2013 Session: 765

• Using Mobile Apps to Measure Spatial Travel-Behavior Changes of Carsharing Users
  Abstract: Positioning technologies in commercially-available mobile phones have matured significantly over the last five years, offering new opportunities to collect high resolution spatial travel behavior data for transportation research and operations. This paper discusses the use of a global positioning system mobile phone application, TRAC-IT, to collect travel behavior data of carsharing users as part of a variable pricing experiment. A random sample of 30 participants carried a mobile phone with TRAC-IT installed, resulting in over 4 million GPS data points that provided precise geographic and spatio-temporal information. These data informed an analysis of the participants’ geographic footprint by estimating a set of standard-distance ellipses of carsharing and non-carsharing modes. Spatial analysis results show that carsharing users have a much smaller activity space (0.5 square miles) than individuals not using carsharing over the same period (7.8 square miles). The activity space of carsharing users contracts while using carsharing as a mode of transport (0.2 square miles for carsharing versus 0.5 square miles for other modes). This may be because carsharing users do not have access to a private vehicle and, therefore, rely on carsharing to conduct out-of-home required trips for maintenance activities, such as grocery shopping.
  Authors: Concas, Sisinnio; Barbeau, Sean J.; Winters, Philip L.; Georggi, Nevine Labib; Bond, Julie M.
  Authors: Concas, Sisinnio; Barbeau, Sean J.; Winters, Philip L.; Georggi, Nevine Labib; Bond, Julie M.
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology
  Session: 765
  Paper Number: 13-1107
• Future Mobility Survey: Experience in Developing a Smart-Phone-Based Travel Survey in Singapore
  Abstract: The Future Mobility Survey (FMS) is a smartphone-based prompted-recall travel survey that aims to support data collection initiatives for transport modeling purposes. This paper details the considerations that have gone into its development, including the smartphone apps for iPhone and Android platforms, the online activity diary and user interface, and the background intelligence for processing collected data into activity locations and travel traces. We discuss the various trade-offs regarding user comprehension, resource use, and participant burden, including findings from usability tests and a pilot study. We find that close attention should be paid to the simplicity of the user interaction, determinations of activity locations (such as the positive/false negative trade-off in their automatic classification), and the clarity of interactions in the activity diary. The FMS system design and implementation provides pragmatic, useful insights into the development of similar platforms and approaches for travel/activity surveys.
  Authors: Cottrill, Caitlin D.; Pereira, Francisco C.; Zhao, Fang; Dias, Inês Ferreira; Lim, Hock Beng; Ben-Akiva, Moshe E.; Zegras, P. Christopher
  Authors: Cottrill, Caitlin D.; Pereira, Francisco C.; Zhao, Fang; Dias, Inês Ferreira; Lim, Hock Beng; Ben-Akiva, Moshe E.; Zegras, P. Christopher
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology
  Session: 765
  Paper Number: 13-4849
• Twitter Interactions as a Data Source for Transportation Incidents
  Abstract: Twitter is a microblogging platform that contains a large amount of publically accessible user generated content. This content consists of short social interactions between users. These interactions often describe day-to-day events, and can include location information, making them potentially suitable for use in transportation-related analysis. This paper evaluates the use of data from public social interactions on Twitter as a potential complement to traffic incident data. We compare incident records from the California Highway Patrol with Twitter messages related to roadway events over the same time period. Relationships between the two datasets are evaluated by visualizing the density of incidents and tweets that coincide near the same location. Additionally, the content of Twitter messages is weighted by its relevance to traffic incidents. This weighting is then compared to the time and space proximity of the message to an incident record to determine if more vivid Twitter messages may correspond to the presence of incidents. Twitter information is interesting because it is inexpensive, readily accessible, has broad geographic coverage, and provides a uniquely passenger-centric perspective. It is expected that this research will lead to a better understanding of the potential for information from Twitter to add context to other traffic measurements as a supplemental data source.
  Authors: Mai, Eric; Hranac, Rob
  Authors: Mai, Eric; Hranac, Rob
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology
  Session: 765
  Paper Number: 13-1636
