Using Automatic Vehicle Location Data to Model and Identify Determinants of Bus Bunching

Rashidi, Soroush; Ranjitkar, Prakash; Orosz, Csaba; Hooper, Andy

doi:10.1016/j.trpro.2017.05.170

Cited by 17 publications

(9 citation statements)

References 11 publications

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“…There are also articles focused on information systems providing on-line information about position of vehicles. Articles [16], [17] and [18] can be mentioned as examples.…”

Section: Fig 1 Illustration Of Problem With Nttij or Nttjimentioning

confidence: 99%

“…The article [18] is also focused on bus bunching and on prevention of it. Bus bunching occurs, when vehicles are going in short subsequent headway time.…”

Section: Fig 1 Illustration Of Problem With Nttij or Nttjimentioning

confidence: 99%

“…These results can allow important extension of optimization possibilities. For instance, the effect of bus bunching [18] can be prevented by using of these data. Spare time position (e.g.…”

Section: Model Formulationmentioning

confidence: 99%

“…In an extreme case when all the inputs will be equal (the same) in the case of all UPT lines, MPOM algorithm will find the same (equal) result for all the UPT lines. This will directly lead to bus bunching effect [18]. For example, all of lines operated in the same headway of LH = 20 min will depart e.g.…”

Section: Timetable Synchronization Within Upt Subsystem Based On Datamentioning

confidence: 99%

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Timetable synchronisation: Urban public transport in busy hubs of long-distance transport

Bulíček

2018

MATEC Web Conf.

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The article is focused on timetable synchronization problem. Interconnection of urban public transport and long-distance transport in busy hubs is researched. Intension is to find adequate time positions of urban public transport arrivals and departures with an effort to minimize passengers’ time loss related to this interchange. Timetable of long distance transport is considered as constant (given). Timetable of urban public transport is result of optimization (and presupposed to be periodic). Nonlinear integer mathematical programming model is formulated. Model is implemented in Microsoft Excel Solver. Parallel way based on exhaustive-search algorithm is introduced as well. This algorithm provides extended set of output data able to be used in transport planning. Two different criterions are proposed. Each interchange (pair of vehicles) has the same weight in the case of uniform approach. Intensity approach considers interchanges as weighted by numbers of interchanging passengers. Some additional modifications of proposed model are mentioned for possibility to regard some important technological aspects and to reach more individualized solution. Model is illustrated by the case study located to Pardubice main station as a hub of passenger railway transport. Interface with urban public transport is mentioned as an illustrational example of synchronization.

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“…There are also articles focused on information systems providing on-line information about position of vehicles. Articles [16], [17] and [18] can be mentioned as examples.…”

Section: Fig 1 Illustration Of Problem With Nttij or Nttjimentioning

confidence: 99%

“…The article [18] is also focused on bus bunching and on prevention of it. Bus bunching occurs, when vehicles are going in short subsequent headway time.…”

Section: Fig 1 Illustration Of Problem With Nttij or Nttjimentioning

confidence: 99%

“…These results can allow important extension of optimization possibilities. For instance, the effect of bus bunching [18] can be prevented by using of these data. Spare time position (e.g.…”

Section: Model Formulationmentioning

confidence: 99%

Section: Timetable Synchronization Within Upt Subsystem Based On Datamentioning

confidence: 99%

See 2 more Smart Citations

Timetable synchronisation: Urban public transport in busy hubs of long-distance transport

Bulíček

2018

MATEC Web Conf.

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“…When headway deviations become too large and corresponding headways very small, bus bunching typically occurs. Bus bunching events, during which consecutive buses run too close together and arrive simultaneously at the same stop (Rashidi et al, 2017;Verbich et al, 2016), result in poor service reliability. Increased waiting times, lower comfort in buses and inefficient capacity utilization are considered as the main impacts of bus bunching (Diab et al, 2015;Moreira-Matias et al, 2014).…”

Section: The Bus Bunching Problem: Overviewmentioning

confidence: 99%

Optimal design for urban transit systems

Ηλιοπούλου¹

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Οι δυσμενείς επιπτώσεις των ορυκτών καυσίμων στο περιβάλλον και τη δημόσια υγεία επιτάσσουν την προσαρμογή των σύγχρονων συγκοινωνιακών δικτύων στα πρότυπα της βιώσιμης ανάπτυξης. Προς αυτή την κατεύθυνση καθοριστικό ρόλο διαδραματίζει ο σχεδιασμός των συστημάτων αστικών συγκοινωνιών με άξονες τη μείωση των περιβαλλοντικών επιπτώσεων και παράλληλα, την αύξηση της δημοτικότητας και οικονομικής βιωσιμότητάς τους. Παράλληλα, οι πρόσφατες εξελίξεις στις τεχνολογίες επικοινωνίας, συλλογής δεδομένων και ασύρματης μεταφοράς ενέργειας συνθέτουν μια νέα πραγματικότητα για τις αστικές συγκοινωνίες, δημιουργώντας την ανάγκη για νέα συστήματα υποστήριξης αποφάσεων. Στο πλαίσιο αυτό, το αντικείμενο της παρούσας διατριβής είναι η ανάπτυξη ενός μεθοδολογικού πλαισίου βασισμένου σε αλγορίθμους τεχνητής νοημοσύνης και επιστήμης δεδομένων για τον σχεδιασμό συστημάτων αστικών συγκοινωνιών αξιοποιώντας τις πρόσφατες εξελίξεις στις τεχνολογίες μεταφοράς ηλεκτρικής ενέργειας και στα Ευφυή Συστήματα Μεταφοράς. Ειδικούς στόχους της διατριβής αποτελούν: α). η ανάπτυξη ενός ολοκληρωμένου μοντέλου σχεδιασμού για ένα δίκτυο αστικών συγκοινωνιών με αποκλειστική χρήση στόλου ηλεκτρικών λεωφορείων υπό διαφορετικές διαμορφώσεις υποδομής φόρτισης, β). η ανάπτυξη ενός πλαισίου για τη βελτίωση του σχεδιασμού των αστικών συγκοινωνιών με επίκεντρο το πρόβλημα του συνωστισμού λεωφορείων (bus bunching) και γ). η ανάπτυξη κατάλληλων εργαλείων και μεθόδων εκμετάλλευσης των δεδομένων αυτόματου εντοπισμού θέσης (AVL). Το προτεινόμενο μεθοδολογικό πλαίσιο εφαρμόζεται σε δίκτυα αναφοράς από την διεθνή βιβλιογραφία και δεδομένα από το Σύστημα Συγκοινωνιών της Αθήνας, αντίστοιχα.

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Methodology to Detect Bus Stop Influence Zones Utilizing Facebook Prophet Changepoint Detection Method

Maltaş

Özen

Saraçoğlu³

2023

KSCE J Civ Eng

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Using Automatic Vehicle Location Data to Model and Identify Determinants of Bus Bunching

Cited by 17 publications

References 11 publications

Timetable synchronisation: Urban public transport in busy hubs of long-distance transport

Timetable synchronisation: Urban public transport in busy hubs of long-distance transport

Optimal design for urban transit systems

Methodology to Detect Bus Stop Influence Zones Utilizing Facebook Prophet Changepoint Detection Method

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