Karthik Charan Konduri (corresponding), University of Connecticut

Transcription

1 Paper Author (s) Karthik Charan Konduri (corresponding), University of Connecticut Asif Rehan, University of Connecticut Ashrafur Rahman, University of Connecticut Nicholas E. Lownes, University of Connecticut Paper Title & Number Crowdsourcing Real-time Traveler Information Services: An Exploratory Analysis Of Data Quality [ITM # 55] Abstract Traditional approaches to providing travel information are based on physical sensor networks that are prohibitively expensive to install, costly to maintain and operate, limited in their coverage and suffer from unreliability. However, technological advances combined with participatory paradigms of information sharing such as crowdsourcing, offer an alternative solution to providing traveler information services that can overcome these challenges. In crowdsourcing, individuals instead of physical sensors serve to collect data and contribute to content repositories; the information then gets processed and distributed to other users. Crowdsourcing is gaining in popularity because of the growing penetration of internet-enabled and location aware handheld devices. However, despite its potential, a number of issues about its feasibility and applicability remain. In this regard, there are three main issues, namely, quality and validity of crowdsourced data; algorithms and approaches for synthesizing structured and unstructured crowdsourced data; and understanding participant behaviors as they relate to motivations for participation, and incentives for continued involvement. The main focus of the proposed research is to explore the issue of quality and validity of crowdsourecd data by deploying a crowdsourced project for providing real-time transit information for the shuttles serving the University of Connecticut. Statement of Financial Interest This is just a research endeavour and there is no financial interest involved. Statement of Innovation The research proposed aims to add to the state of research and practice on crowdsourcing as a potential solution for real-time traveler information systems. The proposed traveler information implementation (shuttle services fitted with on-board GPS) provides a unique opportunity to compare crowdsourced data and explore its applicability for providing real-time traveler information services. The proposed research will comprise one of the very few studies that have qualitatively and quantitatively assessed the issues of data quality and validity associated with crowdsourced data. Further, the availability of on-

2 board baseline data allows for developing synthetic experiments to identify optimal levels of participation for providing accurate and usable real-time traveler information.

3 CROWDSOURCING REAL-TIME TRAVELER INFORMATION SERVICES: AN EXPLORATORY ANALYSIS OF DATA QUALITY Karthik C Konduri, Asif Rehan, Ashrafur Rahman, and Nicholas Lownes Department of Civil and Environmental Engineering, University of Connecticut Corresponding Author: Karthik C Konduri, kkonduri@engr.uconn.edu ABSTRACT Traditional approaches to providing travel information are based on physical sensor networks that are prohibitively expensive to install, costly to maintain and operate, limited in their coverage and suffer from unreliability. However, technological advances combined with participatory paradigms of information sharing such as crowdsourcing, offer an alternative solution to providing traveler information services that can overcome these challenges. In crowdsourcing, individuals instead of physical sensors serve to collect data and contribute to content repositories; the information then gets processed and distributed to other users. Crowdsourcing is gaining in popularity because of the growing penetration of internet-enabled and location aware handheld devices. However, despite its potential, a number of issues about its feasibility and applicability remain. In this regard, there are three main issues, namely, quality and validity of crowdsourced data; algorithms and approaches for synthesizing structured and unstructured crowdsourced data; and understanding participant behaviors as they relate to motivations for participation, and incentives for continued involvement. The main focus of the proposed research is to explore the issue of quality and validity of crowdsourecd data by deploying a crowdsourced project for providing real-time transit information for the shuttles serving the University of Connecticut. INTRODUCTION There has been a paradigm shift in the transportation policies being considered to alleviate congestion by planners and policymakers from capacity oriented strategies to strategies aimed at managing the demand for travel more commonly referred to as Travel Demand Management (TDM). The shift to TDM policies has also been furthered due to the changing landscape of funding, challenges associated with adding new infrastructure, implications of existing mobility patterns on air quality, energy consumption and climate, and sustainability considerations of urban regions. TDM includes a variety of policies aimed at improving accessibility, providing information about alternatives, promoting alternative modes, improving transportation predictability, reducing unreliability and enhancing system performance (FHWA 2013). Intelligent Transportation Systems (ITS) are a popular TDM strategy that utilize advances in information and communication technologies (e.g., computers, telecommunications, location services, data) to address transportation issues. Real-time traveler information services (RTIS) is a subset of ITS which utilizes technological advances for providing up-to-date information about transportation networks so that individuals can make efficient pre-trip and en-route transportation choices, and