  Practice-Ready: Yes
• Non-coverage Errors in Travel Surveys Due to Mobile Phone Only Households
  Abstract: National and regional household travel surveys have conventionally sampled landline telephone households through list-assisted random digit dialing. However, a recent increase in “mobile phone only” households results in either non-coverage or under-coverage of a growing segment of the population. This can potentially cause a substantial bias in the representativeness of travel behavior toward the target population. To cover mobile phone only households, an address-based sampling method is of interest. This study explores whether the characteristics and travel behavior of mobile phone only households differ from those of households with landline telephones. In addition, this study quantifies the extent of non-coverage errors in the surveys in terms of the respondents’ travel behavior. Along with census data, the mobile phone only sample (N=2,988) was compared with the landline telephone sample (N=7,774) drawn from the 2008 National Capitol Region Household Travel Survey. Results show that the mobile phone only sample consists of relatively more single-person households, younger individuals, and Blacks/Asians/Hispanics, which are generally identified as hard-to-reach groups. Statistical models were developed to examine differences in travel behavior (e.g., trip-making), suggesting that the mobile phone only households make more transit (27%) and walking (18%) trips. This is partly due to the spatial distribution of the residential locations between the two groups, which are found to be statistically significant. Regarding non-coverage errors, results show that the inclusion of the mobile phone only households can reduce the errors, especially for transit and walking travel behaviors. The implications for travel survey methods are further discussed.
  Authors: Son, Sanghoon; Khattak, Asad J.; Kim, Nak-Kyeong
  Authors: Son, Sanghoon; Khattak, Asad J.; Kim, Nak-Kyeong
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology
  Session: 765
  Paper Number: 13-2028
  Practice-Ready: Yes
• Using Mobile Apps to Measure Spatial Travel-Behavior Changes of Carsharing Users
  Authors: Concas, Sisinnio
  Authors: Concas, Sisinnio
  Year: 2013
  Document Type: Presentation
  Subject: Data and Information Technology
  Session: 765
  Paper Number: 13-1107
• Twitter Interactions as a Data Source for Transportation Incidents
  Authors: Hranac, Rob
  Authors: Hranac, Rob
  Year: 2013
  Document Type: Presentation
  Subject: Data and Information Technology
  Session: 765
  Paper Number: 13-1636
• Non-coverage Errors in Travel Surveys Due to Mobile Phone Only Households
  Authors: Son, Sanghoon
  Authors: Son, Sanghoon
  Year: 2013
  Document Type: Presentation
  Subject: Data and Information Technology
  Session: 765
  Paper Number: 13-2028
• Dubuque Smart Travel
  Authors: Ravada, Chandra
  Authors: Ravada, Chandra
  Year: 2013
  Document Type: Presentation
  Subject: Data and Information Technology
  Session: 765
  Paper Number: 13-2917
• Future Mobility Survey: Experience in Developing a Smart-Phone-Based Travel Survey in Singapore
  Authors: Cottrill, Caitlin
  Authors: Cottrill, Caitlin
  Year: 2013
  Document Type: Presentation
  Subject: Data and Information Technology
  Session: 765
  Paper Number: 13-4849
• Dubuque Smart Travel
  Abstract:

  Efficient transportation and transit system design requires a detailed understanding of travel patterns within an urban area. In the past, transportation planners have relied on limited survey data that includes little information about choice riders or non-users of transit. Transportation and transit agencies need a technology based data gathering system and route optimization process to address these challenges. In this first of a kind project, researchers have designed a system that computes origin destination models based on three sources of data – 1,000+ volunteers providing movement sampling through a smartphone application; 500+ volunteers providing movement sampling in transit using smart cards; and Airsage Inc. aggregating movement of 15,000 mobile phones in the Dubuque area. The smartphone based data gathering offers the richest and finest spatio-temporal granularity of information. This data is then analyzed to identify trips based on activity by time of day. The model from Airsage offers the largest sample size although at coarser spatio-temporal granularity. The analysis and the origin destination models are then used to design transit routes to optimize performance indicators such as average journey time, headways, and wait time. These optimal routes when implemented will substantially help transit agencies meet demand while still saving operating expenses.

  Authors: Ravada, Chandra
  Authors: Ravada, Chandra
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology
  Session: 765
  Paper Number: 13-2917
  Practice-Ready: Yes