1 Introduction
2 Literature review
2.1 Strategic level
2.1.1 Transit assignment
2.1.2 Network design
2.2 Tactical level planning
2.2.1 Optimal timetabling
2.2.2 Origin-destination and transfer inference
2.2.3 Activity modeling
2.3 Operational level
2.4 Real-time operations
2.4.1 Real-time trip planning
2.4.2 Real-time control
Author | Date | Data | Research Purpose | Level |
---|---|---|---|---|
Yan et al. | 2006 | Loop Detector | Optimal bus route design | Strategic |
Liu et al. | 2017 | AFC | Optimal bus route design | Strategic |
Li and Bertini | 2008 | AVL | Optimal bus stop spacing | Strategic |
Poon et al. | 2004 | AFC | Transit assignment | Strategic |
Fung et al. | 2005 | AFC | Transit assignment | Strategic |
Vuk and Hansen | 2006 | APC | Transit assignment | Strategic |
Bouman et al. | 2012 | AFC | Transit assignment | Strategic |
Schmöcker et al. | 2013 | AFC | Transit assignment | Strategic |
Ordóñez Medina and Erath | 2013 | AFC | Transit assignment | Strategic |
Lovric et al | 2013 | AFC | Transit assignment | Strategic |
Zhu, et al. | 2014 | AFC | Transit assignment | Strategic |
Li et al. | 2015 | AVL | Transit assignment | Strategic |
Chen and Nie | 2015 | AVL | Transit assignment | Strategic |
Fourie et al | 2016 | AFC, AVL | Transit assignment | Strategic |
Ali et al. | 2016 | AFC | Transit assignment | Strategic |
Tavassoli et al. | 2018 | AFC, GTFS | Transit assignment | Strategic |
Hadas and Shnaiderman | 2012 | AVL, APC | Optimal frequency setting | Tactical |
Patnaik et al. | 2006 | APC | Optimal headway setting | Tactical |
Gkiotsalitis and Cats | 2018 | AVL, APC, GTFS | Optimal headway setting | Tactical |
Yan et al. | 2006 | Loop Detector | Timetabling | Tactical |
Mazloumi et al. | 2012 | AVL | Timetabling | Tactical |
Yan et al. | 2012 | AVL | Timetabling | Tactical |
Sun et al. | 2014 | AFC | Timetabling | Tactical |
Wang et al. | 2017 | AFC | Timetabling | Tactical |
Guo et al. | 2017 | AFC | Timetabling | Tactical |
Kusakabe et al. | 2010 | AFC | Route choice modeling | Tactical |
Zhou and Xu | 2012 | AFC | Route choice modeling | Tactical |
Van der Hurk et al. | 2013 | AFC | Route choice modeling | Tactical |
Trepanier et al | 2007 | AFC | OD flow estimation | Tactical |
Mc Cord et al. | 2010 | APC | OD flow estimation | Tactical |
Munizaga and Palma | 2012 | AVL, AFC | OD flow estimation | Tactical |
Gordon et al. | 2013 | AVL, AFC | OD flow estimation | Tactical |
Ji et al. | 2014 | APC | OD flow estimation | Tactical |
Ji et al. | 2015 | APC | OD flow estimation | Tactical |
Xu et al. | 2016 | AFC | OD flow estimation | Tactical |
Sanchez-Martinez | 2017 | AFC, AVL | OD flow estimation | Tactical |
Gordon et al. | 2018 | Farebox, AFC | OD flow estimation | Tactical |
Ji et al. | 2011 | AFC | OD flow modeling | Tactical |
Ma et al. | 2013 | AFC | OD flow modeling | Tactical |
Hofmann et al. | 2009 | AFC | Transfer identification | Tactical |
Hong et al. | 2016 | AFC | Transfer identification | Tactical |
Yap et al. | 2017 | AFC | Transfer identification | Tactical |
Han and Sohn | 2016 | AFC | Activity detection | Tactical |
Goulet-Langlois et al. | 2016 | AFC | Activity detection | Tactical |
Ma et al. | 2017 | AFC | Activity detection | Tactical |
Zou et al. | 2018 | AFC | Activity detection | Tactical |
Qi et al. | 2018 | AFC | Activity detection | Tactical |
Agard et al. | 2006 | AFC | Pattern detection | Tactical |
Morency et al. | 2007 | AFC | Pattern detection | Tactical |
Agard | 2009 | AFC | Pattern detection | Tactical |
Sun et al. | 2013 | AFC | Pattern detection | Tactical |
El Mahrsi et al. | 2015 | AFC | Pattern detection | Tactical |
Kieu et al. | 2015 | AFC | Pattern detection | Tactical |
Kieu et al. | 2015 | AFC | Pattern detection | Tactical |
Ghaemi et al. | 2017 | AFC | Pattern detection | Tactical |
Zhao et al. | 2017 | AFC, AVL | Pattern detection | Tactical |
Kieu et al. | 2018 | AFC | Pattern detection | Tactical |
Shen et al. | 2016 | AVL | Vehicle Scheduling | Operational |
Shen et al. | 2016 | AVL | Vehicle Scheduling | Operational |
Shen et al. | 2017 | AVL | Vehicle Scheduling | Operational |
Eberlein et al. | 2001 | AVI | Optimal Control | Real-Time |
Zolfaghari et al. | 2004 | AVL | Optimal Control | Real-Time |
Yu and Yang | 2009 | AVL | Optimal Control | Real-Time |
Chen et al. | 2013 | AVL,APC | Optimal Control | Real-Time |
Asgharzadeh and Shafahi | 2017 | AVL,APC | Optimal Control | Real-Time |
Luo et al. | 2017 | AVL, AFC | Optimal Control | Real-Time |
Berrebi et al. | 2018 | AVL,APC | Optimal Control | Real-Time |
Berrebi et al. | 2018 | AVL | Optimal Control | Real-Time |
Hickman | 2003 | AVL | Trip planning | Real-Time |
Tien et al. | 2011 | AVL | Trip planning | Real-Time |
Zhang et al. | 2011 | AVL | Trip planning | Real-Time |
Li et al. | 2012 | AVL | Trip planning | Real-Time |
Chen et al. | 2016 | GTFS | Trip planning | Real-Time |
3 Main findings and research gaps
3.1 Practical challenges arising in ITS data exploitation
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Additional data processing required: Many AVL and AFC systems do not archive data in a readily utilized manner, as they are primarily designed for system monitoring [8]. This means that additional data processing and analysis are required in order to render this data useful to transit planners [4, 5, 96].
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Lack of integration among various data sources: Cumbersome procedures are required, so that the inputs required by a planning/design model, specific practitioners’ knowledge and the outputs of monitoring systems may be consolidated in a common framework.
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Different degrees of fleet penetration: While AVL systems are typically installed on entire bus fleets, the same is not true for APCs which may be deployed on 10–15% of the fleet [8, 46]. The availability of passenger demand data or lack thereof dictates the analysis that can be undertaken, as without APC/AFC the latter is inevitably limited to operational characteristics such as speed, delay and reliability.
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Current state of practice: The role of optimization-based approaches has been somewhat limited to supporting decision-makers rather than actually deciding, while most studies address “stylized” problem settings, lacking the degree of realism required in practice [6].
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Increased computational requirements: Planning models require the execution of more computationally intense tasks, while traditionally used well-known algorithms must be modified in the case of real time information [9].
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Operators’ data-sharing policies: Certain operators have adopted a data-sharing stance, spurring ITS related research. This, however, is not the typical case, as limited data sample availability is often reported because of privacy concerns and operators’ restrictions.