the use of social media and offer a first step in this direction.

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A cost-minimization model for bus fleet allocation featuring the tactical generation of short-turning and interlining options

Abstract: A cost-minimization model for bus fleet allocation featuring the tactical generation of short-turning and interlining options

Cited by 72 publications

References 55 publications

Robust Network-Wide Bus Scheduling With Transfer Synchronizations

Robust Network-Wide Bus Scheduling With Transfer Synchronizations

Multi-constrained bus holding control in time windows with branch and bound and alternating minimization

Investigating the relation between social media activities and the mobility and activity types of users

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