4 transportation agencies can optimize operations to ensure maximum capacity utilization. Traditional solutions to traveler information services rely on fixed sensor networks (in the form of loop detectors, and traffic detection cameras), augmented with reports from first responders about planned and unplanned network perturbations to provide real-time traveler information services. The information provided by traditional solutions to RTIS is often limited in its coverage because of high cost of installing sensors across entire networks. Data is also often delayed owing to limitations of communication and transmission technologies and sensor network unreliability leads to a downtime of RTIS. Additionally, physical sensor networks are prohibitively expensive to maintain and operate (USDOT 2010). Recent advances, however, in information and communication technologies in the form of smartphones and portable handheld devices combined with participatory paradigms of information sharing, such as crowdsourcing, offer the ability to overcome challenges associated with traditional information provision solutions. In this new wave of traveler information services, consumers with location-aware and data-enabled devices serve as a network of mobile sensors providing information about prevailing network conditions. This approach of traveler information has potentially wider coverage, almost real-time currency, and redundancy in data collection due to the participatory paradigm of information sharing. Additionally, since the end users serve as sensors, there is little investment involved in deploying the sensor network and minimal cost is incurred in maintaining and operating the information services. Crowdsourcing has gained in popularity for providing information services because of the growing penetration of data-enabled and location-aware handheld devices (Boulos et al. 2011, USDOT 2010). The participatory paradigm has even been implemented by private sector companies, such as INRIX, and NAVTEQ, to mine travel behaviors and provide traveler information services. However, the emphasis by these providers is on traveler information services in major metropolitan areas; coverage is mostly limited to higher roadway functional classes (mostly interstates) because of the demand for such data by consumers such as media outlets, and navigation service providers. The participatory paradigm of crowdsourcing holds a lot of promise for providing high-quality, real-time traveler information services in small and big regions for not only interstates, but across all functional classes of roadways and across all modes of transportation. It has the potential for augmenting and, in some cases, replacing traditional traveler information services based on physical sensor networks. Although the potential is widely apparent, as evidenced by crowdsourcing projects (e.g. INRIX 2013, NAVTEQ 2013) and the utilization of crowdsourced data for policy analysis (recently released Urban Mobility Report utilizes data from INRIX - TTI 2012), a number of concerns nevertheless remain and issues abound about their applicability as a complete real-time traveler information services solution as described in the following three subsections (Yousuf 2011, USDOT 2010).

