11 January, 2017
Thanks to people counters and innovative technological solutions like WiFi tracking, urban areas will ensure efficient and sustainable public transport systems. In this post we will see 7 outcomes of using our Passenger Analytics tool.
The world’s population is increasingly city-based: 53% of it currently lives in urban areas and by 2050 this number is expected to reach 67%*. This growing urbanisation leads to an increasing demand for transport, which requires a corresponding increase in mass transit supply for it to be absorbed. Systems for counting people and analysing passenger behaviour will foster an orderly growth in urban transportation.
Passenger demand, which is the key for planning an efficient public transport network, is usually unknown. This lack of information results in a public transport service that is not aligned with real passengers’ needs, which causes overcrowding and long waiting times on the one hand, with inefficient over provision on the other. Ultimately, the results are costly while the users are often dissatisfied.
Traditionally, information regarding passenger demand has been collected through manual procedures (household surveys, on-board questionnaires or manual counting), as we talked about in our last post, which only provides point in time information relating to specific time periods.
However, nowadays with people counters and WiFi tracking, passengers’ behaviour can be known and public transport can be improved drastically. The seven main outcomes of implementing a system like that are:
– Reduction of travel times: total daily commute time is 80 minutes on average across cities around the world. As we estimate to reduce the travel time per trip by 2-15%**, this would equate to a reduction of travel time between 1,6 to 12 minutes every day per person, which would represent up to 3 days a year.
– Increase in user satisfaction: the satisfaction of Europeans for public urban transport is on average 64%***. We see that there is room to improve the service as none of the variables that affect satisfaction reach 70%, such as provision of information connecting services, routes taken by the different lines, or passenger security.
– Increase in number of passengers: Public Transport has been fare more successful with much greater growth in overall passenger volumes and trips per capita where there is a high quality of service . For instance, Germany has shown a steady increase of 25% of passengers from 1991 to 2010 (going from 9,2 to 11,5 billion)****. As shown in the study of Buehler, R. and Pucher, J. this rising is widely explained by the improvement in their services through regional coordination of ticketing and timetables, and realtime information at stations and on vehicles.
– Reduction of traffic congestions and greenhouse emissions: it has been demonstrated that Intelligent Transport Systems like the Passenger Analytics have the potential to reduce CO2 emissions by 10-15%, other environmental emissions (CO, NOx, PM10) by 2-20%, fuel consumption by 5-15% and traffic congestion by 12-30%***** .
– Optimisation of staffing schedules: knowing that staffing costs represent an average of 25% of total operation costs of a public transport operator******, this being reduced by 1-2% would have a considerable impact on their profit.
– Significant cost savings in labour intensive manual procedures such as home surveys, manual counting and on-board questionnaires: costs regarding customers surveys easily reach €500.000 – €1M a year for large agencies*******. With Passenger Analytics, it would cost €60.000 yearly to equip 200 vehicles of a large agency, and they would have continuous data of their passengers, throughout all year.
– Respect privacy concerns: as 3D ToF sensors do not identify any personal data and Wi-Fi tracking implements privacy controls by design.
* Arthur D. Little (2014), The Future of Urban Mobility 2.0
** Moovit (2016), Global Transit Usage Report
*** InnovITS (2013), Impact Study on Intelligent Mobility
**** Flash Eurobarometer 382b (2014), Europeans’ satisfaction with urban transport
***** Buehler, R., Pucher, J., (2012), Demand for Public Transport in Germany and the USA: An Analysis of Rider Characteristics
****** Smith, NC, Veryard DW and Kilvington RP (2009), Relative costs and benefits of modal transport solutions
******* Weinstein A., Granger-Bevan S., Newmark G. and Nixon H. (2015), Comparing Data Quality and Cost from Three Modes of On-Board Transit Passenger Surveys