Detecting weak public transport connections from cellphone and public transport data

Holleczek, Thomas; Yu, Liang; Lee, Joseph Kang; Senn, Oliver; Ratti, Carlo; Jaillet, Patrick

doi:10.1145/2640087.2644196

Cited by 21 publications

(24 citation statements)

References 7 publications

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“…The algorithm was then adjusted to attain population levels that were very similar to the official statistics. Similar methods of scaling GSM data to the total population have been applied in other transport studies (5,17,21).…”

Section: Gsm Datamentioning

confidence: 99%

Investigating Potential Transit Ridership by Fusing Smartcard and Global System for Mobile Communications Data

Regt

Cats

Oort

et al. 2017

Transportation Research Record: Journal of the Transportation R

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The public transport industry faces challenges in catering to the variety of mobility patterns and corresponding needs and preferences of passengers. Travel habit surveys provide information on overall travel demand as well as its spatial variation. However, that information often does not include information on temporal variations. By applying data fusion to smartcard and Global System for Mobile Communications (GSM) data, researchers were able to examine spatial and temporal patterns of public transport usage versus overall travel demand. The analysis was performed by contrasting different spatial and temporal levels of smartcard and GSM data. The methodology was applied to a case study in Rotterdam, Netherlands, to analyze whether the current service span is adequate. The results suggested that there is potential demand for extending public transport service on both ends. In the early mornings, right before transit operations are resumed, an hourly increase in visitor occupancy of 33% to 88% was observed in several zones, showing potential demand for additional public transport services. The proposed data fusion method was shown to be valuable in supporting tactical transit planning and decision making and can easily be applied to other origin-destination transport data.

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Section: Gsm Datamentioning

confidence: 99%

Investigating Potential Transit Ridership by Fusing Smartcard and Global System for Mobile Communications Data

Regt

Cats

Oort

et al. 2017

Transportation Research Record: Journal of the Transportation R

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show abstract

“…Furthermore, this does not address the fact that spatially, building energy use changes throughout the day as people go to and from work and home. Future work might attempt to quantify the spatial ebb and flow of people using a combination of surveys, census data, and methods using call detail records to derive home versus work locations as shown in Holleczek et al (2014). Building energy use intensity might be modelled by season and diurnally based on factors such as building occupancy, building age, form, and function.…”

Section: Emission Inventoriesmentioning

confidence: 99%

A mobile sensor network to map carbon dioxide emissions in urban environments

et al. 2017

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Abstract. A method for directly measuring carbon dioxide (CO 2 ) emissions using a mobile sensor network in cities at fine spatial resolution was developed and tested. First, a compact, mobile system was built using an infrared gas analyzer combined with open-source hardware to control, georeference, and log measurements of CO 2 mixing ratios on vehicles (car, bicycles). Second, two measurement campaigns, one in summer and one in winter (heating season) were carried out. Five mobile sensors were deployed within a 1 × 12.7 km transect across the city of Vancouver, BC, Canada. The sensors were operated for 3.5 h on pre-defined routes to map CO 2 mixing ratios at street level, which were then averaged to 100 × 100 m grid cells. The averaged CO 2 mixing ratios of all grids in the study area were 417.9 ppm in summer and 442.5 ppm in winter. In both campaigns, mixing ratios were highest in the grid cells of the downtown core and along arterial roads and lowest in parks and well vegetated residential areas. Third, an aerodynamic resistance approach to calculating emissions was used to derive CO 2 emissions from the gridded CO 2 mixing ratio measurements in conjunction with mixing ratios and fluxes collected from a 28 m tall eddycovariance tower located within the study area. These measured emissions showed a range of −12 to 226 CO 2 ha −1 h −1 in summer and of −14 to 163 kg CO 2 ha −1 h −1 in winter, with an average of 35.1 kg CO 2 ha −1 h −1 (summer) and 25.9 kg CO 2 ha −1 h −1 (winter). Fourth, an independent emissions inventory was developed for the study area using buildings energy simulations from a previous study and routinely available traffic counts. The emissions inventory for the same area averaged to 22.06 kg CO 2 ha −1 h −1 (summer) and 28.76 kg CO 2 ha −1 h −1 (winter) and was used to compare against the measured emissions from the mobile sensor network. The comparison on a grid-by-grid basis showed linearity between CO 2 mixing ratios and the emissions inventory (R 2 = 0.53 in summer and R 2 = 0.47 in winter). Also, 87 % (summer) and 94 % (winter) of measured grid cells show a difference within ±1 order of magnitude, and 49 % (summer) and 69 % (winter) show an error of less than a factor 2. Although associated with considerable errors at the individual grid cell level, the study demonstrates a promising method of using a network of mobile sensors and an aerodynamic resistance approach to rapidly map greenhouse gases at high spatial resolution across cities. The method could be improved by longer measurements and a refined calculation of the aerodynamic resistance.

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“…Holleczek et al (2014) or GPS coordinates to analyse and describe the patterns that characterise people behaviour (i.e. Jiang et al (2009) and Liu et al (2012).…”

Section: Mobility Patterns With Intelligent Transport Systemsmentioning

confidence: 99%