crowdsourcing the collection of public transportation data
TRANSCRIPT
Crowdsourcing the Collection of Public Transportation Data
Dr. Chris BoneDepartment of Geography
University of Oregon
Research Team
• Ken Kato, Associate Director, InfoGraphics Lab• Jacob Bartruff, Lead Developer, InfoGraphics Lab• Marc Schlossberg, Professor, PPPM• Seth Kenbeek, Research Assistant• Nathan Mosurinjohn, Research Assistant
2
• Tom Schwetz, Planning and Development Manager• Andy Vobora, Director of Customer Service and Planning
University of Oregon
Lane Transit District
3
Transportation Behavior Data
1. Who is travelling where and why?2. When are people travelling?3. How are people travelling?
4
Historically
Manual counts
5
Automated Counting Systems
www.roadtrafficsafety.org
Limitations of Traditional Data Collection
• Initial investment costs are the most significant barrier to implementing APCS (Boyle 2008).
• The amount of post-collection processing required to account for counting biases (Strathman and Hopper 1991).
7
Limitations of Traditional Data Collection
Next Generation Data Collection Methods
9
10
Smart Cards
• Swiped at location-based points of entry and exit.
• Provides data on the number of individuals that enter a transit system at a specific location and time.
11
Smart Cards
• Smart cards provide a more cost-effective means of digital passenger counting because these technologies already exist in some transit networks and do not require additional infrastructure.
• But, commuters are passive providers of data as smart cards track their movements through space and time without being able to collect more diverse data on their travel behavior.
Location-based Services
• Transfer the responsibility of data production over to transit riders.
12
13
Location-based Services
Location-Based Systems
• Perhaps the main constraint on having social media proliferate into an industry standard of transportation data is the commuter’s willingness to volunteer information on their location (Cotrill 2014).
• Transit riders volunteering data through social media applications are generally forced to forego privacy about their location to the general public.
14
Research Question
How do transportation agencies collect more strategic forms of data about their ridership while respecting the privacy of its riders?
15
Research Objective
• This study evaluates how LBS can be utilized to solicit data from transit commuters while minimizing infringement on locational privacy.
• We asses how two different forms of existing LBS technologies – Bluetooth sensors and geofences – offer ways to capture snapshots of commuter locations to determine where and when a commuter enters and exits a public transportation vehicle.
16
Methods
17
Methods
A customized mobile phone application was developed for this study that connects users to a transportation network and collects data at specific times during an individual’s commute.
18
19
Methods
• We selected these two types of LBS:
o BLE beacons are physical sensors that broadcast signals to connect with applications on a mobile device.
o Geofences are digital perimeters existing in a computation system (i.e. not in physical space) that connect with an application when the geographic coordinates of both technologies overlap.
20
19
19
How did you get here?How long did it take you?
Where did you come from?
How was your experience?
Where are you going?
19
How did you get here?How long did it take you?
Where did you come from?
How was your experience?
Where are you going?
19
geofencing
19
bluetooth sensor
26
19
28
29
Case Study
• Three weeks• 25 participants• University Students and Staff• LTD Staff• 50% geofence• 50% BLE beacons
Results
30
31
Crowdsourced Data
32
Crowdsourced Data
33
Crowdsourced Data
34
Crowdsourced Data
35
Assessing Error
36
Assessing Error
User Critiques
1. When did the survey appear on your phone?
• For the beacon application, the majority of participants stated that the survey appeared immediately after they entered the bus or only after they had been on the bus for a significant amount of time.
• Only one beacon participant stated that the survey appeared as they approached the bus stop.
37
User Critiques
1. When did the survey appear on your phone?
• Multiple geofence participants stated that the survey was triggered when they approached the bus stop, which is not unexpected since the geofence triggers the connection to the application once the commuter enters the digital perimeter around the stop.
38
User Critiques
2. How often did the survey appear when riding the bus ?
• Most beacon participants stated that the survey either always appeared or appeared most of the time during their trip.
• However, the geofence participants again demonstrated more variable responses, where only three stated that the survey always appeared, and some stated that the survey appeared only some or few of the times.
39
User Critiques
3. Other critiques?
• For the beacon applications, critiques of the application were limited to the survey appearing while buses were passing in the other direction and the connection of the application being affected by the location of the participant on the bus, specifically that the signal appeared weaker at the back of the bus.
40
User Critiques
3. Other critiques?
• The geofence participants had relatively more critiques, most of which were focused on the inconsistency in which the survey would appear on their phone.
• These comments allude to the possibility that the geofenceapplications experienced notable difficulties capturing the moment at which commuters entered a bus.
41
Looking Forward
42
Looking Forward
• It is evident that both BLE and geofence technologies have promise and challenges for facilitating the crowdsourcing of data by commuters in any given transit network.
• Our approach seeks a middle ground between passive data collection methods such as APCS and smart cards, and those methods that seek to continually track individuals through space.
43
Looking Forward
• This middle ground comes with the tradeoff between the promise of big data acquisition and having commuters relinquish some amount of locational privacy.
• Whether this compromise is operational at the transit agency level depends on how such applications are made available to commuters and, in the end, how willing commuters are to engage in a crowdsourced data collection enterprise in which the returns to them are not evident.
44
• A worthwhile pursuit moving forward would be to survey transit riders to evaluate how likely crowdsource data collection applications are to be adopted across demographic groups in different cities.
• Additionally, further research is needed to identify the types of data analytics that could utilize the of data collected by this middle ground approach, both for characterizing the overall commuting population as well as reducing the various errors or sources of uncertainty introduced by these technologies.
45
Looking Forward
Summary
• The results show that using these types of location-based services offer an effective approach to collecting richer data than traditional means while requiring only minimal data on individual location.
• With further data processing and application refinement, the methods presented here have the potential for deployment in transportation agencies that operate at a variety of scales.
46