5 Qualifying Crowdsourced Data Although, participatory paradigms such as crowdsourcing hold potential for providing rich traveler information data, they also suffer from data quality issues. First, the data suffers from sparsity when users are not pursuing trips on the network which can limit the ability to provide continuous real-time traveler information. For example, data from crowdsourced projects will generally be sparse during off-peak periods (early morning, mid-day, night) when fewer trips are pursued in general because of fixed activity commitments such as work and school. Second, the expanse of traveler data that can be provided is limited by the locations accessed and the roadway links visited by individuals as they pursue their trips and fulfill their activities. Finally, the validity of the data shared is a concern. The accuracy of the locations obtained from the smartphones is dependent on the technique (Wi-Fi, Cell Phone Tower Triangulation, GPS) used. This is more so a concern for actively contributed user content. For example, if users report information about unplanned network disruptions like accidents, there are no established protocols to filter out actual incidents from the false-positive reports intentionally submitted to pollute the data (Mashhadi, and Capra 2011). Therefore, there is a need to qualify the data obtained from crowdsourced projects by comparing them against data obtained from traditional traveler information services based on fixed physical sensor networks. Additionally, there is a need to explore levels of participation desired from crowdsourcing projects (i.e. quantity of crowdsourced data) to support accurate and usable real-time traveler information. Synthesizing Crowdsourced Data Technological advances have facilitated information ubiquity and have transformed how individuals go about their daily lives. There is a growing trend to collect and provide near real-time information about prevailing conditions. This is especially true for traveler information systems because conditions on roadway networks vary from day-to-day (often significantly) due to planned network events (in the form of roadway closures, diversions, special events) and unplanned network events (in the form of accidents and incidents). Travelers, as a result, want information about prevailing conditions so that they can plan and execute their daily activity and travel needs accordingly. The participatory paradigm of crowdsourcing, with participants serving as a network of mobile sensors offers a solution for providing real-time traveler information. The challenge, however, is to synthesize the information in light of the above noted data quality issues (i.e, sparsity, expanse, and validity) to provide accurate real-time traveler information. This is further exacerbated when structured data (in the form of trajectories, speeds, and spatio-temporal logs) need to be combined with unstructured data (in the form of accident reports, and first respondent reports) to provide representative traveler information. Therefore, what is needed are novel data fusion and data mining techniques that can be utilized to synthesize the shared information.

6 Understanding Crowdsourcers Finally, the key assumption for the success of a crowdsourcing project is the availability of participants (or users) who act as sensors and share information about network conditions. In this regard, there are two important issues: (1) recruiting participants to share and contribute information, and (2) ensuring continued involvement of existing participants. The appeal of getting access to high quality real-time traveler information and the ability to make informed decisions should lead to the recruitment of new users. Additionally, as market penetration of smartphone increases, participation numbers in crowdsourcing projects will likely increase. It stands to reason, then, that recruiting new individuals may not be a major concern. Concerns due exist, however, regarding continued participation of existing users. This is more so the case where users are asked to both actively share information about surroundings (e.g., observations about surroundings and prevailing conditions) and passively share location data. Very little research has explored participant behaviors and their motivations for sharing information, especially in crowdsourced projects aimed at providing traveler information services. Questions that remain unanswered include: Why do individuals actively share information? What is the value gained? How is information being used by the participants? An increased awareness about participant behaviors will allow crowdsourcing projects to build evolving and sustainable business models. The main focus of this paper is in addressing the first issue related to crowdsourced data namely, the quality and validity. The results presented in this paper are part of a larger exploration aimed at addressing the three issues identified above related to RTIS solutions based on the concept of crowdsourcing. In this research, a crowdsourcing project will be implemented to provide real-time transit information services for the shuttle buses that serve the Storrs campus of University of Connecticut. The project will be deployed as a smartphone app wherein participants can access and share real-time information about shuttle services. Data collected from the crowdsourcing transit app will be analyzed to conduct two lines of inquiry. First, the data collected will be used to characterize issues of data quality such as sparsity, expanse, and validity. Moreover, because the shuttles are already fitted with on-board GPS units, infrastructures are in place to obtain a rich baseline against which quality of the data can be compared and characterized. Second, a major concern with crowdsourced projects is levels of participation (i.e. the quantity of data) required to predict and provide accurate real-time information about networks. Based on quality parameters obtained from the first line of inquiry, simulation experiments using the onboard GPS data will be conducted to identify optimal participation levels to provide accurate real-time transit information. METHODOLOGY In the following subsection, the pilot crowdsourcing project that will be deployed is described and in the second subsection, approaches to characterize the quality and validity of crowdsourced information are presented.

