Smart card data-centric replication of the multi-modal public transport system in Singapore

Liu, Xiaodong; Zhou, Yuan; Rau, Andreas

doi:10.1016/j.jtrangeo.2018.02.004

Cited by 16 publications

(12 citation statements)

References 14 publications

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“…Meegahapola et al and Liu et al show that it is possible to rebuild bus routes in Singapore and London, even when GPS traces are not available [4]- [6]. This means that, with some adjustments, the methods used in this paper can be replicated to these cities.…”

Section: Data-driven Analysismentioning

confidence: 99%

See 1 more Smart Citation

Per-Vehicle Coverage in a Bus-Based General-Purpose Sensor Network

Cruz

Couto

Costa

et al. 2020

IEEE Wireless Commun. Lett.

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Vehicle-based sensing is a valuable paradigm to serve third-party applications, but understanding the coverage of such networks is not trivial. For urban buses, trajectories are spatially predictable, but traffic conditions can hinder data timeliness. In this paper, we propose a metric to quantify the contribution of individual buses to the coverage of different applications. We apply this metric to bus mobility traces from Rio de Janeiro for applications of waste management, air quality, and noise monitoring, ranking buses accordingly. We show that bus contribution is strongly related to the application-23% of buses change more than 2,000 places in different application rankings.

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Section: Data-driven Analysismentioning

confidence: 99%

“…These previous works identify the coverage of a bus-based network but lack an estimation of individual bus contribution in a multi-application scenario. Moreover, some works analyze the mobility of buses for smart city applications [4]- [6]. These works focus on the transport system itself.…”

Section: Introductionmentioning

confidence: 99%

Per-Vehicle Coverage in a Bus-Based General-Purpose Sensor Network

Cruz

Couto

Costa

et al. 2020

IEEE Wireless Commun. Lett.

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“…Modeling Since the 1960s, one of the most prominent and widely acknowledged transport modeling approaches has been the four-stage modeling 44 . It is widely used as a systematic framework for both public and private transport modeling, which follows the sequence of (a) trip generation, (b) trip distribution, (c) modal split, and (d) trip assignment.…”

Section: Theme 2: Use Of Big Data In Ptmentioning

confidence: 99%

“…Proposed and implemented methods such as "trajectory rebuilding", "fare matching", "segment tagging", "desired line/stop visualization", "commuter identification" and "scenario analysis" using smart card data Also, an attempt has been made to create online data-driven platform for performance measurement in Beijing, China 48 . Liu et al 44 proposed a method to replicate the multimodal PT system using smart card data and the resulting replication covers about 96% of trips made in PT in Singapore. Also, using cell phone data, a comprehensive dataset was built for para-transit service for performance improvement in Nairobi, Kenya 70 .…”

Section: Smart Card Datamentioning

confidence: 99%

Emerging Big Data Sources for Public Transport Planning: A Systematic Review on Current State of Art and Future Research Directions

Zannat

Choudhury

2019

J Indian Inst Sci

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Introduction Rapid urbanization and associated increase in population are resulting in a higher growth of motorized traffic flow in urban areas. As a consequence, cities are experiencing different problems such as air pollution, road accidents, and congestions. In response to these problems, public transportation (PT) could help to reduce air pollution, road congestion and travel time, and dependency on non-renewable energy, which benefit both

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“…Figure 6(a) visualises bus lane schemes according to data from Singapore (LTA, 2018) which highlight full-day bus lanes mostly paved with Portland cement concrete in the city centre. Figure 6(b) shows the daily bus volumes generated using the method in (Liu, Zhou, & Rau, 2018). The high daily bus volumes are concentrated along bus lanes, with 1500-3000 buses equivalent to 23-46 million equivalent single axle loads in 10 tons (10t-ESALs) accumulated over a service life of 30 years.…”

Section: Field-measured Road Profilesmentioning

confidence: 99%

Road infrastructure design towards passenger ride comfort for autonomous public transport

Van¹,

Ron²

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The rapid developments of smart technology and the vision to smart cities have led to parallel developments of various innovative transport means. Among them, road-based autonomous public transport (APT) is being planned, designed and developed worldwide such as the Dynamic Autonomous Road Transit (DART) in the city of Singapore. Innovative vehicle configuration and operation in the APT shall demand new requirements for design and construction of the infrastructure concerning the ride comfort of onboard passengers. This study aims at supporting the required update of guidelines for the design of roadway (e.g. horizontal alignment design), for vehicle speed recommendation along existing infrastructure and for pavement maintenance concerning passenger ride comfort in which DART is considered as a case study for APT application. Bottom-up and top-down approaches are used, in which the former method uses existing bus vehicles and network as a base-line reference while the latter method is based on APT's vehicle specification and operation.At first, this study starts with a comprehensive review of ride comfort thresholds, recent approaches to evaluate passenger ride comfort onboard and to assess road surface irregularity, where research gaps are pointed out for further study. The interaction between passenger-vehicle-pavement is studied through numerical analysis. Given the similarities between regular bus and APT, the former system has been investigated and detailed bus mathematical models are constructed and validated to evaluate the pavement quality that affects passenger ride comfort. Based on the comparison of different bus models regarding their simulation complexity and performance, a refined Bus Ride Index (BRI) is proposed as a key achievement of this research study. At the same time, regression relationships are established between road geometrical design (e.g. horizontal curve radius) and passenger perceptions at different postures onboard the bus. Herein, experimental study with analytical analysis is carried out to evaluate principal factors (e.g. lateral acceleration, lateral jerk and duration of turning movement) affecting passenger-vehicle-road interaction and ride discomfort based on existing bus fleet and road network.Finally, the DART concept is examined from different perspectives such as network analysis, traffic operation and road infrastructure design.The findings indicate that research on geometry design and road maintenance based on passenger ride comfort for APT is important to support the new vehicle concept. In the urban context, while road geometry concerning the horizontal curve is contributing to the upper range (uncomfortable, very uncomfortable to extremely uncomfortable), road roughness is the principal factor affecting passenger ride quality in the lower range of discomfort (not uncomfortable, a little uncomfortable, fairly uncomfortable to uncomfortable). The developed BRI offers a faster, inexpensive, convenient and more suitable method for evaluating bus lane roughness regarding p...

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Smart card data-centric replication of the multi-modal public transport system in Singapore

Cited by 16 publications

References 14 publications

Per-Vehicle Coverage in a Bus-Based General-Purpose Sensor Network

Per-Vehicle Coverage in a Bus-Based General-Purpose Sensor Network

Emerging Big Data Sources for Public Transport Planning: A Systematic Review on Current State of Art and Future Research Directions

Road infrastructure design towards passenger ride comfort for autonomous public transport

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