7 Crowdsourcing App for Real-Time Transit Information One of the main aims of this effort is to evaluate the feasibility and applicability of crowdsourcing as a platform for providing real-time traveler information services for the shuttle bus service serving Storrs campus of the University of Connecticut. The shuttle service was selected for this project because the buses are already fitted with onboard GPS units giving access to a baseline (known) data against which the crowdsourced data can be compared and characterized. The smartphone application will include two key features. First, the app will provide both expected (schedules on which they run) and actual (real-time information about the location, speed, and arrival time among others) transit information. Second, the application will feature user interfaces to share data; all participants will passively share data about transit trips (through their smartphones) and actively share data about prevailing network conditions (network events, weather conditions and other events). The research team is currently exploring existing crowdsourcing projects including Waze (Waze 2013), INRIX (INRIX 2013), Cycletracks (SFCTA 2013) and Tiramisu (Tiramisu 2013). These projects also provide traveler information services and they are in very advanced stages of deployment and usage. Source code for some of these projects is available under open-source agreements and is being utilized to build the crowdsourcing app for this research. Efforts are also underway to recruit representative sample of participants from different demographic groups that use the shuttle system to capture the variability and differences in behaviors across different demographic groups. After recruitment, participants will be asked to log their transit trips and share information about the prevailing conditions as they pursue trips on the shuttles. Qualifying Crowdsourced Data After deploying the application, quality of the data will be characterized as follows: Passively and actively collected data will be put through comprehensive data filtering procedures to create historical content repositories that can be mined to provide traveler information. For passively collected data about transit trip trajectories, standard data filtering techniques will be used to ensure that trips reported indeed correspond to the transit trip (Reddy et al. 2009). Meanwhile actively shared information regarding the surrounding environment, including network events such as accidents and road closures, will be tallied against information about such events from appropriate data sources (e.g., local newspapers, websites, visual inspection) to assess the data quality. Further, data models will be implemented to ensure data integrity and overcome data pollution - a known issue with activity contributed crowdsourced data (Mashhadi, and Capra 2011). As noted in the previous subsection, the bus shuttles have on-board GPS tracker system in place that can provide accurate baseline information against which the crowdsourced data can be validated. The spatio-temporal traces of reported trips

8 will be compared against on-board GPS data using data mining techniques such as pattern matching (Kang and Yong 2010, Roh et al. 2010) and map matching to assess quality of the information. More specifically, the emphasis will be on quantitatively characterizing issues with crowdsourced data namely, sparsity, expanse, and validity. A major concern with crowdsourced projects is to identify levels of participation (i.e. the quantity of data) required to accurately predict and provide real-time information about networks. The shuttle service with its on-board GPS data will be utilized to produce synthetic data that mimics varying levels of user participation in the crowdsoured project. Optimization techniques will be employed to identify optimal levels of participation for desired levels of sparsity, expanse, and validity. This exploration will provide rich insights into crowdsourcing as a solution for providing high-quality real-time traveler information. RESEARCH CONTRIBUTION The research proposed aims to add to the state of research and practice on crowdsourcing as a potential solution for real-time traveler information systems. The proposed traveler information implementation (shuttle services fitted with on-board GPS) provides a unique opportunity to compare crowdsourced data and explore its applicability for providing real-time traveler information services. The proposed research will comprise one of the very few studies that have qualitatively and quantitatively assessed the issues of data quality and validity associated with crowdsourced data. Further, the availability of on-board baseline data allows for developing synthetic experiments to identify optimal levels of participation for providing accurate and usable real-time traveler information. REFERENCES Boulos, N.K., Resch, B., Crowle, D.N., Breslin, J.G., Sohn, G., Burtner, R., Pike, W.A., Jezierski, E., and Chuang, K.S. (2011) Crowdsourcing, Citizen Sensing and Sensor Web Technologies for Public and Environmental Health Surveillance and Crisis Management: Trends, OGC, Standards, and Application Examples. International Journal of Health Geographics, 10. Kang, J. and Yong, H. (2010) Mining Spatio-Temporal Patterns in Trajectory Data. Journal of Information Processing Systems, 6(4), pp Liu, H., Van Zuylen, H.J., van Link, H., Chen, Y., and Zhang, K. (2005) Prediction of Urban Travel Times with Intersection Delays. Proceedings of the IEEE Intelligent Transportation Systems Conference. Mangharam, R., Lee, I., and Sokolsky, O. (2008) Real-Time Traffic Congestion Prediction. Real-Time and Embedded Systems Lab. papers (Accessed February 19, 2013).

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